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500+ Business Research Topics

Business research is an essential component of any successful organization, as it allows companies to make informed decisions based on data-driven insights. Whether it’s market research to identify new opportunities, or analyzing internal processes to improve efficiency, there are a vast array of business research topics that companies can explore. With the constantly evolving business landscape, it’s critical for organizations to stay up-to-date with the latest research trends and best practices to remain competitive. In this post, we’ll explore some of the most compelling business research topics that are currently being studied, providing insights and actionable recommendations for businesses of all sizes.

Business Research Topics

Business Research Topics are as follows:

• The impact of social media on consumer behavior
• Strategies for enhancing customer satisfaction in the service industry
• The effectiveness of mobile marketing campaigns
• Exploring the factors influencing employee turnover
• The role of leadership in organizational culture
• Investigating the relationship between corporate social responsibility and financial performance
• Assessing the impact of employee engagement on organizational performance
• The challenges and opportunities of global supply chain management
• Analyzing the effectiveness of e-commerce platforms
• Investigating the impact of organizational culture on employee motivation
• The role of corporate governance in ensuring ethical business practices
• Examining the impact of digital marketing on brand equity
• Strategies for managing diversity and inclusion in the workplace
• Exploring the effects of employee empowerment on job satisfaction
• The role of innovation in business growth
• Analyzing the impact of mergers and acquisitions on company performance
• Investigating the impact of workplace design on employee productivity
• The challenges and opportunities of international business expansion
• Strategies for managing talent in the knowledge economy
• The role of artificial intelligence in transforming business operations
• Examining the impact of customer loyalty programs on retention and revenue
• Investigating the relationship between corporate social responsibility and brand reputation
• The role of emotional intelligence in effective leadership
• The impact of digital transformation on small and medium-sized enterprises
• Analyzing the effectiveness of green marketing strategies
• The role of entrepreneurship in economic development
• Investigating the impact of employee training and development on organizational performance
• The challenges and opportunities of omnichannel retailing
• Examining the impact of organizational change on employee morale and productivity
• The role of corporate social responsibility in attracting and retaining millennial talent
• Analyzing the impact of employee motivation on organizational culture
• Investigating the impact of workplace diversity on team performance
• The challenges and opportunities of blockchain technology in business operations
• Strategies for managing cross-functional teams
• The role of big data analytics in business decision-making
• Examining the impact of corporate social responsibility on customer loyalty
• Investigating the relationship between corporate social responsibility and employee engagement
• The impact of social media marketing on customer engagement and brand loyalty.
• The effectiveness of AI in improving customer service and satisfaction.
• The role of entrepreneurship in economic development and job creation.
• The impact of the gig economy on the labor market.
• The effects of corporate social responsibility on company profitability.
• The role of data analytics in predicting consumer behavior and market trends.
• The effects of globalization on the competitiveness of small businesses.
• The impact of e-commerce on traditional brick-and-mortar retail.
• The role of emotional intelligence in leadership and team management.
• The effects of workplace diversity on employee productivity and satisfaction.
• The role of corporate culture in employee retention and satisfaction.
• The impact of employee training and development on company performance.
• The effectiveness of performance-based pay structures on employee motivation.
• The impact of sustainability practices on company reputation and profitability.
• The effects of artificial intelligence on job displacement and the future of work.
• The role of innovation in the growth and success of small businesses.
• The impact of government regulations on business operations and profitability.
• The effects of organizational structure on company performance and efficiency.
• The role of emotional labor in service industries.
• The impact of employee empowerment on job satisfaction and retention.
• The effects of workplace flexibility on employee productivity and well-being.
• The role of emotional intelligence in negotiation and conflict resolution.
• The impact of branding on consumer behavior and purchase decisions.
• The effects of customer experience on brand loyalty and advocacy.
• The role of storytelling in marketing and advertising.
• The impact of consumer psychology on pricing strategies and sales.
• The effects of influencer marketing on consumer behavior and brand loyalty.
• The role of trust in online transactions and e-commerce.
• The impact of product design on consumer perception and purchasing decisions.
• The effects of customer satisfaction on company profitability and growth.
• The role of social entrepreneurship in addressing societal problems and creating value.
• The impact of corporate governance on company performance and stakeholder relations.
• The effects of workplace harassment on employee well-being and company culture.
• The role of strategic planning in the success of small businesses.
• The impact of technology on supply chain management and logistics.
• The effects of customer segmentation on marketing strategies and sales.
• The role of corporate philanthropy in building brand reputation and loyalty.
• The impact of intellectual property protection on innovation and creativity.
• The effects of trade policies on international business operations and profitability.
• The role of strategic partnerships in business growth and expansion.
• The impact of digital transformation on organizational structure and operations.
• The effects of leadership styles on employee motivation and performance.
• The role of corporate social activism in shaping public opinion and brand reputation.
• The impact of mergers and acquisitions on company performance and stakeholder value.
• The effects of workplace automation on job displacement and re-skilling.
• The role of cross-cultural communication in international business operations.
• The impact of workplace stress on employee health and productivity.
• The effects of customer reviews and ratings on online sales and reputation.
• The role of competitive intelligence in market research and strategy development.
• The impact of brand identity on consumer trust and loyalty.
• The impact of organizational structure on innovation and creativity
• Analyzing the effectiveness of virtual teams in global organizations
• The role of corporate social responsibility in crisis management
• The challenges and opportunities of online marketplaces
• Strategies for managing cultural diversity in multinational corporations
• The impact of employer branding on employee retention
• Investigating the impact of corporate social responsibility on investor behavior
• The role of technology in enhancing customer experience
• Analyzing the impact of social responsibility initiatives on customer satisfaction
• Investigating the impact of supply chain disruptions on business performance
• The role of business ethics in organizational decision-making
• The challenges and opportunities of artificial intelligence in customer service
• Strategies for managing employee burnout and stress in the workplace.
• Impact of social media on consumer behavior and its implications for businesses.
• The impact of corporate social responsibility on company performance.
• An analysis of the relationship between employee satisfaction and customer loyalty.
• The effect of advertising on consumer behavior.
• A study on the effectiveness of social media marketing in building brand image.
• The impact of technological innovations on business strategy and operations.
• The relationship between leadership style and employee motivation.
• A study of the effects of corporate culture on employee engagement.
• An analysis of the factors influencing consumer buying behavior.
• The effectiveness of training and development programs in enhancing employee performance.
• The impact of global economic factors on business decision-making.
• The role of organizational communication in achieving business goals.
• The relationship between customer satisfaction and business success.
• A study of the challenges and opportunities in international business.
• The effectiveness of supply chain management in improving business performance.
• An analysis of the factors influencing customer loyalty in the hospitality industry.
• The impact of employee turnover on organizational performance.
• A study of the impact of corporate governance on company performance.
• The role of innovation in business growth and success.
• An analysis of the relationship between marketing and sales performance.
• The effect of organizational structure on employee behavior.
• A study of the impact of cultural differences on business negotiations.
• The effectiveness of pricing strategies in increasing sales revenue.
• The impact of customer service on customer loyalty.
• A study of the role of human resource management in business success.
• The impact of e-commerce on traditional brick-and-mortar businesses.
• An analysis of the relationship between employee empowerment and job satisfaction.
• The effectiveness of customer relationship management in building brand loyalty.
• The role of business ethics in corporate decision-making.
• A study of the impact of digital marketing on consumer behavior.
• The effect of organizational culture on employee turnover.
• An analysis of the factors influencing employee engagement in the workplace.
• The impact of social media on business communication and marketing.
• A study of the relationship between customer service and customer loyalty in the airline industry.
• The role of diversity and inclusion in business success.
• The effectiveness of performance management systems in improving employee performance.
• The impact of corporate social responsibility on employee engagement.
• A study of the factors influencing business expansion into new markets.
• The role of brand identity in customer loyalty and retention.
• The effectiveness of change management strategies in organizational change.
• The impact of organizational structure on organizational performance.
• A study of the impact of technology on the future of work.
• The relationship between innovation and competitive advantage in the marketplace.
• The effect of employee training on organizational performance.
• An analysis of the impact of online reviews on consumer behavior.
• The role of leadership in shaping organizational culture.
• The effectiveness of talent management strategies in retaining top talent.
• The impact of globalization on small and medium-sized enterprises.
• A study of the relationship between corporate social responsibility and brand reputation.
• The effectiveness of employee retention strategies in reducing turnover rates.
• The role of emotional intelligence in leadership and employee engagement.
• The impact of digital marketing on customer behavior
• The role of organizational culture in employee engagement and retention
• The effects of employee training and development on organizational performance
• The relationship between corporate social responsibility and financial performance
• The impact of globalization on business strategy
• The importance of supply chain management in achieving competitive advantage
• The role of innovation in business growth and sustainability
• The impact of e-commerce on traditional retail businesses
• The role of leadership in managing change in organizations
• The effects of workplace diversity on organizational performance
• The impact of social media on brand image and reputation
• The relationship between employee motivation and productivity
• The role of organizational structure in promoting innovation
• The effects of customer service on customer loyalty
• The impact of globalization on small businesses
• The role of corporate governance in preventing unethical behavior
• The effects of technology on job design and work organization
• The relationship between employee satisfaction and turnover
• The impact of mergers and acquisitions on organizational culture
• The effects of employee benefits on job satisfaction
• The impact of cultural differences on international business negotiations
• The role of strategic planning in organizational success
• The effects of organizational change on employee stress and burnout
• The impact of business ethics on customer trust and loyalty
• The role of human resource management in achieving competitive advantage
• The effects of outsourcing on organizational performance
• The impact of diversity and inclusion on team performance
• The role of corporate social responsibility in brand differentiation
• The effects of leadership style on organizational culture
• The Impact of Digital Marketing on Brand Equity: A Study of E-commerce Businesses
• Investigating the Relationship between Employee Engagement and Organizational Performance
• Analyzing the Effects of Corporate Social Responsibility on Customer Loyalty and Firm Performance
• An Empirical Study of the Factors Affecting Entrepreneurial Success in the Technology Sector
• The Influence of Organizational Culture on Employee Motivation and Job Satisfaction: A Case Study of a Service Industry
• Investigating the Impact of Organizational Change on Employee Resistance: A Comparative Study of Two Organizations
• An Exploration of the Impact of Artificial Intelligence on Supply Chain Management
• Examining the Relationship between Leadership Styles and Employee Creativity in Innovative Organizations
• Investigating the Effectiveness of Performance Appraisal Systems in Improving Employee Performance
• Analyzing the Role of Emotional Intelligence in Leadership Effectiveness: A Study of Senior Managers
• The Impact of Transformational Leadership on Employee Motivation and Job Satisfaction in the Healthcare Sector
• Evaluating the Effectiveness of Talent Management Strategies in Enhancing Organizational Performance
• A Study of the Effects of Customer Relationship Management on Customer Retention and Loyalty
• Investigating the Impact of Corporate Governance on Firm Performance: Evidence from Emerging Markets
• The Relationship between Intellectual Capital and Firm Performance: A Case Study of Technology Firms
• Analyzing the Effectiveness of Diversity Management in Improving Organizational Performance
• The Impact of Internationalization on the Performance of Small and Medium-sized Enterprises: A Comparative Study of Developed and Developing Countries
• Examining the Relationship between Corporate Social Responsibility and Financial Performance: A Study of Listed Firms
• Investigating the Influence of Entrepreneurial Orientation on Firm Performance in Emerging Markets
• Analyzing the Impact of E-commerce on Traditional Retail Business Models: A Study of Brick-and-Mortar Stores
• The Effect of Corporate Reputation on Customer Loyalty and Firm Performance: A Study of the Banking Sector
• Investigating the Factors Affecting Consumer Adoption of Mobile Payment Systems
• The Role of Corporate Social Responsibility in Attracting and Retaining Millennial Employees
• Analyzing the Impact of Social Media Marketing on Brand Awareness and Consumer Purchase Intentions
• A Study of the Effects of Employee Training and Development on Job Performance
• Investigating the Relationship between Corporate Culture and Employee Turnover: A Study of Multinational Companies
• The Impact of Business Process Reengineering on Organizational Performance: A Study of Service Industries
• An Empirical Study of the Factors Affecting Internationalization Strategies of Small and Medium-sized Enterprises
• The Effect of Strategic Human Resource Management on Firm Performance: A Study of Manufacturing Firms
• Investigating the Influence of Leadership on Organizational Culture: A Comparative Study of Two Organizations
• The Impact of Technology Adoption on Organizational Productivity: A Study of the Healthcare Sector
• Analyzing the Effects of Brand Personality on Consumer Purchase Intentions: A Study of Luxury Brands
• The Relationship between Corporate Social Responsibility and Customer Perceptions of Product Quality: A Study of the Food and Beverage Industry
• Investigating the Effectiveness of Performance Management Systems in Improving Employee Performance: A Study of a Public Sector Organization
• The Impact of Business Ethics on Firm Performance: A Study of the Banking Industry
• Examining the Relationship between Employee Engagement and Customer Satisfaction in the Service Industry
• Investigating the Influence of Entrepreneurial Networking on Firm Performance: A Study of Small and Medium-sized Enterprises
• The Effect of Corporate Social Responsibility on Employee Retention: A Study of High-tech Firms
• The impact of workplace communication on employee engagement
• The role of customer feedback in improving service quality
• The effects of employee empowerment on job satisfaction
• The impact of innovation on customer satisfaction
• The role of knowledge management in organizational learning
• The effects of product innovation on market share
• The impact of business location on customer behavior
• The role of financial management in business success
• The effects of corporate social responsibility on employee engagement
• The impact of cultural intelligence on cross-cultural communication
• The role of social media in crisis management
• The effects of corporate branding on customer loyalty
• The impact of globalization on consumer behavior
• The role of emotional intelligence in leadership effectiveness
• The effects of employee involvement in decision-making on job satisfaction
• The impact of business strategy on market share
• The role of corporate culture in promoting ethical behavior
• The effects of corporate social responsibility on investor behavior
• The impact of sustainability on brand image and reputation
• The role of corporate social responsibility in reducing carbon emissions.
• The effectiveness of loyalty programs on customer retention
• The benefits of remote work for employee productivity
• The impact of environmental sustainability on consumer purchasing decisions
• The role of brand identity in consumer loyalty
• The relationship between employee satisfaction and customer satisfaction
• The impact of e-commerce on traditional brick-and-mortar stores
• The effectiveness of online advertising on consumer behavior
• The impact of leadership styles on employee motivation
• The role of corporate social responsibility in brand perception
• The impact of workplace diversity on organizational performance
• The effectiveness of gamification in employee training programs
• The impact of pricing strategies on consumer behavior
• The effectiveness of mobile marketing on consumer engagement
• The impact of emotional intelligence on leadership effectiveness
• The role of customer service in consumer loyalty
• The impact of technology on supply chain management
• The effectiveness of employee training programs on job performance
• The impact of culture on consumer behavior
• The effectiveness of performance appraisal systems on employee motivation
• The impact of social responsibility on organizational performance
• The role of innovation in business success
• The impact of ethical leadership on organizational culture
• The effectiveness of cross-functional teams in project management
• The impact of government regulations on business operations
• The role of strategic planning in business growth
• The impact of emotional intelligence on team dynamics
• The effectiveness of supply chain management on customer satisfaction
• The impact of workplace culture on employee satisfaction
• The role of employee engagement in organizational success
• The impact of globalization on organizational culture
• The effectiveness of virtual teams in project management
• The impact of employee turnover on organizational performance
• The role of corporate social responsibility in talent acquisition
• The impact of technology on employee training and development
• The effectiveness of knowledge management on organizational learning
• The impact of organizational structure on employee motivation
• The role of innovation in organizational change
• The impact of cultural intelligence on global business operations
• The effectiveness of marketing strategies on brand perception
• The impact of change management on organizational culture
• The role of leadership in organizational transformation
• The impact of employee empowerment on job satisfaction
• The effectiveness of project management methodologies on project success
• The impact of workplace communication on team performance
• The role of emotional intelligence in conflict resolution
• The impact of employee motivation on job performance
• The effectiveness of diversity and inclusion initiatives on organizational performance.
• The impact of social media on consumer behavior and buying decisions
• The role of diversity and inclusion in corporate culture and its effects on employee retention and productivity
• The effectiveness of remote work policies on job satisfaction and work-life balance
• The impact of customer experience on brand loyalty and revenue growth
• The effects of environmental sustainability practices on corporate reputation and financial performance
• The role of corporate social responsibility in consumer purchasing decisions
• The effectiveness of leadership styles on team performance and productivity
• The effects of employee motivation on job performance and turnover
• The impact of technology on supply chain management and logistics efficiency
• The role of emotional intelligence in effective leadership and team dynamics
• The impact of artificial intelligence and automation on job displacement and workforce trends
• The effects of brand image on consumer perception and purchasing decisions
• The role of corporate culture in promoting innovation and creativity
• The impact of e-commerce on traditional brick-and-mortar retail businesses
• The effects of corporate governance on financial reporting and transparency
• The effectiveness of performance-based compensation on employee motivation and productivity
• The impact of online reviews and ratings on consumer trust and brand reputation
• The effects of workplace diversity on innovation and creativity
• The impact of mobile technology on marketing strategies and consumer behavior
• The role of emotional intelligence in customer service and satisfaction
• The effects of corporate reputation on financial performance and stakeholder trust
• The impact of artificial intelligence on customer service and support
• The role of organizational culture in promoting ethical behavior and decision-making
• The effects of corporate social responsibility on employee engagement and satisfaction
• The impact of employee turnover on organizational performance and profitability
• The role of customer satisfaction in promoting brand loyalty and advocacy
• The effects of workplace flexibility on employee morale and productivity
• The impact of employee wellness programs on absenteeism and healthcare costs
• The role of data analytics in business decision-making and strategy formulation
• The effects of brand personality on consumer behavior and perception
• The impact of social media marketing on brand awareness and customer engagement
• The role of organizational justice in promoting employee satisfaction and retention
• The effects of corporate branding on employee motivation and loyalty
• The impact of online advertising on consumer behavior and purchasing decisions
• The role of corporate entrepreneurship in promoting innovation and growth
• The effects of cultural intelligence on cross-cultural communication and business success
• The impact of workplace diversity on customer satisfaction and loyalty
• The role of ethical leadership in promoting employee trust and commitment
• The effects of job stress on employee health and well-being
• The impact of supply chain disruptions on business operations and financial performance
• The role of organizational learning in promoting continuous improvement and innovation
• The effects of employee engagement on customer satisfaction and loyalty
• The impact of brand extensions on brand equity and consumer behavior
• The role of strategic alliances in promoting business growth and competitiveness
• The effects of corporate transparency on stakeholder trust and loyalty
• The impact of digital transformation on business models and competitiveness
• The role of business ethics in promoting corporate social responsibility and sustainability
• The effects of employee empowerment on job satisfaction and organizational performance.
• The role of corporate governance in mitigating unethical behavior in multinational corporations.
• The effects of cultural diversity on team performance in multinational corporations.
• The impact of corporate social responsibility on consumer loyalty and brand reputation.
• The relationship between organizational culture and employee engagement in service industries.
• The impact of globalization on the competitiveness of small and medium enterprises (SMEs).
• The effectiveness of performance-based pay systems on employee motivation and productivity.
• The relationship between innovation and corporate performance in the pharmaceutical industry.
• The impact of digital marketing on the traditional marketing mix.
• The role of emotional intelligence in leadership effectiveness in cross-cultural teams.
• The relationship between corporate social responsibility and financial performance in the banking sector.
• The impact of diversity management on employee satisfaction and retention in multinational corporations.
• The relationship between leadership style and organizational culture in family-owned businesses.
• The impact of e-commerce on supply chain management.
• The effectiveness of training and development programs on employee performance in the retail sector.
• The impact of global economic trends on strategic decision-making in multinational corporations.
• The relationship between ethical leadership and employee job satisfaction in the healthcare industry.
• The impact of employee empowerment on organizational performance in the manufacturing sector.
• The relationship between corporate social responsibility and employee well-being in the hospitality industry.
• The impact of artificial intelligence on customer service in the banking industry.
• The relationship between emotional intelligence and employee creativity in the technology industry.
• The impact of big data analytics on customer relationship management in the telecommunications industry.
• The relationship between organizational culture and innovation in the automotive industry.
• The impact of internationalization on the performance of SMEs in emerging markets.
• The effectiveness of performance appraisal systems on employee motivation and retention in the public sector.
• The relationship between diversity management and innovation in the pharmaceutical industry.
• The impact of social entrepreneurship on economic development in developing countries.
• The relationship between transformational leadership and organizational change in the energy sector.
• The impact of online customer reviews on brand reputation in the hospitality industry.
• The effectiveness of leadership development programs on employee engagement in the finance industry.
• The relationship between corporate social responsibility and employee turnover in the retail sector.
• The impact of artificial intelligence on the recruitment and selection process in the technology industry.
• The relationship between organizational culture and employee creativity in the fashion industry.
• The impact of digital transformation on business models in the insurance industry.
• The relationship between employee engagement and customer satisfaction in the service industry.
• The impact of mergers and acquisitions on organizational culture and employee morale.
• The effectiveness of knowledge management systems on organizational performance in the consulting industry.
• The impact of social media marketing on brand loyalty in the food and beverage industry.
• The relationship between emotional intelligence and customer satisfaction in the airline industry.
• The impact of blockchain technology on supply chain management in the logistics industry.
• The relationship between corporate social responsibility and employee engagement in the technology industry.
• The impact of digitalization on talent management practices in the hospitality industry.
• The effectiveness of reward and recognition programs on employee motivation in the manufacturing industry.
• The impact of industry 4.0 on organizational structure and culture in the aerospace industry.
• The relationship between leadership style and team performance in the construction industry.
• The impact of artificial intelligence on financial forecasting and decision-making in the banking sector.
• The relationship between corporate social responsibility and customer loyalty in the automotive industry.
• The impact of virtual teams on employee communication and collaboration in the pharmaceutical industry.
• The impact of remote work on employee productivity and job satisfaction
• The effects of social media marketing on customer engagement and brand loyalty
• The role of artificial intelligence in streamlining supply chain management
• The effectiveness of employee training and development programs on organizational performance
• The impact of diversity and inclusion initiatives on organizational culture and employee satisfaction
• The role of corporate social responsibility in enhancing brand reputation and customer loyalty
• The effects of e-commerce on small businesses and local economies
• The impact of big data analytics on marketing strategies and customer insights
• The effects of employee empowerment on organizational innovation and performance
• The impact of globalization on the hospitality industry
• The effects of corporate governance on organizational performance and financial outcomes
• The role of customer satisfaction in driving business growth and profitability
• The impact of artificial intelligence on financial forecasting and risk management
• The effects of corporate culture on employee engagement and retention
• The role of green marketing in promoting environmental sustainability and brand reputation
• The impact of digital transformation on the retail industry
• The effects of employee motivation on job performance and organizational productivity
• The role of customer experience in enhancing brand loyalty and advocacy
• The impact of international trade agreements on global business practices
• The effects of artificial intelligence on customer service and support
• The role of organizational communication in facilitating teamwork and collaboration
• The impact of corporate social responsibility on employee motivation and retention
• The effects of global economic instability on business decision-making
• The role of leadership styles in organizational change management
• The impact of social media influencers on consumer behavior and purchasing decisions
• The effects of employee well-being on organizational productivity and profitability
• The role of innovation in driving business growth and competitive advantage
• The impact of digital marketing on consumer behavior and brand perception
• The role of strategic planning in organizational success and sustainability
• The impact of e-commerce on consumer privacy and data security
• The effects of corporate reputation on customer acquisition and retention
• The role of diversity and inclusion in organizational creativity and innovation
• The impact of artificial intelligence on customer relationship management
• The effects of customer feedback on product development and innovation
• The role of employee job satisfaction in reducing turnover and absenteeism
• The impact of global competition on business strategy and innovation
• The effects of corporate branding on customer loyalty and advocacy
• The role of digital transformation in enhancing organizational agility and responsiveness
• The effects of employee empowerment on customer satisfaction and loyalty
• The role of entrepreneurial leadership in driving business innovation and growth
• The impact of digital disruption on traditional business models
• The effects of organizational culture on innovation and creativity
• The role of marketing research in developing effective marketing strategies
• The impact of social media on customer relationship management
• The effects of employee engagement on organizational innovation and competitiveness
• The role of strategic partnerships in promoting business growth and expansion
• The impact of global trends on business innovation and entrepreneurship

