customer satisfaction research report

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Customer Satisfaction Research: What it is + How to do it?

Customer satisfaction research is essential for businesses looking to build long-term customer relationships. It provides organizations with essential insights into their customers’ thinking and tastes.

Customers who are satisfied with the quality of service are more likely to become loyal customers. In this blog, we will explore customer satisfaction research and how to do it for customer-centric success.

What is customer satisfaction research?

Customer satisfaction research is a systematic process of collecting, analyzing, and interpreting data that allows companies to measure the satisfaction level of customers when purchasing a product or service from their brand.

This research is useful to identify satisfied customers who are loyal defenders of your brand and who are dissatisfied to follow up on their demands.

There are many reasons to measure customer satisfaction. Customer satisfaction research offers great insights, so your team can focus on meeting customer expectations or flagging potential issues that may affect your business growth.

Importance of conducting a satisfaction study

Customer satisfaction research allows business managers and owners to discover that keeping current customers costs less than getting new ones.

One way to collect information about customer satisfaction is by conducting online surveys, which will help you make the necessary changes to improve your business and maintain customer loyalty.

Responding to customer complaints and concerns don’t always mean knowing their needs. Satisfaction surveys allow companies to understand what is working, what needs to be improved, and why.

To provide better customer service, it’s important to understand how they feel and allow them to explain why they feel that way. Only then can you adapt your services and offer an experience that makes you stand out from the competition.

Companies carry out satisfaction studies for different objectives. Among the most important uses of this mechanism are:

• Know what are the areas that need to be improved in the business.
• Know the opinion of customers about your brand. 
• Find out what the true needs of customers are.
• Create better customer retention strategies.
• Know if the market strategies that are carried out are working. 
• Meet customer expectations.

How to carry out customer satisfaction research?

Customer satisfaction research takes several steps to get a thorough and accurate insight into your customer experiences and perspectives. Here’s a step-by-step method you can follow for carrying out customer satisfaction research:

Step 1: Define Research Objectives

Defining precise and well-structured research objectives is an essential first step in every customer satisfaction research project. These objectives will guide you through the whole research process and ensure that the research remains focused, relevant, and connected with your business goals.

To define research objectives, follow the steps outlined below:

• Identify the Objectives: Start by identifying the overall objectives of your customer satisfaction research.
• Break Down Objectives: Divide the purpose into specific objectives. Each objective should be specific and address a different component of customer satisfaction.
• SMART Criteria: Make sure your objectives are SMART—specific, measurable, attainable, relevant, and time-bound.
• Prioritize: If you have several objectives, prioritize them according to relevance and potential impact.

Step 2: Select Research Methodology

Selecting an appropriate research technique is a vital decision that will define your overall research process. Your approach will influence the type of data you gather, the level of insights you get, and the general validity of your findings. Here are some examples of research methodology.

• Surveys: Surveys are a popular and versatile method for collecting data on customer satisfaction. You can gather qualitative and quantitative data through structured questions.

Customer Satisfaction Score (CSAT) is the most straightforward of the customer satisfaction survey methodologies. Surveys are well-suited for measuring customer satisfaction scores, Net Promoter Scores (NPS), and other quantitative metrics.

• Interviews: Interviews will enable you to have in-depth interactions with customers. You can get valuable qualitative insights into customer experiences through phone interviews or in-person chats.
• Focus Groups: In a focus group, a small group of customers shares their experiences, ideas, and impressions in a guided session. This strategy encourages group interactions by allowing participants to respond to each other’s comments.
• Observations: Observational research refers to directly monitoring customers as they interact with your products or services. This strategy will provide you insights into user behavior and reactions in real time.

Step 3: Develop Customer Satisfaction Surveys

Developing well-crafted customer satisfaction surveys is an important stage in customer satisfaction research. It serves as the primary tool for gathering customer data and insights.

A well-crafted customer satisfaction survey will ensure that you get relevant and meaningful data. It will also motivate you to make improvements and increase customer satisfaction. You can develop a robust customer satisfaction survey by following the steps below:

• Define Research Objectives: Before developing survey questions, ensure you understand the research objectives. Determine which aspects of customer satisfaction you want to measure and what insights you want to get.
• Choose Question Types: Remember the research objectives when creating customer satisfaction survey questions. Select appropriate question types that align with your research objectives. It will help you to capture different dimensions of customer satisfaction. To quantify responses, include closed-ended questions with Likert scales, multiple-choice options, and ranking scales. Include open-ended questions. It will encourage your customers to provide thorough comments and insights.
• Order and Flow: Organize the survey questions logically, begin with general questions, and then proceed to more specialized and complicated topics. Keep a balance between qualitative and quantitative questions.
• Avoid Leading Questions: Leading questions will unintentionally influence your respondents and compromise the accuracy of their responses. So, avoid including leading questions and design questions that are neutral and unbiased.
• Incorporate Demographic Questions: Demographic questions (e.g., age, gender, location) will help you to segment responses and analyze satisfaction across different customer segments. So include it.
• Mobile-Friendly Design: Make sure your survey is mobile-friendly and displays properly on different screen sizes.

Step 4: Sampling Strategy

Sampling ensures that the findings are representative of your whole customer base. It will enable you to make correct decisions and judgments. A well-planned sampling method will help you reduce biases and increase your findings’ generalization.

Depending on your research objectives and available resources, you can use a variety of sampling methods . Here are a few common approaches:

• Simple Random Sampling : It ensures that every person in the population has an equal chance of being chosen.
• Stratified Random Sampling : This sampling method divides your population into subgroups based on specified criteria.
• Convenience Sampling : This method selects participants who are easily accessible, such as customers who frequently visit your physical store or online store.

Step 5: Data Collection and Analysis

In this step, you will collect data from your target audience, arrange and evaluate the data systematically, and generate useful insights to make informed decisions.

Use statistical tools to analyze trends, correlations, and distributions for quantitative data. Calculate measures such as averages, percentages, and standard deviations. You can visually represent the findings using graphs, charts, and tables.

Use qualitative analysis tools for qualitative data. Content analysis, thematic analysis, and sentiment analysis are all common methodologies you can use. These strategies will help you identify repeating themes, attitudes, and patterns in open-ended responses.

Step 6: Implement Changes

The implementation phase of customer satisfaction research is where insights and recommendations are implemented. Here, you will turn data-driven findings into real improvements that directly influence the customer experience.

Create a detailed implementation plan for each identified improvement. Implementing changes based on research findings involves careful planning, cooperation, and a dedication to providing greater customer value.

Define specific tasks, time frames, responsible parties, and key performance indicators (KPIs) to measure the effectiveness of each effort. Prioritize the actionable recommendations that are most likely to improve customer satisfaction and retention significantly.

Step 7: Communication and Regular Feedback Loop

Transparency is essential for maintaining trust and credibility with your customers. Share the research’s findings and the responses that were made. Let your customers know that their opinions are taken seriously and have resulted in concrete improvements.

Customer satisfaction will remain a dynamic and changing emphasis of your business strategy if you establish a continual feedback loop. Here are some tips for creating and keeping a consistent feedback loop:

• Scheduled Surveys: Conduct customer satisfaction surveys quarterly, semi-annually, or yearly. 
• Incorporate Feedback Mechanisms: Integrate feedback mechanisms into various touchpoints, such as post-purchase follow-up emails, customer service interactions, or feedback forms on your website.
• Feedback Analysis: Analyze the customer feedback you received from each cycle in detail. Identify recurring themes, popular trends, and problem areas.
• Action Planning: Create action plans for additional improvements based on the newly acquired insight.
• Implementation: Implement the suggested modification and changes in every relevant part of your business.

Advantages of carrying out a satisfaction study

Carrying out a satisfaction study has great benefits for your organization:

• Obtain valuable information from customers

Doing customer satisfaction research allows you to obtain information about your customers, determine how happy they are with your company, and correct what is wrong.

• Establish priorities

The satisfaction study results allow you to discover which areas of your business need more attention, such as customer service, the sales closing process, etc.

• Customer retention

If your customers are satisfied with your products, it is possible that they will stay in your business. Maintaining a high level of customer satisfaction is extremely important to the overall success of your organization. 

• Maintain your reputation

A satisfaction study allows you to interact with consumers and show them that you care about their needs and opinions. In particular, they offer to improve the customer experience if you make the changes.

• Maintain customer loyalty

If you want to maintain customer loyalty, a satisfaction survey will give you the opportunity to listen to their feedback and improve your brand.

• Get new customers

People feel more confident buying from transparent companies, so post the feedback you get from current customers to show that you allow any kind of feedback and value it. 

• An advantage over the competition

There is a lot of competition in the market today, so any advantage you may have needs to be made known. Show current and potential customers the areas in which you excel.

Conducting customer satisfaction research with QuestionPro

One of the best ways to find out the opinion of customers and their needs is through online surveys, which allow you to collect information and perform data analysis to make better business decisions.

With QuestionPro, you can find out how satisfied your customers are by asking a Net Promoter Score question, which will let you know if consumers are promoters or detractors of your brand. 

Other types of questions that will help you gather information for your study are: 

• Multiple Choice Questions
• Closed questions
• Open text questions
• Order and Ranking Questions

You can track customer satisfaction and measure how happy your existing customers are with your business, brand, and customer initiatives by using QuestionPro’s customer satisfaction survey templates and survey questions. These customer satisfaction survey examples help ensure a higher survey completion and response rate for your market research.

Find out what customers think! Carry out customer satisfaction research and collect the necessary information to improve the consumer experience. Contact us and learn how to measure customer satisfaction using QuestionPro.

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Instant insights, infinite possibilities

How to analyze customer feedback to get better insights

Last updated

16 April 2023

Reviewed by

While continual customer feedback is essential, without a practical and logical way of digesting those findings, it’s unlikely that feedback will lead to actionable changes. 

That’s where analysis can help. Customer feedback analysis helps you turn raw customer feedback into a reliable source of knowledge covering paint points, satisfaction levels, usability, loyalty, and more. 

• What is customer feedback analysis?

A customer feedback analysis is a process by which to turn customer feedback into actionable insights . Completing a best practice analysis means you’re more likely to deeply understand what your customers want and need from your company. It can also help you attract new customers while retaining your current ones.

A customer feedback analysis typically operates in five main steps:  

Collating customer feedback into a readable and understandable report 

Analyzing the feedback to deeply understand the messages from your customers

Paying attention to common themes or patterns across the feedback

Deciphering which common issues are the most critical to be solved 

Ensuring that you address those issues

• Why analyzing customer feedback is critical

Paying attention to what your customers have to say is one of the most important ways to guarantee long-term business success. Customer feedback is a critical way to understand where issues and clunky aspects arise so that you get the chance to do better for your customers. 

While receiving negative feedback can be tricky, in the words of Bill Gates, “[y]our most unhappy customers are your greatest source of learning.” 

When customers do provide feedback, it’s essential to react to it and see how you can improve your offering. Doing so can have many positive effects, including: 

Business growth . Businesses that react to customer feedback are more likely to stay competitive and continue growing.

Customer experience . Analyzing feedback can streamline areas of friction across products, improving the overall customer experience .

Boosted NPS . The Net Promoter Score (NPS) is a common way of calculating customer loyalty. Participants are more likely to be satisfied if you’re continually improving your products for their benefit. 

• The challenge of analyzing customer feedback

While it’s a critical aspect of improving a business, analyzing customer feedback isn’t necessarily straightforward. 

Feedback comes from a range of different sources—surveys, social media comments, call center conversations, and more. The broad range of sources means that feedback comes in various forms—written comments, conversational feedback, and scores through surveys. Categorizing, understanding, and acting upon that diverse feedback can be challenging. 

The quality of feedback can impact the analysis too. Some people may use complex language when giving feedback. Others may have poor literacy making their point harder to decipher, leaving room for ambiguity or misunderstandings. 

To uncover the most relevant and helpful insights, discernment is necessary. 

• The difference between insightful and non-insightful data

That discernment means recognizing the difference between data that’s insightful––and therefore helpful to the business––and data that’s non-insightful––and therefore useless. 

Non-insightful data is feedback that doesn’t tell you anything new or is irrelevant to your business––such as feedback written by internet trolls or feedback that tells you about an issue that’s already being fixed. What’s more, feedback from random people who are not part of your target audience might be completely useless.

Insightful data tells you something new or can add weight to a proposal to optimize or release a new feature. 

If many customers, for example, are mentioning that the payment aspect of your website is challenging to use, that’s a great case for a more seamless payment feature. As each customer gives this feedback, it proves there’s a real need to prioritize this improvement. If not, your customers could soon drop off. 

Insightful data can lead a company to:

Take critical actions, including bug fixes, optimization, new features, and even new product releases 

Make changes to the business strategy 

Validate (or invalidate) new ideas and plans 

How to discern insightful and useless data

Discerning whether data is useful or not can be tricky. These are some questions to ask when sorting data:

Is this feedback authentic?

Do we fully understand what the customer wants to tell us? Should we follow up and ask more questions (instead of making assumptions)? 

Does this feedback tell us about a new issue?

Does this feedback add weight to a proposed fix or improvement?

Is this feedback providing us with something new?

Do we know the motivations and reasons behind particular behavior?

Paying attention to these questions as you sort feedback will help ensure you rely on the most insightful data. 

Keep in mind that feedback will sometimes be something you may not want to hear. But it’s essential to pay attention to negative comments, as they can help drive business improvements. 

• The process: getting actionable insights from customer feedback 

Listening to feedback is one thing, but turning it into actionable insights for your business is another. That’s why we recommend these best practice steps to effectively gather, analyze, and act upon comments from customers.

1. Gather customer feedback

Customer feedback can come from a variety of sources.

The most common sources of feedback include:

Customer surveys

Surveys are one of the most common ways to collect customer responses. That’s because they’re a quick, cost-effective way to gather large amounts of data. 

Net Promoter Score (NPS)

The NPS is a helpful way of discovering customer satisfaction across the business, not just in one area. The NPS asks participants to rank the likelihood that they would recommend your product or service to someone they know (a friend or colleague). In sum, the NPS provides macro-level insights about the current satisfaction of your customers with your business. To get an actionable insight about your score, you need to include an open-ended follow-up question asking why your customer gave a particular score. 

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Your Net Promoter Score is calculated by subtracting the percentage of Detractors from the percentage of Promoters.

Customer Effort Score Surveys (CES)

The CES is a benchmark that helps you measure the perceived effort customers must put in to achieve a result, whether that’s resolving an issue, fulfilling a task, or getting the information they need. 

It’s calculated by asking customers whether a particular interaction was easy. This can lead you to focus on particular processes and collect more evidence about emerging friction points, enabling you to act on the flaws and ultimately streamline processes to be simpler and more satisfying for the customer. 

Customer Satisfaction Surveys (CSAT)

The CSAT survey is a key method of measuring customer satisfaction . The survey asks customers how satisfied they are with a certain product, service, or interaction. This means it focuses on one experience and is a helpful way to quickly see whether an isolated process is working well for the users. 

Customer reviews

When your customers are talking about your business, it’s a crucial time to be listening. Reviews are critical when it comes to business. A whopping 98% of those surveyed read online reviews for local businesses. That means reviews can be the difference between a customer deciding to use your business or not. 

Social media comments

Comments made on social media are also important, especially given that they appear publicly, so in a way, they act like reviews too. 

Call center feedback

Call center notes can be very important to understand how people feel when they contact your customer service center. Conversations between customers may reveal some interesting insights. 

