Advanced Customer Analytics

Targeting, Valuing, Segmenting and Loyalty Techniques

Mike Grigsby

• Publisher: Kogan Page
• Year: 2016
• ISBN:
  9780749477165
• Edition: 1
• Topic: Betriebswirtschaft
• Subtopic: Marketingforschung

Advanced Customer Analytics provides a clear guide to the specific analytical challenges faced by the retail sector. The book covers the nature and scale of data obtained in transactions, relative proximity to the consumer and the need to monitor customer behaviour across multiple channels. The book advocates a category management approach, taking into account the need to understand the consumer mindset through elasticity modelling and discount strategies, as well as targeted marketing and loyalty design.A practical, no-nonsense approach to complex scenarios is taken throughout, breaking down tasks into easily digestible steps. The use of a fictional retail analyst 'Scott' helps to provide accessible examples of practice. Advanced Customer Analytics does not skirt around the complexities of this subject but offers conceptual support to steer retail marketers towards making the right choices for analysing their data. Online resources include a selection of datasets to support specific chapters.

INTERLUDE

Now that we’ve got through the preliminaries (overview of statistics, data and the retail industry) it’s time to focus on what this book is really trying to do: give marketers insights into customer behaviour that will drive the business. Each chapter will start with a general marketing question and then provide an analytic answer to that question. Then a business case will detail the use and output involved in using marketing science to answer the marketing strategy question.

This means that, after the Chapter 2 introduction to regression and factor analysis, reading the book in order isn’t necessary. Generally, I’d suggest flipping through to find the marketing problem you’re dealing with and that chapter will give you an analytic answer to that problem. This means that each chapter is nearly stand-alone.

However, it is also true that the chapters on dependent variable techniques (eg regression modelling) are organized in increasing complexity: ordinary regression, logistic regression and Poisson regression, survival modelling and simultaneous equations. So if your marketing problem involved, say, multiple stages and the answer is simultaneous equations, while it is generally designed to ‘stand alone’, you might want to review ordinary regression.

05

Understanding and estimating demand

Introduction

Business objective

Using ordinary regression to estimate demand

Properties of estimators

A note on time series data: autocorrelation

Dummy variables

Business case

Conclusion

MARKETING QUESTION

How can I estimate demand? What things impact demand?

ANALYTIC SOLUTION

Ordinary regression

Introduction

So our first question is a basic and very important one: how do you estimate demand? By ‘demand’ is typically meant (especially in retail circles) units or quantity. That is, the number of total items purchased. This is not the number of transactions or amount of net revenue but the count of items in the basket. Two packs of batteries count as two units even if they are only one stock-keeping unit (SKU).

Is there an issue in defining the dependent variable as units when one unit can include a set of AA batteries as well as a giant screen TV? No, not really. Regression is always ‘on average’ and a unit is a unit, on average. However the merchandizers construct the product hierarchy in defining units is what we’ll use. Besides, what else can we do? Remember, a simplifying assumption always puts us on the right path.

(It is possible of course to add deeper granularity by making a demand model not just total units but units of...