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business analytics research

Business analytics research requires a rigorous approach to model formulation and estimation as well as the skills to analyse the outputs of these models. Our Business Analytics scholars regularly publish in leading international journals. Particular fields of interest include:

• big data analytics 
• applied econometrics
• electricity markets
• financial econometrics and quantitative risk forecasting
• Bayesian methods
• forecasting, sensitivity analysis
• micro-econometrics, multivariate statistical methods
• panel data methods and models
• scheduling problems
• statistical machine learning
• stochastic non-life insurance and actuarial problems
• supply chains
• testing and modelling structural change
• time series and forecasting.

We welcome approaches from potential PhD students with an interest in any of these areas.

Meet our academics and research students.

Head of Discipline

Associate Professor  Dmytro Matsypura

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Associate Professor Anastasios Panagiotelis (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor  Daniel Oron

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr  Nam Ho-Nguyen

Dr  Stephen Tierney

Dr  Chao Wang

Dr Wilson Chen

Dr  Bern Conlon

Dr Qin Fang

Dr  Simon Loria

Dr  Pablo Montero-Manso

Dr Bradley Rava

Dr  Marcel Scharth

Dr Firouzeh Taghikhah

Dr Alison Wong

Adjunct Senior Lecturer

Dr  Steven Sommer

Adjunct Lecturer

Research associates, postdoctoral research associate.

Dr  Tomas Ignacio Lagos

Honorary and emeritus staff

Emeritus professor.

Professor Eddie Anderson

Professor Robert Bartels

Honorary Professors

Professor Robert Kohn

Professor Ganna Pogrebna

Professor Michael Smith

Honorary Associates

John Goodhew

Hoda Davarzani

John Watkins

David Grafton

Yakov Zinder

Higher degree by research students

View our current  higher degree by research students . 

Research groups

Time series and forecasting research group, productivity, efficiency and measurement analytics (pema), research seminars.

The Discipline of Business Analytics holds a regular seminar series. Seminars are usually held on Fridays at 11am in Room 5070, Abercrombie Building (H70) . The seminar organiser is Bradley Rava .

Please email  [email protected]  if you wish to be included in the BA seminar series mailing list.

Below is an outline of our recent and upcoming activity. 

2018 seminars

Finding critical links for closeness centrality.

