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ANALYTICS SERVICES
Case study for global financial services major
OVERVIEW
• Atlas helps a global financial services organization identify prime customers and
best growth opportunities in its commercial portfolio
• The client wanted to increase the average revenue per customer for its
commercial finance portfolio, which included a very broad spectrum of
companies requiring finance for equipment, commercial vehicles, corporate
aircrafts and manufacturing & office infrastructure.
SOLUTION
• To improve average revenue per customer, it was imperative to identify
following:
• Which of the ‘low’ and ‘medium’ value customers are most likely to become
‘high’ value customers.
• What products should be offered to these customers?
• Therefore, the solution framework had following components:
• Customer segmentation based on average annual revenue.
• Development of customer profiles based on demographics, transaction
history and other significant variables.
• A ‘look-alike’ model to identify which of the ‘low’ and ‘medium’ value
customers are most likely to become ‘high’ value customers.
• Market basket analysis to recommend what products should be offered to
the selected customers.
RESULTS
• The two models delivered enabled the client more accurate targeting of
potential high value customers with right products through more effective
marketing communications to improve average annual revenue per customer
and marketing ROI

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BD Financial services

  • 1. ANALYTICS SERVICES Case study for global financial services major
  • 2. OVERVIEW • Atlas helps a global financial services organization identify prime customers and best growth opportunities in its commercial portfolio • The client wanted to increase the average revenue per customer for its commercial finance portfolio, which included a very broad spectrum of companies requiring finance for equipment, commercial vehicles, corporate aircrafts and manufacturing & office infrastructure.
  • 3. SOLUTION • To improve average revenue per customer, it was imperative to identify following: • Which of the ‘low’ and ‘medium’ value customers are most likely to become ‘high’ value customers. • What products should be offered to these customers? • Therefore, the solution framework had following components: • Customer segmentation based on average annual revenue. • Development of customer profiles based on demographics, transaction history and other significant variables. • A ‘look-alike’ model to identify which of the ‘low’ and ‘medium’ value customers are most likely to become ‘high’ value customers. • Market basket analysis to recommend what products should be offered to the selected customers.
  • 4. RESULTS • The two models delivered enabled the client more accurate targeting of potential high value customers with right products through more effective marketing communications to improve average annual revenue per customer and marketing ROI