Chat conversations

Similar to call center feedback, feedback received in live chat can be very useful for seeing trends and themes emerge.  

Customer feedback interviews

These allow you to speak directly to your customers, either over the phone or in person, to discover what their pain points are, how your competitors compare, and what their overall experience with your brand is. 

2. Categorizing customer feedback

A categorization system is important to digest this large amount of information from multiple sources. Rather than lumping all feedback together, use helpful categories to break it down and turn the feedback into actions. 

It can be helpful to place feedback into two main categories: 

Type of feedback. First, classify the feedback into types. This means deciding whether it relates to a bug, a feature request, general feedback, a usability issue, or user education.

Feedback theme. Then decide which area of the business the feedback relates to. It could relate to payments, onboarding, the app, sales, marketing, the website, user profiles, or more. This is achieved through conducting a thematic analysis .

This categorization helps to group feedback into manageable sections. 

3. Code the feedback

Once feedback is categorized, it’s the best practice to turn feedback into a code that defines what the user is requesting or speaking about. 

The code defines exactly what the issue is to make the feedback simple to understand. Say, for example, a customer said they would like to edit their Google documents within your platform. The resulting code could be the “ability to integrate with Google Docs.” Similar feedback that comes through can then fall under this code.

Each piece of feedback should be given a code to ensure it’s ultimately actionable. 

4. Analyze feedback codes

Coding feedback can be a complex task. It’s often necessary to go through the codes multiple times to ensure you’ve covered all feedback notes. 

A piece of feedback may initially receive just one code. But after going through it once more, you may realize that you need to break the feedback down into two or more codes to cover all requests and comments. 

This way, you’ll have a more thorough approach to coding. 

5. Score feedback codes

Once you complete the coding process, it’s then useful to see which codes are most popular. Group similar codes together to build an overall picture of which pieces of feedback ought to be actioned more quickly. 

Give a score to each piece of feedback. This will help you begin to see patterns and understand what feedback is most common and should therefore be categorized. 

6. Provide a summary

Once you’ve collated the feedback and completed the coding process, summarize the analysis into a shareable and digestible document. 

You should share this across teams and stakeholders to ensure feedback is listened to, understood, and actionable throughout the business. 

• Utilize tools for customer feedback analysis

Collating feedback manually can be a painstaking process––particularly when working with large data sets from multiple sources. Manual processing can also increase the chances of errors. 

Making use of tools for customer feedback that are specifically designed for analysis is something many businesses lean into. Tools can fasten the process, increase accuracy, keep all your data and insights in one place, and ensure that feedback is actioned effectively across the business.

Dovetail, for example, allows you to get from data to insights fast. With Dovetail, you can store all feedback in one place while uncovering insights across all kinds of customer touch-points—whether it’s from user interviews, product feedback, or surveys. From there, it’s simple to see patterns, collate insights, and share feedback across your business for fast action. 

• Feedback analysis template

To help you start better analyzing customer data, we’re sharing this customer feedback survey analysis template .

Analyze your data to identify common themes and patterns within the responses, which can provide useful information for making informed decisions. This example demonstrates how you can import feedback, tag raw data to capture useful observations, and then transform your findings into actionable insights.

• Analyze feedback for better insights

Customer feedback is a key process to better understand your customers—their needs, wants, and pain points. 

Deeply understanding your customers and listening to what they have to say helps you deliver products that are easy to use, satisfying, and ultimately solve the problems they were designed for. 

Customer feedback analysis provides a best practice way to gather feedback, categorize it, and turn it into useful actions, all for the benefit of your business––and, importantly, your customers.

Learn more about customer analysis software

What is a customer feedback strategy.

A customer feedback strategy is the process of gathering responses and insights from your customers and then using those to drive positive change across the business. 

Which analytics is best for analyzing customer feedback?

There’s no one right way to gain or analyze customer feedback. However, score-based feedback tools such as the NPS and CSAT can be simpler to analyze than other types of open feedback. 

When analyzing feedback, consider your business objectives to decide on the right approach for your organization. 

What are the limitations of AI in customer feedback analysis?

AI can be very powerful for customer analysis, speeding up the process of gaining insights while increasing accuracy. However, there are limitations. Where there’s little or missing data, the results can be less reliable. The better the data collected, the more relevant and helpful the AI feedback analysis will be.

Should you be using a customer insights hub?

Do you want to discover previous customer research faster?

Do you share your customer research findings with others?

Do you analyze customer research data?

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What is Customer Research? Definition, Types, Examples and Best Practices

By Nick Jain

Published on: June 26, 2023

Table of Contents

What is Customer Research?

Customer research is defined as the systematic process of gathering and analyzing information about customers, their behaviors, needs, preferences, and experiences. It involves qualitative and quantitative studies to understand the target audience in order to make informed business decisions and develop effective strategies to meet expectations on customer experience and product/ service demands.

Customer research aims to provide insights into various aspects of the customer journey, including their motivations, purchase behaviors, satisfaction levels, and pain points. It helps organizations gain a deep understanding of their customers, enabling them to tailor their products, services, and marketing efforts to better meet customer expectations.

The key components of customer research typically include the following:

• Research Objectives: Clearly defining the objectives and goals of the research is crucial. This involves determining what specific information or insights the organization aims to gather from customer research. Research objectives help guide the research process and ensure that the collected data is relevant and aligned with the organization’s needs.
• Target Audience Definition: Identifying the target audience or customer segment is essential. This involves determining the specific group of customers or potential customers that the research will focus on. The target audience should be representative of the organization’s customer base or the intended market.
• Research Methodology: Choosing the appropriate research methods and techniques is important to gather relevant data. The methodology may include a combination of quantitative and qualitative observation approaches such as surveys, interviews, focus groups , or data analytics. The chosen methods should align with the research objectives and provide the desired depth and breadth of insights.
• Data Collection: Conducting data collection activities is a core component of customer research. This involves implementing the selected research methods to collect data from the target audience. It may include distributing surveys, conducting interviews or focus groups , observing customer behaviors, or analyzing existing data sources. Proper data collection techniques ensure the accuracy and reliability of the gathered information.
• Data Analysis: Once the data is collected, it needs to be analyzed to extract meaningful insights. Data analysis involves organizing, categorizing, and interpreting the collected data. This may include quantitative research using statistical techniques, such as descriptive statistics or regression analysis, and qualitative research involving the identification of patterns, themes, and trends in the data. The goal is to derive actionable insights that can inform decision-making.
• Findings and Insights: Communicating the research findings and insights is a critical component. This involves summarizing and presenting the results in a clear and understandable manner. The findings should address the research objectives and provide valuable insights into customer behaviors, preferences, needs, or pain points. Visualizations, reports, presentations, or dashboards may be used to effectively convey the information.
• Recommendations: Based on the research findings, recommendations are made to guide business decisions and actions. Recommendations should be practical, actionable, and aligned with the organization’s goals. They may involve suggestions for product improvements, marketing strategies, customer experience enhancements, market segmentation approaches, or any other relevant areas.
• Iteration and Continuous Improvement: Customer research is an iterative process. Organizations should continuously gather customer feedback and update their understanding of customer needs and preferences. The insights gained from research should be regularly incorporated into business strategies and practices. This iterative approach ensures that the organization remains responsive to customer expectations and market changes.

Types of Customer Research

There are various types of customer research that organizations can conduct to gather insights into customer experiences , behavior, and preferences. Some of the common types of customer research include:

• Customer Satisfaction Research

Customer satisfaction research focuses on measuring customer satisfaction levels with a product, service, or overall experience. It often involves surveys or feedback forms to gather customer opinions and perceptions. Customer satisfaction research helps organizations identify areas for improvement, gauge customer loyalty, and track changes in customer satisfaction over time.

• Customer Needs and Preferences Research

This type of research aims to uncover the needs, preferences, and expectations of customers. It helps organizations understand what customers value, what drives their purchasing decisions, and what features or attributes they desire in a product or service. Customer needs and preferences research can involve surveys, interviews, focus groups , or ethnographic research methods.

• Customer Experience (CX) Research

CX research focuses on understanding how users interact with a product, website, or service. It involves observing and analyzing user behaviors, attitudes, and perceptions to identify usability issues, pain points, and opportunities for improvement. The insights gained from CX research help organizations enhance the customer experience and increase satisfaction.

• Brand Perception Research

Brand perception research aims to understand how customers perceive a brand and its reputation in the market. It involves gathering customer feedback on brand awareness, brand image, brand associations, and brand loyalty. Brand perception research helps organizations assess the effectiveness of their branding strategies, identify brand strengths and weaknesses, and make informed decisions to enhance brand positioning.

• Customer Segmentation Research

Customer segmentation research involves grouping customers into distinct segments based on common characteristics, behaviors, or needs. It helps organizations understand their customer base and tailor their marketing strategies and offerings to specific customer segments. Customer segmentation research can involve data analysis, surveys, or clustering techniques to identify meaningful customer segments.

• Competitive Research

Competitive research focuses on analyzing competitors’ strategies, products, and customer experiences . It aims to gain insights into the competitive landscape and identify opportunities for differentiation. Competitive research involves analyzing competitors’ websites, conducting mystery shopping, monitoring social media, and gathering intelligence through industry reports or secondary research.

• Customer Journey Mapping

Customer journey mapping involves visualizing and understanding the end-to-end customer experience across various touchpoints and interactions with a company. It helps organizations identify pain points, gaps, and opportunities for improvement at each stage of the customer journey. Customer journey mapping can be done through a combination of data analysis, customer feedback , and qualitative research methods .

These are just a few examples of the types of customer research organizations can conduct. The choice of research type depends on the specific research objectives, the nature of the industry or market, and the information needed to make informed business decisions.

Learn more: What is Customer Feedback?

How to Conduct Customer Research: 10 Key Steps

Conducting customer research involves a systematic approach to gathering insights about customers and their preferences. Here are the key steps to conduct customer research effectively:

1. Define Research Objectives: Clearly define the specific objectives of your customer research. Determine what information or insights you seek to gather and how you plan to use the research findings. This will guide the entire research process and ensure that it remains focused and aligned with your goals.

2. Identify Target Audience: Identify the specific target audience or customer segment you want to study. Consider factors such as demographics, location, behavior, or any other relevant criteria. The target audience should be representative of your customer base or the market you wish to understand.

3. Choose Research Methods: Select the appropriate research methods (such as quantitative , qualitative research ) and techniques that will help you gather the desired information from your target audience. This may include surveys, interviews, focus groups , observational research (such as quantitative , and qualitative observation ), data analytics, or a combination of these methods. Consider the advantages, limitations, and resource requirements of each method.

4. Develop Research Instruments: Design the research instruments, such as survey questionnaires, interview guides, or discussion protocols, based on your research objectives. Ensure that the instruments are clear, concise, and structured to gather the necessary data. Use validated scales or questions when available and pilot test the instruments to identify any issues or areas for improvement.

5. Recruit Participants: Recruit participants who fit your target audience criteria and are willing to participate in the research. Depending on the research methods chosen, recruitment can be done through various channels such as online panels, customer databases, social media, or targeted advertising. Clearly communicate the purpose and benefits of the research to encourage participation.

6. Conduct Data Collection: Implement the chosen research methods to collect data from your participants. Administer surveys, conduct interviews or focus groups , observe customer behaviors, or analyze existing data sources. Ensure that the data collection process follows ethical guidelines, respects privacy, and maintains data confidentiality.

7. Analyze Data: Once the data is collected, analyze it to derive meaningful insights. Use appropriate data analysis techniques based on the nature of your data and research objectives. This may involve quantitative research and analysis using statistical methods, qualitative research and analysis using thematic coding or content analysis, or a combination of both. Ensure that the data analysis is rigorous, systematic, and aligned with your research objectives.

8. Interpret Findings: Interpret the research findings to gain insights into customer behaviors, preferences, needs, or perceptions. Analyze patterns, trends, and relationships in the data and relate them back to your research objectives. Look for key themes, outliers, or significant findings that can inform your decision-making.

9. Communicate Results: Present the research findings in a clear and concise manner. Prepare reports, presentations, or visualizations that effectively communicate the insights to stakeholders. Tailor the communication format to the needs and preferences of your target audience, ensuring that the findings are easily understandable and actionable.

10. Apply Insights: Apply the insights gained from customer research to inform your business decisions and strategies. Use the findings to enhance product development, refine marketing strategies, improve customer experiences , or address specific pain points. Regularly revisit the research findings and incorporate them into your ongoing business practices.

Remember that customer research is an iterative process. As you implement the insights gained, monitor the outcomes and consider conducting follow-up research to assess the impact and gather further insights. Continuous customer research helps organizations stay informed about evolving customer needs and preferences, enabling them to stay competitive and customer-centric.

Learn more: What is Quantitative Market Research?

Examples of Customer Research Questions

​​Here are some examples of customer research questions that businesses might ask:

• What factors influenced your decision to purchase our product/service?
• How did you first hear about our company?
• What specific features or aspects of our product/service do you find most valuable?
• What improvements or enhancements would you like to see in our product/service?
• How likely are you to recommend our product/service to others? Why?
• What obstacles or challenges did you encounter when using our product/service?
• How does our product/service compare to competitors in the market?
• How satisfied are you with the level of customer support you received?
• What are your expectations for pricing and value in relation to our product/service?
• How frequently do you use our product/service, and for what purposes?

These questions can help businesses gain insights into customer preferences, satisfaction levels, purchasing behavior, and areas for improvement. It’s important to tailor the questions to the specific industry, product, or service being researched to gather the most relevant information.

Top 10 Best Practices for Customer Research

When conducting customer research, it’s essential to follow best practices to ensure accurate and valuable insights. Here are some best practices for customer research:

1. Clearly define research objectives

Start by identifying the specific goals and objectives of your customer research. What do you want to learn or achieve through the research? This will guide your research approach and help you focus on the most relevant questions and areas of investigation.

2. Use a mix of qualitative and quantitative methods

Combining qualitative and quantitative research methods can provide a comprehensive understanding of your customers. Qualitative methods , such as interviews or focus groups, offer in-depth insights and allow you to explore customer motivations and experiences. Quantitative methods , like surveys or data analysis, provide statistical data and help you identify patterns and trends.

3. Identify your target audience

Clearly define the characteristics and demographics of your target audience. This will help you select the right participants for your research and ensure that customer feedback represents your customer base accurately.

4. Create unbiased and neutral questions

Formulate questions that are clear, unbiased, and neutral to avoid leading or influencing participants’ responses. Use open-ended questions to encourage participants to provide detailed and honest feedback.

5. Use a variety of data collection methods

Explore various data collection methods to gather customer insights. These can include surveys, interviews, focus groups , social media listening, website analytics, customer feedback forms, or online reviews. Employing multiple methods (such as quantitative research methods , qualitative research methods , etc.) can provide a more comprehensive view of customer opinions and behaviors.

6. Engage with customers at different touchpoints

Interact with customers throughout their journey with your product or service. This can include pre-purchase, purchase, and post-purchase stages. Collect feedback at different touchpoints to understand the entire customer experience and identify areas for improvement.

7. Maintain confidentiality and anonymity

Assure participants that their responses will be kept confidential and anonymous. This encourages honest and unbiased feedback. Respect privacy regulations and data protection guidelines when collecting and storing customer data.

8. Analyze and interpret data systematically

Once you have collected the data, analyze it systematically. Look for patterns, trends, and common themes. Identify key insights and use them to inform your decision-making process. Consider using data visualization techniques to present findings in a clear and concise manner.

