Win-win situation for customer satisfaction and lead generation

It has been common to see different kinds of personalized recommendation on e-commerce sites. “People also bought” recommendation is too common nowadays “People like you also bought” (calculated based on a variety of attributes) is not surprising also. In fact, personalized recommendation engine can do far more than that. Our personalized recommendation engine is well-designed to maximize conversion and retention rate with a mixed matrix of factorization and neural network. How? We track the individual behavior of a user down to fine details, like how many times they click to a certain product, which kind of product is added to cart but being removed later, or how long do they stay on a product page etc. With the power of AI, we are able to predict how an individual customer will behave and which kind of product is his/her key interest, hence achieving specific KPIs like increasing sales, retention rate or referrals.

Today it’s not enough to just attract visitors to your site. The mission is not completed before you convert them into paying customers. The key to success is to provide personalized product recommendations that fit their desire and interest. Unify their user experience and make them feel like you understand them in-depth.

With our powerful personalized recommendation engine, you can now touch a customer’s heart with accurate predictions and cross-sell the right products. Our AI specialists can help to build a custom recommendation engine for your website and online store. You are now able to make use of offline store data for more precise analytics and rock your sales.

Case Study

A UK-based C2C marketplace startup is eager to increase the sales on their marketplace. However, they find it hard to discover what kinds of products that users are interested in. Our well-trained AI engine helps to determine which listings to show and their rankings based on a user’s behavioral data. For example, if a user typed a certain keywords, or if he/she viewed several items in the same category, the engine would recommend related items for the specific user. After implementation, the 5-day retention rate boost up to 22% within 2 weeks.

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