What are Marketing Analytics? - Part I Introduction

Hi,
In keeping with the spirit of the blog, I make a feeble attempt at talking about marketing analytics - a subject that gave me nightmares at XLRI. I write under the umbrella of correction and criticism is solicited. This is a three part story where we try to learn about marketing analytics. This part is just an introduction. The next part will try to delve into the finer points of X-Sell, Retention and Up-Sell; What is the kind of data looked for.

Imagine this. You are the marketing veep of a stationary company. You have a gut feeling that the new white board marker you make is going to be a hit. but your colleague veep finance, the CEO and MD tell you not sell your dreams to them. "Get me numbers" growls the Chairman. Now what the hell are you going to do?? you twiddle your thumbs, bite finger nails to the very roots. so you go to your manager and growl "I want numbers." Now the manager who is a wise guy and an alley cat. He knows all about numbers. But problem is he needs somebody to program the whole damn thing. Now about the products and how they are selling, he has a disks full of raw data. he has to cull useful information from that. For example he has to find which distributor is selling the most. Drill down further from there and find which areas of the distributor reports most sales. Drill down further to the retailer. That is one customer. Then there is the bulk-order customer like corporates who strut around with presentations . The output might look something like this or it can be more graphical. What in effect the manager is trying to do is find out the best probability of making maximum through put and which wholeseller/distributor/retailer is most likely and least likely to meet this requirement.
The next aspect that most marketers worry about is X-Sell (pronounced cross sell). Would a customer move from a one product towards another offering from the same company. Ex. will a credit card user avail of this new "low interest loan" that we have. For this they have the demographics and other parameters. So can the numbers churn out information useful for this. Again the most likely candidates and least likely candidates are found.
The next one is the bane and ruin of many a marketing golden boy. Retention is a worrisome word. How likely is a customer move away from me? The marketer has to come up with solutions to problems like this. Most of you must have changed your car's service center at least once. Now the reasons are aplenty, but the service center (if it wants to) can retain customers if and only if it has some data with it which can be converted to information. You might be surprised, but a simple fact is that customers of a car service center are retained when they are directly in touch with the actual mechanic working on their car and not the handsome face of the suited front office exec. Retention strategies always keep marketers on their feet and make them sleep one eye closed.
The last major aspect is Up-Sell. Make a customer move from his drab master card to a gold card. Will a customer be able to. Remember the form you filled out when applying for a card. All that data is not just thrown away. That form is a wealth of information. Say you applied for a card 3 years ago, the bank calls you and talks about add on cards, life time free gold card and other goodies. Not every one gets such a call. Only Most likely customers are targeted.

Now marketing analytics is about getting that kind of numbers offering drill-down capabilities to get down as far as possible; nay as far down as wanted. It is about culling meaningful information from volumes of data, might be dating as far from a decade or so. Marketing analytics are to a marketer what taste buds are to a wine taster.

Yours Truly
Manikantan a.k.a Mani/Naren