• Effective implementation of marketing strategies – An estimated 94% customers across segments were identified as marketable and a potential 8x marketing spend ROI was unlocked

Customized customer segmentation to target promo-sensitive customers with relevant offers

Infrequent renters engaged with regular outbound-marketing campaigns

Problem Statement & Challenge

A leading entertainment company that operates in the movie streaming and rentals industry via automated retail kiosks, wanted to analyze customer engagement on their digital platform and leverage data-driven insights to discern accurate, personalized marketing strategies and tactics. 


TheMathCompany worked with the client company to customize customer segmentation so that they could run personalized promotions for each customer based on historical purchasing patterns and other relevant factors like rental time and historical promotional data. The three-step process for setting up the customer segmentation tool would help effectively implement and curate marketing strategies.


Data-driven personalization strategies are seen to help businesses gain 5x to 8x times ROI on marketing spend. Consumers are likely to be annoyed when ads served are not aligned to their needs or interests, and when brands do not directly engage and respond to customer needs with custom offers, consumers are prone to switching brands. Setting up effective marketing strategies that best appeal to different customer segments can not only improve customer relations but also drive personalized marketing campaigns & subsequently increase ROI rates.
Keeping these outcomes in mind, TheMathCompany drafted a three-step solutioning approach to create a customized segmentation tool that the client can leverage to map the right marketing mix or marketing strategies, to relevant customer segments.  

Step 1: Exploratory Data Analysis

EDA was undertaken to observe any trend or learning from customers’ engagement on the client’s digital platform. Customers’ transactional behavior was analyzed over a year and pertinent metrics were determined and selected for the analysis, such as:
- Transactional metrics - rentals, purchases
- Customer loyalty metrics – customer membership, loyalty points usage
- Customer-behavior centric metrics – preferred movie genre, preferred game genre, marketability factor

Step 2: Customer Segmentation

By using a combination of clustering and classification along with a rule-based approach, suitable behavior segments were identified for each customer. K-Means Clustering helped identify behavioral segments. Following which, classification techniques were leveraged to segment customers with lesser amount of data. For the new and lapsed customers, independent segments were created. Post the segmentation process, profiling exercise was undertaken across each of the behaviors and identities to further understand the different kinds of segments created.

Step 3: Deployment

The solution deployed was then validated by running campaigns on specific customer-segments. Most of the segments were directly used in marketing strategies such as incentivizing promo sensitive customers to increase rental orders, identifying electronic sell through or EST customers for purchase offers, keeping frequent customers engaged with targeted content suggestions. The segments are refreshed at a weekly cadence to include new customers and account for migration of existing customers to relevant segments due to a change in their behavior.
Here's a closer look at the solutioning process: 

Here's a closer look at the various steps involved in the customer segmentation process:

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