A multi system operator- telecom
Cross sell and Upsell optimization
Problem
The client’s marketing team needed insights into viewership data to design targeted campaigns and effectively reach potential customers.
Solution
An AI system was developed that leverages feature engineering to derive meaningful attributes from viewership data. Multiple classification ML models were used to identify customer segments and assess their propensity, enabling more precise targeting.
Outcome
The system successfully identified a large group of customers with favorable attributes, enabling more accurate targeting with ads. This improved customer segmentation resulted in a higher conversion rate, optimizing campaign effectiveness.


Leveraging for targeted advertisement campaigns
An omni-channel retailer partnered with an MSO to access viewership data for designing targeted campaigns to acquire customers within six months. To aid in this, they engaged in a solution to provide data-driven insights for better advertisement targeting. Leveraging demographic data from third-party providers and viewership data from the MSO, a robust AI solution was developed. The solution included feature engineering to generate meaningful derived attributes such as network, day of week, and daypart affinity, significantly enhancing model accuracy. Multiple classification machine learning models were built to identify distinct customer segments and their propensity to visit the store.



The data-driven approach successfully extended reach by identifying 10% of over 2 million customers with similar attributes to existing ones, largely consisting of individuals aged 50+, with medium-to-high income and college degrees. Ads were more precisely targeted, generating significant cost savings. Additionally, deploying the model boosted the customer conversion rate from 21% to over 39%, resulting in improved campaign performance and greater business outcomes.
Advertisements that hit the target.
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