9. Continuously iterate and improve

Customer research should be an ongoing process. Regularly revisit your research objectives and update your research methods to reflect changing customer needs and preferences. Continuously gather customer feedback and make improvements based on customer insights.

10. Communicate findings and take action

Share the results of your customer research with relevant stakeholders within your organization. Communicate the key findings, insights, and recommendations. Use the research findings to inform strategic decisions, product development, marketing strategies, and customer support initiatives.

By following these best practices, you can conduct effective customer research that provides valuable insights and helps you better understand and serve your customers.

Learn more: What is Qualitative Research?

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How To Design Customer Satisfaction Surveys That Get Results [+ Templates]

Updated: May 02, 2024

Published: June 15, 2022

On a recent cross-country flight, I made a pitstop in the restroom. On the way out, I was asked to rate my experience by pressing a button — one of four faces ranging from a happy smiley face to a red angry face. My selection was a quick, painless customer satisfaction survey.

Knowing what your customers think and feel makes all the difference when improving your user experience. So, what is a customer satisfaction survey, and what are the best practices for creating one?

I took a deep dive, so you don’t have to. Below, I’ll share some knowledge from my personal and professional experience. I’ll also get some other experts to weigh in. Let’s get started.

Table of Contents

What is a customer satisfaction survey?

Why customer satisfaction surveys are important, customer satisfaction survey examples & templates, customer satisfaction question types & survey design, customer satisfaction survey best practices, how to use & implement survey results.

Customer satisfaction surveys gather information on customers’ experiences through a questionnaire. It’s a basic measure of customers’ level of happiness (or unhappiness) with your business.

You might ask respondents if your products or services are useful, your website or app is easy to use, your staff is friendly, or your restroom is clean. Here’s an example of a customer feedback survey from HubSpot:

Going about our lives, we take part in customer surveys almost every day — sometimes multiple times a day — often unknowingly.

For example, if I hop in an Uber to head to my local coffee shop, I’ll get asked to rate my driver after they drop me off. After I’ve ordered my cup of joe, the coffee shop might ask me to rate my drink or the staff member who served it to me.

If companies are asking us to take customer satisfaction surveys so often, they must have some value, right? You bet.

In my career as a marketer and co-founder of Omniscient Digital , I’ve used various types of surveys — from basic one-question surveys to longer questionnaires — to get to the root of customer experiences. Here’s why they matter.

Customer satisfaction surveys are about more than confirming how much your customers love your brand. Let me explain where their real value lies.

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• Net Promoter Score
• Customer Effort Score

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1. Customer feedback improves your product and your customer’s overall experience.

From my experience as a marketer, customer feedback is a goldmine of information on more than just satisfaction levels. When customers tell you what they like and don’t like about products or services, you’ll find out what you should definitely keep and what you might want to change.

For a real-world perspective, I talked to Amy Maret , HubSpot’s principal researcher for trends and thought leadership and a former market research manager. According to Maret, satisfaction surveys allow businesses to track their most important relationship — their relationship with customers.

“We see over and over that having an exceptional customer experience is absolutely critical to a business’s success, so being able to see in real-time how your customers are feeling, diagnose potential issues, and act quickly as soon as satisfaction starts trending down is a huge advantage to any organization,” Maret says.

I agree with Maret on the importance of customer relationships, and I think that you can strengthen your relationship by simply listening to customers .

2. Feedback can improve customer retention.

If I don’t know why customers churn, I can’t do much to keep them — or win them back if they’ve already left. Essentially, I can’t know a customer’s thoughts if they don’t tell me.

Take my experience at a new coffee shop. I started going regularly until a barista told me that they had stopped serving oat milk. They offered alternatives, but I wasn’t interested and switched shops.

What’s the lesson? I’m not one to complain over minor things, but had I been given a customer satisfaction survey, I would have mentioned my milk issue.

Maret agrees that businesses should ask if customers are satisfied, but also why. In fact, Maret notes that the best surveys go beyond just measuring KPIs and actually identify which factors impact customer satisfaction.

“As soon as the business sees unsatisfied customers in their survey, they can go deeper into what exactly is causing that dissatisfaction and address those problems directly — which we know from our research leads directly to happier customers that are less likely to churn,” Maret says.

With a well-designed survey given at the right time, you can identify issues and resolve them, helping you increase customer loyalty .

3. Feedback identifies happy customers who can become advocates.

If customers are genuinely happy with your product or service (and not just using it because they can’t find an alternative), they can become an extension of your marketing team — and they do it all for free!

When I moved to a new city, I asked around the office if anyone knew a good place for a haircut. Suddenly, everyone became an advocate for their hairdresser. I got information on everything I needed to know, including prices, personalities, and free extras, like top-tier snacks and drinks.

These recommendations were both honest and enthusiastic, and I found them more convincing than any paid marketing campaign.

If you can identify your brand advocates through customer surveys, you can show your appreciation for them and even incentivize their word-of-mouth marketin g.

4. Customer feedback helps inform decisions.

When it comes down to making important business decisions, you need to get the input of all the important people in the room, and that includes your customers.

From a basic customer survey, I learned that something as simple as a new user interface for a website can annoy previously satisfied customers. As you might imagine, I held urgent discussions with my team to see how we could continue to satisfy loyal customers.

To show you how you can retain customers and have them shout your company’s benefits from the rooftops, I’ll share some customer satisfaction survey examples and templates.

Customer satisfaction surveys come in a few common forms, usually executed using a popular response scale methodology, like:

• Net Promoter Score® (NPS).
• Customer Satisfaction Score (CSAT).
• Customer Effort Score (CES).

Each of these types of customer satisfaction surveys measures something slightly different, so it’s important to consider the specifics if you hope to use the data wisely.

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Net Promoter Score (NPS)

The NPS is a popular survey methodology, especially for those in the technology space.

It's rare to see a survey that doesn't use this famous question: “How likely is it that you would recommend this company to a friend or colleague?”

Ask your customers this question with HubSpot's customer feedback software .

Okay, you got me — this first example doesn’t technically ask customers about their level of satisfaction. But, I’ve found that customer satisfaction and willingness to recommend are often directly linked.

If I don’t like a new product/service, I won’t recommend it to friends. Sounds straightforward? Not exactly. When it comes to subjective experience and preferences, things can get a little tricky.

For example, if I watch a new movie that doesn’t appeal to me because it’s too quirky, I might still recommend it to a friend who loves that kind of thing.

So, how does this affect businesses? Well, if you build a great product, you might have a good NPS, even if not every potential customer can benefit from it.

Let’s look at how the NPS is measured:

On a rating scale of 0–10, your detractors score you 0–6, passives score you 7 or 8, and promoters score you 9 or 10. Your NPS score is your percentage of promoters minus your percentage of detractors.

For example, if 70% of my respondents are promoters and 20% are detractors, my NPS is +50. If you think +50 sounds disappointing, you might be surprised. According to market research firm B2B International, the average NPS for business-to-business (B2B) firms is +25 to +33.

Customer Satisfaction Score (CSAT)

For me, CSAT is the easiest customer satisfaction survey type to use and respond to.

Usually, it takes the form of a question phrased like, “How satisfied were you with your experience today?” and a corresponding survey scale, which is generally from 1 to 5.

Here’s what these numbers mean.

• 1 = Very unsatisfied
• 2 = Unsatisfied
• 3 = Neutral
• 4 = Satisfied
• 5 = Very satisfied

To get your CSAT score, expressed as a percentage, you’ll need this equation:

(Number of positive responses (4 or 5)/Number of people surveyed) x 100

Opinions differ on this, but I think a good CSAT score is 75% to 85%. Higher is better, but don’t be too worried if you don’t hit a perfect 100% (it’s tricker than you might think).

As someone who has answered countless CSAT surveys and given plenty to customers, I can say that the results are a pretty useful indicator of how a customer feels at that moment, but you need to take the results with a pinch of salt.

For example, if I’m shopping for clothes online and encounter serious issues at the checkout page (no, I don’t want to become a member), I get frustrated. When my payment finally goes through, and I’m asked about my satisfaction level, I might say that it is 1 or 2.

However, when the clothes are delivered quickly and look and fit great, my overall satisfaction level could rise considerably. So, you need to be specific when asking this question (e.g., “How satisfied are you with the checkout experience?”) and get your timing right. (You can easily ask your customers about their satisfaction level with HubSpot’s customer feedback tool .)

In some cases, a simple satisfaction survey won’t give you the answers you’re looking for. Instead, you might want to ask about ease of experience.

Enter the CES.

Customer Effort Score (CES)

The CES is a useful metric for measuring a customer’s service experience. Generally, your survey answer options should range from “very difficult” to “very easy.”

I’ve used these CES options to ask customers if they could easily navigate a website to get what they needed and if using an app was challenging.

A few answers of “very difficult” aren’t usually cause for concern, but a high percentage of them means you should consider redesigning your product or service.

Ask your customers this question with HubSpot's customer feedback tool.

It’s impossible to understate the importance of survey design. It’s the foundation your survey is built on, and it can affect everything from response rates to accuracy. If the design is wrong, the data won't be useful to answer your questions about your customers.

With that in mind, I’ll take you through the different types of questions that underpin your survey design.

1. Binary Scale Questions

The first type of survey question is a simple binary distinction:

• “Was your experience satisfying?”
• “Did our product meet expectations?”
• “Did you find what you were looking for?”

The options for all of these are dichotomous, such as yes/no or thumbs up/thumbs down.

I think you’ll agree that the benefit of this survey type lies in its simplicity. When I use this question type, I want a straightforward answer from customers, nothing more.

In some cases, as you lengthen the survey scale, you end up with data you can’t use. As Jared Spool , co-founder of Center Centre , said in a talk , “Anytime you’re enlarging the scale to see higher-resolution data, it’s probably a flag that the data means nothing.”

If I’m finished browsing an ecommerce website and about to exit the page, I often get asked a binary question about my experience. Because it’s easy to answer, I don’t mind doing it, even if I’m in a hurry.

Bear in mind that binary questions have some shortcomings. For example, if a restaurant asks me the binary, “Did you like your meal?” and I answer, “No,” they won’t know if it’s because I found my food too salty, cold, or both.

When to use: I recommend using this question type when you only need a yes/no answer, and there’s no real ambiguity about what the answer means for your business. If you think your target audience could reasonably answer, “Well, kind of,” then choose a different question type.

2. Multiple-Choice Questions

Multiple-choice questions have three or more mutually exclusive answers. These tend to be used to collect categorical variables , like names and labels.

When I’m creating surveys that I’m going to conduct data analysis on later, multiple-choice questions are my go-to. That’s because I can easily segment the data based on different categorical variables to get the valuable insights I need.

Let’s say I have an accounting software company. I can ask respondents what their job title or business industry is when soliciting feedback. Then, when I’m analyzing the data, I can compare the satisfaction scores by job title or industry. I might find that CEOs don’t like the software’s expense, while their finance teams love its functionality.

When to use: I use this question type when I want to glean deep insights from survey responses, and you should, too. However, if you just want basic information from customers and don’t have the time or resources for data analysis, skip this one.

3. Scale Questions

Many popular satisfaction surveys are based on scale questions. For example, the CSAT asks, “How satisfied are you with your experience?” and you rate the experience on a scale of 1 to 5 (a Likert scale ).

You can design scale questions so that they’re answered using numbers (1-5), words (strongly agree), emojis (😀), and more.

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Like other question types, they’re not without faults. For example, if I ask customers to rate my product and they give it a low score, I’m left guessing why exactly they gave me that score.

When to use: Use Likert scale questions and other scale types when you want a more specific response than a binary answer. They’re generally suitable if you don’t care too much about the reason behind a response.

4. Semantic Differential

Similar to scale questions, semantic differential scales are based on binary statements, such as disagree and agree, but respondents can choose from a wide number of points between them.

That means customers don't have to pick just one or the other — they can choose a point between the two poles that reflects their experience accurately.

Here’s what a semantic differential question looks like in practice:

Say a website owner is testing out a new homepage design. So, they ask visitors a semantic differential question, “Do you like the new website design?” They let respondents answer using two extreme options of “I don’t like it” and “I like it” or multiple unlabelled options in between.

This can give the website owner a good handle on respondents’ actual perceptions of the new design, particularly if there are follow-up questions about specific design elements.

When to use: Use this type of question if you want respondents to be able to interpret the answer continuum as they see fit. This can help you find out their strength of satisfaction or dissatisfaction without introducing bias from labels like “neutral” or “somewhat satisfied.”

5. Open-Ended Questions

None of the surveys I’ve discussed above tell you the why of an experience — you only get the what. To find out the why, you need qualitative data , which you can get from this type of customer satisfaction survey question.

Maret notes that qualitative questions allow customers to tell you the real why behind their satisfaction, without you having to make assumptions about what matters to them.

“When you want to dig deeper into motivations and underlying factors, it is helpful to hear from customers in their own words. But be careful — too many open-ended questions in one survey can cause respondent fatigue — potentially frustrating your customers and damaging your data quality,” Maret says.

I’ll add to her warning with one of my own: Make sure you’re asking clear questions without letting your bias or expectations slip through.

For example, if you write, “Tell us, in your own words, why our products are so great,” you leave little room for collecting customer feedback that’s critical of your products, and that’s a missed opportunity.

In many of my surveys, I’ll add some open-ended questions to take an in-depth look at customer expectations, customer needs, and more. Often, the insights I get from open-ended questions are the most valuable, but it can take a lot of time and resources to mine these insights.

When to use: Use open-ended questions when you have the time to really dig deep into the answers.

You’ll often see this type of question at the end of a survey with multiple question types — I have found that it helps respondents share thoughts that aren’t covered in other questions.

To bring you closer to creating the most suitable customer satisfaction survey for your business, I’ve collected some tried-and-tested best practices for you to follow.

• Choose the right survey type.
• Choose the right survey questions.
• Send surveys at the right moment in the customer journey.
• Ask for customer feedback regularly.
• Limit the number of survey questions.
• Consider different ways to ask questions.
• Test your survey.
• Follow up with respondents.

1. Choose the right survey type.

If you want quantitative data, I don’t recommend open-ended questions. But there’s nothing stopping you from having a mixed survey type to cover all your bases. If you go this route, I recommend a logical, consistent approach to keep readers on track.

For example, I find that open-ended questions work well at the end of a group of closed questions or as the final section of a survey.

2. Choose the right survey questions.

Remember I said to be careful of your question phrasing? I meant it. Make sure you’re asking the right questions, which should be clear and free from bias.

3. Send surveys at the right moment in the customer journey.

If you measure customer sentiment at the right time, you’ll get actionable results — and a chance to make things right with customers before it’s too late. The correct stage will largely depend on your business type, but in general, you should send out surveys shortly after a purchase.

If you’ve lost a customer, an exit survey can tell you why. But if you spot signs of dissatisfaction and survey customers before they’ve left, you can make a real difference.

For example, I once got negative feedback from a customer just before they were about to switch to a competitor. When I told them I understood their concerns and would work with them to make things right, they appreciated feeling listened to and retained our services.

4. Ask for customer feedback regularly.

By getting regular customer feedback from different touchpoints (e.g., in-app, email surveys, and social media), you can better understand how customers feel about your product/service in the long term.

For example, for complex apps, I recommend checking in with customers early (within the first week post-purchase) in case they are frustrated by technical challenges. You can offer tutorials to help and check in regularly afterward (e.g., monthly or quarterly).

5. Limit the number of survey questions.

If you ask too many questions, your customers might start to answer them on autopilot. This makes surveys a waste of time for them and a waste of time and money for you.

