Challenge

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.

Solution

We helped our client establish optimal pricing and monitor competitors’ actions 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​.​

Recommendations engine 1

Result

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.

Deliverables:​​

1. Collaborative suggestions – live algorithm providing customer-level propensity scores to watch a show that someone similar to them has seen​
2. Machine learning – live algorithm which retrains the itself based on actual customer behavior
3. Holistic 360 RENG –three modules (metadata, collaborative and machine learning) providing one-set of recommendations unique to each customer​

Challenge

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.

Solution

We helped our client establish optimal pricing and monitor competitors’ actions 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​.​

Recommendations engine 1

Result

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.

Deliverables:​​

1. Collaborative suggestions – live algorithm providing customer-level propensity scores to watch a show that someone similar to them has seen​
2. Machine learning – live algorithm which retrains the itself based on actual customer behavior
3. Holistic 360 RENG –three modules (metadata, collaborative and machine learning) providing one-set of recommendations unique to each customer​