Customer Analytics

Customer analytics is all about predicting behaviour of individual customers. The right marketing ensures that the businesses are able to deliver the right message to the right customer. With marketing, we also want to be able to influence the behaviour of individual customers. Thus, it is more important to focus on the “individual behaviour of customers” rather than the “average behaviour of customers.” How can we use customer centric analytics? Businesses need to:

• Understand buying behaviour of existing customers
• Understand why prospects are not yet customers
• Decide which products they should launch
• Forecast performance and understand drivers
• Allocate resources across products and customers

The right model will depend on the type of data that we’re looking at. So we need to inspect what kind of customer level data might we have.

• Choice data (choosing between Coca-Cola and Pepsi, AT&T and Verizon)
• Count (how much is being purchased, how much quantity on a particular shopping occasion)
• Timing (when do I become a customer, how long do I stay as a customer, how long between visits to a website, how long is it between purchases)
• Multivariate (combining, say, choice and count data – choose a brand and how much is purchased)

Applications in Marketing:

• Measuring marketing effectiveness (ROI)
• Clickstream data and online advertising
• Loyalty programs and CRM
• Social Media

Linear regression is essentially maximum likelihood estimation. Types of Univariate data

• Continuous (linear regression)
• Count
• Choice
  • Between two options (binary choice)
  • Between n options (multinomial choice)
• Timing

Binary Choice Data

• Did you buy the product on this shopping trip?
• Did you acquire service (are you a customer)?
• Did you retain service?
• Did you file a complaint?

All these questions have a yes/no answer.