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Predicting Customer Behavior to Drive Growth for a Leading Bank
Infocepts enabled a leading bank to predict customer behavior with Databricks, boosting marketing efficiency and improving conversion rates.
The Challenge
A leading bank faced multiple challenges in optimizing its customer targeting, engagement, and retention strategies.
- The bank depended on manual methods to assess customer engagement, using transaction frequency as a simplistic proxy. This approach lacked the depth needed to understand customer value accurately.
- Without automated scoring mechanisms, such as Customer Lifetime Value (CLV) and Recency, Frequency, and Monetary (RFM) analysis, the bank struggled to create precise customer segments. This led to ineffective targeting and engagement strategies.
- Due to limited segmentation insights, retention strategies were not adequately tailored. Generic incentives and services often resulted in customer dissatisfaction and sub-optimal retention efforts.
- The lack of a data-driven segmentation model prevented the bank from identifying and focusing on high-potential customers, resulting in missed opportunities for personalization.
The Outcome
Key outcomes included:
- Strategic Marketing and Acquisition: Enabled the creation of personalized campaigns, driving more precise customer engagement and acquisition.
- Customer Retention and Loyalty: Tailored strategies supported retention of high-value customers, fostering loyalty and long-term profitability.
- Risk Management: Enhanced the bank’s credit risk assessment capabilities, allowing for early identification of at-risk customers.
Overall, the solution yielded a 30% improvement in marketing efficiency, significantly boosting the bank’s ability to drive growth through data-driven customer insights and targeted engagement strategies.
The Solution
Infocepts partnered with the bank to address these challenges by implementing a robust customer propensity model powered by Databricks’ advanced machine learning capabilities. Leveraging Databricks’ suite of tools, this end-to-end solution streamlined data processing, automated segmentation, and enabled data-driven customer engagement strategies. Key components of the solution included:
- Data Consolidation and Model Formulation –Infocepts developed a comprehensive data model that unified customer transaction data, using Databricks Delta Lake to manage large datasets efficiently. This model incorporated custom calculations for RFM (Recency, Frequency, Monetary) and CLV (Customer Lifetime Value), enabling precise segmentation based on customer value and behavior. Using Databricks’ MLflow for tracking experiments, the team was able to iterate quickly and enhance model accuracy.
- Targeted Segmentation Strategy –Using the RFM and CLV scores, we segmented customers into low, medium, and high-value categories, allowing the bank to tailor marketing efforts toward high-potential customers. Low-value segments received retention strategies tailored to encourage engagement, while high-value segments were targeted with loyalty rewards to boost customer satisfaction and retention.
- Enhanced Customer Scoring with RFM and CLV –We automated the scoring process, which enabled regular, scheduled updates to customer scores based on dynamic data inputs. This automation empowered the bank to take action based on real-time customer value and engagement insights.
- Accessible Insights for Cross-Functional Use –A detailed scoring table was created, enabling both marketing and sales teams to access and leverage insights through Databricks’ collaborative workspace. This setup facilitated data sharing and aligned strategies to maximize impact, allowing cross-functional teams to prioritize high-impact customers and focus on long-term retention and profitability.
The Outcome
Key outcomes included:
- Strategic Marketing and Acquisition: Enabled the creation of personalized campaigns, driving more precise customer engagement and acquisition.
- Customer Retention and Loyalty: Tailored strategies supported retention of high-value customers, fostering loyalty and long-term profitability.
- Risk Management: Enhanced the bank’s credit risk assessment capabilities, allowing for early identification of at-risk customers.
Overall, the solution yielded a 30% improvement in marketing efficiency, significantly boosting the bank’s ability to drive growth through data-driven customer insights and targeted engagement strategies.
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