Understand product interests & buying behaviors at individual customer level
Understanding Customer journeys is challenging - we help you to find the answers and to use the insights
what we can offer you
Improve customer experience by offering personalized recommendations
Allows A/B testing and optimization of recommendation algorithms
Cross-sell and upsell products more effectively
Cross Sell
By clustering similar customers based on observed buying behavior or other descriptive parameter, we identify white-spot items, which have a high likelihood being interesting for the customer. The results can be further enhanced with time-series models, to predict item trends seasonality effects.
Buying pattern
By analyzing historic transactions we can identify strong customer-product relations, which reflect needs and affinities. These customer individual core/ regular products can be used to identify basket composition patterns and to predict future transactions and customer needs quite accurately.
