Our client, a global telecom, had a legacy recommendations engine (RENG), that was often suggesting repeat recommendations and was not learning from customer choices. Thus, our client was failing to increase customer engagement.
We built a robust measurement framework using large volumes of real-time data, to help evaluate the impact of the RENG on different customer interactions. We also improved the functionality of RENG: eliminated repeat recommendations, wrong recommendations, wrong system flags. We also developed and executed a three-step improvement initiative
We helped our client increase by 17 %
the number of shows watched through recommendations through the refurbished RENG.
The groups with the highest increase in recommendations watched also exhibited 11% lower churn rates than their counterparts, thus driving additional value for the telecom.