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Overview of Customer Analytics Models/Metrics:

Models/Metrics for Customer Acquisition

  • Logistic Regression (mainly used to predict buy/don’t buy, respond/no respond, high risk/low risk, good effect/no effect scenarios)
  • Hazard/Survival Function
  • Customer Life Time Value Model (CLV)
  • Optimal Acquisition Spend (may depends on your organisation’s definition of what an efficient acquisition cost is)

Models/Metrics for Customer Engagement

  • Cross-Sell/Propensity Score Card (ie answers the question how likely are they to respond to products cross-sold to them; eg Telco, sell cable TV to mobile phone customer segment)
  • Look-A-Like/Propensity Score Card (ie customer profiling based on product overlap; eg offering credit cards to the (bank’s) liability customer segment)

Models/Metrics for Customer Retention

  • Revenue Score Card (identification of the most profitable customer segments)
  • Customer Life Time Value Model (a form of customer valuation technique)
  • Value-based Segmentation (a form of retention model to reinvest in the most valuable customer segments)
  • Pareto’s Rule (ie 80% of revenue comes from 20% of your best customers; key idea is to identify who they are)

Models/Metrics for Win Back

  • Attrition Score Card (looks at how likely they are to churn, and if so, are they worth chasing back? Secondary analysis required)
  • Attrition-Revenue Potential Model (baseline is to chase after customer segments who are profitable)

Successful execution requires in-depth customer insights to effective marketing activities. To begin with, using data we already have, we can know who are our best and worst customers (understanding customer behaviour), answer questions such as how are these segments of customers likely to behave when we implement certain promotions (predict future behaviour), how do we leverage data to extract maximum value from our marketing investments (action plan), ensuring our desired action plans are successfully executed (operationalise) and finally, how do we learn from our marketing activities and improve on our next promotional campaign (measure and improve)?

As we can see, with the power of data analytics, marketing can become the nerve centre of a business, be in the vanguard armed with insights and opportunities to pave the way in the profit war against our competitors.