When it comes to survey questions, I always recommend quality over quantity, especially when the results will inform company decisions.

Sticking to around 10-15 questions is a good rule of thumb, but also consider how long the survey will take. For example, I answer binary questions really quickly, but I spend some time mulling over my answers to open-ended questions.

6. Consider different ways to ask questions.

By asking similar questions but in different ways, you can get unexpected but valuable answers from customers. This can tease out customer pain points that they hadn’t considered initially.

For example, “Is the app fast?” and “Is the app slow at times?” can yield different answers.

7. Test your survey.

Don’t just send your survey out without any input from others. I usually get colleagues to read over my questions to check if they hit the mark. Then, I send them to a small group of customers to ensure they’re interpreted correctly before I release them to the masses.

8. Follow up with respondents.

If your survey isn’t anonymous, then check in with customers and reply to their grievances. Address all their points, and try not to get too defensive if you think their responses are inaccurate.

After your customer satisfaction survey is done and dusted, you can’t just put your feet up. Here’s what you need to do:

• If you collect quantitative surveys, clean the data and use analysis software to find out its implications. I generate reports from survey data and use them as slides in company meetings or to show customers we take their feedback seriously.
• For qualitative surveys, read over the results thoroughly and try to identify themes in responses. I’ll constantly take notes on one computer monitor while I’m looking at survey results on another.
• Next, develop a list of recommended actions and group them by priority. For example, if your website can’t accept payments, that’s an urgent concern. But, if a customer has a helpful suggestion that might cost a lot of money to implement, you can discuss it in detail with team members.
• Take any actions necessary, and tell customers about the steps you’ve taken based on their feedback.

To help bring you new insights into implementing satisfaction survey results, I reached out to Simon Bacher , CEO and co-founder of Ling , a language learning app, to find out his team’s approach.

He says the team at Ling analyzes what aspects of gamification users love, such as the app’s banana points system or the badges earned through completing challenges.

“Furthermore, surveys reveal user preferences for different learning styles, enabling us to tailor the gamification experience to better suit their needs. Once we've implemented changes, we track how users engage with the updated features through app analytics and surveys,” Bacher says.

Results Begin with a Survey

Customer satisfaction surveys are generally a great idea, provided you put some thought into designing them, distribute them at the right time and frequency, and — most importantly — do something about what you’ve found out from them.

Customers appreciate the simple act of sharing their thoughts, but in my experience, they won’t be truly satisfied unless a brand’s products or services meet their needs.

I hope these tips on survey design, use, and implementation help you in your efforts to improve your customers’ experience. Many of them have worked for me, but I’m always on the lookout for new ways to improve customer satisfaction.

Net Promoter, Net Promoter System, Net Promoter Score, NPS and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld and Satmetrix Systems, Inc.

Editor’s Note: This post was originally published in February 2020 and has been updated for comprehensiveness.

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How do I measure customer satisfaction?

Types of customer experience surveys, types of customer satisfaction survey questions, additional questions to ask in your customer satisfaction survey, customer satisfaction survey design best practices, linking customer satisfaction surveys to your customer journey, how to turn your customer feedback into action, customer satisfaction survey templates, try qualtrics for free, customer satisfaction (csat) surveys: questions & template.

21 min read Consumers expect an exceptional experience with your company, and unfortunately, people talk about bad customer experiences more than they’ll brag about good ones. Read on to learn why satisfaction data is valuable information, and how to optimize your customer satisfaction surveys for useful insights.

What is customer satisfaction (CSAT) survey?

A customer satisfaction (CSAT) survey is used to determine a CSAT score by asking customers the question ‘How satisfied are you with [organization]? Answers range from 1-5 with 5 being “highly satisfied” and 1 being “highly unsatisfied”.

They are used to understand your customer’s satisfaction levels with your organization’s products, services, or experiences. This is one type of customer experience survey and can be used to gauge customers’ needs, understand problems with your products and/or services, or segment customers by their score. They often use rating scales to measure changes over time, and gain a deeper understanding of whether or not you’re meeting the customer’s expectations.

Get started with our free customer satisfaction survey template

Why use customer satisfaction surveys?

Customer satisfaction is at the core of human experience, reflecting customers’ liking of a company’s business activities. A customer satisfaction survey is a great way to understand how your customer feels about your business and their customer journey, and to nail down exactly what new customers might like about your offering.

There are several reasons why measuring your audience’s views with a customer satisfaction survey can be beneficial to your brand.

Customers will leave if they don’t feel like their experience was worth it

Our recent 2022 Global Consumer Trends Report found that 62% of customers think brands need to care about them more. With 9.5% of your revenue at risk from customers walking after a bad experience, knowing how your customers feel is financially beneficial. Gathering feedback via a customer satisfaction survey means you can figure out how to care for customers – and to monitor changes in customer sentiment before small issues become real problems.

A satisfying customer experience is worth the money for your target audience

Our research also found that 60% of consumers would buy more if businesses treated them better . Measuring which interactions and experiences your customers value will help you to judge what they will pay more for.

Satisfied customers will spread the word

Americans will mention a positive experience to an average of nine people and a negative experience to an average of 16 . This can have a knock-on effect for your brand reputation. According to Nielsen , 84% of consumers they surveyed thought word-of-mouth was the most trustworthy recommendation type. With every experience potentially attracting or pushing away future customers that hear of you this way, it’s vital to monitor how your customers feel with a customer satisfaction survey.

Satisfaction is a great indicator of retention, loyalty, and likelihood to repurchase

High levels of satisfaction (with pleasurable experiences) are strong predictors of customer and client retention and product repurchase. Customer satisfaction data that answers why loyal customers or clients enjoyed their experience helps the company recreate these experiences in the future. Effective businesses focus on creating and reinforcing world-class experiences so that they retain existing customers and add new customers.

A well-timed customer satisfaction survey can help you hone your customer journey

Mapping your customer journey is an important step of understanding your customers’ interactions with your brand – and for building out your customer lifecycle. However, it’s not enough to just create your journey map. You need to know how your customers feel at each stage of their experience with your brand, from the first interaction to getting in touch with customer service representatives, to making a purchase. A customer satisfaction survey can put your finger on the pulse of customer sentiment and give you a great sense of where your journey needs updating maximum efficacy.

To understand how satisfied your customers are, you need to understand the key drivers behind their experiences. The best way of discovering not only how your customers feel, but what has caused them to feel the way they do is by creating customer satisfaction surveys.

However, customer satisfaction feedback can be nebulous. Giving your customers a framework for their feedback – such as likelihood to recommend with a scale of 0 – 10 – can help you contrast and compare answers over time, as well as develop insights and action across multiple relationships.

Choosing the type of customer satisfaction survey you wish to create will help you to develop a metric for measuring – and improving – your customer satisfaction.

There are a few ways you can measure customer experience through customer or client satisfaction surveys. The first question you need to answer is what metrics you want to use.

The most commonly used metrics are:

• Net Promoter Score (NPS) ® – Probably the most popular measure of customer affinity towards your company. Created and trademarked by Bain & Company, the net promoter system involves a quick survey that typically asks “How likely are you to recommend [company name] to a friend” with a Likert scale question from 0-10
• Customer Effort Score (CES)   – This metric measures how hard it was for a customer to be able to complete the task that prompted their interaction. This survey question could look like, “How easy was it to deal with our company today?” This survey and measurement system can be useful for post-interaction surveys with customer service or support teams
• Customer Satisfaction (CSAT) – This is a commonly used measure for product and services to rate how happy consumers are with what they purchased. The typical survey question to collect this feedback looks like, “How would you rate your overall satisfaction with the [goods/service] you received?” then offers a Likert scale question type between 1-5 with 5 being “highly satisfied” and 1 being “highly unsatisfied”

In this article, we will be focusing on customer satisfaction surveys (CSAT).

When building your customer satisfaction survey questions, the type of question you choose to ask can make a big difference to the insights you receive and your ability to improve the experience.

Here are the types and some sample customer satisfaction (CSAT) questions to help you decide which will get you the answers you’re looking for.

Likert scale questions

A Likert Scale question provides customers with options for their response from one extreme to another (i.e. satisfied to unsatisfied), with or without a neutral response.

For example, a five-point Likert scale question might look like this:

How satisfied are you with our service?

• Very satisfied
• Moderately satisfied
• Neither satisfied nor dissatisfied
• Moderately dissatisfied
• Very dissatisfied

An even Likert scale question removes the middle response to provide a binary choice.

These types of customer satisfaction survey questions are simple to understand and answer, and will provide you with quantifiable customer satisfaction data.

However, the real attitude of your customers can’t usually be deduced by just this type of question alone. Customers might have specific drivers that aren’t highlighted by this question, or they might feel reluctant to choose an “extreme” option, even if it is true for their experience.

Binary questions

Binary questions provide you with the quickest response to any feedback survey question. By asking a simple yes/no question (or its equivalent), you can get the general sense of whether customers’ needs have been met.

For example, you could ask:

Were you satisfied with your experience with us?

[Smiley face/Unhappy face]

Did you find what you were looking for today?

Again, this does not provide you with the full context of their answer.

Multiple choice questions

Multiple choice questions can enable you to find out more about customers and their experiences.

Which of our services was most useful for you today?

[Service A] [Service B] [Service C]

This type of question can be more leading for your customers, as you are providing the text answers for them to choose from. However, it can offer you more insight than a simple binary or Likert scale question.

Open-ended questions

This type of question allows your customer to provide a description in their own words of how satisfied they are with your products or services.

What could we have improved on today?

[Open text box]

Open-ended questions can give you a much more specific insight into a particular customer’s problems or highlights. However, they are also higher effort for your customers – which may put them off responding.

Get started with our free Customer Satisfaction Survey template

Adding additional questions can help you sort through and take action on your customer feedback — just remember that shorter is generally better when it comes to survey completion rate.

Usage frequency

Edit the usage frequency options below so that they are relevant to your industry or product. This helps you understand the user’s skill level with your product/service.

• Once a Month
• Every 2-3 Months
• 2-3 Times a Month

Product and usage survey questions

Product and usage survey questions can give you greater insight into how your customer base uses your products and services. Not only that, but you can learn more about how they feel about them as well. This can help inform not only how you approach customers, but also with your product development efforts. Incorporating customer feedback about your products, you know how to better meet their needs and improve their experience.

Product and usage survey questions you could ask include:

• How often do you use our products/services?
• Which key features of our products/our services are the most useful?
• How easy do you find our products/our services to use?
• Do our products/services provide value for money?
• Are there any features that you would like to see in our products/services?
• What problem are you trying to solve by using our products/our services?

Demographic Questions

Demographic questions can be helpful in understanding what audiences or customer segments you are excelling with or under serving. We recommend getting as much of this data from your customer database or CRM, instead of asking for it in a survey whenever possible. Below are some potential demographic questions you can add to your customer satisfaction survey.

• Employment Status
• Household Income
• Marital Status
• Children/dependents
• Location (zip code)
• Ethnic background

Psychographic survey questions

Unlike demographic survey questions, psychographic survey questions are more focused on psychological criteria. These questions can cover activities, interests and opinions, giving you a fuller picture of your customer profile . These questions can be open-ended, binary or multiple choice.

Psychographic questions might cover:

• Attitudes toward a certain product or service
• Religious beliefs
• Political affiliations
• Likes and dislikes towards certain topics
• Personal reasons behind purchases

Satisfaction category questions

This type of question helps you identify satisfaction key drivers and highlight the areas of a customer’s experience that are important, allowing you to align product and service priorities. Below are potential categories of drivers.

• Overall Quality
• Purchase Experience
• Installation/Onboarding
• Warranty/Repair Experience

Open text feedback question

This question allows customers to provide unsolicited open-text feedback and their response to your customer satisfaction survey and mention specific topics or experiences for your team to review.

Action/ follow-up questions

This is a simple question asking if it’s okay if a member of the team reaches out to the respondent to try and understand and resolve any pain points.

Properly constructed customer satisfaction surveys and questionnaires provide the insights that are the foundation for benchmarking customer happiness. Depending on what customer metrics you intend to use, it will determine what type of survey questions you need to ask your customers. Below are a few best practices:

• Ask for overall company rating first – This satisfaction survey question gives you great initial insight and allows you to compare to industry and internal benchmarks over time.
• Allow for open text feedback – Open text questions allow you to collect open-ended responses from your respondents. You can gain more detail about your customer’s experiences and you might uncover new insights you didn’t expect.
• Optimize for mobile – Many consumers are now completing surveys on mobile devices or within mobile apps, so your survey must be optimized for mobile devices . If it is too complicated for a mobile respondent, survey participation will decrease.
• Ask double-barrel questions – These questions touch on more than one issue, but only allow for one response. They are confusing for the respondent, and you’ll get skewed data because you don’t know which question the respondent is answering.
• Make the survey too long – The majority of CSAT surveys should be less than 10 questions. People won’t finish long surveys.
• Use internal or industry jargon- Your customers must be able to clearly understand each question without hesitation and using internal or industry jargon is confusing to respondents.

Asking your customers about their experiences at any time might seem useful, but ideally you will link your customer satisfaction survey to specific points in the customer journey. Proper timing of customer satisfaction surveys depends on the type of product or service provided, the type and number of customers served, the longevity and frequency of customer/supplier interactions, and the intended use of the results.

Nevertheless, timing when to send a customer satisfaction survey is extremely important no matter the circumstances.

Best practices include:

• Asking for responses shortly after the customer journey touchpoint has occurred: The experience should be fresh in your respondent’s mind so you get the most honest answers and gain insights that are accurate.
• Use multiple channels to give customers options they’ll prefer: You can solicit feedback face-to-face when they leave your store, email, online survey, phone, or within your mobile app.
• Avoid survey fatigue: Don’t quiz the same customers again and again throughout all the points of their journey – figure out when delivering a survey will give you the most useful insights.
• Take action once you have customer satisfaction data: There’s no use learning that part of your journey puts off your customers and then leaving the problem to fester. Take action to make changes once you know what experiences make customers feel less satisfied.

Delivering customer satisfaction surveys at the right points in the journey

Let’s look at an example of a customer journey from the airline industry. A customer satisfaction survey can be sent at every touchpoint in the process.

• After the customer books their flight – Feedback after the initial purchase is important because you want to understand if the person was satisfied with their checkout or purchase experience. Send an email with a link to an online survey after the customer purchases their flight to find out how satisfied they were with the booking process. Consumers want easy transactions, so look for ease-of-use in your data.
• After the actual flight – Post-purchase evaluations reflect the satisfaction of the individual customer at the time of product or service delivery (or shortly thereafter). This can be a transactional NPS or customer satisfaction survey and sent by email.
• After a customer service encounter- If the customer initiates contact with a customer service representative, a customer effort score (CES) survey should be sent immediately after the issue was resolved. For airlines, this could be a call to change a flight date or report lost baggage. The goal is to see how much effort it took to resolve the issue.
• Six months after the flight – To measure the long-term customer loyalty, relational NPS or CSAT surveys can be sent months after the transaction occurred to see if your customers are still loyal to your brand.
• In-app mobile feedback – You can request customer feedback on the mobile app or customer experience through a feedback tab in the app. Getting mobile app feedback is important — only your customers can tell you what will make them more satisfied with their experience.

Measuring customer satisfaction is important but what you do with the data is essential. If your customers take the time to fill out a survey, it’s important they know you’re serious about improving their experience.

