Adventures in Mobile Marketing

The Role of Segmentation

In application and system architecture there’s this concept of generations. Is your CRM system a 2nd, third or fourth generation? Generations generally refer to the sophistication of tools, networked systems, and perhaps size. If a CRM group regularly segments its lists, analyzes those segments, and leverages segmentation in each email it launches (transactional as well as promotional), to me, that is a sophisticated, advanced generationally, CRM system.

Direct mail & catalog marketers have been doing this for a long time- even including more sophisticated scoring and modeling abilities in their lists. One reason I can think of- why direct mail techniques have not been applied full sail to email marketing- is that these are intensive efforts on rapidly changing data, that iterates sometimes twice in one day. Direct mail doesn’t work on that tight a time frame, so they can usually leisurely analyze and segment. Yet there are tools that segment email lists, and the sooner email marketers leverage these tools, the more they will see response rates go up, with smaller email lists that are far more targeted to interested audiences. First, let’s outline three areas that are often confused (I’ve found), and they also mark the technological progression from simple filtering to complex segmentation.

Dynamic content: the template swaps content based on either simple stated logic or static values coming from the email list: for example, product name, which swaps in the hero product photo for each email going out of a specific campaign. The value “pants,” when associated with an individual email record, will tell the template to display a photo of pants. If there is no value, perhaps a default promo product displays, or if “shirt” is in the record, the template knows to display a shirt. Sometimes they can handle a simple is/is not logic too such as a filter.

Filters: Inclusive or exclusive, criteria which determines the final targeted list. You can make these statements complex, “Has not bought, last twelve months, and never bought pants, and has not been cross-sold,” by including all of the logic in one long statement. Each bucket is trimmed by the filter above it. This kind of filtering is limiting, because for every permutation of each criteria, you would have to create an entirely new list. For 3 statements above, that means 9 filter statements need to be written, tested, and output. As you can see it can get complicated, and to recreate this list for each campaign, perhaps they are triggered, transactional emails, then it becomes very burdensome. So we introduce:

Segmentation: Ability to handle multiple logical statements, with a “branching” quality, creates a targeted list with n-dimensional complexity. For example, take the logic above, 3 statements, 3 states of customers. If you imagine it as a matrix, each combination of each state results in a “cell” of the customer base.

has purchased recently? Prod category: Pants? Cross-sold? Cell Label
Y Y Y Recent customer, pants, and cross-sold
N Recent customer, pants, and not cross-sold
N Y Recent Customer, not pants, has been cross-sold
N Recent Customer, not pants, has not been cross-sold
N Y Y Lapsed customer, bought pants, but has been cross-sold
N Lapsed customer, bought pants, hasn’t been cross-sold
N Y Lapsed customer, hasn’t bought pants, has been cross-sold
N Lapsed customer, hasn’t bought pants, ahsn’t been cross-sold (total prospect)

The main benefit comes from the ease with which the marketer creates it. Going in and editing the filters, re-using the segmentation in another campaign, and using more complex (than binary) logic, reveals the deep ability to find hidden niches in your base.

Then the user creates the labels that they care about- perhaps some variations of the population are meaningless? “lapsed customer, no pants, not cross-sold” could be potential to upsell, or perhaps they represent an audience that is truly uninterested in the pants-upsell promotion?

That’s a relatively simple example. Using feedback data, or more complex logic statements, truly shows the strength of these tools. Above, I have chosen binary yes/no choices, but you can use multiple answer logic, as well:
- Has purchased last 3 months, 6 months, 9 months, etc.
- Purchased above median, below, substandard, above standard, etc.

Creating the segmentation logic is good only as the analysis of those segments, determining things like the median price, pruning amounts on cells (according to email cost), and other factors. Make sure that the segmentation tool has appropriate and relevant reporting that will backup the thresholds of each segmentation filter.

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Written on Friday, 21. December 2007 at 16:05 In the category CRM, techniques, technology. Follow the comments via RSS here: RSS-Feed. Read the Comments. Trackbacks- Trackback on this post. Share on FriendFeed

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