Understand your Customers better using Propensity 360

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Our customer is one of the leading Banking firms in Nordics which offers multichannel products and services. They wanted to build a propensity model to understand their customer’s revenue life cycle and find the potential customer segments to upsell their additional services and products.

Requirements

  • Understand customer demographics and how they are changing at a macro level.
  • Robust to market changes and require less historical data of customers.
  • Personalized in-depth ROI analysis for B2B customers to forecast and predict their revenue in affiliated branch accounts.
  • Understanding the customer’s behavioral analysis based on the RFM parameters.
  • Identify the customer segments where targeted marketing can be applied.
  • Predict the campaign performance for the new products or services on clientele.

Challenges

  • The complexity of the data leads to additional work in formulating and structuring the model to perform accurately
  • Fulfilling different needs of customers based on business needs involves a huge volume of data and a longer processing time.
  • Adhering to Data Governance policies to set the policy standards as per GDP.
  • Modeling the use cases to support multiple cloud platforms and on-premises systems.

Solution Design

  • The objective is to formulate and combine the different use cases such as Lead Conversion, Propensity to engage, Customer Lifetime Value, and Churn rate forecasting.
  • To achieve an AI solution, we have processed millions of transactional, survey, and Ticket data to create multiple machine learning models to predict and forecast outcomes.
  • To handle the huge volume of complex input data, we built a big data pipeline using specifically designed data structures on Apache Spark and the Hadoop platform which can import data from several sources.
  • After structuring the data, we develop different KPIs depending on the business and customer modeling needs.
  • We use clustering algorithms to differentiate customers on basis of characteristic features.
  • The recency of transactions over a defined period, Frequency of the usage of products or services, and Monetary value of the transaction.
  • We also account for clients’ purchase behavior, time taken to purchase, spending patterns, and social similarity groups.
  • We employed multi-level ensemble modeling techniques to generate data-driven business insights limiting bias from the development team.
  • With these insights, we were able to identify client microsegments and forecast the future revenues for individual microclusters, do behavioral analysis and check customer drift.
  • We developed user-friendly, easy-to-understand dashboards to analyze observational changes in customer patterns and formulate new marketing strategies which improved ROI.

Business Value Addition

  • Performing quasi-statistical experiments to research customer demographics and behavioral analysis to cater to the needs of customers improving customer retention.
  • Achieving higher precision, recall, and F1 scores with improved modeling techniques.
  • Explainable model outputs and formulating more effective relationships with data points (90% AUC).
  • The propensity score matching was effectively applied across almost 90% of the client customer population including new customers, inactive clients, and those with less or close to zero historical data.
  • Enhanced conversion rate up to 80% towards consumer product offering and better positioning.
  • Cost reduction by 40% by early prediction of churn customers.
  • Personalized product offerings tailored using propensity score with a significant increase in customer engagements.
  • Detected potential churn and offered better services.

Author: Aniket Anil Chaudhary

Data Science

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