How Online Platforms Use Science, Data, and Human Psychology to Turn Reviews into Revenue

In the era before internet information was freely accessible, consumers relied heavily on agents to make purchasing decisions about services or products they could not see or experience firsthand. Relying on professional knowledge, market insights, and an understanding of consumer psychology, agents played a critical role in transactions. Even today, agents still hold an important place in fierce global competition. However, professional services come at a price, and purchasing through agents incurs agency fees. Furthermore, because agents possess more product information than ordinary consumers, situations of information asymmetry can arise where negative information gets concealed. Consumers may make wrong decisions based on incomplete information, indirectly harming their own interests.

With the popularization of internet information and the rise of B2C platforms, consumers can now directly contact manufacturers, bypassing traditional agents. However, these online platforms do not provide the professional services of agents; they merely offer a space for companies and consumers to exchange transaction information. How can these platforms ensure consumers feel confident about their purchases? They devised a low-cost, highly efficient method: allowing consumers to exchange information directly on the platform. By utilizing concepts from behavioral psychology, they made consumers more comfortable purchasing online and consequently increased sales. What STEAM concepts have these platforms applied, and what can we learn from them?

Letting Science Lead the Way: Understanding Consumer Psychology

While individual behavior may not always be predictable, group behavior generally follows patterns and exhibits a degree of reproducibility. The science that studies human behavior is psychology. In consumer behavior, a frequently occurring psychological phenomenon is the Bandwagon Effect, also known as the Herd Effect. When you see that a desired product has over 200 orders, and you scroll down to find detailed positive reviews, it becomes easier to generate an impulse to buy, as this implies the product's reliability.

However, platforms cannot simply allow users to leave comments unchecked. Psychology introduces the concept of Negativity Bias, which suggests that when faced with positive and negative evaluations of a similar nature, people tend to amplify the negative reviews and ignore the positive ones. A single irresponsible or one-sided comment is enough to damage a product's reputation. Platforms need to strike a balance between presenting honest user feedback and maintaining a product's image.

"To what extent do emotional comments versus objective evaluations influence consumer decision-making? How should platforms make trade-offs when filtering through numerous comments?"

These are scientific questions that require in-depth exploration, and they sit at the heart of how modern e-commerce platforms are designed.

Review Engineering: Leveraging Big Data to Extract Sales-Driving Reviews

The concept of Marketing 4.0 was introduced in 2016, emphasizing the importance of human-centric digital marketing strategies in an age of information overload. Customer mobility has increased, and consumers can easily find alternatives at any moment. Brands require more precise and efficient strategies to attract and retain them.

In this context, how do online platforms filter out high-impact reviews and convert them into sales? One study utilized artificial intelligence to conduct textual sentiment analysis on large volumes of platform reviews, finding that the sentiment, rating, and satisfaction reflected in user reviews influence purchase intent more than brand loyalty does.

"E-commerce platforms possess tens of thousands of product reviews. By utilizing big data analysis and machine learning, it is not difficult to find the correlation between each review style and sales volume."

Platforms can categorize consumers, filter effective positive and negative reviews, and highlight them to improve sales conversion rates. This is why e-tailers invest significant resources in data analysis and engineering planning during platform design.

Weighting with Mathematical Models: Presenting the Most Persuasive Reviews

On online platforms, general users typically provide simple or empty comments, casually assigning a star rating. The traditional average rating method is easily skewed by malicious or thoughtless reviews. However, platforms also have highly engaged users who provide detailed, nuanced reviews, complete with photos and personal reflections. Their opinions carry far more weight than those of casual reviewers.

If the influence of these high-quality contributors is increased within the rating system, the platform's recommendation results become more professional and credible. This is not a simple average. It is a mathematically weighted model designed to surface what is most useful to the next buyer.

"Using science to explore human nature, employing big data technology and mathematical models to guide marketing engineering, and using the humanities to connect merchants and customers — these all demonstrate the important role of STEAM in digital marketing."

Mastering digital marketing strategies in the digital age, including SEO, social media marketing, and data analysis, has become an essential skill in business. Understanding consumer psychology and applying data science can make marketing strategies more effective and provide reliable support for promotional planning. The review system, so familiar and seemingly simple, is in fact one of the most sophisticated applications of STEAM thinking in everyday commerce.

References

1. Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296–320.

2. Kotler, P., Kartajaya, H., & Setiawan, I. (2016). Marketing 4.0: Moving from Traditional to Digital.

3. Hossain, M. S. (2024). Textual feature engineering for purchase intent and customer satisfaction: Insights from marketing 4.0 and sentiment. Sustainable Futures, 8, 100385.

About the Author

RoRono is a STEM educator and school administrator born in Hong Kong, now based in Japan. Holding a Master's in Education (STEM) from The University of Hong Kong, where he graduated on the Dean's Honor List, he brings a decade of experience in curriculum design, extracurricular programming, and school development across Hong Kong's education sector.