This article was first published on SingularityNET - Medium
Our study on the behavior of digital marketplaces participants will help us develop a reputation algorithm that can perform more effective product recommendations.
Recommendations often dictate our digital activities. What should we buy? Which video should we watch next? Tech Corporations are tackling all of these questions, albeit imperfectly, for now.
As the age of artificial intelligence draws near, the impact of recommendation engines on our daily lives is only set to increase. The SingularityNET team is creating a reputation algorithm which will help us to perform more effective and contextual product recommendations for prospective buyers.
This reputation algorithm will be useful in a wide variety of online marketplaces. Currently, we are testing the algorithm by performing a large-scale simulation which tries to mimic real-world marketplaces as much as possible. In this article, we will explain our study on the behavior of digital marketplaces participants.
How many users leave reviews?
Multiple studies have already concluded that the percentage of users who leave a review while visiting online marketplaces depends on whether they were asked to do so.
According to the BrightLocal consumer review survey, if asked, approximately 71% of consumers will leave a review for a business. Similarly, our analysis of Fiverr, a freelance services marketplace, showed that the platform’s persistent ask for a review results in feedback from roughly 60–80% of its users.
Thus, we can conclude that if online marketplaces ask their participants for reviews, then up to 70% of their participants will oblige, depending on the system we have in place.
Our study of user behavior revealed that the persistence of a website or business in asking for reviews also makes a difference in the actions of their customers. Some websites are particularly insistent in asking for a review, to the point that it obstructs user behavior — while other ...
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SingularityNET - Medium