Decision tree analysis for a consumer credit firm

  • Mapped key decision factors influencing credit card adoption

  • Identified customer profiles most likely to convert for each card type

  • Optimised marketing efforts through data-driven targeting

CLIENT CHALLENGES

  • A US-based consumer credit firm approached us to understand the thought process behind a customer’s decision to apply for different types of credit cards
  • The client wanted to analyse survey data to determine which customer traits influence decisions to opt for student, cashback, travel or premium cards
  • The goal was to segment the customer base and enhance conversion rates through targeted marketing

OUR APPROACH

  • Step 1: Data collection

    • We conducted a structured survey dataset from 5,000 respondents covering the following:

      • Demographics (age, income, student status)

      • Behavioural traits (online activity, response to offers)

      • Channel interaction (email/SMS/phone)

      • Product preference across four card types

  • Step 2: Decision tree modelling

    • We used a classification and regression tree (CART) model to do the following:

      • Predict the most likely credit card type a customer would apply for

      • Understand the hierarchical influence of various features

      • Map the sequential logic of customer decisions

  • Step 3: Segmentation analysis

    • Used Gini Index to split decision nodes and identify the most important variables

    • Mapped customer journeys to interpretable decision rules

    • Validated findings against actual conversion outcomes from past campaign data

IMPACT DELIVERED

  • Create targeted campaigns by age, income and credit score brackets
  • Optimise email-based marketing for high-response segments
  • Increase cashback card conversions by 18% through segment-specific messaging
  • Identify that 42% of potential applicants favoured a cashback offering under specific thresholds
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