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CASE STUDY


 Adap ve Cognizance model uses advanced


 machine learning algorithms to priori ze

 offers based on dynamic tracking of


 individual customer behaviour and

 touch point affinity pa erns.









 To improve offer acceptance rate by
 iden fying and recommending offers that
 Objec ve
 serve customer's contextual needs






 Flytxt Customer Value Management Solu on
 used mul -stage filtering process to recommend

 right offers. It dynamically ranks offers from                     IMPACT
 Solu on
 eligible offer basket based on their fitment to
 customers current needs




                                                                                        Retailer

 Demographic Profiles, usage and recharge         Customer engagement              touch points – App
 data, recent touch point interac ons                   improved                       and USSD
 Data                                                                                contributed to
 Analyzed                                                   6X
                                                    over touch points                     88%
                                                                                      of the total
                                                                                      conversions



 Advanced machine learning based

 recommenda on and priori za on of offers
 Analy cs
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