• Close the loop – Respond quickly after receiving negative feedback from your customers. This is a chance to keep your customer loyal. 70 percent of consumers said they would be more likely to do business with an organization again if their complaint was handled well the first time.
• Analyze for trends – Understand what metrics you’re looking to improve and see if there are patterns on these specific items. For instance, if 30 percent of respondents say the customer service wait time was too long, you know you need to improve in that area.
• Company-wide effort- Every department must be on board to keep the customer satisfied. If customers complain about a product feature, the product department must be willing to receive the data and fix it. If customers complain about the service, customer service representatives need to understand how to fix the issues more effectively. Make sure the right people have the right visibility with role-based CX dashboards and analytics .

Do you want to go deeper into customer insights and create loyal and satisfied customers?

Though looking at customer satisfaction survey examples is helpful, we’ve gone one step further to create a customer satisfaction survey template to get you started.

Our prebuilt customer satisfaction survey template can be used in your customer experience management (CXM) to start properly measuring customer satisfaction. Keep in mind, all of these customer satisfaction surveys can be used today when you sign up for a FREE Qualtrics account .

Other Customer Feedback Resources:

• Customer Feedback – What to Collect and When
• Omni Channel Customer Feedback
• How to increase survey response rates

Related resources

Measuring customer satisfaction 22 min read, what is csat 8 min read, customer delight 18 min read, improving customer satisfaction 11 min read, customer satisfaction 16 min read.

Customer Service

Omnichannel Customer Service 13 min read

Generative ai customer service 10 min read, request demo.

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10 Ways to Boost Customer Satisfaction

• G. Tomas M. Hult
• Forrest V. Morgeson

Takeaways from an analysis of millions of consumer data points.

Customer satisfaction is at its lowest point in the past two decades. Companies must focus on 10 areas of the customer experience to improve satisfaction without sacrificing revenue. The authors base their findings on research at the ACSI — analyzing millions of customer data points — and research that we conducted for The Reign of the Customer : Customer-Centric Approaches to Improving Customer Satisfaction. For three decades, the ACSI has been a leading satisfaction index (cause-and-effect metric) connected to the quality of brands sold by companies with significant market share in the United States.

Despite all the effort and money poured into CX tools by companies, customer satisfaction continues to decline . In the United States, it is now at its lowest level in nearly two decades, per data from the American Customer Satisfaction Index (ACSI). Consumer sentiment is also at its lowest in more than two decades. This negative dynamic in the customer-centric ecosystem in which we now live creates the challenge of figuring out what is going wrong and what companies can do to fix it.

• GH G. Tomas M. Hult is part of the leadership team at the American Customer Satisfaction Index (ACSI); coauthor of The Reign of the Customer: Customer-Centric Approaches to Improving Customer Satisfaction ; and professor in the Broad College of Business at Michigan State University. He is also a member of the Expert Networks of the World Economic Forum and the United Nations’ World Investment Forum.
• FM Forrest V. Morgeson is an assistant professor in the Broad College of Business at Michigan State University; (Former) Director of Research at the American Customer Satisfaction Index (ACSI); and coauthor of The Reign of the Customer: Customer-Centric Approaches to Improving Customer Satisfaction .

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ORIGINAL RESEARCH article

Service quality and customer satisfaction in the post pandemic world: a study of saudi auto care industry.

• 1 College of Business Administration, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
• 2 Department of Management Sciences, University of Baluchistan, Quetta, Pakistan

The aim of this research is to examine the impact of service quality on customer satisfaction in the post pandemic world in auto care industry. The car care vendor in the study made effective use of social media to provide responsive updates to the customers in the post pandemic world; such use of social media provides bases for service quality and customer satisfaction. The study examined the relationship between service quality and customer satisfaction using the SERVQUAL framework. According to the findings, empathy, reliability, assurance, responsiveness, and tangibles have a significant positive relationship with customer satisfaction. Our findings suggest that it is critical for workshops to recognize the service quality factors that contribute to customer satisfaction. Findings also suggest that empathy, assurance, reliability, responsiveness, and tangibles contribute to customer satisfaction. Auto repair industry must regularly provide personal attention, greet customers in a friendly manner, deliver cars after services, notify customers when additional repairs are required, and take the time to clarify problems to customers. Furthermore, workshops must screen and hire courteous staff who can clearly communicate the services required to customers both in-person and online and effectively communicate the risks associated with repairs. Service quality seems to be aided by prompt services.

Introduction

The previous studies on the effect of pandemic have focused on the behavior related to preventative measures to protect the health of the customers; however, less attention has been paid to the influence of pandemic on customer outcomes. To fill this gap, the SERVQUAL framework was employed to examine the changes in customers’ social media behaviors that have occurred since the pandemic was declared ( Mason et al., 2021 ). In the post pandemic world, the parameters for customer satisfaction have changed considerably ( Monmousseau et al., 2020 ; Srivastava and Kumar, 2021 ; Wu et al., 2021 ). Pandemic has made personal interaction more challenging ( Brown, 2020 ). To be less vulnerable to becoming severely ill with the virus, customers prefer touchless digital mediums of communications. For example, Mason et al. (2021) concluded that pandemic has altered customers’ needs, shopping and purchasing behaviors, and post purchase satisfaction levels. Keeping in view the public healthcare concerns, the governmental pandemic mitigation policies also promotes touchless mediums for shopping; therefore, the role of social media as a communication tool stands to increase at a time when social distancing is a common practice; social media provides avenues for buyers to interact with sellers without physical contact. Thus, the use of social media gains critical importance, especially after the pandemic ( Mason et al., 2021 ), and the businesses may find new opportunities to gain competitive advantage through their use of effective social media strategies.

The car care industry uses traditional means of customer communications. The company in this study made use of social media in improving their service quality through effective and safe communication with their customers. The use of social media to provide updates to customers played a significant role in improving service quality and satisfaction ( Ramanathan et al., 2017 ). The company in the study used Snapchat to provide updates on the work, thus minimizing the customers’ need to physically visit the car care facility. This use of social media gave a significant boost to the responsiveness aspect of the service quality.

Service quality and customer satisfaction are important aspects of business since a company’s growth is largely dependent on how well it maintains its customers through service and how well they keep their customers satisfied ( Edward and Sahadev, 2011 ). According to Chang et al. (2017) ; customer satisfaction is expected to result from good service efficiency, which will improve customer engagement and interrelationship. González et al. (2007) asserted that customer satisfaction is linked to high service quality, which makes businesses more competitive in the marketplace. This study uses the SERVQUAL framework to define service quality. This framework uses five dimensions to account for service quality, namely, tangibles, reliability, responsiveness, assurance, and empathy. Identifying issues in service and customer satisfaction can lead to high service quality. Furthermore, service quality can be characterized by analyzing the variations between planned and perceived service. Service quality and customer satisfaction have a positive relationship.

Recognizing and meeting customer expectations through high levels of service quality help distinguish the company’s services from those of its rivals ( Dominic et al., 2010 ). Social media plays a critical role in shaping these service quality-related variables. Specifically, in the context coronavirus disease 2019 (COVID-19), where customers hesitated to visit auto workshops physically, the importance of online platforms such as auto workshops’ social media pages on Instagram and Facebook has increased, where customers try to get information and book appointment. For example, responsiveness is not only physical responsiveness but also digital means of communication. The car care company in this study uses social media as mode of communication with their customers due to physical interaction restriction caused by the pandemic.

Service quality becomes a critical element of success in car care industry because customer contact is one of the most important business processes ( Lambert, 2010 ). Saudi Arabia is one of the Middle East’s largest new vehicle sales and auto part markets. Saudi Arabia’s car repair industry has grown to be a significant market for automakers from all over the world. As a result, the aim of this research was to see how service quality affects customer satisfaction in the Saudi auto repair industry.

This aim of this research was to answer the following research questions:

(i) What is the contribution of individual dimensions of SERVQUAL on customer perceived service quality of car care industry in Saudi Arabia?

(ii) What is the impact of perceived service quality on customer satisfaction in car care industry in Saudi Arabia?

Literature Review

The concept of service has been defined since the 1980s by Churchill and Surprenant (1982) together with Asubonteng et al. (1996) , who popularized the customer satisfaction theory through measuring the firm’s actual service delivery in conformity with the expectations of customers, as defined by the attainment of perceived quality, and that is meeting the customers’ wants and needs beyond their aspirations. With this premise, Armstrong et al. (1997) later expanded the concept of service into the five dimensions of service quality that comprised tangibles, reliability, responsiveness, assurance, and empathy.

Extant literature on service delivery focuses on the traditional emphasis on the contact between the customer and service provider ( Mechinda and Patterson, 2011 ; Han et al., 2021 ). Doucet (2004) explained that the quality in these traditional settings depends on the design of the location and the behavior of the service provider. More recently, the proliferation of the internet has led to the emergence of the online service centers. In these cases, communication both in-person and online plays a critical role in the quality of service rendered. It follows that service quality in hybrid settings depends on quality of communications on social media as well as the behavioral interactions between the customer and the service provider ( Doucet, 2004 ; Palese and Usai, 2018 ). These factors require subjective assessments by the concerned parties, which means that different persons will have varied assessments of the quality of service received.

SERVQUAL Dimensions

Service quality has been described with the help of five quality dimensions, namely, tangibles, reliability, responsiveness, assurance, and empathy. Definitions relating to these variables have been modified by different authors. The relationship between various dimensions of service quality differs based on particular services.

The tangible aspects of a service have a significant influence on perception of service quality. These comprise the external aspects of a service that influence external customer satisfaction. The key aspects of tangibility include price, ranking relative to competitors, marketing communication and actualization, and word-of-mouth effects ( Ismagilova et al., 2019 ), which enhance the perception of service quality of customers ( Santos, 2002 ). These aspects extend beyond SERVQUAL’s definition of quality within the car care industry settings. Thus, we proposed the following hypothesis:

Hypotheses 1a: Tangibles are positively related with perceived service quality.

Reliability

Reliability is attributed to accountability and quality. There are a bunch of precursors that likewise aid basic methodology for shaping clients’ perspectives toward administration quality and reliability in the car care industry in Saudi ( Korda and Snoj, 2010 ; Omar et al., 2015 ). A portion of these predecessors is identified with car repair benefits and includes the convenient accessibility of assets, specialist’s expertise level and productive issue determination, correspondence quality, client care quality, an exhibition of information, client esteem, proficiency of staff, representatives’ capacity to tune in to client inquiries and respond emphatically to their necessities and protests, security, workers’ dependability, more limited holding up time and quickness, actual prompts, cost of administration, accessibility of issue recuperation frameworks, responsibility, guarantees, for example, mistake-free administrations, generally association’s picture and workers’ politeness, and responsiveness. Despite the innovative changes happening in the car care industry and the instructive degree of car administrations suppliers in Saudi Arabia, car care suppliers in the territory are taught about the need to continually refresh their insight into the advancements in the area of vehicle workshops and the components of administration. Thus, we argued that reliability is important to enhance the perception of service quality of customers.

Hypotheses 1b: Reliability is positively linked with perceived service quality.

Responsiveness

Responsiveness refers to the institution’s ability to provide fast and good quality service in the period. It requires minimizing the waiting duration for all interactions between the customer and the service provider ( Nambisan et al., 2016 ). Nambisan et al. (2016) explained that responsiveness is crucial for enhancing the customers’ perception of service quality. Rather, the institution should provide a fast and professional response as to the failure and recommend alternative actions to address the customer’s needs ( Lee et al., 2000 ). In this light, Nambisan summarizes responsiveness to mean four key actions, i.e., giving individual attention to customers, providing prompt service, active willingness to help guests, and employee availability when required. These aspects help companies to enhance the customers’ perception of service quality. Therefore, we proposed the following hypothesis:

Hypotheses 1c: Responsiveness is positively linked with perceived service quality.

Assurance refers to the skills and competencies used in delivering services to the customers. Wu et al. (2015) explains that employee skills and competencies help to inspire trust and confidence in the customer, which in turn stirs feelings of safety and comfort in the process of service delivery. Customers are more likely to make return visits if they feel confident of the employees’ ability to discharge their tasks. Elmadağ et al. (2008) lists the factors that inspire empathy as competence, politeness, positive attitude, and effective communication as the most important factors in assuring customers. Besides, other factors include operational security of the premises as well as the proven quality of the service provided to the customers. Thus, the assurance has significant contribution in the perception of service quality.

Hypotheses 1d: Assurance is positively related with perceived service quality.

Empathy refers to the quality of individualized attention given to the customers. The service providers go an extra mile to make the customer feel special and valued during the interaction ( Bahadur et al., 2018 ). Murray et al. (2019) explains that empathy requires visualizing the needs of the customer by assuming their position. Murray et al. (2019) lists the qualities that foster empathy as including courtesy and friendliness of staff, understanding the specific needs of the client, giving the client special attention, and taking time to explain the practices and procedure to be undertaken in the service delivery process. Therefore, we proposed the following hypothesis:

Hypotheses 1e: Empathy is positively related with perceived service quality.

Perceived Service Quality and Customer Satisfaction

Customer satisfaction refers to the level of fulfillment expressed by the customer after the service delivery process. This is a subjective assessment of the service based on the five dimensions of service quality. Customer satisfaction is important due to its direct impact on customer retention ( Hansemark and Albinsson, 2004 ; Cao et al., 2018 ; Zhou et al., 2019 ), level of spending ( Fornell et al., 2010 ), and long-term competitiveness of the organization ( Suchánek and Králová, 2019 ). Susskind et al. (2003) describes that service quality has a direct impact on customer satisfaction. For this reason, this research considers that five dimensions of service quality are the important antecedents of customer satisfaction.

Service quality refers to the ability of the service to address the needs of the customers ( Atef, 2011 ). Customers have their own perception of quality before interacting with the organization. The expectancy-confirmation paradigm holds that customers compare their perception with the actual experience to determine their level of satisfaction from the interaction ( Teas, 1993 ). These assessments are based on the five independent factors that influence quality. Consequently, this research considers service quality as an independent variable.

This study attempts to quantify perceived service quality though SERVQUAL dimensions. We proposed that customers place a high premium on service quality as a critical determinant of satisfaction. Moreover, it is argued that satisfaction prompts joy and reliability among customers in Saudi Arabia. These discoveries infer that the perception of service quality is significantly related to satisfaction, and quality insight can be applied across different cultures with negligible contrasts in the result. Car care industry in Saudi Arabia has grave quality problems. To rectify this situation, it is essential to apply quality systems as tools for development. The SERVQUAL is one of these system options. It is used to gauge the service quality using five dimensions that have been time-tested since 1982. Thus, the significance of SERVQUAL in car care industry in Saudi Arabia cannot be overemphasized. The study further suggests that the SERVRQUAL dimension increases the perceived service quality, which in turn increases customer satisfaction. Thus, we proposed the following hypothesis:

Hypothesis 2: The perceived service quality of car care customers is positively linked with their satisfaction.

Methods and Procedures

In this study, we employed a cross-sectional research design. Using a paper-pencil survey, data were collected form auto care workshops situated in the Eastern Province of Saudi Arabia. According to the study by Newsted et al. (1998) , the survey method is valuable for assessing opinions and trends by collecting quantitative data. We adapted survey instruments from previous studies. The final survey was presented to a focus group of two Ph.D. marketing scholars who specialized in survey design marketing research. The survey was modified keeping in view the recommendations suggested by focus group members. We contacted the customers who used social media to check the updates and book the appointment for their vehicle’s service and maintenance. We abstained 130 surveys, 13 of which were excluded due to missing information. Therefore, the final sample encompassed 117 (26 female and 91 male) participants across multiple age groups: 10 aged less than 25 years, 46 aged between 26 and 30 years, 28 aged between 31 and 35 years, 21 aged between 36 and 40 years, and 12 aged older than 40 years (for details, refer to Table 1 ). Similarly, the averaged participants were graduates with more than 3 years of auto care service experience.