• Date: 10 Aug 2018 at 11am
• Venue: Rm 3010, Abercrombie Building (H70)
• Speaker: Professor Oleg Prokopyev, Department of Industrial Engineering, University of Pittsburg

Risk management with POE, VaR, CVaR and bPOE: Applications in finance

• Venue: Rm 4150, Abercrombie Building (H70)
• Speaker: Professor Stan Uryasev, Department of Industrial and Systems Engineering, University of Florida

My experience as EIC of OMEGA

• Date: 9 Aug 2018 at 11am
• Venue: Rm 2240, Abercrombie Building (H70)
• Speaker: Prof Benjamin Lev, LeBow College of Business, Drexel University

Heterogeneous component MEM models for forecasting trading volumes

• Date: 27 Jul 2018 at 11am
• Venue: Rm 3190, Abercrombie Building (H70)
• Speaker: Professor Giuseppe Storti, Department of Economics and Statistics, University of Salerno UNISA

Realised stochastic volatility models with generalised asymmetry and periodic long memory

• Date: 1 Jun 2018 at 11am
• Venue: Rm 2290, Abercrombie Building (H70)
• Speaker: Professor Manabu Asai, Faculty of Economics, Soka University

Improving hand hygiene process compliance through process monitoring in healthcare

• Date: 24 May 2018 at 11am
• Venue: Rm 1080, Abercrombie Building (H70)
• Speaker: Associate Professor Chung-Li Tseng, Operations Management, UNSW Business School

Exact IP-based approaches for the longest induced path problem

• Date: 18 May 2018 at 11am
• Speaker: Dr Dmytro Matsypura, Discipline of Business Analytics, The University of Sydney

Bayesian deep net GLM and GLMM

• Date: 11 May 2018 at 11am
• Speaker: Mr Nghia Nguyen, Discipline of Business Analytics, The University of Sydney

Computational intelligence-based predictive snalytics: Applications with multi-output support vector regression

• Date: 13 Apr 2018 at 11am
• Speaker: Professor Yukun Bao, School of Management, Huazhong University of Science and Technology (HUST)

Entrywise functions preserving positivity: Connections between analysis, algebra, combinatorics and statistics

• Date: 5 Apr 2018 at 3.30pm
• Venue: Rm 3120, Abercrombie Building (H70)
• Speaker: Associate Professor Apoorva Khare, Department of Mathematics, Indian Institute of Science

Large-scale multivariate modelling of financial asset returns and portfolio optimisation

• Date: 23 Feb 2018 at 11am
• Speaker: Professor Marc Paolella, Department of Banking and Finance, University of Zurich

Statistical inference on the Canadian middle class

• Date: 16 Feb 2018 at 11am
• Speaker: Professor Russell Davidson, Department of Economics, McGill University

2017 seminars

Heterogeneous structural breaks in panel data models.

• Date: 1 Sep 2017 at 11am
• Venue: Rm 1050, Abercrombie Building (H70)
• Speaker: Dr Wendun Wang, Erasmus School of Economics, Erasmus University

Externalities, optimisation and regulation in queues

• Date: 25 Aug 2017 at 11am
• Speaker: Dr Nadja Klein, Melbourne Business School, University of Melbourne

A partial identification subnetwork approach to discrete games in large networks: An application to quantifying peer effects

• Date: 11 Aug 2017 at 11am
• Speaker: Professor Tong Li, Department of Economics, Vanderbilt University

An introduction to knowledge management and some common entry points

• Date: 4 Aug 2017 at 11am
• Venue: Rm 2090, Abercrombie Building (H70)
• Speaker: Prof Eric Tsui, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University

Two applications of serial inventory systems

• Date: 21 Jul 2017 at 11:00am
• Venue: Rm 5070, Abercrombie Building (H70)
• Speaker: Associate Professor Ying Rong, Operations Management, Shanghai Jiao Tong University

Methods of matrix factorisation

• Date: 2 Jun 2017 at 11am
• Speaker: Professor Wray Buntine, Master of Data Science, Monash University

Optimisation and equilibrium problems in engineering

• Date: 26 May 2017 at 11am
• Speaker: Prof Steven Gabriel, Department of Mechanical Engineering, University of Maryland

Exact subsampling MCMC

• Date: 12 May 2017 at 11am
• Speaker: Dr Matias Quiroz, UNSW Business School, University of New South Wales

Effects of taxes and safety net pensions on life-cycle labor supply, savings and human capital: The case of Australia

• Date: 21 Apr 2017 at 11am
• Speaker: Dr Fedor Iskhakov, College of Business and Economics, Australian National University

Trial-offer markets with social influence: The impact of different ranking policies

• Date: 18 Apr 2017 at 11am
• Venue: Rm 5040, Abercrombie Building (H70)
• Speaker: Dr Gerardo Berbeglia, Melbourne Business School, University of Melbourne

Conditionally optimal weights and forward-looking approaches to combining forecasts

• Date: 7 Apr 2017 at 11am
• Speaker: Dr Andrey Vasnev, Discipline of Business Analytics, The University of Sydney

A flexible generalised hyberbolic option pricing model and its special cases

• Date: 31 Mar 2017 at 11am
• Speaker: Dr Simon Kwok, School of Economics, The University of Sydney

Scheduling with variable processing times: Complexity results and approximation algorithms

• Date: 24 Mar 2017 at 11:00am
• Speaker: Associate Professor Daniel Oron, Discipline of Business Analytics, The University of Sydney

Modelling insurance losses using contaminated generalised beta type-2 distribution

• Date: 17 Mar 2017 at 11am
• Speaker: Dr Boris Choy, Discipline of Business Analytics, The University of Sydney

How (not) to get what you ask for: Survey mode effects on self-reported substance use

• Date: 24 Feb 2017 at 11am
• Speaker: Dr Bin Peng, School of Mathematics, University of Technology Sydney

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211 Business Topics For Research Paper [Updated]

Are you looking for intriguing business topics to explore in your research paper? Whether you’re a student delving into the world of business studies or a seasoned professional seeking fresh insights, selecting the right topic is crucial. In this blog, we’ll walk you through a diverse array of business topics for research paper. From management strategies to emerging trends like sustainability and digital transformation, there’s something for everyone. Let’s dive in!

What Are The Characteristics of Business Research Topics?

Table of Contents

Business research topics possess several key characteristics that distinguish them from other types of research topics. These characteristics include:

• Relevance: Business research topics should address current issues, trends, and challenges facing the business world. They should be of interest to academics, practitioners, and policymakers alike.
• Practicality: Business research topics should have real-world applicability and relevance to industry practices. They should offer insights that can be implemented to improve organizational performance, decision-making, and strategy.
• Interdisciplinary Nature: Business research often draws from multiple disciplines such as economics, management, marketing, finance, and psychology. Topics should be interdisciplinary in nature, incorporating insights from various fields to provide comprehensive analysis.
• Data-Driven: Business research relies heavily on empirical evidence and data analysis. Topics should lend themselves to quantitative, qualitative, or mixed-method research approaches, depending on the research question and objectives.
• Innovation and Creativity: Business research topics should encourage innovative thinking and creative problem-solving. They should explore emerging trends, disruptive technologies, and novel approaches to address business challenges.
• Ethical Considerations: Ethical considerations are paramount in business research. Topics should adhere to ethical principles and guidelines, ensuring the protection of participants’ rights and the integrity of research findings.
• Global Perspective: Business research topics should consider the global context and implications of business decisions and practices. They should explore cross-cultural differences, international markets, and global trends shaping the business landscape.
• Impact: Business research topics should have the potential to generate meaningful insights and contribute to the advancement of knowledge in the field. They should address pressing issues and offer practical solutions that can drive positive change in organizations and society.

By embodying these characteristics, business research topics can effectively address the complexities and challenges of the modern business environment, providing valuable insights for academic scholarship and practical application.

211 Business Topics For Research Paper

• The Impact of Leadership Styles on Employee Motivation
• Strategies for Managing Multicultural Teams Effectively
• The Role of Emotional Intelligence in Leadership Success
• Marketing Strategies for Small Businesses on a Limited Budget
• The Influence of Social Media Marketing on Consumer Behavior
• Brand Loyalty: Factors Influencing Consumer Purchase Decisions
• Ethical Considerations in Advertising Practices
• Financial Risk Management in Multinational Corporations
• Corporate Governance and Financial Performance
• The Role of Financial Derivatives in Hedging Market Risks
• Success Factors for Startups in Competitive Markets
• Innovation and Entrepreneurship: Key Drivers of Economic Growth
• Challenges and Opportunities in Scaling a Business Globally
• Ethical Dilemmas in Business Decision-Making
• Corporate Social Responsibility Practices and Brand Image
• Balancing Profit Motives with Social and Environmental Concerns
• The Business Case for Sustainability Initiatives
• Renewable Energy Adoption in Businesses
• Circular Economy Models and Business Sustainability
• The Impact of Digital Technologies on Traditional Business Models
• E-Commerce Trends and Consumer Preferences
• Cybersecurity Challenges in E-Commerce Transactions
• The Benefits of Diversity in the Workplace
• Strategies for Promoting Gender Equality in Leadership Roles
• Addressing Unconscious Bias in Recruitment Processes
• The Impact of Remote Work on Employee Productivity
• Flexible Work Arrangements and Work-Life Balance
• The Role of Corporate Culture in Employee Engagement
• Talent Management Strategies for Attracting and Retaining Top Talent
• Performance Appraisal Systems: Best Practices and Challenges
• Workplace Diversity and Inclusion Initiatives
• Employee Training and Development Programs
• Change Management Strategies for Organizational Transformation
• Crisis Management and Business Continuity Planning
• Supply Chain Resilience: Lessons Learned from Disruptions
• Sustainable Sourcing Practices in Supply Chain Management
• Inventory Management Strategies for Reducing Costs
• Logistics Optimization for Efficient Operations
• The Impact of Globalization on Supply Chain Networks
• Strategic Alliances and Collaborative Partnerships in Business
• Mergers and Acquisitions: Drivers and Challenges
• Corporate Restructuring Strategies for Turnaround Success
• The Role of Corporate Social Responsibility in Building Customer Trust
• Reputation Management in the Digital Age
• Crisis Communication Strategies for Managing Reputational Risks
• Customer Relationship Management: Strategies for Enhancing Customer Loyalty
• Personalization Techniques in Marketing and Customer Service
• Omnichannel Retailing: Integrating Online and Offline Channels
• The Future of Brick-and-Mortar Retail in the Digital Era
• Pricing Strategies for Maximizing Profitability
• Revenue Management Techniques in Hospitality Industry
• Brand Extension Strategies and Brand Equity
• Customer Experience Management: Best Practices and Trends
• The Impact of Artificial Intelligence on Business Operations
• Machine Learning Applications in Marketing and Sales
• Automation and Robotics in Manufacturing Processes
• Blockchain Technology: Opportunities and Challenges for Businesses
• Augmented Reality and Virtual Reality in Marketing
• Data Privacy and Security Concerns in the Digital Age
• The Role of Big Data Analytics in Business Decision-Making
• Predictive Analytics for Sales Forecasting and Demand Planning
• Customer Segmentation Techniques for Targeted Marketing
• The Influence of Cultural Factors on Consumer Behavior
• Cross-Cultural Marketing Strategies for Global Brands
• International Market Entry Strategies: Modes of Entry and Risks
• Exporting vs. Foreign Direct Investment: Pros and Cons
• Market Entry Strategies for Emerging Markets
• The Impact of Political and Economic Factors on International Business
• Foreign Exchange Risk Management Strategies
• Cultural Intelligence and Global Leadership Effectiveness
• The Role of Multinational Corporations in Economic Development
• Corporate Governance Practices in Different Countries
• Comparative Analysis of Business Laws and Regulations
• Intellectual Property Rights Protection in Global Business
• The Influence of Cultural Differences on Negotiation Styles
• Cross-Border Mergers and Acquisitions: Legal and Cultural Challenges
• International Trade Agreements and Their Impact on Businesses
• The Role of Non-Governmental Organizations in Sustainable Development
• Corporate Philanthropy and Social Impact Investing
• Microfinance and Economic Empowerment of Women
• Entrepreneurship Ecosystems and Innovation Hubs
• Government Policies and Support for Small Businesses
• Venture Capital Financing and Startup Growth
• Crowdfunding Platforms: Opportunities for Entrepreneurs
• Social Entrepreneurship: Business Models for Social Change
• Innovation Clusters and Regional Economic Development
• Angel Investors and Their Role in Startup Funding
• Technology Incubators: Nurturing Startup Innovation
• Intellectual Property Rights Protection for Startup Innovations
• Business Model Innovation: Disrupting Traditional Industries
• The Impact of Climate Change on Business Operations
• Green Technologies and Sustainable Business Practices
• Carbon Footprint Reduction Strategies for Businesses
• Environmental Management Systems and Certification
• Corporate Reporting on Environmental Performance
• Circular Economy Business Models: Closing the Loop
• Sustainable Supply Chain Management Practices
• The Role of Renewable Energy in Achieving Carbon Neutrality
• Smart Cities and Sustainable Urban Development
• Green Building Technologies and Sustainable Construction
• The Influence of Cultural Factors on Entrepreneurship
• Gender Differences in Entrepreneurial Intentions and Success
• Social Capital and Networking for Entrepreneurial Ventures
• Family Business Succession Planning and Governance
• Corporate Entrepreneurship: Fostering Innovation Within Organizations
• Franchising: Opportunities and Challenges for Entrepreneurs
• Online Platforms and the Gig Economy
• Digital Nomads: Remote Work and Entrepreneurship
• The Sharing Economy: Business Models and Regulation
• Blockchain Applications in Supply Chain Traceability
• Cryptocurrency Adoption in Business Transactions
• Initial Coin Offerings (ICOs) and Tokenization of Assets
• Decentralized Finance (DeFi) and Its Implications for Traditional Banking
• Smart Contracts and Their Potential in Business Operations
• Privacy-Preserving Technologies in Data Sharing
• Cryptocurrency Exchanges: Regulation and Security Issues
• Central Bank Digital Currencies (CBDCs) and Monetary Policy
• The Impact of Artificial Intelligence on Financial Services
• Robo-Advisors and Algorithmic Trading in Wealth Management
• Fintech Startups and Disruption in Traditional Banking
• Peer-to-Peer Lending Platforms: Opportunities and Risks
• Digital Identity Management Systems and Security
• Regulatory Challenges in Fintech Innovation
• Financial Inclusion and Access to Banking Services
• Green Finance: Sustainable Investment Strategies
• Socially Responsible Investing and ESG Criteria
• Impact Investing: Financing Social and Environmental Projects
• Microfinance Institutions and Poverty Alleviation
• Financial Literacy Programs and Consumer Empowerment
• Behavioral Finance: Understanding Investor Behavior
• Risk Management Strategies for Financial Institutions
• Corporate Fraud Detection and Prevention Measures
• Financial Market Volatility and Risk Hedging Strategies
• The Role of Central Banks in Monetary Policy Implementation
• Financial Stability and Systemic Risk Management
• Corporate Governance Practices in Banking Sector
• Credit Risk Assessment Models and Default Prediction
• Asset Allocation Strategies for Portfolio Diversification
• Real Estate Investment Strategies for Wealth Accumulation
• Commercial Property Valuation Methods
• Real Estate Crowdfunding Platforms: Opportunities for Investors
• Property Management Best Practices for Rental Properties
• Real Estate Development and Urban Planning
• Mortgage Market Trends and Homeownership Rates
• Affordable Housing Initiatives and Government Policies
• The Impact of Interest Rates on Real Estate Investments
• Sustainable Architecture and Green Building Design
• Real Estate Investment Trusts (REITs) and Tax Implications
• The Influence of Demographic Trends on Housing Demand
• Residential Property Flipping Strategies and Risks
• Health and Wellness Tourism: Trends and Opportunities
• Medical Tourism Destinations and Quality of Care
• Wellness Retreats and Spa Resorts: Business Models
• The Impact of Technology on Healthcare Delivery
• Telemedicine and Remote Patient Monitoring
• Healthcare Data Security and Privacy Regulations
• Healthcare Financing Models: Insurance vs. Out-of-Pocket
• Value-Based Healthcare Delivery and Payment Models
• Healthcare Workforce Challenges and Solutions
• Healthcare Infrastructure Development in Emerging Markets
• The Role of Artificial Intelligence in Healthcare Diagnosis
• Precision Medicine: Personalized Treatment Approaches
• Pharmaceutical Industry Trends and Drug Development
• Biotechnology Innovations in Healthcare Solutions
• Mental Health Awareness and Support Services
• Telehealth Adoption and Patient Engagement
• Chronic Disease Management Programs and Prevention
• Health Information Exchange Platforms: Interoperability Challenges
• Patient-Centered Care Models and Outcomes
• The Influence of Healthcare Policies on Access to Care
• Human Resource Management in the Hospitality Industry
• Employee Training and Development in Tourism Sector
• Quality Service Delivery in the Hotel Industry
• Revenue Management Strategies for Hospitality Businesses
• Destination Marketing and Tourism Promotion Campaigns
• Sustainable Tourism Practices and Eco-Friendly Resorts
• Technology Integration in Travel and Tourism Services
• Cultural Heritage Tourism and Conservation Efforts
• Adventure Tourism: Risks and Safety Measures
• The Role of Online Travel Agencies in Tourism Distribution
• Sustainable Transportation Solutions for Tourism
• Food and Beverage Management in Hospitality Operations
• Wellness Tourism: Trends and Market Segmentation
• Airbnb and Short-Term Rental Market Dynamics
• Wellness Retreats and Spas: Market Positioning Strategies
• Community-Based Tourism Development Initiatives
• Luxury Travel Market: Trends and Consumer Preferences
• Aviation Industry Trends and Airline Marketing Strategies
• Sustainable Event Management Practices
• Convention and Exhibition Tourism: Economic Impact
• Destination Management Organizations and Tourism Planning
• Customer Relationship Management in the Tourism Sector
• Online Reputation Management for Hospitality Businesses
• Accessibility and Inclusivity in Tourism Infrastructure
• Cultural Tourism: Heritage Preservation and Promotion
• Agritourism: Farm-to-Table Experiences and Trends
• The Impact of Climate Change on Tourism Destinations
• Wildlife Tourism: Conservation and Responsible Practices
• Wellness Tourism in Developing Countries: Challenges and Opportunities
• The Role of Tour Operators in Sustainable Tourism Development
• Virtual Reality Applications in Tourism Marketing
• The Rise of Medical Tourism: Market Growth and Challenges
• Responsible Travel and Ethical Tourism Practices
• Event Marketing Strategies for Business Success
• Sponsorship and Partnership Opportunities in Event Management
• Technology Integration in Event Planning and Execution
• Event Risk Management and Contingency Planning
• Corporate Event Planning: Trends and Best Practices
• Trade Show Marketing Strategies for Exhibitors
• Sports Event Management : From Planning to Execution
• Sustainable Event Certification Programs and Standards

