Analytics and Dynamic Customer Strategy by John F. Tanner Jr

Author:John F. Tanner, Jr. [John F. Tanner, Jr.]
Language: eng
Format: epub
Published: 2014-05-31T08:36:09+00:00

Trim Size: 6in x 9in

Tanner

c06.tex

V2 - 05/05/2014

10:38am

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A N A L Y T I C S

A N D

D Y N A M I C

C U S T O M E R

S T R A T E G Y

into a spreadsheet and sorting the table based on high frequencies and

high values. There’s really not much more to it. You can also factor in

the frequency with which carts are abandoned. Divide the number of

successful carts with product X going in first by the total number of carts

with product X going in first (adding in the ones that were abandoned).

Now you use that proportion as the dependent variable and start looking

at variables that predict whether or not the basket will convert using

regression analysis.

Summary

Analytics can be separated into three categories based on the purpose:

reporting, discovery, and production. Reporting analytics enable us to

take complex systems and simplify them for monitoring, as well as for

comparison. Discovery analytics are used to understand why something

is happening, to test relationships in our conceptual map, or to identify

new opportunities. Production analytics are models routinely and auto-

matically applied as data streams so that our systems can make the right

offers and other decisions.

Lead scoring and affinity models are common production models.

But discovery models depend on the type of data being used, such as

cluster analysis used for segmentation. Since you have choices regarding

the format of data when you create your data strategy, knowing how

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