Recommendations

The Recommendation Engine: Self-learning. Context-based. Personalized.

FACT-Finder’s Recommendation Engine analyses your store’s click and purchase history to determine corresponding appropriate products, categories, and attributes. If necessary, it is possible to use either single or multiple products for a recommendation source, including (for example) the complete contents of the shopping cart. If the cart contains a shirt and a tie, FACT-Finder might displays items that would match them both: for example, a jacket. Your recommendations will become more precise by using FACT-Finder’s Personalization module. The Recommendation Engine then considers the customer’s individual preferences along with the „wisdom of the crowd”, including colours, brands, and other factors.
You can also base the recommendations on complex sets of rules. For example, this could be useful in retailing pharmaceuticals, so as to recommend only products that do not have reciprocal effects with the medications already selected. In addition, you can use whitelist and blacklist entries to precisely determine which recommendations should and should not be made for specific products.

The Recommendation Engine tool provides product recommendations based on actual sales information. Depending on the way the systems are integrated, this sales information can come from an export file provided by you and/or can be generated from the tracking information obtained while FACT-Finder is running.

The Recommendation Engine can be controlled on different levels: From the matrixes, FACT-Finder determines the meaning of levels as well as the amount of recommendations and a possible self-reference (products from the same category may be recommended). Additionally, you can create manual Recommendations to recommend or prevent specific products. Manual recommendations are created in a similar way as campaigns.

If you have licenced Personalization in addition to the Recommendation Engine, then recommendations can also be created based on the known preferences of users. If both modules are present, you can find the configuration within the sub menu Recommendations.