How To Prepare Research Paper?

Preparing a research paper involves several key steps, from selecting a topic to writing and formatting the final document. Here’s a comprehensive guide on how to prepare a research paper:

• Select a Topic: Choose a topic that interests you and aligns with the requirements of your assignment or research objectives. Consider the scope of the topic, its relevance, and the availability of resources for conducting research.
• Conduct Background Research: Read up on books and studies that talk about the same things you want to research. This will help you see what people already know, find out where there are still things we don’t know, and make your research questions or ideas better.
• Develop a Research Question or Thesis Statement: Formulate a clear and focused research question or thesis statement that guides your study. Your research question should be specific, relevant, and capable of being answered through empirical investigation.
• Create an Outline: Organize your ideas and research findings into a logical structure by creating an outline for your research paper. Outline the introduction, literature review, methodology, results, discussion, and conclusion sections, along with any subheadings or subsections.
• Write the Introduction: Begin your research paper with an interesting introduction. Share some basic info about your topic, explain why your study is important, and clearly state what you’ll be focusing on in your research. The introduction should also outline the structure of the paper.
• Review the Literature: Conduct a comprehensive review of relevant literature to provide context for your study, support your arguments, and identify gaps in existing research. Summarize key findings, theories, and methodologies from previous studies in your literature review.
• Describe the Methodology: Clearly explain the research design, methods, and procedures used to collect and analyze data. Include details on the population/sample, data collection instruments, data analysis techniques, and any ethical considerations.
• Present the Results: Report the findings of your study in a clear and concise manner. Use tables, graphs, or charts to present quantitative data, and provide descriptive analysis for qualitative data. Ensure that your results are relevant to your research question or thesis statement.
• Discuss the Implications: Interpret the results of your study and discuss their implications that are for theory, practice, or policy. Analyze the strengths and limitations of your research, address any unexpected findings, and propose recommendations for future research or action.
• Write the Conclusion: Summarize the key findings and contributions of your study in the conclusion section. Restate your research question or thesis statement, review the main points that you have discussed in the paper, and highlight the significance of your research in advancing knowledge in the field.
• Revise and Edit: Review your research paper for clarity, coherence, and accuracy. Ensure that your arguments are well-supported by evidence, your writing is concise and precise, and your paper follows the appropriate style and formatting guidelines.
• Cite Sources: Acknowledge the contributions of other scholars by properly citing their work in your research paper. Use a consistent citation style (e.g., APA, MLA, Chicago) and include a reference list or bibliography at the end of your paper.
• Proofread: Carefully proofread your research paper to correct any spelling, grammar, or punctuation errors. Pay attention to formatting details such as margins, font size, and line spacing to ensure consistency throughout the document.
• Get Feedback: Seek feedback from peers, instructors, or mentors to improve the quality of your research paper. Consider their suggestions for revision and make appropriate changes to strengthen your arguments and clarify your writing.
• Finalize the Paper: Make any final revisions or edits based on feedback and proofreading, and then finalize your research paper for submission. Double-check all formatting requirements and ensure that your paper is properly formatted and ready for submission.

Final Thoughts

Researching business topics offers a unique opportunity to delve into the complexities of the modern economy and explore innovative solutions to real-world challenges.

Whether you’re passionate about leadership, marketing, finance, entrepreneurship, or corporate social responsibility, there’s a wealth of knowledge waiting to be discovered. So roll up your sleeves, sharpen your analytical skills, and get ready to make your mark in the world of business research! I hope you find the best and most relevant answer to business topics for research paper. 

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Top 10 Analytics And Business Intelligence Trends For 2024

Business Intelligence Trends 2024

1) Artificial Intelligence

2) data security.

3) Data Discovery

4) D&A Sustainability

5) Data Sharing

6) continuous intelligence, 7) data literacy, 8) natural language processing (nlp).

9) Predictive & Prescriptive Analytics Tools

10) Embedded Analytics

Over the past decade, business intelligence has been revolutionized. Data exploded and became big. And just like that, we all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards . The rise of self-service analytics democratized the data product chain. Suddenly, advanced analytics wasn’t just for analysts.

2023 was a particularly major year for the business intelligence industry. The trends we presented last year will continue to play out through 2024. But the BI landscape is evolving, and the future of business intelligence is playing now, with emerging trends to watch. In 2024, BI tools and strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics, but what is the best BI solution for their specific needs?

Businesses are no longer wondering if visualizations improve analyses but what is the best way to tell each data story, especially with the help of modern BI dashboard software . 2024 will be the year of data security and discovery: clean and secure data combined with a simple and powerful presentation. It will also be a year of collaborative BI and AI. We are excited to see what this new year will bring. Read on to see our top 10 business intelligence trends for 2024!

Let’s Discuss The Top Trends In Business Intelligence

We will start analyzing what is new in business intelligence with AI. This trend is wildly being covered by Gartner in their latest Strategic Technology Trends report, combining AI with engineering and hyper-automation and concentrating on the level of security in which AI risks developing vulnerable points of attack.

Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix, or the Master Control Program of Tron), AI is not yet on the verge of destroying us, despite the legit warnings of some reputed scientists and tech-entrepreneurs.

While we work on programs to avoid such inconvenience , AI and machine learning are revolutionizing the way we interact with our analytics and data management, while increments in security measures must be taken into account. The fact is that it is and will affect our lives, whether we like it or not.

It is expected that in the coming year, AI will evolve into a more responsible and scalable technology as organizations will require a lot more from AI-based systems. According to Gartner’s Data and Analytics research for 2021, with COVID-19 completely changing the business landscape, historical data will no longer be the main driver of AI-based technologies. In change, these solutions will need to work with smaller datasets and more adaptive machine learning while also being compliant with new privacy regulations. This concept is known as ethical AI, and it aims to ensure that organizations use AI systems in a way that will not break the law. To this day, many organizations have faced legal issues for illegally collecting user data. The Facebook and Cambridge Analytica scandal is a perfect example of that. 

In that sense, implementing systems and models to ensure the correct use of AI-related technologies will become even more important in the coming years. In fact, the US government recently released a blueprint for the “AI Bill of Rights” , presenting 5 principles that should guide the design, use, and deployment of automated systems “to protect the American public in the age of artificial intelligence”.

In response to this increasing need for AI accountability, Gartner presents  AI TRiSM as one of the concepts that will help organizations ensure “AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection”. This cross-functional framework needs to be implemented from the earliest stages of system design and involve people from compliance, legal, IT, and analytics for a successful approach. By 2026, businesses that apply this framework to their AI models are expected to be 50% more successful in adoption, business goals, and user acceptance. 

It can’t be denied that AI is still a topic of concern even today. The number of AI-based applications has become so big that many IT professionals don’t even know how to use or interpret them. This leaves the doors open for breaches and financial losses that can significantly impact companies and customers alike. As a response, terms such as explainable AI (XAI) will be at the center of the conversation during 2024. XAI is an emerging field that aims to apply specific processes and methods to allow humans to understand the results and outputs created by machine learning and AI algorithms. The end goal of this field is to ensure trust and transparency with these systems to give humans control over them. 

AI-based business analytics

When it comes to analytics, businesses are evolving from static, passive reports of things that have already happened to proactive analytics with dashboards that help them see what is happening at every second and give alerts when something is not how it should be. Solutions such as an AI algorithm based on the most advanced neural networks provide high accuracy in anomaly detection as it learns from historical trends and patterns. That way, any unexpected event will be immediately registered, and the system will notify the user.

Another feature that AI has on offer in BI solutions is the upscaled insights capability. It basically fully analyzes your dataset automatically without needing effort on your end. You simply choose the data source you want to analyze and the column/variable (for instance, revenue) that the algorithm should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and what-if analysis. That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.

Time gain is also present in the form of AI assistants. Tools have started to develop AI features that enable users to communicate with the software in plain language - the user types a question or request, and the AI generates the best possible answer. If this is something you are interested in, then keep reading because we will dive into it in more detail later in the post with the natural language processing trend.

The demand for real-time online data analysis software is increasing, and the arrival of the IoT (Internet of Things) also brings a countless amount of data, promoting statistical analysis and management at the top of the priority list. However, businesses today want to go further, and Adaptive AI might be the answer. As stated by Gartner, Adaptive AI systems “support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise”. What makes these systems so interesting for companies today is the fact that they can learn from behavioral patterns and adjust to real-world changes, making it easier to make fast and improved decisions. 

In that same realm, Generative AI is another technology that has revolutionized the industry in 2023 and will continue to do so in 2024.  It basically enables AI systems to generate text, images, audio, and other types of content based on human-generated input. A famous example of Generative AI is ChatGTP. In 2023, the tool revolutionized the industry with its ability to generate well-written texts based on a short input. However, as with many AI-related innovations, ChatGTP was quickly scrutinized because it could generate biases, copyright infringement, fake news, and more if not used ethically. 

From a business perspective, using technologies like Adaptive and Generative AI has facilitated several processes, including data collection, cleaning, and analysis, which can be automated and tailored to the company’s needs. Risk management is another area in which these technologies thrive. Businesses can use Generative AI to predict any kind of fraud or attack, as well as generate risk simulations and test strategies in an imaginary scenario. 

Overall, we cannot deny the value of AI and how it has continued to develop over the years. That said, it is fundamental for regulators and decision-makers to ensure ethical and secure measures are being imposed when implementing these systems. It all comes back to security, and we will discuss it in more detail in our next trend.  

As you saw with our extensive AI trend, data and information security have been on everyone’s lips in 2023, and they will continue to buzz the world in 2024. The implementation of privacy regulations such as the GDPR ( General Data Protection Regulation ) in the EU, the CCPA ( California Consumer Privacy Act ) in the USA, and the LGPD ( General Personal Data Protection Law ) in Brazil have set building blocks for data security and management of customers personal information.