Table 1. Demographic information.

We measured service quality dimensions using 20 indicators. Customer satisfaction of the restaurant customers was assessed using 4-item scale (for detail, refer to Table 2 ). In this research, the 5-point Likert scale from 1 = strongly disagree to 5 = strongly agree was used.

Table 2. Constructs and items included in the questionnaire.

Control Variables

Following the previous research, customer’s gender and age were controlled to examine the influence of service quality dimensions on customer satisfaction.

Data Analysis and Results

For data analysis and hypotheses testing, we employed the structural equation modeling (SEM) based on the partial least squares (PLS) in Smart-PLS. Smart-PLS 3 is a powerful tool, which is used for the confirmatory factor analysis (CFA) and SEM ( Nachtigall et al., 2003 ). Research suggests that CFA is the best approach to examine the reliability and validity of the constructs. We employed SEM for hypotheses testing because it is a multivariate data analysis technique, which is commonly used in the social sciences ( González et al., 2008 ).

Common Method Bias

To ensure that common method bias (CMB) is not a serious concern for our results, we employed procedural and statistical and procedural remedies. During data collection, each survey in the research contained a covering letter explaining the purpose of the study and guaranteed the full anonymity of the participants. Moreover, it was mentioned in the cover letter that there was no right and wrong questions, and respondents’ answers would neither be related to their personalities nor disclosed to anyone. According to Podsakoff et al. (2003) , the confidentiality of the responses can assist to minimize the possibility of CMB. Furthermore, CMB was verified through the Harman’s single-factor test ( Podsakoff et al., 2003 ). All items in this research framework were categorized into six factors, among which the first factor explained 19.01% of the variance. Thus, our results showed that CMB was not an issue in our research. Moreover, using both tolerance value and the variance inflation factors (VIFs), we assessed the level of multicollinearity among the independent variables. Our results indicate that the tolerance values for all dimensions of service quality were above the recommended threshold point of 0.10 ( Cohen et al., 2003 ), and VIF scores were between 1.4 and 1.8, which suggested the absence of multicollinearity; thus, it is not a serious issue for this study.

Measurement Model

We performed CFA to analyze the reliability and validity of the constructs. The measurement model was assessed by examining the content, convergent, and discriminant validities. To assess the content validity, we reviewed the relevant literature and pilot test the survey. We used item loadings, Cronbach’s alpha, composite reliability (CR), and the average variance extracted (AVE) ( Fornell and Larcker, 1981b ) to assess the convergent validity. The findings of CFA illustrate that all item loadings are greater than 0.70. The acceptable threshold levels for all values were met, as the value of Cronbach’s alpha and CR was greater than 0.70 for all constructs ( Fornell and Larcker, 1981b ), and the AVE for all variables was above 0.50 ( Tabachnick and Fidell, 2007 ; see Table 3 ). Thus, these findings show acceptable convergent validity.

Table 3. Item loadings, Cronbach’s alpha, composite reliability, and average variance extracted.

To analyze the discriminant validity, we evaluated the discriminant validity by matching the association between correlation among variables and the square root of the AVE of the variables ( Fornell and Larcker, 1981a ). The results demonstrate that the square roots of AVE are above the correlation among constructs, hence showing a satisfactory discriminant validity, therefore, indicating an acceptable discriminant validity. Moreover, descriptive statistics and correlations are provided in Table 4 .

Table 4. Descriptive statistics and correlations.

Structural Model and Hypotheses Testing

After establishing the acceptable reliability and validity in the measurement model, we examined the relationship among variables and analyzed the hypotheses based on the examination of standardized paths. The path significance of proposed relations were calculated using the SEM through the bootstrap resampling technique ( Henseler et al., 2009 ), with 2,000 iterations of resampling. The proposed research framework contains five dimensions of service quality (i.e., tangibles of the auto care, reliability of the auto care, responsiveness of the auto care, assurance of the auto care, and empathy of the auto care) and customer satisfaction of auto care. The results show that five dimensions of service quality are significantly related to customer’s perception of service quality of auto care; thus, hypotheses 1a, 1b, 1c, 1d, and 1e were supported. Figure 1 shows that the service quality of auto care is a significant determinant of customer satisfaction of auto care industry (β = 0.85, p < 0.001), supporting hypothesis 2. The result in Figure 1 also shows that 73.8% of the variation exists in customer satisfaction of auto care.

Figure 1. Results of the research model tests. *** p < 0.001.

The main purpose of this research was to assess the relationship between service quality and customer satisfaction in the post pandemic world in Saudi Arabia. This study was designed to examine how satisfaction of auto care customers is influenced by service quality, especially, when pandemic was declared, and due to health concerns, the customers were reluctant to visit workshops physically ( Mason et al., 2021 ). It appears that after the pandemic, customers were increasingly using online platforms for purchasing goods and services. This study reveals how customers of auto repair in Saudi perceive service quality and see how applicable SERVQUAL model across with five dimensions, including tangibles, responsiveness, reliability, assurance, and empathy measure service quality. The findings of this research show that five dimensions of SERVQUAL are positively related to the service quality perception of auto care customers in Saudi Arabia. Moreover, service quality perceptions are positively linked with customer satisfaction. These results indicate that auto care customers view service quality as an important antecedent of their satisfaction. The findings indicate that the customers perceive the service quality as a basic service expectation and will not bear the extra cost for this criterion. In this research, the positive connection between service quality and customer satisfaction is also consistent with previous studies (e.g., González et al., 2007 ; Gallarza-Granizo et al., 2020 ; Cai et al., 2021 ). Thus, service quality plays a key role in satisfying customers. These findings suggest that service organizations, like auto repair industry in Saudi Arabia could enhance satisfaction of their customers through improving service quality. Because of pandemic, people are reluctant to visit auto care workshops, and they try to book appointment through social media; so, by improving the quality of management of their social media pages, the workshops can provide accurate information for monitoring, maintaining, and improving service quality ( Sofyani et al., 2020 ). More specifically, social media, which allows individuals to interact remotely, appears to be gaining significant importance as a tool for identifying customers’ products and service needs. Increasingly, customers are also increasingly engaging with retailers through social media to search and shop for product and services options, evaluate the alternatives, and make purchases.

Furthermore, the research on the customer service quality can be held essential since it acts as a means for the promotion of the competitiveness of an organization. Precisely, the knowledge about the customers’ view concerning service quality can be used by organizations as a tool to improve their customer services. For example, knowledge of the required customer service would help in the facilitation of training programs oriented toward the enlightenment of the overall employees on the practices to improve and offer high-quality customer services. Besides, information concerning customer services would be essential in decision-making process concerning the marketing campaigns of the firm, hence generating competitive advantage of the organization in the marketplace. Findings show that customers demand more from auto repair, so the company must work hard to increase all service quality dimensions to improve customer satisfaction. Thus, organizations ought to venture in customer services initiatives to harness high-quality services.

Managerial Implications

The findings of this research indicate a strong association between SERVQUAL dimensions and perceived service quality. Perception of higher service quality leads to higher level of customer satisfaction among Saudi car care customers. In particular, the results indicate high scores for reliability, empathy, tangibles, and responsiveness. These are clear indications that the immense budgetary allocation has enabled these institutions to develop capacity. Nevertheless, the lack of a strong human resource base remains a key challenge in the car care industry. The effective use of social media plays a critical role in the responsiveness dimension of service quality. Companies need to develop their digital and social media marketing strategies in the post pandemic world to better satisfy their customers.

Saudi Arabia requires a large and well-trained human resource base. This requires intensive investment in training and development. Most of these workers have a limited contract, which reduced their focus on long-term dedication. Consequently, the government should provide longer-term contracts for workers in this critical sector. The contracts should include training on tailored courses to serve the identified needs in effective communication with the customers using digital media. We suggested that the auto car care workshops should provide training to their workers, particularly, on service technicians to enhance their skills that will help to deliver fast and reliable service to their auto customers.

Moreover, the auto car care workshops also provide customer care- or customer handling-related training especially for the service marketing personnel who handles customer directly for them to better understand the customer needs and expectations. This can be done at least once a year. This will help auto care workshops to improve their service quality.

Limitation and Future Research Direction

This research is not without limitations. First, the findings of this study are based on data collected from a single source and at a single point of time, which might be subjected to CMB ( Podsakoff et al., 2003 ). Future research can collect data from different points of time to validate the findings of this research. Second, this research was carried out with data obtained from Saudi auto car care customers; the findings of this research might be different because the research framework was retested in a different cultural context. Therefore, more research is needed to improve the understanding of the principles of service quality and customer satisfaction, as well as how they are evaluated, since these concepts are critical for service organizations’ sustainability and development. A greater sample size should be used in a similar study so that the findings could be applied to a larger population. Research on the effect of inadequate customer service on customer satisfaction, the impact of customer retention strategies on customer satisfaction levels, and the impact of regulatory policies on customer satisfaction is also recommended. Third, because most of the participants participated in this research are men, future studies should obtain data from female participants and provide more insights into the difference between male and female customers’ satisfaction levels. Moreover, due to limitation of time, the sample was collected from the eastern province. Consequently, further research should include a larger and more representative sample of the Saudi population. Because of the non-probability sampling approach used in this research, the results obtained cannot be generalized to a wide range of similar auto repair services situations, even though the methodology used in this study could be extended to these similar situations. Since the sample size considered is not that large, expectations could vary significantly. When compared with the significance of conducting this form of analysis, the limitations mentioned above are minor. Such research should be conducted on a regular basis to track service quality and customer satisfaction levels and, as a result, make appropriate changes to correct any vulnerability that may exist.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SZ helped in designing the study. ZH helped in designing and writing the manuscript. MAA helped in data collection and analysis and writing the manuscript. SUR repositioned and fine-tuned the manuscript, wrote the introduction, and provided feedback on the manuscript.

This study was received funding from University Research Fund.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : auto care, customer satisfaction, service quality, Saudi Arabia, pandemic (COVID-19)

Citation: Zygiaris S, Hameed Z, Ayidh Alsubaie M and Ur Rehman S (2022) Service Quality and Customer Satisfaction in the Post Pandemic World: A Study of Saudi Auto Care Industry. Front. Psychol. 13:842141. doi: 10.3389/fpsyg.2022.842141

Received: 23 December 2021; Accepted: 07 February 2022; Published: 11 March 2022.

Reviewed by:

Copyright © 2022 Zygiaris, Hameed, Ayidh Alsubaie and Ur Rehman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zahid Hameed, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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An empirical research on customer satisfaction study: a consideration of different levels of performance

Yu-cheng lee.

1 Department of Technology Management, Chung-Hua University, Hsinchu, 300 Taiwan

Yu-Che Wang

2 Department of Business Administration, Chung-Hua University, Hsinchu, 300 Taiwan

Shu-Chiung Lu

3 PhD Program of Technology Management, Chung-Hua University, Hsinchu, 300 Taiwan

4 Department of Food and Beverage Management, Lee-Ming Institute of Technology, New Taipei City, 243 Taiwan

Yi-Fang Hsieh

6 Department of Food and Beverage Management, Taipei College of Maritime Technology, New Taipei City, 251 Taiwan

Chih-Hung Chien

5 Department of Business Administration, Lee-Ming Institute of Technology, New Taipei City, 243 Taiwan

Sang-Bing Tsai

7 Zhongshan Institute, University of Electronic Science and Technology of China, Dongguan, 528402 Guangdong China

8 School of Economics and Management, Shanghai Maritime University, Shanghai, 201306 China

9 Law School, Nankai University, Tianjin, 300071 China

10 School of Business, Dalian University of Technology, Panjin, 124221 China

11 College of Business Administration, Dongguan University of Technology, Dongguan, 523808 Guangdong China

12 Department of Psychology, Universidad Santo Tomas de Oriente y Medio Día, Granada, Nicaragua

Weiwei Dong

13 School of Economics and Management, Shanghai Institute of Technology, Shanghai, 201418 China

Customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Customers should be managed as assets, and that customers vary in their needs, preferences, and buying behavior. This study applied the Taiwan Customer Satisfaction Index model to a tourism factory to analyze customer satisfaction and loyalty. We surveyed 242 customers served by one tourism factory organizations in Taiwan. A partial least squares was performed to analyze and test the theoretical model. The results show that perceived quality had the greatest influence on the customer satisfaction for satisfied and dissatisfied customers. In addition, in terms of customer loyalty, the customer satisfaction is more important than image for satisfied and dissatisfied customers. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Traditional manufacturing factories converted for tourism purposes, have become a popular leisure industry in Taiwan. The tourism factories has experienced significant growth in recent years, and more and more tourism factories emphasized service quality improvement, and customized service that contributes to a tourism factory’s image and competitiveness in Taiwan (Wu and Zheng 2014 ). Therefore, tourism factories has become of greater economic importance in Taiwan. By becoming a tourism factory, companies can establish a connection between consumers and the brand, generate additional income from entrance tickets and on-site sales, and eventually add value to service innovations (Tsai et al. 2012 ). Because of these incentives, the Taiwanese tourism factory industry has become highly competitive. Customer satisfaction is seen as very important in this case.

Numerous empirical studies have indicated that service quality and customer satisfaction lead to the profitability of a firm (Anderson et al. 1994 ; Eklof et al. 1999 ; Ittner and Larcker 1996 ; Fornell 1992 ; Anderson and Sullivan 1993 ; Zeithaml 2000 ). Anderson and Sullivan ( 1993 ) stated that a firm’s future profitability depends on satisfying current customers. Anderson et al. ( 1994 ) found a significant relationship between customer satisfaction and return on assets. High quality leads to high levels of customer retention, increase loyalty, and positive word of mouth, which in turn are strongly related to profitability (Reichheld and Sasser 1990 ). In a tourism factory setting, customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Kutner and Cripps ( 1997 ) indicated that customers should be managed as assets, and that customers vary in their needs, preferences, buying behavior, and price sensitivity. A tourism factory remains competitive by increasing its service quality relative to that of competitors. Delivering superior customer value and satisfaction is crucial to firm competitiveness (Kotler and Armstrong 1997 ; Weitz and Jap 1995 ; Deng et al. 2013 ). It is crucial to know what customers value most and helps firms allocating resource utilization for continuously improvement based on their needs and wants. The findings of Customer Satisfaction Index (CSI) studies can serve as predictors of a company’s profitability and market value (Anderson et al. 1994 ; Eklof et al. 1999 ; Chiu et al. 2011 ). Such findings provide useful information regarding customer behavior based on a uniform method of customer satisfaction, and offer a unique opportunity to test hypotheses (Anderson et al. 1997 ).

The basic structure of the CSI model has been developed over a number of years and is based on well-established theories and approaches to consumer behavior, customer satisfaction, and product and service quality in the fields of brands, trade, industry, and business (Fornell 1992 ; Fornell et al. 1996 ). In addition, the CSI model leads to superior reliability and validity for interpreting repurchase behavior according to customer satisfaction changes (Fornell 1992 ). These CSIs are fundamentally similar in measurement model (i.e. causal model), they have some obvious distinctions in model’s structure and variable’s selection. Take full advantages of other nations’ experiences can establish the Taiwan CSI Model which is suited for Taiwan’s characters. Thus, the ACSI and ECSI have been used as a foundation for developing the Taiwan Customer Satisfaction Index (TCSI). The TCSI was developed by Chung Hua University and the Chinese Society for Quality in Taiwan. The TCSI provides Taiwan with a fair and objective index for producing vital information that can help the country, industries, and companies improve competitiveness. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs (Fig.  1 ). The relationships among the different aspects of the TCSI are different from those of the ACSI, but are the same as those of the ECSI (Lee et al. 2005 , 2006 ).

