Machine Learning in Marketing Research

In today’s rapidly evolving business landscape, market research has become an indispensable tool for organizations seeking to gain a competitive edge. While traditional market research methods have proven valuable, the advent of machine learning has opened up a new realm of possibilities, revolutionizing the way businesses collect, analyze, and interpret data. In this article, we will explore the intersection of machine learning and market research, delving into the innovative applications, the benefits it offers, and how it empowers businesses to make data-driven decisions with unparalleled precision and efficiency.

Detecting logical inconsistencies

Machine learning is a game-changer in market research, enabling the seamless detection of logical inconsistencies within survey data. Logical inconsistencies in the context of market research refer to inconsistencies, contradictions, or anomalies in survey data. Leveraging AI and ML models automates this process, reducing the need for manual data analysis and interpretation, saving valuable time and effort. These models possess the remarkable capability to analyze vast volumes of survey data quickly and efficiently, even handling millions of responses. Their sophisticated pattern recognition abilities unveil complex and subtle inconsistencies, non-linear relationships, and hidden patterns that might escape manual scrutiny.

ML models continuously learn and adapt, honing their skills in identifying inconsistencies where traditional methods may struggle. By using Natural Language (NL) statements, ML models can effectively process survey questions, benefiting from rule-based approaches, NLP techniques, and pre-trained models like ChatGPT. Combining neural models like LLM, specific models like NLI, as well as syntactic, lexical, and semantic models enhances the detection of various dimensions of inconsistencies. This enables market researchers to set a benchmark by computing consistency scores for concluded surveys, leading to valuable insights and empowering businesses to make well-informed decisions. The added value of ML lies in its autonomous ability to discern and flag inconsistencies promptly, ensuring a proactive approach to survey data analysis.

Logical inconsistencies example from ChatGPT

Detecting fraudulent answers

Machine learning proves invaluable in detecting fraudulent answers within survey data, going beyond merely identifying logical inconsistencies to ensure data integrity and accuracy. Traditionally, manually selecting features for fraud detection was subjective and time-consuming, but ML models excel at encompassing a wide array of features simultaneously. By leveraging ML algorithms, the model learns to distinguish deceptive responses, relieving the need for manual feature setting. Logical consistencies serve as one of the features to identify potential fraud, alongside time between answers, answer position patterns, statistics on open-ennded answers, user agent and user behavior analysis, and so on.

There are several ways to approach the model construction. In a supervised learning case, the data in the dataset is labeled as fraudulent or non-fraudulent prior to learning, while in a semi-supervised learning case, a small labeled set is used to generate additional labelled data from the larger (previously unlabelled) set. The ML model construction involves multiple steps, from exploratory analysis, data pre-processing, and splitting the dataset into training and test sets, to training the ML model, cross-validation, and evaluating the model’s performance on unseen data. With ML’s precision, accuracy, and robustness, fraudulent responses can be effectively detected, safeguarding the integrity of market research findings.

Example of fraudulent answers

Closing remarks

Machine learning will be crucial for future market research, automating the detection of logical inconsistencies in surveys and uncovering fraudulent responses. By employing artificial intelligence and machine learning models, organizations can swiftly analyze extensive data, saving time and effort. These models continuously learn and adapt, identifying complex patterns with precision and efficiency in analyses.

In an upcoming blog on this topic, we will delve into advanced profiling, synthetic data, and emotion analysis, unveiling new possibilities for machine learning in market research and enabling even more informed business decisions.