Customer Analytics for Financial Services

Customer Analytics & Insights: Overview

Financial Services Increased competition due entry of retailers and telecom organizations in offering financial products and ever changing customer expectations and behavior due to technology advancement and experience from other industries require financial institution to move toward customer centricity. The analytics is one of the key enablers to achieve the customer centricity. Customer Analytics can help in identifying customer needs and preferences using predictive and advanced analytics.

Financial institutes and banks leverage Customer analytics for increasing share of wallet & profitability from a customer, creating consistent customer experience across channels, and improving customer satisfaction & loyalty.

Objectives of Customer Analytics are

Understanding customer behavior

How are customers using products and services? How can customer be educated on financial products? How engage or active are the customers with a product relationship? What are the product or channel interaction patterns for the customers?

Identifying customer needs and preferences

What are the current product and service needs? What are the future needs of the customers? What are the communication preferences?

Engaging customers with the bank

How frequently customers interact with the bank? What channels do the customers use? Which channel is used for what services?

In one of the surveys, “89 percent of banking and financial markets CEOs say their top priority is to better understand, predict and give customers what they want”1. Also, significant number of banks does not use predictive modeling2, so there is huge scope to contribute by using analytics for the banks.

Customer Life Cycle

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Typical Analytics involved in customer analytics for banks and financial services are as follow

  • Life Stage, Transactional and Behavioral Segmentation
  • Activation and Sales Funnel Analytics
  • Profiling and ad-hoc analyses
  • Product Cannibalization and Product Penetration based insights
  • Cross Sell and ResponseModel Development
  • Customer Life Time Value (CLTV) Modeling
  • Customer Dormancy and Attrition Modeling
  • Pricing &Profitability
  • Share of wallet (SoW) Analysis
  • Web Analytics, Channel Usage and Preference Analytics
  • Event Driven Marketing (EDM)
  • Social Media Analytics (SMA)

References

1. http://www.bankingreview.nl/download/25887

2. http://celent.com/reports/customer-analytics-retail-banking-why-here-why-now