The Taiwan Customer Satisfaction Index model

The traditional CSI model for measuring customer satisfaction and loyalty is restricted and does not consider the performance of firms. Moreover, as theoretical and empirical research has shown, the relationship between attribute-level performance and overall satisfaction is asymmetric. If the asymmetries are not considered, the impact of the different attributes on overall satisfaction is not correctly evaluated (Anderson and Mittal 2000 ; Matzler and Sauerwein 2002 ; Mittal et al. 1998 ; Matzler et al. 2003 , 2004 ). Few studies have investigated CSI models that contain different levels of performance (satisfaction), especially in relation to satisfaction levels of a tourism factory. To evaluate overall satisfaction accurately, the impact of the different levels of performance should be considered (Matzler et al. 2004 ). The purpose of this study is to apply the TCSI model that contains different levels of performance to improve and ensure the understanding of firm operational efficiency by managers in the tourism factory. A partial least squares (PLS) was performed to test the theoretical model due to having been successfully applied to customer satisfaction analysis. The PLS is well suited for predictive applications (Barclay et al. 1995 ) and using path coefficients that regard the reasons for customer satisfaction or dissatisfaction and providing latent variable scores that could be used to report customer satisfaction scores. Our findings provide support for the application of TCSI model to derive tourist satisfaction information.

Literature review

National customer satisfaction index (csi).

The CSI model includes a structural equation with estimated parameters of hidden categories and category relationships. The CSI can clearly define the relationships between different categories and provide predictions. The basic CSI model is a structural equation model with latent variables which are calculated as weighted averages of their measurement variables, and the PLS estimation method calculates the weights and provide maximum predictive power of the ultimate dependent variable (Kristensen et al. 2001 ). Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ).

Although the core of the models are in most respects standard, they have some obvious distinctions in model’s structure and variable’s selection so that their results cannot be compared with each other and some variations between the SCSB (Swedish), the ACSI (American), the ECSI (European), the NCSB (Norwegian) and other indices. For example, the image factor is not employed in the ACSI model (Johnson et al. 2001 ); the NCSB eliminated customer expectation and replaced with corporate image; the ECSI model does not include the customer complaint as a consequence of satisfaction. Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ). The ECSI model distinguishes service quality from product quality (Kristensen et al. 2001 ) and the NCSB model applies SERVQUAL instrument to evaluate service quality (Johnson et al. 2001 ). A quality measure of a single customer satisfaction index is typically developed according to a certain type of culture or the culture of a certain country. When developing a system for measuring or evaluating a certain country or district’s customer satisfaction level, a specialized customer satisfaction index should be developed.

As such, the ACSI and ECSI were used as a foundation to develop the TCSI. The TCSI was developed by Chung Hua University and the Chinese Society for Quality. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs. The TCSI assumes that currently: (1) Taiwan corporations have ability of dealing with customer complaints; customer complaints have already changed from a factor that influences customer satisfaction results to a factor that affects quality perception; (2) The expectations, satisfaction and loyalty of customers are affected by the image of the corporation. The concept that customer complaints are not calculated into the TCSI model is that they were removed based on the ECSI model (Lee et al. 2005 , 2006 , 2014a , b ; Guo and Tsai 2015 ; Tsai et al. 2015a , b ; 2016a ).

TCSI model and service quality

Service quality is frequently used by both researchers and practitioners to evaluate customer satisfaction. It is generally accepted that customer satisfaction depends on the quality of the product or service offered (Anderson and Sullivan 1993 ). Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the NCSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). Ryzin et al. ( 2004 ) applied the ACSI to U.S. local government services and indicated that the perceived quality of public schools, police, road conditions, and subway service were the most salient drivers of satisfaction, but that the significance of each service varied among income, race, and geography. Hsu ( 2008 ) proposed an index for online customer satisfaction based on the ACSI and found that e-service quality was more determinative than other factors (e.g., trust and perceived value) for customer satisfaction. To deliver superior service quality, an online business must first understand how customers perceive and evaluate its service quality. This study developed a basic model for using the TCSI to analyze Taiwan’s tourism factory services. The theoretical model comprised 14 observation variables and the following six constructs: image, customer expectations, perceived quality, perceived value, customer satisfaction, and loyalty.

Research methods

The measurement scale items for this study were primarily designed using the questionnaire from the TCSI model. In designing the questionnaire, a 10-point Likert scale (with anchors ranging from strongly disagree to strongly agree) was used to reduce the statistical problem of extreme skewness (Fornell et al. 1996 ; Qu et al. 2015 ; Tsai 2016 ; Tsai et al. 2016b ; Zhou et al. 2016 ). A total of 14 items, organized into six constructs, were included in the questionnaire. The primary questionnaire was pretested on 30 customers who had visited a tourism factory. Because the TCSI model is preliminary research in the tourism factory, this study convened a focus group to decide final attributes of model. The focus group was composed of one manager of tourism factory, one professor in Hospitality Management, and two customers with experience of tourism factory.

We used the TCSI model (Fig.  1 ) to structure our research. From this structure and the basic theories of the ACSI and ECSI, we established the following hypotheses:

Image has a strong influence on tourist expectations.

Image has a strong influence on tourist satisfaction.

Image has a strong influence on tourist loyalty.

Tourist expectations have a strong influence on perceived quality.

Tourist expectations have a strong influence on perceived values.

Tourist expectations have a strong influence on tourist satisfaction.

Perceived quality has a strong influence on perceived value.

Perceived quality has a strong influence on tourist satisfaction.

Perceived value has a strong influence on tourist satisfaction.

Customer satisfaction has a strong influence on tourist loyalty.

The content of our surveys were separated into two parts; customer satisfaction and personal information. The definitions and processing of above categories are listed below:

• Part 1 of the survey assessed customer satisfaction by measuring customer levels of tourism factory image, expectations, quality perceptions, value perceptions, satisfaction, and loyalty toward their experience, and used these constructs to indirectly survey the customer’s overall evaluation of the services provided by the tourism factory.
• Part 2 of the survey collected personal information: gender, age, family situation, education, income, profession, and residence.

The six constructs are defined as follows:

• Image reflects the levels of overall impression of the tourism factory as measured by two items: (1) word-of-mouth reputation, (2) responsibility toward concerned parties that the tourist had toward the tourism factory before traveling.
• Customer expectations refer to the levels of overall expectations as measured by two items: (1) expectations regarding the service of employees, (2) expectations regarding reliability that the tourist had before the experience at the tourism factory.
• Perceived quality was measured using three survey measures: (1) the overall evaluation, (2) perceptions of reliability, (3) perceptions of customization that the tourist had after the experience at the tourism factory.
• Perceived value was measured using two items: (1) the cost in terms of money and time (2) a comparison with other tourism factories.
• Customer satisfaction represents the levels of overall satisfaction was captured by two items: (1) meeting of expectations, (2) closeness to the ideal tourism factory.
• Loyalty was measured using three survey measures: (1) the probabilities of visiting the tourism factory again (2) attending another activity held by the tourism factory, (3) recommending the tourism factory to others.

Data collection and analysis

The survey sites selected for this study was the parking lots of one food tourism factory in Taipei, Taiwan. A domestic group package and individual tourists were a major source of respondents who were willing to participate in the survey and completed the questionnaires themselves based on their perceptions of their factory tour experience. Four research assistants were trained to conduct the survey regarding to questionnaire distribution and sampling.

To minimize prospective biases of visiting patterns, the survey was conducted at different times of day and days of week—Tuesday, Thursday, Saturday for the first week; Monday, Wednesday, Friday and Sunday for the next week. The afternoon time period was used first then the morning time period in the following weeks. The data were collected over 1 month period.

Of 300 tourists invited to complete the questionnaire, 242 effective responses were obtained (usable response rate of 80.6 %). The sample of tourists contained more females (55.7 %) than males (44.35 %). More than half of the respondents had a college degree or higher, 28 % were students, and 36.8 % had an annual household income of US $10,000–$20,000. The majority of the respondents (63.7 %) were aged 20–40 years.

Comparison of the TCSI models for satisfied and dissatisfied customers

Researchers have claimed that satisfaction levels differ according to gender, age, socioeconomic status, and residence (Bryant and Cha 1996 ). Moreover, the needs, preferences, buying behavior, and price sensitivity of customers vary (Kutner and Cripps 1997 ). Previous studies have demonstrated that it is crucial to measure the relative impact of each attribute for high and low performance (satisfaction) (Matzler et al. 2003 , 2004 ). To determine the reasons for differences, a satisfaction scale was used to group the sample into satisfied (8–10) and dissatisfied (1–7) customers.

The research model was tested using SmartPLS 3.0 software, which is suited for highly complex predictive models (Wold 1985 ; Barclay et al. 1995 ). In particular, it has been successfully applied to customer satisfaction analysis. The PLS method is a useful tool for obtaining indicator weights and predicting latent variables and includes estimating path coefficients and R 2 values. The path coefficients indicate the strengths of the relationships between the dependent and independent variables, and the R 2 values represent the amount of variance explained by the independent variables. Using Smart PLS, we determined the path coefficients. Figures  2 and ​ and3 3 show ten path estimates corresponding to the ten research hypothesis of TCSI model for satisfied and dissatisfied customers. Every path coefficient was obtained by bootstrapping the computation of R 2 and performing a t test for each hypothesis. Fornell et al. ( 1996 ) demonstrated that the ability to explain the influential latent variables in a model is an indicator of model performance, in particular the customer satisfaction and customer loyalty variables. From the results shown, the R 2 values for the customer satisfaction were 0.53 vs. 0.50, respectively; and the R 2 value for customer loyalty were 0.64 vs. 0.60, respectively. Thus, the TCSI model explained 53 vs. 50 % of the variance in customer satisfaction; 64 vs. 60 % of that in customer loyalty as well.

Path estimate of the TCSI model for satisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

Path estimate of the TCSI model for dissatisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

According to the path coefficients shown in Figs.  2 and ​ and3, 3 , image positively affected customer expectations (β = 0.58 vs. 0.37), the customer satisfaction (β = 0.16 vs. 0.11), and customer loyalty (β = 0.47 vs. 0.16). Therefore, H1–H3 were accepted. Customer expectations were significantly related to perceived quality (β = 0.94 vs. 0.83). However, customer expectations were not significantly related to perceived value shown as dotted line (β = −0.01 vs. −0.20) or the customer satisfaction, shown as dotted line (β = −0.21 vs. −0.32). Thus, H4 was accepted but H5 and H6 were not accepted. Perceived value positively affected the customer satisfaction (β = 0.27 vs. 0.14), supporting H7. Accordingly, the analysis showed that each of the antecedent constructs had a reasonable power to explain the overall customer satisfaction. Furthermore, perceived quality positively affected the customer satisfaction (β = 0.70 vs. 0.62), as did perceived value (β = 0.83 vs. 0.74). These results confirm H8 and H9. The path coefficient between the customer satisfaction and customer loyalty was positive and significant (β = 0.63 vs. 0.53). This study tested the suitability of two TCSI models by analyzing the tourism factories in Taiwan. The results showed that the TCSI models were all close fit for this type of research. This study provides empirical evidence of the causal relationships among perceived quality, image, perceived value, perceived expectations, customer satisfaction, and customer loyalty.

To observe the effects of antecedent constructs of perceived value (e.g., customer expectation and perceived quality), customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Furthermore, satisfied customers were affected more by perceived quality (β = 0.83 vs. 0.74), as shown in Table  1 . Regarding the effect of the antecedents of customer satisfaction (e.g., image, customer expectations, perceived value and perceived quality), the total effects of perceived quality on the customer satisfaction of satisfied and dissatisfied customers were 0.92 and 0.72. The total effects of image on the customer satisfaction of satisfied and dissatisfied customers were 0.45 and 0.19. Thus, the satisfaction level of satisfied customers was affected more by perceived quality. Consequently, regarding customer satisfaction, perceived quality is more important than image for satisfied and dissatisfied customers. Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the CSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). This is consistent with the results of previous research ( O’Loughlin and Coenders 2002 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Chin and Liu 2015 ; Chin et al. 2016 ).

Table 1

Path estimates of the satisfied and dissatisfied customer CSI model

CS customer satisfaction

* p < 0.05; ** p < 0.01; *** p < 0.001

With respect to the effect of the antecedents of customer loyalty (e.g., image and customer satisfaction), the total effects of image on customer loyalty for satisfied and dissatisfied customers were 0.57 and 0.21. In other words, the customer loyalty of satisfied customers was affected more by customer satisfaction. Customer satisfaction was significantly related to the customer loyalty of both satisfied and dissatisfied customers, and satisfied customers were affected more by customer satisfaction ( β  = 0.63 vs. 0.14). Consequently, regarding customer loyalty, customer satisfaction is more important than image for both satisfied and dissatisfied customers. Numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Grønholdt et al. 2000 ). This study empirically supports the notion that customer satisfaction is positively related to customer loyalty.

The TCSI model has a predictive capability that can help tourism factory managers improve customer satisfaction based on different performance levels. Our model enables managers to determine the specific factors that significantly affect overall customer satisfaction and loyalty within a tourism factory. This study also helps managers to address different customer segments (e.g., satisfied vs. dissatisfied); because the purchase behaviors of customers differ, they must be treated differently. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively.

Fornell et al. ( 1996 ) demonstrated that the ability to explain influential latent variables in a model, particularly customer satisfaction and customer loyalty variables, is an indicator of model performance. However, the results of this study indicate that customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Moreover, they were affected more by perceived quality of customer satisfaction. Numerous researchers have found that the construct of customer expectations used in the ACSI model does not significantly affect the level of customer satisfaction (Johnson et al. 1996 , 2001 ; Martensen et al. 2000 ; Anderson and Sullivan 1993 ).

Through the overall effects, this study derived several theoretical findings. First, the factors with the largest influence on customer satisfaction were perceived quality and perceived expectations, despite the results showing that customer expectations were not significantly related to perceived value or customer satisfaction. Hence, customer expectations indirectly affected customer satisfaction through perceived quality. Accordingly, perceived quality had the greatest influence on customer satisfaction. Likewise, our results also show that satisfied customers were affected more by perceived quality than dissatisfied customers. This study determined that perceived quality, whether directly or indirectly, positively influenced customer satisfaction. This result is consistent with those of Cronin and Taylor ( 1992 ), Cronin et al. ( 2000 ), Hsu ( 2008 ), Ladhari ( 2009 ), Terblanche and Boshoff ( 2010 ), Deng et al. ( 2013 ), and Yazdanpanah et al. ( 2013 ).