Moreover, the recent overturn by the European Court of Justice of the legal framework called Data Privacy Shield hasn't made software companies' lives much easier. The Shield was a legal framework that enabled them to transfer data from the EU to the USA, but with recent legal developments causing the invalidation of the process, companies that have their headquarters in the US don't have the right to transfer any of the EU data subjects.

Actually, a similar situation happened in 2015 when the EU and the USA had no legally valid agreements on this matter for a while. Many US-based (software) providers argue that they use European servers, and there is no data transfer to the US at all. However, from a legal perspective, even this solution is questionable, as, in theory, the US judiciary could force US-based businesses to reveal even data from EU-based servers. In essence, the information that is located in the EU needs to stay in the EU. In practice, that means that EU-based businesses that use in the current situation, US-based software vendors that store any kind of data for them are taking hazards as they operate in a legal grey area. For companies such as datapine , this doesn't represent a big issue since the registration, business, and servers are located in the EU. 

Taking all this into account, businesses have been forced to invest in security to stay compliant with the new regulations and also to protect themselves from cybercrime. In fact, global spending on cybersecurity products is expected to reach $1.75 trillion in the next 5 years. This is not a surprise to the experts as, during 2020 and the beginning of COVID-19, companies of all sizes were forced to mutate from physical to digital, and, to accelerate the transformation, they relied on online services, leaving a gap for cybercriminals to attack. According to the 2023 KPMG CEO Outlook Pulse survey , cybersecurity is among the top 10 “risks to growth” topics for CEOs in the coming years. Even more concerning is that 27% of the surveyed CEOs admit to being unprepared for a potential attack, which increased compared to 24% in the previous year. 

This might change now that company boards recognize cybersecurity as an overall business risk more than an IT-related issue. According to Gartner's Cybersecurity Predictions for 2023-2024, by 2026, 70% of boards will include one member with cybersecurity expertise. 

Amongst the measures organizations are taking in the coming years, we will see an increase in adopting the Zero Trust framework. Zero Trust doesn’t describe a specific technology but an approach in which businesses remove the “implicit trust” from all computing infrastructure by verifying every stage of digital interaction from devices to users, regardless of location. This means every user who wants to interact with the company's systems needs to be validated and verified. According to Gartner, by 2026, 10% of large enterprises will have a “comprehensible, mature and measurable” Zero Trust program in place, compared to the less than 1% that have one today. However, almost half of them might fail as a Zero Trust approach requires full organizational involvement and connection to business goals to succeed. 

The concern in cybersecurity also presents a challenge for SaaS BI tools as they need to ensure they offer a secure product that clients will trust with their sensitive data. Like any other cloud BI solution, online business intelligence software is also subjected to security risks. Some of them include processing data quickly to provide real-time insights that might be subjected to regulatory compliance, vulnerabilities when moving data from user’s systems to the BI tool’s cloud, or when the tool provides access to data from multiple devices that may be unsafe and exposed to attacks. To prevent any of this from happening, BI software needs to have a clear focus on security. 

One of the latest trends in business intelligence to help SaaS BI solutions stay safe is cybersecurity mesh architecture. Cybersecurity mesh is a composable and scalable security control that protects digital assets that reside in applications, in the cloud, IoT, and others. It seeks to establish a defined security perimeter around a person or a specific point with a more modular approach, enabling users to securely access data from their smartphones. One of Gartner's cybersecurity predictions for 2021-2022 stated that by the end of 2024, organizations adopting cybersecurity mesh architecture will reduce the financial impact of security incidents by around 90% . Since data breaches have been regularly in the news, buzzing industries, and average users, the demand for security products and services is understandable.

With these security threats increasing, businesses must adopt an organizational approach to protect their data. That is why data governance will remain one of the hottest topics related to security in 2024. This concept refers to a set of processes, policies, and roles that ensure appropriate valuation, creation, consumption, and control of business data at a strategic, tactical, and operational level. It establishes roles and responsibilities regarding who can manipulate the data, in which situation, and with what tools and methods to ensure a secure and efficient data management process. 

In the past years, due to tighter regulations, such as GDPR, organizations were obligated to ensure a secure environment for sensitive data, enhancing the need for stronger governance processes. As we mentioned earlier, companies of all sizes are exposed to attacks and breaches, leaving massive amounts of sensitive information from customers, suppliers, employees, and more exposed to misuse. In that sense, implementing a well-crafted governance plan will help organizations comply with government regulations while setting the perfect environment to use quality data and achieve their goals. 

In today's highly competitive business environment, where data collection keeps growing every second, data governance becomes a mandatory practice. A well-implemented governance framework not only assists organizations in staying compliant but also in minimizing risks, reducing costs, improving communication from an internal and external point of view, and achieving strategic goals, among other things. 

3) Data Discovery/Visualization

Data discovery using visuals has opened the analytical doors to a wider audience, and it is expected to keep growing in the coming years. As stated by a survey conducted by the Business Application Research Center, data discovery was already listed in the top 6 business intelligence trends by the importance hierarchy for 2023 and is expected to keep growing in 2024. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.

*Source: Business Application Research Center (click image to enlarge)*

Essentially, data discovery is the process of collecting data from various internal and external sources and using advanced visual analytics tools to consolidate all the information. This allows businesses to engage every relevant stakeholder with the information by empowering them to intuitively analyze and manipulate it and extract actionable insights. To achieve this, businesses of all sizes turn to modern solutions such as business intelligence tools that offer data integration, interactive visualizations, a user-friendly interface, and the flexibility to work with big amounts of data efficiently and intuitively.

An essential element to consider is that data discovery tools depend upon a process, and the generated findings will bring business value. It requires understanding the relationship between data through data preparation, visual analysis, and guided advanced analytics. “The high demand for data discovery solutions reflects a huge shift in the BI world towards increased data usage and the extraction of insights,” the Research Center emphasizes. Using online data visualization tools to perform those actions is an invaluable resource for producing relevant insights and creating a sustainable decision-making process. That being said, business users require software that is:

• Easy to use
• Agile and flexible
• Reduces time to insight
• Allows easy handling of a high volume and variety of data

Discovering trends in business operations that you didn’t even know existed or enabling immediate actions when a business anomaly occurs have become invaluable tools in effectively managing businesses of all sizes.

Data visualization has evolved into a state-of-the-art solution to present and interact with numerous graphics on a single screen, whether it's focused on developing sales charts or comprehensive interactive reports. The point is that data discovery is a process that enables decision-makers to reveal insights, and by using visualizations, teams have the chance to spot trends and major outliers within minutes.

In 2024, the dashboard will continue to be a major visual communication tool that will enhance collaboration between teams by being the analytical hub of a project. But more than just a visualization tool, KPI dashboards will take their interactivity features to the next level with technologies such as AI-based alarms and real-time data. Since humans process visual information better, the data discovery trend will be one of the most important BI trends in 2024.

4) D&A Sustainability

Moving on with our list of the new trends in business intelligence, we have data and analytics (D&A) sustainability. The topic, also mentioned in Gartner’s 2023 Data and Analytics Trends, is one of the most important ones we will discuss in this post, as climate change remains a global concern for the next years. 

In recent years, businesses started diving into sustainability mostly as a marketing tactic to brand themselves as “conscious”. As the topic becomes increasingly important, with new regulations forcing organizations to report on their ESG initiatives , decision-makers have realized that sustainability also represents a big way to reduce operating costs and increase overall profitability and efficiency. That is where D&A sustainability comes into the picture. 

Now that businesses of all sizes and across industries have realized the hidden potential of sustainability, we will start to see many using data and analytics as a way to boost their strategies and make the most out of their efforts. By tracking important metrics like energy consumption, gas emissions, labor rights, supply chain performance, and others, organizations can extract valuable insights to guide their sustainability journey. 

In 2024 and beyond, we can expect organizations to use D&A sustainability to anticipate changes in demand and adjust their resource purchases and usage to be more financially intelligent. However, we will also see other factors coming into play besides just purely resource-related data. Production levels, sales volume, employee headcounts, and even weather data will help paint a more accurate picture to facilitate real-time decision-making. 

We can also expect to see different tools emerge to help track sustainability data from a past, present, and future perspective, providing a big competitive advantage for companies that manage to adopt it correctly. That being said, ensuring all employees and relevant stakeholders are involved in the process is also necessary. Implementing training instances to engage employees with the process is a good way to start. 

Linking ESG initiatives to business outcomes is not an easy task. As of today, sustainability analytics is valuable for three main reasons: the first one is to stay compliant with the law, the second one is to track the performance of ESG goals, and the third one is to uncover new opportunities to keep integrating sustainability into operations. Organizational leaders must take charge to ensure all these aspects are covered and supported with the best tools and technologies. 

It is no secret that sustainability has transitioned from a buzzword to a mandatory practice in the business world. It is a growing trend that we will see everywhere in 2024 and many more years to come. 

Data and analytics have become a business’s most valuable competitive asset. Making informed decisions based on accurate insights can skyrocket success to a whole new level. That being said, analyzing data and extracting insights is not enough. Especially considering how accessible it has become to extract and manage valuable business data. To really extract the maximum potential out of your analytical journey, it is necessary to ensure full organizational adoption through powerful data sharing practices, which leads us to our next trend. 

Gartner already identified data sharing as one of the top 10 data and analytics trends for 2023. Stating that businesses that implement efficient data sharing processes with internal and external stakeholders will outperform their competitors on most business value metrics. 

While the importance of data sharing might seem obvious to some, it presents a challenge for most organizations as, for decades, it was the norm to say, “don’t share data unless…”. The issue is that in today’s context, where most businesses are undergoing digital transformations, not sharing data can be detrimental, as everyone across the company needs to be united to connect analytics to general business goals. In that sense, Gartner advises organizations to switch their mindset to “must share data unless..”. Doing so will enable more robust data and analytics strategies, empowering stakeholders to make agile and informed decisions. 

Changing the mindset might not be easy, and organizations that don’t take it seriously might fail in the process. Gartner suggests establishing trust-based mechanisms to ensure decision-makers trust the data they collect and use to inform their strategies. This way, they will feel confident in using it, sharing it, and re-sharing it with those who might need it. This can be easily done by tracking data quality metrics and implementing catalogs to compile all the information related to the trustworthiness of the data. 

When discussing data sharing, the term " self-service BI ” quickly pops up because those solutions do not require an IT team to access, interpret, and understand all the data. These online BI tools make sharing easier by generating automated reports that can be scheduled at specific times and to specific people. For instance, they enable you to set up business intelligence alerts and share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices, which enhances the decision-making and problem-solving processes critical for today's ever-changing environment. This is especially necessary now that the pandemic has forced businesses to shift to a home office dynamic in which collaboration needs to be supported by the right tools more than ever. 

Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI solutions . However, data sharing does not only occur around the exchange or updates of some documents. It has to track the progress of meetings, calls, e-mail exchanges, and ideas collection. More recent insights predict that collaborative business intelligence will become more connected to greater systems and larger sets of users. The team’s performance will be affected, and the decision-making process will thrive in this new concept. 

In fact, it is expected that, in 2024, data sharing will move further from just sharing insights and will start from earlier stages. Starting from data exploration and spreading across the entire analytical workflow for a more efficient decision-making process that includes every stakeholder, regardless of location. This last point is especially important when considering the growing security concerns many businesses face today. Implementing a collaborative BI approach enables every stakeholder and data user to be accountable for the decisions he or she makes, ensuring a more secure workflow. 

In response to all these changes, data analytics and BI providers are prioritizing collaboration for 2024, introducing multiple capabilities that connect users at every stage of their work and with a level of interactivity that breaks the barriers between data and analytics and the different business functions. A recent survey shows that 75% of executives say their business functions are competing rather than collaborating. This presents a major challenge, especially for companies still undergoing a digital transformation due to the pandemic. By implementing a collaborative approach supported by the right tools and processes, developers and average business users are expected to work together under the same analytics umbrella, enabling more united communication and a productive work environment. Let’s see how it will be developed in the business intelligence trends topics of 2024.

Next, in our list of trends in data analytics, we will talk about continuous intelligence (CI). Gartner defines the concept as a “design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events”. 

It basically describes the use of tools and processes to facilitate the integration of real-time analytics into business operations with the help of augmented analytics. Traditionally, the analytical process has relied on predefined metrics that are tracked on specified schedules. CI is a machine-driven approach that automates the extraction of data insights no matter how many data sources or massive volumes of data need to be handled, providing businesses with a continuous and frictionless flow of real-time insights. 

The concept was born out of a necessity for an integrated analytical approach to keep up with modern organizations' demands in the digital revolution. Having data silos and decentralized analytical processes can only lead to a waste of resources and valuable time. With continuous intelligence, organizations can go further from analyzing static metrics that must be constantly updated to being able to identify trends, growth opportunities, and anomalies that might remain hidden otherwise. 

So, it is clear that all CI applications have real-time analysis at their core. However, historical data also plays a pivotal role in the process. For example, you might be a manufacturing company analyzing machine performance in real time and realize, in just seconds, that a specific part of the machine is about to fail, which can help you implement corrective measures immediately. Complementing the live data you just got, you can use historical data to understand how many times this same machine has failed in the past few weeks, months, or even years. Allowing you to get a 360-degree view and make the most efficient decisions.  

CI tools are expected to offer automated, real-time data ingestion, simplify and unify data collection, management, and analysis, and use advanced in-memory technology to store and manage historical information. Combining these solutions will give businesses the power to optimize their day-to-day operations by spending less time shifting through massive amounts of data and more time focusing on what really matters. Plus, they can significantly accelerate the time to action in any business scenario thanks to various features, like dynamic alerting and event triggering powered by AI and ML algorithms. 

In 2024 and the years ahead, we can expect more and more organizations to adopt CI technologies to make smarter decisions live and with less manual work. CI offers a shift from traditional BI processes based on curated historical data to AI-driven augmented analytics with real-time insights that allow for efficient and agile responses to unexpected events. 

