RECO Engine recommends static and dynamic offer based on the customer’s behavioral patterns using Collaborative Filtering and KNN algorithms. Recommendation engine heightens customer engagement by acting on the customers’ historical behavior and what they do in the current moment. It provides profound insight into each customer’s interests and past interactions to help delivering highly personalized content including marketing offers and product recommendations.
Next Best Offer, based on forecasting model, captures a customer’s usage patterns and interactions in the real time. The solution helps to turn individual customer interactions into relevant, contextual and personalized engagement for inbound marketing. It creates opportunities for deep market segmentation, better conversion, higher lifetime value and greater retention throughout the customer life cycle.
Top X best Offers recommendation based on user profile
Combination of various offers for better uptakes
Offer push based on real time trigger
Configurable Action item based on subscriber response
Recommend Next offer in case of:
Previous offer rejected
Previous offer fulfilled
Previous offer expired
Previous offer delivered
Rule based, segmented offer creation (static or dynamic/real time offer based on rule)
Offer Display in the desired sequence through various channels (sequence may alter based on the pre-defined rule, for example, if someone has taken the offer, then the offer can be moved to a lower level/ removed from the system.
Off-the-shelf sampling techniques for better segment selection
Complete report of offer-checkers and offer-takers are made available in the desired format on a daily basis.
Integration and real time updating of NBO offers with all other platforms
Engage with customers contextually across inbound and outbound channels
Next best offer recommendation for better conversion rate
Detailed analysis with control/test groups at each stage: Pre-Analysis, Ongoing Analysis and Post Analysis