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E-commerce Analytics

E-commerce industries have the collective communication outlets or tools used to store and deliver information or data. In these industries, as the number of customers grows, so does the amount of real-time data. Here, the success and failure of your business strategy depends on how you manage your data. Finding meaning in terabytes of data may seem impossible, but it is easy for Metatron. Metatron helps you find ways to do more with your existing network, respond to changing patterns, manage customer exits, and predict the most profitable expansion strategies. As competition gets tougher and consumer demands grow, Metatron helps you find and keep what you want.


B is a startup that provides integrated mileage service in the bay area of the United States. It has grown rapidly and has attracted a large number of customers. However, B has slowed its growth in recent years, so they needed to know how customers were dissatisfied with their service use behavior and how they were functioning, and how return rates changed over time after customers signed up.
In addition, in order to provide more benefits to merchants, they had to analyze various data such as shoppers’ move patterns, purchase patterns, and which coupons were most effective when they were issued. However, they were not even able to try to analyze the situation where the data was growing rapidly and scattered around.

Business Impact

Company B suffered from even a simple data inquiry due to a large amount of data. After introducing Metatron, company B has gathered them into one place. Metatron’s sub-second data processing provides a basis for quick data analyzing without creating a separate data mart. This finally allows self-discovery of data by analysts in each department.


Service association analysis reflecting customer pattern

If customers are not satisfied with the initial service several times, the re-visit rate will be drastically lowered or withdrawn. So, at this point, customers’ minds need to be stable for their service to continue growing. It is also important to raise the retention rate by offering coupons at an appropriate time to tempt customers who have recently been using the service. Company B analyzed the user behavior log through Metatron. They analyzed the time took, search keywords, pages users stayed on and items purchased together. By analyzing the similarity between items, possible user interest items and most effective coupon for sales were suggested. In addition, by analyzing the purchase pattern by age and sex groups of the customers, informing the sellers of who they should target was possible.