An International Entertainment Company Leveraged a Customer Segmentation Tool for Effective Marketing Strategies & Unlocked a Potential 8x ROI on Mark






Customer Segmentation Tool

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

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.

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.

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