Second, the factors with the most influence on customer loyalty were image and customer satisfaction. The results of this study demonstrate that the customer loyalty of satisfied customers was affected more by customer satisfaction. Consequently, regarding customer loyalty, customer satisfaction is more important than image for satisfied customers. Lee ( 2015 ) found that higher overall satisfaction increased the possibility that visitors will recommend and reattend tourism factory activities. Moreover, numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Su 2004; Deng et al. 2013 ). In initial experiments on ECSI, corporate image was assumed to have direct influences on customer expectation, satisfaction, and loyalty. Subsequent experiments in Denmark proved that image affected only expectation and satisfaction and had no relationship with loyalty (Martensen et al. 2000 ). In early attempts to build the ECSI model, image was defined as a variable involving not only a company’s overall image but products or brand awareness; thus image is readily connected with customer expectation and perception. Therefore, this study contributes to relevant research by providing empirical support for the notion that customer satisfaction is positively related to customer loyalty.

In addition to theoretical implications, this study has several managerial implications. First, the TCSI model has a satisfactory predictive capability that can help tourism factory managers to examine customer satisfaction more closely and to understand explicit influences on customer satisfaction for different customer segments by assessing the accurate causal relationships involved. In contrast to general customer satisfaction surveys, the TCSI model cannot obtain information on post-purchase customer behavior to improve customer satisfaction and achieve competitive advantage.

Second, this study not only indicated that each of the antecedent constructs had reasonable power to explain customer satisfaction and loyalty but also showed that perceived quality exerts the largest influence on the customer satisfaction of Taiwan’s tourism factory industry. Therefore, continually, Taiwan’s tourism factories must endeavor to enhance their customer satisfaction, ideally by improving service quality. Managers of Taiwan’s tourism factories must ensure that service providers deliver consistently high service quality.

Third, this research determined that the factors having the most influence on customer loyalty were image and customer satisfaction. Therefore, managers of Taiwan’s tourism factories should allow customer expectations to be fulfilled through experiences, thereby raising their overall level of satisfaction. Regarding image, which refers to a brand name and its related associations, when tourists regard a tourism factory as having a positive image, they tend to perceive higher value of its products and services. This leads to a higher level of customer satisfaction and increased chances of customers’ reattending tourism factory activities.

Different performance levels exist in how tourists express their opinions about various aspects of service quality and satisfaction with tourism factories. Customer segments can have different preferences depending on their needs and purchase behavior. Our findings indicate that tourists belonging to different customer segments (e.g., satisfied vs. dissatisfied) expressed differences toward service quality and customer satisfaction. Thus, the management of Taiwan’s tourism factories must notice the needs of different market segments to meet their individual expectations. This study proposes two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Limitations and suggestions for future research

This study has some limitations. First, the tourism factory surveyed in this study was a food tourism factory operating in Taipei, Taiwan, and the present findings cannot be generalized to the all tourism factory industries. Second, the sample size was quite small for tourists (N = 242). Future research should collect a greater number of samples and include a more diverse range of tourists. Third, this study was preliminary research on tourism factories, and domestic group package tourists were a major source of the respondents. Future studies should collect data from international tourists as well.

Authors’ contributions

Writing: S-CL; providing case and idea: Y-CL, Y-CW, Y-FH, C-HC; providing revised advice: S-BT, WD. All authors read and approved the final manuscript.

Acknowledgements

Department of Technology Management, Chung-Hua University, Hsinchu, Taiwan. This work was supported by University of Electronic Science Technology of China, Zhongshan Institute (414YKQ01 and 415YKQ08).

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Yu-Cheng Lee, Email: moc.liamg@861eelrd .

Yu-Che Wang, Email: wt.ude.uhc@gnawyrrej .

Shu-Chiung Lu, Email: moc.liamg@56ulecarg .

Yi-Fang Hsieh, Email: moc.liamg@gnafiyheish .

Chih-Hung Chien, Email: moc.liamtoh@neihctsirhc .

Sang-Bing Tsai, Phone: +86-22-2350-8785, Email: moc.liamtoh@gnibgnas .

Weiwei Dong, Email: moc.361@4949gnodiewiew .

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• IT Services & Applications
• Application Performance Management Market Report, 2030

Application Performance Management Market Size, Share & Trends Analysis Report By Platform Type (Software, Service), By Deployment Mode (On-premise, Cloud, Hybrid), By Enterprise Size, By Access Type, And Segment Forecasts, 2024 - 2030

• Report ID: GVR-4-68040-452-2
• Number of Report Pages: 120
• Format: PDF, Horizon Databook
• Historical Range: 2018 - 2022
• Forecast Period: 2024 - 2030 
• Industry: Technology
• Report Summary
• Table of Contents
• Segmentation
• Methodology
• Request a FREE Sample Copy

Market Size & Trends

The global application performance management market size was estimated at USD 6.56 billion in 2023 and is anticipated to grow at a CAGR of 13.7% from 2024 to 2030. The market's growth is largely driven by the surge in remote work settings, which has heightened the need for seamless application performance across distributed environments. As organizations increasingly rely on digital platforms to support remote operations, ensuring optimal application functionality has become critical for maintaining productivity and user satisfaction.

Furthermore, the adoption of DevOps methodologies has augmented the focus on application performance management (APM), as development and IT operations teams seek to accelerate software delivery while minimizing downtime. This shift toward continuous integration and continuous deployment mode (CI/CD) has made APM tools indispensable for monitoring application health, troubleshooting issues in real time, and ensuring the performance and reliability of critical business applications, fueling the market's expansion.

The increasing adoption of digital technologies, particularly mobile and cloud computing , is a key driver of the market's growth. As businesses continue to shift towards mobile-first strategies and leverage cloud-based infrastructure, ensuring optimal performance across these platforms has become critical. As of 2024, according to BuildFire, a U.S.-based app development platform, the Google Play Store offers 2.87 billion apps for download. Mobile app usage is particularly high among millennials, with 21% of them opening an app more than 50 times a day. Overall, 49% of users open apps at least 11 times daily, while mobile apps account for 70% of all digital media time in the U.S. On average, smartphone users engage with ten apps daily and use around 30 different apps each month.

APM tools enable organizations to monitor and manage the performance of applications running on diverse and distributed environments, including mobile devices and cloud-based systems. With the rising complexity of multi-cloud environments and the need for consistent performance across mobile applications , APM solutions are becoming essential for tracking and optimizing application health in real time. This growing reliance on mobile and cloud technologies has heightened the demand for APM tools as businesses strive to deliver uninterrupted, high-quality digital experiences to their users, ultimately contributing to the market's expansion.

Platform Type Insights

The software segment accounted for the largest market share of 66.5% in 2023 due to the increasing complexity of modern IT infrastructures and the expanding use of cloud-based applications. As businesses adopt more distributed and hybrid environments, the need for robust software solutions to monitor, manage, and optimize application performance has become essential. APM software tools provide comprehensive insights into application behavior, enabling IT teams to identify bottlenecks quickly, troubleshoot issues, and ensure that applications run smoothly.  According to Eurostat, in 2023, 75.3% of enterprises acquired advanced cloud services, including security software, database hosting, or computing platforms for application development, testing, or deployment mode.

The service segment is anticipated to grow at the fastest CAGR over the forecast period due to the increasing focus on improving customer experience and operational efficiency. APM services help organizations identify performance bottlenecks, optimize resource utilization, and reduce downtime, leading to improved application availability and faster issue resolution. By leveraging the insights provided by APM tools and services, businesses can proactively address performance issues before they impact access types, enhancing user satisfaction and retention.

Deployment Mode Insights

The cloud segment accounted for the largest market share of 52.6% in 2023. The scalability and flexibility offered by cloud APM tools are significant growth drivers in this segment. Cloud-based APM solutions allow organizations to scale their monitoring capabilities as their application environments expand without requiring substantial investments in on-premise infrastructure.

The on-premises segment is anticipated to grow at the fastest CAGR over the forecast period due to the growing demand for greater control over data security and privacy. Many organizations, particularly those in heavily regulated industries such as finance, healthcare, and government, prefer on-premises solutions to ensure compliance with stringent data protection laws.

Enterprise Size Insights

The large enterprises segment accounted for the largest market share, 59.5%, in 2023. As these organizations integrate more digital technologies, such as artificial intelligence (AI), big data, and Internet of Things (IoT) systems, into their operations, the need for effective performance management becomes even more critical. APM solutions enable large enterprises to monitor and optimize the performance of these technologies, ensuring that they function seamlessly within the broader IT ecosystem.

The SMEs segment is anticipated to grow at the fastest CAGR over the forecast period. The increasing availability of subscription-based pricing models and cloud-based APM solutions has lowered the entry barrier for SMEs, enabling them to benefit from advanced performance management capabilities without substantial upfront investments.

Access Type Insights

The web APM segment had the largest market share of 61.8% in 2023 due to the widespread adoption of cloud computing and the proliferation of hybrid cloud environments. As companies migrate to cloud-based infrastructures, they require APM solutions that can effectively monitor and manage applications across diverse environments, including public, private, and multi-cloud setups.

The mobile APM segment is anticipated to grow at the fastest CAGR over the forecast period. The rise of mobile commerce and transactions has amplified the importance of maintaining high-performance standards. Businesses relying on mobile apps for financial transactions or other critical functions must ensure that their applications perform optimally to prevent disruptions and maintain user trust. Moreover, the integration of APM tools with DevOps practices enhances the development and deployment mode process. By providing real-time performance data and analytics, mobile APM solutions enable development teams to identify and address issues during the development cycle, leading to faster and more efficient releases.

Regional Insights

The application performance management market in North America held the largest global revenue share of 37.0% in 2023 due to the growing demand for real-time monitoring and instant visibility into application performance. Businesses require immediate insights to quickly resolve performance bottlenecks and ensure seamless user experiences. APM solutions that offer real-time dashboards and alerts are becoming more popular in addressing this need.

U.S. Application Performance Management Market Trends

The application performance management market in the U.S. is expected to grow significantly from 2024 to 2030. AI and machine learning technologies are increasingly being integrated into APM solutions. These advancements provide predictive analytics, automated root cause analysis, and anomaly detection, helping businesses proactively manage performance issues and optimize their applications.

Europe Application Performance Management Market Trends

The application performance management market in Europe is growing significantly at a CAGR of 13.9% from 2024 to 2030. There is an increasing demand for APM solutions tailored to specific industry sectors, such as finance, healthcare, and retail. These sector-specific solutions address unique performance management needs and regulatory requirements within different industries.

Asia Pacific Application Performance Management Market Trends

Asia Pacific is growing significantly, with a CAGR of 15.7% from 2024 to 2030. Businesses in the region are accelerating their digital transformation efforts, which include adopting cloud technologies, microservices, and containerization. This shift creates a strong demand for APM solutions that can manage and monitor performance across increasingly complex IT environments.

Key Application Performance Management Company Insights

Key players operating in the application performance management (APM) industry include Akamai Technologies Inc., Broadcom Inc., Datadog Inc., Microsoft, New Relic Inc., and Oracle. The companies are focusing on various strategic initiatives, including new product development, partnerships & collaborations, and agreements to gain a competitive advantage over their rivals. The following are some instances of such initiatives.

In August 2024, Broadcom Inc. unveiled VMware Tanzu Platform 10, a new solution for advancing intelligent application delivery within private cloud settings. This platform is designed to streamline teams' development workflows and enhance governance and operational efficiency.

In July 2024, New Relic, Inc. introduced an AI-powered Digital Experience Monitoring (DEM), a fully integrated solution designed to optimize application performance and proactively prevent disruptions in digital experiences. This enterprise-grade solution provides real-time insights and comprehensive visibility across web, mobile, and AI applications, enabling organizations to deliver seamless digital experiences across various platforms. Key features include mobile user journeys, logs, and session replay functionality improvements.

In June 2024, Akamai Technologies successfully acquired Noname Security, a U.S.-based API security provider. This strategic acquisition is anticipated to enhance Akamai's capacity to address the increasing demand for API security solutions as the use of APIs continues to grow. By integrating Noname Security's expertise, Akamai Technologies aims to strengthen its offerings and better meet evolving market needs.

Key Application Performance Management Companies:

The following are the leading companies in the application performance management market. These companies collectively hold the largest market share and dictate industry trends.

• Akamai Technologies
• AppDynamics
• Broadcom Inc.
• Datadog Inc.
• Dynatrace LLC
• OpenText Corporation
• New Relic Inc.

Application Performance Management Market Report Scope

Global Application Performance Management Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2030. For this study, Grand View Research has segmented the global application performance management market report based on platform type, deployment mode, enterprise size, access type, and region.

Platform Type Outlook (Revenue, USD Billion, 2018 - 2030)

Deployment and Integration

Training and Education

Support and Maintenance

Deployment Mode Outlook (Revenue, USD Billion, 2018 - 2030)

Enterprise Size Outlook (Revenue, USD Billion, 2018 - 2030)

Large Enterprises

Access Type Outlook (Revenue, USD Billion, 2018 - 2030)

Application Performance Management Regional Outlook (Revenue, USD Billion, 2018 - 2030)

North America

Asia Pacific

South Korea

Latin America

Middle East & Africa

Kingdom of Saudi Arabia

South Africa

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Exploring heterogeneous differences between Chinese and Western customer preferences for restaurant attributes from online reviews

• Published: 09 September 2024

Cite this article

• Dian Liu 1 ,
• Wenshuang Zhao 1 ,
• Vijayan Sugumaran 2 &
• Jing Zhang   ORCID: orcid.org/0000-0002-8083-2565 3  

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Consumer behavior varies across different countries due to their distinct cultural backgrounds. Gaining a comprehensive understanding of this influence can greatly assist restaurant managers in achieving higher business performance. However, academic inquiry into cross-cultural differences in customer preferences for specific restaurant attributes, such as décor, food variety, and reservation, remains scarce, warranting further scholarly investigation. This paper analyses customer preferences for specific restaurant attributes based on aspect-level sentiment analysis of online reviews from Chinese and Western customers. We adopt ordinary least squares regression to analyze the impact of country on customer attention to different restaurant attributes and carry out quantile regression on customer satisfaction to determine the satisfaction variance in different service performance level. The results show that Chinese and Western customers demonstrate divergent levels of attention and satisfaction towards specific attributes. Specifically, Chinese customers exhibit higher interest and satisfaction in non-functional attributes, such as View , while allocating less attention to value-oriented attributes like Portion size of dish. Moreover, the impact of country on customer satisfaction displays heterogeneity, exhibiting a U-shaped variation across performance levels. To elucidate these differences, we delve into unique cultural elements in China, such as Confucian values and face culture, within the framework of Hofstede's cultural dimensions. Our work delves into the specific attribute-level preferences of Western and Chinese consumers, highlighting the heterogeneity of these preference differences at different performance levels. It underscores for managers the importance of considering consumer preference differences in conjunction with their own service performamce levels.

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Acknowledgements

This research was supported by programs granted by the National Natural Science Foundation of China (NSFC) (No. 72001131, 71901053 and 72031004), and the Key Project of Social Science Planning Foundation of Liaoning Province (No. L23AGL008).

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SILC Business School, Shanghai University, Shanghai, 201899, People’s Republic of China

Dian Liu & Wenshuang Zhao

Department of Decision and Information Sciences, School of Business Administration, Oakland University, 442 Elliott Hall, Rochester, MI, 48309, USA

Vijayan Sugumaran

School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian, 116024, People’s Republic of China

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See Figs. 4 , 5 , 6 and Table 17 .

The quantile regression results for satisfaction to sub-attributes of Core Product

The quantile regression results for satisfaction to sub-attributes of the Environment

The quantile regression results for satisfaction to sub-attributes of Service-related and Value

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Liu, D., Zhao, W., Sugumaran, V. et al. Exploring heterogeneous differences between Chinese and Western customer preferences for restaurant attributes from online reviews. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09889-4

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Accepted : 09 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1007/s10660-024-09889-4

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