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MILLIONS OF DOLLARS HAVE NOW BEEN INVESTED to bring sophisticated CRM systems online. Unfortunately, many are busy collecting and storing lots of data -- but very little useful information. If your CRM system isn't earning it's keep, here are some tips on how to leverage the data you have to build a stronger (and more profitable!) relationship with your customers and your bank account. |
THE CHALLENGE
Most managers are well aware that customer segmentation is a popular and powerful tool for dealing with different customers appropriately. However, there are many ways to segment customers. What segmentation approach makes the most sense?
Some companies, CRM software vendors and marketing consultants use very sophisticated segmentation methods. They point with pride to using some exotic statistical technique to select the “best” 20,000 names from your database of customer information.
However, these techniques are typically used in a “black box” fashion. That is, they are generally used with no explanation about how they work internally to select the "best" names. The data go in, the “crank is turned” and out come the results. Sometimes the names are simply segmented into deciles and it is left to the marketer to discover which segments can and cannot be contacted profitably.
Because the results of these techniques are delivered to the manager without explanation, they cannot easily be used to design offers or creative messages. How could a manager create relevant offers and effective communication plans for each segment if they do not know the characteristics of each of the segments? This has to change!
THE GOAL
Successful managers work to change customer behavior, not just predict it. If they focus only upon predicting responses and sales, they miss the point of marketing entirely. The objective of data-driven marketing is to influence customer behavior in ways that improve a company's bottom-line profits. Period.
So, a good database management technique should tell the marketer at least two things about each customer segment:
- How is the segment unique in a way that allows the marketer to predict how the segment will respond to a specific offer.
- Will the segment respond in a way that allows the marketer to decide not only whether or not to contact them, but also when and how often to contact them.
THE SECRET:
Statistical Modeling is Tactical; Marketing is Strategic
Using statistical techniques to segment customers is an effective tactic -- but only that. How those segments are used defines a strategy. For example, consider the two following scenarios for a organization that has a database with 100,000 names.
Scenario One: The marketing manager has 20,000 mail pieces ready for a September campaign. So, she asks the statistician to select the 20,000 “best” names. He does so using an advanced, neural-networking technique that has been repeatedly shown to be highly predictive of who will respond.
The 20,000 pieces are identical and each customer will get the same offer. The offer, which is $20 off any order over $100, has been the most effective overall in the past. The mail pieces go out, and the campaign is a success. Responses are very close to what was predicted, and all segments within the 20,000 pieces perform above break-even.
Scenario Two: The marketing manager is planning a September campaign. She sits down with the statistician and reviews past campaigns, current customer segments, and past offer performance. They realize that September has been a strong month in the past and they estimate they can reach 40,000 customers profitably. They also discovered that customers with lower average orders tended not to respond well to their best performing overall offer, which is $20 off any order over $100. In contrast, customers with very high average orders often placed smaller orders than usual when given that offer.
They create 40,000 mail pieces that can be versioned for three different offers. Customers with low average orders get $10 off any order over $50. Typical customers get $20 off any order over $100, and customers with very large average orders get $100 off any order over $500.
The mail pieces go out, and the campaign is a success. All segments within the 40,000 pieces perform at or above break-even. Furthermore, the low average order segment had a higher average order than usual, with most spending over $50. The high average order segment had a much higher average order than usual as well. Interestingly, response rates were better than expected in the low average order segment, and about the same in the high average order segment, despite the increase in average order.
Now that you have read both scenarios, which do you think is a more advanced use of the database? Which is a more effective use of segmentation? Which one is more effective in increasing sales, profits and building business relationship with customers?
UNDERSTANDING IS THE KEY
Marketing is communication, and proper communication requires understanding. Someone once said “Never tell a story without making a point, and never make a point without telling a story.” The point of effective segmentation modeling is that you can select (and de-select) names very efficiently. You can also explore segmentation based upon other information from the database or your CRM system, such as frequency of purchase.
As marketers, we should demand that a segmentation technique tell a story -- regardless of how simple or exotic the technique itself may be. Understanding the story is the key to success. Without understanding the story, it is impossible to develop the right creative message, presentation or best offer.
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