As data becomes the foundation of strategic decisions for businesses of all sizes, understanding and using this data as a collaborative tool that everyone in the organization can use becomes critical for success. That said, data literacy will be one of the relevant data analytics trends to look out for in 2024. 

Data literacy is defined as the ability to understand, read, write, and communicate data in a specific context. This means understanding the techniques and methods used to analyze the data as well as the tools and technologies implemented. According to Gartner , poor data literacy is listed as the second-biggest roadblock to the success of the CDO’s office, and it adds that by 2024, data literacy will become essential in driving business value. 

Even with the rise of self-service tools that are accessible to everyone, data literacy continues to be the foundation of a successful data-driven culture. Business leaders are responsible for providing the needed training and tools to the entire organization to empower everyone to work with data and analytics. To achieve a successful data literacy process, a careful assessment of the skills of employees and managers needs to be made in order to identify weak spots and gaps. Gartner recommends starting by identifying fluent data users that can serve as “mediators” for non-skilled groups as well as identifying communication barriers where data is failing its purpose. With all this knowledge in hand, the creation of targeted training instances will become an easier task. 

In the long run, with the proper training and the right tools, users from all levels of knowledge will be able to perform advanced analysis and use data as their main language. With technologies such as predictive analytics becoming accessible for regular users, data science will no longer need to be performed by experts- shifting these professionals to focus on other advanced tasks such as Machine Learning or MLOps. In fact, according to Gartner, it is expected that by 2025, the shortage of data scientists will no longer be an obstacle to businesses adopting advanced technological processes. That said, data literacy will be one of the most important business intelligence market trends in the coming year.

Natural Language Processing (NLP) is one of the recent trends in business intelligence that is revolutionizing how companies approach their analytical processes. Considered amongst the most powerful branches of AI, NLP enables computers and machines to understand, learn from, and interpret human language in a spoken or written form, and it can be divided into two subsets: natural language understanding (NLU) and natural language generation (NLG). NLU focuses on understanding the meaning behind text and speech, while NLG focuses on text generation based on specific data input. 

The growth of this trend has been such in the past years that its $3 billion worldwide market revenue from 2017 is expected to be almost 14 times larger by 2025, reaching $43 billion, according to research by Statista. This is not surprising as language-processing applications are already present in our daily lives in the shape of car navigation systems, smart voice assistants like Siri or Alexa, autocomplete text features on our phones, and translation apps, just to name a few. 

Considering all of that, it is not surprising that businesses have begun to adopt this technology to manage the large amounts of unstructured text data they gather from different sources such as emails, social media, or surveys. As a response, multiple BI software providers offer their users language insight features. There are two major use cases for which language processing is becoming increasingly popular in the BI industry. Let’s look at them in more detail below: 

BI data assistant: Similar to the chatbots we see on multiple websites today, a data assistant is integrated into BI software to answer any analytical questions that a user might have. All you need to do is write a question in human language, and the assistant will provide you with the answer. As the technology matured in the past years, AI-based assistants went from simply showing search results for users to analyze to being able to filter and organize the data to generate analytical insights as an answer. This development has also helped democratize data as non-technical users can simply type a question, and the software will automatically show them an answer without needing complicated calculations or analysis. 

Sentiment analysis : Also known as opinion mining, it is the process of analyzing text data to identify the emotional tone behind it. Businesses often use it to analyze comments on social media, emails, blog posts, webchats, and more and define if the tone of what is being said is negative, positive, or neutral. Through this, organizations can extract useful insights regarding product development and brand positioning, as well as understand pain points to improve the customer experience on different touch points. 

NPL is one of the business intelligence emerging trends we will see developing in multiple areas over the coming years. BI software that exploits this capability with a self-service approach will gain a competitive advantage by allowing users to conduct efficient analysis without the need for any calculations. We will definitely be watching how this technology develops in 2024.

9) Predictive & Prescriptive Analytics Tools

Business analytics of tomorrow is focused on the future and tries to answer the question: what will happen? How can we make it happen? Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among BI professionals, especially since big data is becoming the main focus of analytics processes being leveraged by big enterprises and small and medium-sized businesses.

Predictive analytics is the practice of extracting information from existing data sets to forecast future probabilities. It’s an extension of data mining that refers only to past data. Predictive analytics includes estimated future data and, therefore, always includes the possibility of errors from its definition, although those errors steadily decrease as software that manages large volumes of data today becomes smarter and more efficient. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessments. Applied to business, predictive analytics is used to analyze current data and historical facts to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities. In contrast, bankers use it to generate a credit score – the number generated by a predictive model that incorporates all the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

Predictive analytics must also become accessible for everyone, and in 2024, we will witness even more relevance that will cater to that notion. Self-service analytical possibilities are becoming a criterion for BI vendors and companies alike; both can profit from it and bring more value to their businesses. The predictive models, in practice, use mathematical models, in other words, forecast engines, to predict future happenings. Users simply select past data points, and the software automatically calculates predictions based on historical and current data, as shown in the example:

**click to enlarge**

Among different predictive analytics methods, two are quite popular among data scientists: artificial neural networks (ANN) and autoregressive integrated moving averages (ARIMA).

In artificial neural networks, data is processed in a similar way as in biological neurons. Technology duplicates biology: information flows into the mathematical neuron, is processed by it, and the results flow out. This single process becomes a mathematical formula that is repeated multiple times. As in the human brain, the power of neural networks lies in their capability to connect sets of neurons together in layers and create a multidimensional network. The input to the second layer is from the output of the first layer, and the situation repeats itself with every layer. This procedure allows for capturing associations or discovering regularities within a set of patterns with a considerable volume, number of variables, or diversity of the data. 

ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future. The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be considered when making predictions. Each part of ARIMA takes care of different sides of model creation – the autoregressive part (AR) tries to estimate the current value by considering the previous one. Any difference between predicted data and real value is used by the moving average (MA) part. We can check if these values are normal, random, and stationary – with constant variation. Any deviations in these points can bring insight into the data series behavior, predict new anomalies, or help to discover underlying patterns not visible by the bare eye. ARIMA techniques are complex, and concluding the results may not be as straightforward as for more basic statistical analysis approaches. However, once the basic principles are grasped, the ARIMA provides a powerful predictive analysis tool.

Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and which steps are taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics tries to see what the effect of future decisions will be to adjust the decisions before they are actually made. This greatly improves decision-making, as future outcomes are considered in the prediction. Prescriptive analytics can help you optimize scheduling, production, inventory, and supply chain design to deliver what your customers want in the most optimized way, and these are some of the emerging trends in business intelligence 2024 that we will hear more about.

When data analytics occurs within a user’s natural workflow, embedded analytics is the name of the game. Businesses have recognized the potential of embedding various BI components, such as dashboards or reports, into their own application, thus improving their decision-making processes and increasing productivity. Formerly strangled by spreadsheets, companies have realized how utilizing embedded dashboards enables them to provide higher value within their own applications. In fact, according to Allied Market research, the embedded analytics market is projected to reach $77.52 BN by 2026, with a CAGR of 13.6% from 2017 to 2023 , and this is one of the business analytics topics we will hear even more in 2024.

Whether you need to create a sales report or send multiple dashboards to clients, embedded analytics is becoming a standard in business operations. In 2024, we will see even more companies adopting it. Departments and company owners seek professional solutions to present their data without building their own software. By simply white labeling the chosen application, organizations can achieve a polished presentation and reporting they can offer consumers.

More than just embedding a dashboard or BI features in an application, embedding analytics allows for collaboration by keeping every single stakeholder involved. By allowing clients and employees to manipulate the data in a well-known environment, you facilitate the extraction of insights from every area of your business. This makes it one of the fastest-growing business intelligence trends on this list. 

Business Wire recently published a report called “Global Embedded Analytics Market (2021 to 2026) - Growth, Trends, COVID-19 Impact, and Forecasts,” in which they mention that “organizations are deploying embedded analytics solutions to realize significant gains in revenue growth, marketplace expansion, and competitive advantage.” They also add that embedding analytics will grow significantly in the healthcare industry in the coming years. Considering the massive amounts of data that hospitals collect, which got even bigger with COVID-19 and telemedicine interactions, healthcare businesses “switch from paying for service volume toward service value”. By using powerful healthcare analytics software that can be embedded, hospital managers can extract valuable insights that will help them optimize processes from a clinical, operational, and financial point of view. 

This is one of the trends in business analytics that can be implemented immediately since many vendors already offer this opportunity and ensure that the application works seamlessly and without much complexity.

What Are The Analytics & Business Intelligence Trends For 2024?

We’ve summed up in this article what the near future of business intelligence looks like for us. Here are the top 10 analytics and business intelligence trends we will talk about in 2024:

• Artificial Intelligence
• Data Security
• Data Discovery/Visualization
• D&A Sustainability 
• Data Sharing 
• Continuous Intelligence 
• Data Literacy
• Natural Language Processing
• Predictive And Prescriptive Analytics Tools
• Embedded Analytics

Become Data-driven In 2024!

Being data-driven is no longer an ideal; it is an expectation in the modern business world. 2024 will be an exciting year of looking past all the hype and moving towards extracting the maximum value from state-of-the-art online business intelligence software . We hope you enjoyed this overview, and stay tuned for more business intelligence industry trends!

If you’re ready to start your business intelligence journey, or keep up with the 2024 trends, trying our software for a 14-day trial will do the trick! And it’s completely free!

Research challenges and opportunities in business analytics

• BIO5, Institute of
• Management Information Systems
• Remote Sensing / Spatial Analysis - GIDP
• Computer Science

Research output : Contribution to journal › Article › peer-review

There are plenty of definitions proposed for business analytics–some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

• Business analytics
• descriptive analytics
• machine learning
• network science
• predictive analytics
• prescriptive analytics

ASJC Scopus subject areas

• Information Systems
• Industrial and Manufacturing Engineering

Access to Document

• 10.1080/2573234X.2018.1507324

Other files and links

• Link to publication in Scopus

Fingerprint

• Industry Engineering & Materials Science 100%
• Outcomes Research Business & Economics 40%
• Encapsulation Engineering & Materials Science 32%
• Diagnostics Business & Economics 30%
• Decision Making Business & Economics 28%
• Labels Engineering & Materials Science 24%
• Decision making Engineering & Materials Science 20%
• Methodology Business & Economics 16%

T1 - Research challenges and opportunities in business analytics

AU - Delen, Dursun

AU - Ram, Sudha

N1 - Publisher Copyright: © 2018 Operational Research Society.

PY - 2018/1/2

Y1 - 2018/1/2

N2 - There are plenty of definitions proposed for business analytics–some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

AB - There are plenty of definitions proposed for business analytics–some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

KW - Business analytics

KW - descriptive analytics

KW - machine learning

KW - network science

KW - predictive analytics

KW - prescriptive analytics

UR - http://www.scopus.com/inward/record.url?scp=85067402716&partnerID=8YFLogxK

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Book series

Business Analytics in Practice

About this book series.

• Ali Emrouznejad

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Business analytics is a type of analytics that helps organizations mine, process, and visualize important business data and take advantage of patterns in their businesses that they would not see otherwise.

Business analytics is the process by which companies use data either created by their operations or publicly available data, to solve business problems, monitor their business fundamentals, identify new opportunities for growth and better serve their customers. As the saying goes, you can’t measure what you can’t see.

Business analytics involves either individual pieces of data or data sets which are either stored on-premise or in the cloud. Data sets that increase beyond a certain threshold are commonly referred to as big data, which requires significant computational power to access and analyze. Business analytics uses data exploration, data visualization, integrated dashboards and more, to allow users access to usable data and insights.

As companies increasingly digitize their businesses, business analytics is more important than ever before. Delivering advanced data analytics and AI with an integrated workflow drives organizations to implement smarter, faster and more accurate data-driven decisions.

Business analytics also delivers business optimization strategies that help organizations visualize and take advantage of patterns in their businesses that they would not see otherwise.

The world changes so fast, and organizations need to adapt quickly based on the information. Success today depends on many elements, but, primarily, organizations need access to the right data and insights fast so executives can act decisively.

Those who can make quick strategic decisions with the right information at hand often have a huge competitive advantage. With business analytics, organizations can make confident business decisions informed by real metrics and insights and take the guesswork out of decision making.

Therefore, many companies have business analysts, whose jobs depend on identifying business intelligence that can help the company make smarter and quicker decisions that produce an advantage over competitors.

Explore IBM's ebook to uncover the value of integrating a business analytics solution that turns insights into action.

Read the guide for data leaders

Business intelligence, which has been around for many years, involves using data on hand to make important business decisions that impact the entire organization. Business intelligence is often thought of as the act of identifying and storing data so as to be used for decision making.

Business analytics 1 (link resides outside ibm.com) takes business intelligence a step further by using that data to ask and answer specific questions about what happened in the past that either a) may happen in the future exactly the same or b) will happen differently because of new or different contexts.

It provides a complete picture of a business, allowing organizations to explain user behavior more effectively. Not only that, but business analytics can also forecast what’s coming in the future, making predictions about changes to business results. 

Business analytics benefits data scientists and advanced data analysts to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis, such as using previous sales data to estimate customer lifetime value, and cluster analysis, such as analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance , human resources , supply chain , marketing , sales , or information technology ; and all industries, including healthcare, financial services and consumer goods.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use to turn that data into insights. Here are some of the most common tools, disciplines and approaches.

• Predictive modeling: Companies will often design or develop new products, enter into new markets or otherwise explore new opportunities for which they have little prior experience or historical data to mine. This is where predictive modeling and predictive analytics shine. Predictive modeling 2 (link resides outside ibm.com) helps organizations avoid issues before they occur, like knowing when a vehicle or tool will break down and intervening before it occurs or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 
• Data mining : This is an extremely important component of business analytics, where mostly automated tools unearth and make sense of raw data to identify patterns, producing key insights. The growing importance of big data makes data mining , also known as knowledge discovery in data (KDD), a critical component of any modern business. Although companies often struggle with scaling their data mining activities as they seek to uncover more insights.
• Data science: The study of how data creates business insights, incorporating elements from mathematics, statistics and computer science. With the increase in data sources and the importance of analyzing that data, data science is become one of the most important jobs in Corporate America and organizations are increasingly reliant on it to certain create actionable insights that impact business outcomes.

