Background and Challenges

In an increasingly competitive digital landscape, a leading information services provider faced the critical challenge of enhancing customer engagement to maximize account value. Managing a diverse portfolio of products, the company recognized the untapped potential within its extensive data on customer interactions and transaction histories. However, manually navigating through approximately 50,000 unique pricing and promotional decisions across various customer segments was proving inefficient and increasingly ineffective.

Determined to transform their approach, the company engaged L.E.K. Consulting to leverage cutting-edge data analytics to unlock powerful cross-sell and upsell opportunities and to significantly reduce the risk of customer attrition. The results were impressive, revealing millions of dollars in potential revenue and identifying customer segments at risk.


To address the client’s challenges, we designed a comprehensive strategy that leveraged advanced analytics to enhance customer engagement and retention.

Hypothesis development: We formed an initial hypothesis about key indicators of product cross-sell/upsell potential and account-retention risk based on current perspectives within the client organization.

Data utilization: We identified and leveraged available data to generate deeper insights into signals of customer potential and risk.

Signal analysis and model development: Our detailed analysis identified the strongest correlations with desired outcomes. This analysis informed the development of “propensity” models that scored clients on cross-sell/upsell potential and attrition risk.

Evaluation: We tested 12 machine learning (ML) model types, including regression-based, tree-based and other ML models. Using a confusion matrix, we evaluated model performance, focusing on key metrics such as accuracy, precision, recall, F1 score and area under curve. Based on these criteria, we selected the best-performing model.

Implementation: We deployed the models into the client’s cloud environment, connecting to key data sources and the customer relationship management system for automatic, real-time analysis and ongoing optimization of customer engagement strategies.


  • Targeted customer interventions. We operationalized the model to run daily, producing real-time alerts for sales managers. These alerts identify accounts with changed statuses (e.g., upsell potential, heightened churn risk) and highlight key variables driving these changes (e.g., decreased product usage). With a 75% accuracy rate, the model identified 6% of customers as high risk for churning and 10% with strong upsell potential, representing millions of dollars in potential revenue.
  • Enhanced sales strategies. The model’s insights enable more strategic outreach for potential cross-sell and upsell opportunities, thereby enhancing overall sales performance. This approach has set a new standard in customer account management, highlighting how targeted data analysis and ML can significantly enhance business outcomes.
  • Ongoing advantages. The client received a database of prioritized customer accounts and a dynamic model that continuously updates account scores and insights, ensuring the sales strategies remain aligned with current data. The predictive model not only met the immediate needs of the client but also provided a dynamic tool for continuous strategic adjustments, ensuring long-term benefits in customer relationship management.

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