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What Direct Marketers Need To Know About Segmentation
Part 1 of a Multi-Part Series
By David Shepard
These four articles first appeared in Direct Magazine
The purpose of this series of articles is to help direct marketers understand the issues and choices involved in a customer segmentation project so that they can manage it effectively from conception to implementation. Along the way we’ll identify common pitfalls that stand in the way of success and suggest ways in which they can be avoided.
In this first issue we’ll discuss the important considerations that must be addressed before starting a segmentation project. In later articles we’ll discus the tools of segmentation: factor analysis, cluster analysis, discriminant analysis, CHAID and logistic regression.
The Goals of Segmentation
The fundamental goal of a segmentation project is to identify groups or segments or clusters (the terms are interchangeable) of customers that from a marketing perspective are meaningfully different from each other.
Obviously we could create segments that differ demographically or in terms of behavior, but will these differences lead to different marketing strategies that will be more effective than a single strategy that treats all existing or potential customers the same way?
Intuitively, you know that your customer base, be it 50,000 or 5,000,000 is not homogeneous. By homogeneous we mean that at any point in time it will contain some good customers, some bad ones; some new customers, some old ones: some young, some old; some rich some less rich, some poor; some price sensitive, some not; some extremely loyal, some not loyal at all; the list goes on and on and on.
And, because of this diversity, it makes little apparent sense to market to all customers the same way, i.e., the same level of marketing effort, the same offer, the same copy, the same creative, etc. Yet, this is what direct marketers do (perhaps without realizing it) when they search for the best control package in direct mail, or the one best script in outbound telemarketing.
When pressed on this point, many direct marketers will point to tests that clearly show that the control packages they have developed over time have out-pulled the segmented packages tested against them. So, assuming the testing was done correctly, the question is was there something wrong with the strategy of segmentation itself or are the weaker results due to how the strategy was implemented? We think it’s more of the later.
So, let’s start our look at the way segmentation projects are initiated.
The first thing you need to decide upon is the “type” or “kind” of variables you want to build your segmentation around. The three primary types are: demographic, behavioral and attitudinal.
Demographic Segmentation
In segmentation systems based on demographic data the emphasis is on describing the customer in terms of their personal characteristics (young singles, young families with children, affluent empty nesters, etc) which through further research can lead to an understanding of their needs, wants and behavior.
While there are several ways to collect customer demographic information, the most common approach is to purchase demographic enhancement data from an outside supplier. The supplier matches your customer list against a large compiled consumer database using name, address, phone number, etc., and then appends the demographic data from the compiled database onto your file. Because compiled databases and matching technologies are not perfect, you will never obtain demographic data for every customer. Usually, “hit rates” will range from 80% to 50%. Records that do not receive this type of demographic data can be “backfilled” (or approximated) by appending geo-demographic data which are usually averages for the area in which they live (average age, income, home value, etc.).
Companies wishing to employ demographic/lifestyle/lifestage segmentation but not wanting to create their own segmentations will frequently turn to products such as Prism or Cohorts to meet their segmentations needs. While a meaningful description of these products and their competitors is well beyond the space allotted, suffice it to say that these systems allow a marketer (through exact name and address matching, or based on the just the zip codes their customers live in) to segment their file into 40 to 60 predefined clusters that may be useful for company specific marketing segmentation purposes.
Behavioral Segmentation
Segmentation systems based on behavioral data emphasize what the customer has purchased (clothing, appliances, big & tall sizes, baby clothes, closeout deals, etc.). This information can be used along with a product cross-sell analysis to suggest other products that the customer may be interested in.
Companies that have a comprehensive customer database in place possess the best possible source of data for a behavioral segmentation, because all of the purchase data is already in the customer database (or should be) the “hit rates” will be 100%.
Behavioral-based segmentation systems are the best fit for organizations that are product-driven, where product line managers call the shots. This type of segmentation system is not as useful for individual customer value development. When a new customer is acquired, the organization has to wait for them to define themselves via their purchases. Depending on the type of business and its characteristic frequency of purchase and/or seasonality, it could take quite a while to obtain enough purchase information to correctly classify a customer.
Even after they have made a few purchases with you, all you know is what they have bought. The customer could be purchasing things that you sell from another source, but you have no way of knowing that. Also, this approach does not give you any insights into how you could influence a customer’s purchase decision.
In a similar fashion, behavioral segmentation is not as useful in product development efforts because you only know what the customer has bought, not what they are most likely buying elsewhere or what un-served needs they may have.
Attitudinal Segmentation
Segmentation systems based on customer attitudes emphasize the nature of the customer’s relationship with your company. Defining the customer in terms of how they feel about your company helps you understand why the customer does business with you and also gives you some understanding of how they position you against the competition. From there, it is possible to anticipate which type of value proposition, or “creative slant”, might best appeal to each customer segment.
The best way to collect customer attitudinal information is to ask them, i.e. do a survey. However, surveys tend to be quite expensive to implement and even if you had the necessary funds available it is almost impossible to comprehend what type of incentive would be necessary to get everyone to respond.
And, and this is the critical question, if we survey only a sample of the customer database, how will those not sampled be assigned to the proper cluster?
To begin exploring this question in detail let’s assume that we’re building a segmentation model for a large retailer with multiple outlets, a situation we can all relate to.
Again, it’s intuitive that not all of the retailer’s customers will feel the same way about the chain. Some shop there because of service, others primarily because of price, others because of the breadth of the product lines, others because of convenience, others because they like the ambience of the stores, still others because it’s the hot new store in town, you get the idea.
Now let’s assume that we all agree with the above and that we decide that we want to create a segmentation scheme that would segment our customer database along the following set of major dimensions: (1) price, (2) service (3) product depth (4) product quality (5) the overall shopping experience and (6) brand image.
How would we go about it?
In our next article we’ll answer this question and difficult related questions: given a segmentation based on a sample, how do we place all of our customers into the proper segment, and if we can’t what should we do.
continue to: What Direct Marketers Need To Know About Segmentation Part 2