Business analytics leverage analytics, the action of deriving insights from data, to drive increases in business performance. There are three types of valuable analytics that are often employed in business analytics situations.

As the phrase implies, this type of analytics describes the data contained within. An example would be a pie chart that breaks down the demographics of a company’s customers. 

This form of analytics mines existing data, identifies patterns and helps companies predict what may happen in the future based on that data. It uses predictive models that data can be fed into to make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season was projecting warm temperatures.

These analytics 3 (link resides outside ibm.com) help organizations make future decisions based on existing information and resources. Every business can use prescriptive analytics by using the existing data to make guesses about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

To maximize an organization’s business analytics, it needs to clean and connect its data, create stunning data visualizations and provide insights on where a particular business is today while helping predict what will happen tomorrow. It usually involves the following components:

First, organizations must identify all of the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Unfortunately, much of the data a company may be sitting on today is not “cleaned,” rendering it useless for real analysis unless it is addressed.

Here are some reasons why an organization’s data may need cleaning:

Incorrect data fields: Due to manual entry or incorrect data transfers, an organization may have bad data mixed with good data. If it has any bad data in the system, it has the potential to render the entire set meaningless.

Outdated data values: Certain data sets, like customer information, may need editing due to customers leaving, product lines being discontinued, or other historical data that no longer is relevant.

Missing data: Companies may have changed how it collects data or the data they collect, which means historic entries may be missing data that is crucial to future analysis. Companies in this situation may need to invest in either manual data entry or identify ways to use algorithms or machine learning to predict what the correct data should be.

Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it may need to merge so that it has all the data in one place. While the foundation of any business analytics approach is first-party data (e.g., data the company has collected from stakeholders and owns), they may want to append third-party data (e.g., data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Companies now can query and quickly parse gigabytes and terabytes of data instantaneously with additional cloud computing. Data scientists can analyze data more effectively using machine learning (ML), algorithms, artificial intelligence (AI) and other technologies. Doing so can produce actionable insights based on an organization’s KPIs.

A company’s data is only as good as it can be understood by humans. Programs can now quickly take voluminous amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders. Data visualization serves several purposes for organizations, helping non-technical people understand analytics concepts, helping others see patterns in multiple data points, or demonstrating a business’s growth or decline. They can help with idea generation, idea illustration, or visual discovery. Data visualization best practices include understanding what visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to ensure the audience it is shared with understands what they’re viewing.

Data management is conducted in tandem with the above, an organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision making.

• Financial and operational planning: Business analytics helps organizations to align financial planning and operations more seamlessly. It does this by setting rules for supply chain management, integrating data across functions, and improving demand forecasting.
• Planning analytics: Planning analytics is an integrated business planning approach that combines uses spreadsheets and database technology to make effective business decisions about topics such as demand and lead generation, operations costs, and technology requirements. Many organizations have historically used tools like Excel for business planning, but some are transitioning to tools like IBM Planning Analytics .
• Integrated sales and marketing planning: Every organization is sitting on historical data about their lead generation, sales conversions, and customer retention success rates. Organizations looking to create accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data using business analytics to easily allocate resources based on performance or changing demand to meet business objectives.
• Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they need to ensure they have the right workforce with the right skills. It is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent analytical and communication skills.

Here’s the type of employees they will need to have to take advantage of the full potential of robust business analytics strategies.

• Data scientists: These employees are usually responsible for managing the algorithms and models that power the company’s business analytics programs. Organizational data scientists either leverage open source libraries, like NTLK, for algorithms to use or build their own to conduct analysis on data. They excel at problem-solving and usually need to know several programming languages, like Python, which helps tap into access out-of-the-box machine learning algorithms, and SQL, which helps extract data from databases to feed into a model. In recent years, an increasing number of schools now offer master of science or bachelor’s degrees in data science where students engage in a degree program coursework that teaches them computer science, statistical modeling, and other mathematical applications.
• Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted and placed into one master database. They are often responsible for ensuring that data can be easily collected and accessed by stakeholders to provide organizations with a single-pane-of-glass view of their data operations.
• Data analysts: Data analysts play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they may be involved in collecting and analyzing the data sets and building the data visualizations or they may just take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up every year and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors that do not.

• More informed decisions: Having a flexible and expansive view of all the data an organization sits on can eliminate uncertainty and prompt an organization to take action quicker. If an organization’s data suggests that sales of a particular product line are precipitously declining, it may decide to discontinue it. If climate risk will affect the harvesting of a raw material another organization depends on, it may need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies. How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics allows them to utilize thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.
• Single-pane view of information: Increase collaboration between departments and line-of-business users because everyone has the same data and is talking from the same playbook. That can expose more unseen patterns, allow different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.
• Enhanced customer service: By knowing what customers want and when and how they want it, organizations will drive happier customers and, therefore, engender greater loyalty. Additionally, by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

The flexibility of spreadsheets. Control of a database. The power of integrated business planning. Now available as a Service on AWS.

AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data. 

Detects application and business risks affecting the customer experience, enabling users to correlate application service level objectives with underlying infrastructure resourcing.

Learn more about business analytics by reading these blogs and articles. 

IBM Planning Analytics has helped support organizations across not only the office of finance – but all departments in their organization.

A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

Predict outcomes with flexible AI-infused forecasting and analyze what-if scenarios in real-time. IBM Planning Analytics is an integrated business planning solution that turns raw data into actionable insights. Deploy as you need, on-premises or on cloud.

1 Business Intelligence vs. Business Analytics  (link resides outside ibm.com), Harvard Business School 2  How predictive analytics can boost product development  (link resides outside ibm.com), McKinsey, August 16, 2018 3  What is prescriptive analytics?  (link resides outside ibm.com), Harvard Business School Blog, November 2, 2021

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• Business Management

Top 10 Business Analytics Project Ideas 2024

Home Blog Business Management Top 10 Business Analytics Project Ideas 2024

As a beginner in business management, one of the most crucial skills is gathering and analyzing data to make informed decisions. Business analytics uses data and statistical methods to extract insights and make data-driven decisions. The good news is that there are countless business analytics project ideas that you can start working on to improve your skills and help your business thrive. This blog will explore the top 10 business analytics project ideas you can do online as a beginner or an experienced professional. So, let’s dive in and discover how you can use business analytics projects to gain a competitive advantage in today’s fast-paced business world. 

Why are Business Analytics Projects Important? 

Business analytics is an amalgamation of business management and data analytics. High-value projects aimed at business development add value to the profile or resume of candidates who opt for a business analytics career. Here are the top 10 business analytics project examples. 

Sales Data Analysis 

It involves the analysis of data on every aspect of a company’s sales. It determines the total number of sales, average monthly sales, demographics of customers, and patterns of selling periods. It allows the company to make informed decisions to prioritize the production of specific products and scale them. To analyze the sales data, students can use different tools and languages. Students can use SQL to extract data from the database. Excel or Google Sheets can clean and analyze data for charts and graphs. For advanced visualizations and dashboards, Tableau or Power BI can be used. Python or R is good for advanced data analysis and statistical modeling, like looking for trends or making predictions. 

Sales Analysis Source Code  

Dataset  

Customer Review Sentiment Analysis 

It is the process of determining the emotional state of customers after they purchase or use the products. It allows the company to realize the possible reasons for customer complaints and measures to improve the features and quality. Students can use Python or R for data analysis. Tools like TextBlob and NLTK for sentiment analysis. 

Reviews Sentiment Source Code  

Market Basket Analysis 

It involves the analysis of the correlation between the ales of different products when combined. It helps improve the business by identifying the best combinations and increasing the preferences of customers for the products. For this project, students can analyze data using the Apriori algorithm. They can use either Python or R programming languages. 

Market Basket Analysis Source Code  

Price Optimization 

It involves investigating historical prices, crucial price factors, the markets where the company operates (and their economic contexts), the profiles of potential clients, etc. Programming Languages like Python or R are suitable for this project. Regression analysis and demand forecasting models are used to analyze the data.

Tensor House Source Code  

Stock Market Data Analysis

The project involves determining the frequency of rise and fall in price, the general trend of average monthly closing prices over the year, and trading volumes. Candidates can select a specific dataset and explore the company’s stock performance history. To analyze the data for this project, Python and R is used. Tools like Pandas and Numpy are used for manipulating the data. 

Stock Market and Analysis Source Code  

Customer Segmentation

It refers to categorizing a company’s clients into different groups based on their purchasing behavior, financial level, interests, needs, and loyalty to the business. It helps optimize marketing campaigns and maximize the profits from each client. The K-means and Hierarchical clustering algorithms are generally used for this project.

Customer Segmentation Source Code  

Fraud Detection

Credit card fraud, identity theft, and cyber-attack are common fraudster challenges faced across various industries. Projects on fraud detection involve choosing a dataset and running statistical analyses to identify fraudulent operations. Machine learning algorithms such as decision trees and logistic regression are used for fraud detection. 

Fraud Detection Source Code  

Equity Research

Equity is the value of the returns received by a company’s shareholders after liquidating all the assets and clearance debts incurred by the company. Equity research plays a crucial role in the successful run of both shareholders and companies. Students can use Excel and Python to analyze the financial datasets for this project. Tools such as ratio analysis and financial statement analysis are in equity research.

Equity Research Source Code  

Social Media Reputation Monitoring

It is the process of gauging the presence and influence of a brand on customers through social media. Using analytical tools and techniques, the project audits, monitors, and interprets social media users’ opinions about the products. It helps revise social media marketing strategies to promote the business. Social media monitoring tools such as Hootsuite and Sprout Social are used to analyze the data.

Real-Time Pollution Analysis

It is a typical data visualization project, allowing the candidates to learn univariate and multivariate data analysis. The methodology can be reproducible to business aspects. Students can use either Python or R to build the project. Matplotlib or Plotly are used for creating visualizations. 

Air Pollution Tracker Source Code  

Business Analytics Project Ideas for Beginners 

Graduates from several fields, including engineering, with an inclination for business, choose management as their career path.  Business management for beginners , augmented with business analytics projects, provide potential platforms to lay a strong foundation to build their career. The following are the most-edifying business analytics projects for students.

Employee Attrition and Performance 

These projects are ideal for acquiring the qualitative analysis skills of employee attrition to find answers for the event’s who, when, and why. They also predict quantitative aspects of human resource dynamics for the organization’s next 5 to 10 years. The balance between attrition and retention is the secret to optimal human resources and talent utilization. To do this, students can use Excel to clean the data. SQL is used for data extraction. Python or R for data analysis. 

Employee Attrition Performance Source Code  

Prediction of Sales in Tourism for the Next Five Years 

This project helps business analysts to improve their skills in applying data mining to determine patterns and correlations among tourism packages and their preferences. It has two approaches: qualitative and quantitative. Both approaches help beginners to hone their analytical and judgmental skills. To predict sales, statistical analysis tools like R or Python are used. Excel and SQL are used for cleaning and extracting data, respectively. 

 Prediction of the Success of an Upcoming Movie 

Business management professionals have a good scope in the film industry as numerous films enter the screen. These projects involve forecasting success based on the analysis of variables, including genre, language, directors, actors, actresses, budget, locations, etc. The prediction depends on the model devised based on the data of predetermined variables associated with previously released movies against their success. Like the other projects, students can use Python or R to predict the success of the upcoming movie.

Prediction of the Fate of a Loan Application 

These projects expose beginners to several machine-learning tools and techniques, and datasets. They also introduce the candidates to various parameters and help them gain the ability to recognize variables under eccentric circumstances. The top 3 machine-learning solution approaches for loan prediction are as follows.

• Support vector machine 
• Random forest

Pandas are the most straightforward and powerful Python libraries for beginners used for the prediction of the fate of loan applications. 

Business Analytics Ideas for MBA Students

ECBA certificate training is among the best options to improve the profile of business analytics aspirants. A merit of this program is the opportunities for business analytics projects for MBA students. Three top business analytics project ideas are as follows.

Predicting Customer Churn Rate

It involves predicting the decline of customer rates. It has scope for stakeholders to identify setbacks in the business. It helps learn several statistical tools, such as SHAP (Shapley Additive exPlanations), RandomSearch, and GridSearch, for univariate and multivariate analysis on a retrieved dataset.

Customer Churn Analysis Source Code  

Prediction of Selling Prices for Different Products 

It refers to the determination of the price of a product that attracts customers with an optimal profit margin. Further, it also helps companies to determine the offers to improve business. These projects help acquire skills to employ machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks that use different metrics to test each of their performances. 

Store Sales Prediction

These projects involve working with numeric and categorical feature variables and performing univariate & bivariate analysis to find the redundancy in variables associated with the store chain of a company. They help the candidates learn machine learning models such as the ARIMA time series model. 

Store Item Demand Forecasting Source Code  

Business Analytics Idea for Intermediate Professionals

Business analytics project ideas for experienced professionals should involve a complex combination of statistical parameters and real-world scenarios to enhance their skills significantly. Following are the business analytics project examples suitable for the intermediate levels.

Creating Product Bundles

It is a method that combines different products from the same company and sells them as a single unit. Under these projects, candidates learn market basket analysis and time series clustering methods to identify product bundles using sales data.

Product Bundle Source Code  

Life Expectancy Analysis

These projects aim to determine the monetary value of the potential consumer of the products and services of a company. Traditionally, government organizations utilize life expectancy analysis to determine the correlation between life expectancy and a nation’s GDP.

Life Expectancy Analysis Source Code  

Building a BI app 

Business intelligence apps or tools play a critical role in finding urgent solutions to issues that cost high for the business. Low to no-code custom apps for decision-making and long-term strategies are invaluable for an organization.

