This essay examines how the marketing discipline will evolve over the next five to ten years, with a particular focus on research methodology and analytics. Drawing on scholarship in artificial intelligence and consumer behavior modeling, the paper argues that marketing is transitioning from a historically backward-looking practice toward a proactive, data-driven science. It discusses how artificial intelligence, big data, and dynamic pricing models are already reshaping how marketers identify customers, predict purchasing decisions, and tailor product offerings. Real-world examples, including digital banking firm NuBank and the credit card industry, illustrate these trends in action. The paper also addresses limitations of traditional research tools such as surveys and focus groups, predicting their gradual replacement by real-time behavioral data.
The marketing discipline will be fundamentally changed going forward and well into the future. The discipline will rely heavily on analytics and overall predictive ability. Artificial intelligence is beginning to enter the discipline as a means of predicting consumer behavior and purchasing decisions. Likewise, technology continues to evolve, allowing marketers to properly pinpoint and isolate variables that are directly correlated to sales. The use of social media websites, streaming viewership rates, time spent online, how people interact online, and other variables now requires specific expertise as it relates to marketing. Marketing has now become an analytical science with many of its variables easily measured and observed. The focus of this essay is placed on marketing research and analytics and how they will evolve over the coming years to encompass much more than reactive pricing data. Instead, the future of marketing research will be far more proactive in nature, with an improved ability to predict how and when a consumer will make a purchase.
Historically, market research has been backward-looking. Researchers often review data related to sales, promotion effectiveness, and purchaser demographics, using these variables to predict future marketing performance. However, this approach is frequently difficult because many of these variables change and have different impacts on one another, making accurate predictions challenging. Research conducted by Fornell and Larcker found that many of these models do a poor job of predicting consumer behavior in highly dynamic markets. Fast-growing markets such as Dallas, Texas, or Atlanta, Georgia, often do not provide ample predictive power. According to the research, older methods are much more applicable to slower-growth, mature markets characterized by little change in consumer demographics (Fornell, 2021).
This landscape has changed significantly with the introduction of big data, data analytics, artificial intelligence, and other methods for curating large amounts of information. Marketers are now transitioning into the role of data scientists, with the ability not only to predict purchasing behavior but also to change pricing dynamically using multiple variables at a single point in time. This dynamism will only improve over time as analytical capabilities, systems, and processes advance. In addition, businesses can leverage these insights to provide more flexible pricing terms for their consumers, clients, and customers.
NuBank, one of the world's fastest-growing digital banks, leverages these capabilities to specifically market its products to clients. Through market research, the company is better able not only to pinpoint the best customers in the regions in which it operates, but also to price loans and other products effectively. As a result, the company spends very little compared to its peers on marketing and advertising. Instead, its client growth has been generated almost entirely from customer referrals, due in part to strong product quality and an ability to target the customers most likely to benefit from its digital products.
Airlines, credit card companies, and vacation websites often use a more rudimentary form of this model to attract and retain customers. Market research has enabled credit card companies not only to pinpoint the most profitable customers, but also to curate product offerings to better meet customer needs and pricing considerations.
"AI replaces surveys with real-time behavioral data"
The marketing discipline is undergoing a fundamental transformation driven by artificial intelligence, big data, and real-time analytics. As the field moves away from backward-looking, reactive research toward proactive, predictive methodologies, marketers must develop data science competencies alongside traditional marketing skills. The coming decade will see continued advancement in how businesses identify, target, and serve customers — making analytical proficiency an essential component of modern marketing education and practice.
You’re 63% through this paper. Sign up to read the remaining 1 section.
Sign Up Now — Instant Access Already a member? Log inAlways verify citation format against your institution’s current style guide requirements.