Understanding and maximizing Customer Lifetime Value (CLTV) is critical for driving sustained growth. This is especially true in competitive retail environments, where acquisition costs are rising and customer expectations are evolving. However, traditional CLTV models often rely only on historical spend and aggregate-level analysis. As a result, they offer limited visibility into future value and lack the granularity needed for targeted interventions.
Our data-driven CLTV solution addressed these challenges by building dynamic, customer-level models. These models incorporated behavioral signals, purchase frequency, and timing of interactions. This enabled precise segmentation and revealed the true value potential of each customer. As a result, the client was able to move from broad-based marketing to more personalized engagement strategies. The outcome was improved retention, higher campaign efficiency, and a significant topline impact through better resource allocation and smarter decision-making.
Problem statement
Our client was facing several challenges in accurately estimating and leveraging Customer Lifetime Value (CLV) to inform strategic decision-making. The existing CLV approach was limited to aggregate-level analysis based solely on historical spend, lacking the granularity required to capture individual customer behavior or future potential. As a result, the CLV estimates did not reflect true customer value, nor did they provide sufficient insight into the behavioral drivers of high-value segments.
Moreover, the analysis was static in nature and failed to account for evolving customer interactions over time. This limited the retailer’s ability to take timely, personalized actions to improve customer engagement or retention. The existing methodology also offered restricted actionability, as it did not differentiate between customers based on predictive potential or enable targeted marketing interventions. To address these limitations, the retailer partnered with MathCo to design a customer-centric, data-driven CLV framework capable of generating dynamic, individualized insights to support long-term growth and more effective customer engagement strategies.
Impact
- Resulted in an estimated $36 million increase in topline performance.
- Business was able to consistently track the retention trend, mitigate any risks and put appropriate tactics in place to drive a 7% retention for some cohorts.
- Targeted Email Campaigns – Sent personalized email campaigns and reduced follow up windows for high value customers.
- Growth Team used the predicted CLV to understand and prioritize retention tactics for customers in higher CLV deciles.
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