Business Intelligence Analysis Source Code  

Are Business Analytics Projects Difficult to Complete? 

Business analytic projects face several challenges that hamper their successful implementation. Technological advancement expands the options for tools and techniques. Still, they create a grey zone wherein the new tools emerge with overlapping functionalities interfering with decision-making. Other reasons for the failure of business analytics projects are: 

• Lack of well-defined and explicit goals 
• Poor data integration 
• Lack of conversion of insights and outcomes into actions.
• Poor adaptations to the ongoing development

Final Thoughts 

Business analytics is blooming parallel to technological advancements, and every business is leveraging analytical tools and techniques to optimize its actions. Whether experienced or fresher, diverse business analytics projects help you upgrade your profile.  KnowledgeHut business management for beginners is highly recommendable for a firm foundation before undertaking analytics projects, as it provides top-quality augmentation to your aptitude for the discipline.

Frequently Asked Questions (FAQs)

Predictive analytics is a branch of analytics that predicts future outcomes using models based on historical data. Businesses use customer data and transaction information to predict the performance of the products and make strategies to optimize profits. 

Popular business analytics tools are SAS business analytics, Sisense, Microstrategy, KNIMETIBCO Spotfire, Tableau big data analytics, Power BI, and Excel.  

The common challenges faced in business analytics projects are: 

• Changing requirements or business needs 
• Conflicts with stakeholders 
• Poorly documented processes 
• Unrealistic timelines. 

Mansoor Mohammed

Mansoor Mohammed is a dynamic and energetic Enterprise Agile Coach, P3M & PMO Consultant, Trainer, Mentor, and Practitioner with over 20 years of experience in Strategy Execution and Business Agility. With a background in Avionics, Financial Services, Banking, Telecommunications, Retail, and Digital, Mansoor has led global infrastructure and software development teams, launched innovative products, and enabled Organizational Change Management. As a results-driven leader, he excels in collaborating, adapting, and driving partnerships with stakeholders at all levels. With expertise in Change Management, Transformation, Lean, Agile, and Organizational Design, Mansoor is passionate about aligning strategic goals and delivering creative solutions for successful business outcomes. Connect with him to explore change, Agile Governance, implementation delivery, and the future of work.

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses, and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

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Research Topics & Ideas: Business

50+ Management Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a business/management-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of  research ideas and topic thought-starters for management-related research degrees (MBAs/DBAs, etc.). These research topics span management strategy, HR, finance, operations, international business and leadership.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the management domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: Business Research Topics

• Business /management strategy
• Human resources (HR) and industrial psychology
• Finance and accounting
• Operations management
• International business
• Actual business dissertations & theses

Strategy-Related Research Topics

• An analysis of the impact of digital transformation on business strategy in consulting firms
• The role of innovation in transportation practices for creating a competitive advantage within the agricultural sector
• Exploring the effect of globalisation on strategic decision-making practices for multinational Fashion brands.
• An evaluation of corporate social responsibility in shaping business strategy, a case study of power utilities in Nigeria
• Analysing the relationship between corporate culture and business strategy in the new digital era, exploring the role of remote working.
• Assessing the impact of sustainability practices on business strategy and performance in the motor vehicle manufacturing industry
• An analysis of the effect of social media on strategic partnerships and alliances development in the insurance industry
• Exploring the role of data-driven decision-making in business strategy developments following supply-chain disruptions in the agricultural sector
• Developing a conceptual framework for assessing the influence of market orientation on business strategy and performance in the video game publishing industry
• A review of strategic cost management best practices in the healthcare sector of Indonesia
• Identification of key strategic considerations required for the effective implementation of Industry 4.0 to develop a circular economy
• Reviewing how Globalisation has affected business model innovation strategies in the education sector
• A comparison of merger and acquisition strategies’ effects on novel product development in the Pharmaceutical industry
• An analysis of market strategy performance during recessions, a retrospective review of the luxury goods market in the US
• Comparing the performance of digital stakeholder engagement strategies and their contribution towards meeting SDGs in the mining sector

Topics & Ideas: Human Resources (HR)

• Exploring the impact of digital employee engagement practices on organizational performance in SMEs
• The role of diversity and inclusion in the workplace
• An evaluation of remote employee training and development programs efficacy in the e-commerce sector
• Comparing the effect of flexible work arrangements on employee satisfaction and productivity across generational divides
• Assessing the relationship between gender-focused employee empowerment programs and job satisfaction in the UAE
• A review of the impact of technology and digitisation on human resource management practices in the construction industry
• An analysis of the role of human resource management in talent acquisition and retention in response to globalisation and crisis, a case study of the South African power utility
• The influence of leadership style on remote working employee motivation and performance in the education sector.
• A comparison of performance appraisal systems for managing employee performance in the luxury retail fashion industry
• An examination of the relationship between work-life balance and job satisfaction in blue-collar workplaces, A systematic review
• Exploring HR personnel’s experiences managing digital workplace bullying in multinational corporations
• Assessing the success of HR team integration following merger and acquisition on employee engagement and performance
• Exploring HR green practices and their effects on retention of millennial talent in the fintech industry
• Assessing the impact of human resources analytics in successfully navigating digital transformation within the healthcare sector
• Exploring the role of HR staff in the development and maintenance of ethical business practices in fintech SMEs
• An analysis of employee perceptions of current HRM practices in a fully remote IT workspace

Topics & Ideas: Finance & Accounting

• An analysis of the effect of employee financial literacy on decision-making in manufacturing start-ups in Ghana
• Assessing the impact of corporate green innovation on financial performance in listed companies in Estonia
• Assessing the effect of corporate governance on financial performance in the mining industry in Papua New Guinea
• An evaluation of financial risk management practices in the construction industry of Saudi Arabia
• Exploring the role of leadership financial literacy in the transition from start-up to scale-up in the retail e-commerce industry.
• A review of influential macroeconomic factors on the adoption of cryptocurrencies as legal tender
• An examination of the use of financial derivatives in risk management
• Exploring the impact of the cryptocurrency disruption on stock trading practices in the EU
• An analysis of the relationship between corporate social responsibility and financial performance in academic publishing houses
• A comparison of financial ratios performance in evaluating E-commerce startups in South Korea.
• An evaluation of the role of government policies in facilitating manufacturing companies’ successful transitioning from start-up to scale-ups in Denmark
• Assessing the financial value associated with industry 4.0 transitions in the Indian pharmaceutical industry
• Exploring the role of effective e-leadership on financial performance in the Nigerian fintech industry
• A review of digital disruptions in CRM practices and their associated financial impact on listed companies during the Covid-19 pandemic
• Exploring the importance of Sharia-based business practices on SME financial performance in multicultural countries

Ideas: Operations Management

• An assessment of the impact of blockchain technology on operations management practices in the transport industry of Estonia
• An evaluation of supply chain disruption management strategies and their impact on business performance in Lithuania
• Exploring the role of lean manufacturing in the automotive industry of Malaysia and its effects on improving operational efficiency
• A critical review of optimal operations management strategies in luxury goods manufacturing for ensuring supply chain resilience
• Exploring the role of globalization on Supply chain diversification, a pre/post analysis of the COVID-19 pandemic
• An analysis of the relationship between quality management and customer satisfaction in subscription-based business models
• Assessing the cost of sustainable sourcing practices on operations management and supply chain resilience in the Cocao industry.
• An examination of the adoption of behavioural predictive analytics in operations management practices, a case study of the
• Italian automotive industry
• Exploring the effect of operational complexity on business performance following digital transformation
• An evaluation of barriers to the implementation of agile methods in project management within governmental institutions
• Assessing how the relationship between operational processes and business strategy change as companies transition from start-ups to scale-ups
• Exploring the relationship between operational management and innovative business models, lessons from the fintech industry
• A review of best practices for operations management facilitating the transition towards a circular economy in the fast food industry
• Exploring the viability of lean manufacturing practices in Vietnam’s plastics industry
• Assessing engagement in cybersecurity considerations associated with operations management practices in industry 4.0 manufacturing

Topics & Ideas: International Business

• The impact of cultural differences in communication on international business relationships
• An evaluation of the role of government import and export policies in shaping international business practices
• The effect of global shipping conditions on international business strategies
• An analysis of the challenges of managing multinational corporations: branch management
• The influence of social media marketing on international business operations
• The role of international trade agreements on business activities in developing countries
• An examination of the impact of currency fluctuations on international business and cost competitiveness
• The relationship between international business and sustainable development: perspectives and benefits
• An evaluation of the challenges and opportunities of doing business in emerging markets such as the renewable energy industry
• An analysis of the role of internationalisation via strategic alliances in international business
• The impact of cross-cultural management on international business performance
• The effect of political instability on international business operations: A case study of Russia
• An analysis of the role of intellectual property rights in an international technology company’s business strategies
• The relationship between corporate social responsibility and international business strategy: a comparative study of different industries
• The impact of technology on international business in the fashion industry

Topics & Ideas: Leadership

• A comparative study of the impact of different leadership styles on organizational performance
• An evaluation of transformational leadership in today’s non-profit organizations
• The role of emotional intelligence in effective leadership and productivity
• An analysis of the relationship between leadership style and employee motivation
• The influence of diversity and inclusion on leadership practices in South Africa
• The impact of Artificial Intelligence technology on leadership in the digital age
• An examination of the challenges of leadership in a rapidly changing business environment: examples from the finance industry
• The relationship between leadership and corporate culture and job satisfaction
• An evaluation of the role of transformational leadership in strategic decision-making
• The use of leadership development programs in enhancing leadership effectiveness in multinational organisations
• The impact of ethical leadership on organizational trust and reputation: an empirical study
• An analysis of the relationship between various leadership styles and employee well-being in healthcare organizations
• The role of leadership in promoting good work-life balance and job satisfaction in the age of remote work
• The influence of leadership on knowledge sharing and innovation in the technology industry
• An investigation of the impact of cultural intelligence on cross-cultural leadership effectiveness in global organizations

Business/Management Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a business-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various management-related degree programs (e.g., MBAs, DBAs, etc.) to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Sustaining Microbreweries Beyond 5 Years (Yanez, 2022)
• Perceived Stakeholder and Stockholder Views: A Comparison Among Accounting Students, Non-Accounting Business Students And Non-Business Students (Shajan, 2020)
• Attitudes Toward Corporate Social Responsibility and the New Ecological Paradigm among Business Students in Southern California (Barullas, 2020)
• Entrepreneurial opportunity alertness in small business: a narrative research study exploring established small business founders’ experience with opportunity alertness in an evolving economic landscape in the Southeastern United States (Hughes, 2019)
• Work-Integrated Learning in Closing Skills Gap in Public Procurement: A Qualitative Phenomenological Study (Culver, 2021)
• Analyzing the Drivers and Barriers to Green Business Practices for Small and Medium Enterprises in Ohio (Purwandani, 2020)
• The Role of Executive Business Travel in a Virtual World (Gale, 2022)
• Outsourcing Security and International Corporate Responsibility: A Critical Analysis of Private Military Companies (PMCs) and Human Rights Violations (Hawkins, 2022)
• Lean-excellence business management for small and medium-sized manufacturing companies in Kurdistan region of Iraq (Mohammad, 2021)
• Science Data Sharing: Applying a Disruptive Technology Platform Business Model (Edwards, 2022)
• Impact of Hurricanes on Small Construction Business and Their Recovery (Sahu, 2022)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Topic Ideation

If you’d like hands-on help to speed up your topic ideation process and ensure that you develop a rock-solid research topic, check our our Topic Kickstarter service below.

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Great help. thanks

Hi, Your work is very educative, it has widened my knowledge. Thank you so much.

Thank you so much for helping me understand how to craft a research topic. I’m pursuing a PGDE. Thank you

Effect of Leadership, computerized accounting systems, risk management and monitoring on the quality of financial Reports among listed banks

May you assist on a possible PhD topic on analyzing economic behaviours within environmental, climate and energy domains, from a gender perspective. I seek to further investigate if/to which extent policies in these domains can be deemed economically unfair from a gender perspective, and whether the effectiveness of the policies can be increased while striving for inequalities not being perpetuated.

healthy work environment and employee diversity, technological innovations and their role in management practices, cultural difference affecting advertising, honesty as a company policy, an analysis of the relationships between quality management and customer satisfaction in subscription based business model,business corruption cases. That I was selected from the above topics.

Research topic accounting

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Lessons from the Bud Light Boycott, One Year Later

• Jura Liaukonyte,
• Anna Tuchman,
• Xinrong Zhu

Six factors that make a brand more susceptible to consumer backlash.

Why did the Bud Light boycott affect the beer brand’s sales when many other boycotts have only marginal or short-term impact? An analysis of sales data confirms that Bud Light suffered a sustained downturn in sales, more pronounced in Republican-leaning counties in the U.S. And it explains several factors that determine how vulnerable a brand is to a boycott. Boycotts can have a bigger impact when a product is more substitutable, when it is more visible, and when consumers feel psychological “ownership” over it.

Taking a social stance has become a rite of passage for contemporary brands that are hoping to resonate with younger, more socially-conscious audiences. In April 2023, Bud Light tried its hand at this strategy, collaborating with transgender influencer Dylan Mulvaney on a social media promotional post. This sparked backlash from several prominent conservatives , leading many conservative figures and groups to call for a boycott of Bud Light.

• Jura Liaukonyte is an Associate Professor at Cornell University’s SC Johnson College of Business. Her research focuses on quantifying the effects of advertising, information, and social media movements on consumer choice.
• Anna Tuchman is an Associate Professor of Marketing at Northwestern University’s Kellogg School of Management. Her research addresses economic questions related to advertising, pricing, and public policy.
• Xinrong Zhu is an Assistant Professor of Marketing at Imperial College London Business School, specializing in quantitative marketing, retail analytics, and the causal impact of policy changes, marketing activities, and politics on consumer behavior.

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