Drilling Down

Turning Customer Data into Profits

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Maximizing marketing ROI with customer behavior analysis


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Micro versus Macro Analytical Approach to CRM

First Published 3/28/01

Users of CRM systems are spending too much time and money on a "micro" view of the customer while ignoring more profitable "macro" analysis.  The micro view, perhaps lead by the "1-to-1" philosophy, is fine if you have the skills to really understand and implement it.

But it seems to lead to what I'd call "over-targeting".  This occurs when there is an attempt to subdivide customers into groups so small they are no longer meaningful or practical for the purpose of measuring and increasing customer value.  Then there's a backlash to this self-inflicted situation, usually "we're drowning in data" or "we have too much data but not any usable information".

The micro approach skips over more fundamental macro or group analysis which leads directly to higher ROI marketing. For example, what is the average value of customers generated by banners versus newsletter ads?  The value of customers whose first purchase is over US $100 versus
customers whose first purchase is under US $100?  These are behavioral characteristics known to affect future customer value in very
pronounced ways.

For example, in the area of collaborative filtering, or product recommendations based on past purchase history, who has not bought a gift for someone in a product category they held no personal interest in, only to be inundated with product offers related to the gift purchase?

As a retailer, do you want to "sell them anything" or "sell them a specific thing"?  By over-targeting people like this, you risk losing a
sale by making offers so targeted they have no relevance.  It would be much better to recognize the person as someone with certain overall purchase patterns, and sell them "anything they want to buy" - a more
macro approach.

To continue the example, a customer who was a heavy cookbook buyer in the past who stopped buying, then for some reason comes back to the site, should not be met with "you bought cookbooks so here's a cookbook offer".  Perhaps the reason they haven't been back is they own enough cookbooks, or bought them as a gift for someone! 

Recency of purchase is the single most powerful predictor of future purchase; and recency has nothing to do with "what" they bought but "when" they bought it.  This customer should be met with "you haven't
been here for awhile, so here's $3 off anything you want to buy", not "here's another cookbook offer".  A macro kind of offer based on their overall behavior, not a micro offer targeted so finely as to discourage an additional purchase from a "lost customer" who decided to come back one more time.

The longer it has been since the customer made a purchase, the higher the discount should be.  This overcomes inertia and gets the customer back on a buying track.  The reverse is also true; current active buyers should not generally be offered discounts until they show signs of defecting.  Very recent purchasers are highly likely to make another purchase and discounting to these people is just throwing money away.  Ignoring these basic behavioral patterns leads to significant customer value loss, as inappropriate discount timing erodes margin.

In summary, people seem to have "skipped over" a lot of the tried and true methods of customer value measurement and management in favor of the latest "1-to-1" toys.  The result is a ton of barely usable data, ineffective and expensive marketing, and a lack of awareness of what really drives customer behavior and how to take advantage of it to increase value.

The Drilling Down book teaches you these tried and true methods for making money with database marketing.  Data mining not required.

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