Look for the similarities

Editor’s note: Peter Flannery is vice president of the HSM Group Ltd., a Scottsdale, Ariz., research firm.

Five methodological guidelines

1. Similarity within segments

A good segmentation must find a set of objects (whether individuals, companies or products) that are similar to each other. Finding similar objects is not always easy. For one, there is the issue of “On what basis (topics) are the objects similar?” In addition, there is the issue of defining similarity: How similar is similar? For example, say you have decided to segment your market by company size. You will still face the issue of defining what counts as similar size ranges. Even with a concrete topic like size, it can be hard to make a clear decision rule about size breaks. Is a company with 25 employees more like a five-employee company or more like a 250-employee company? The solution is often to hope for a logical break in one’s database. For example, one may find that most companies with 25 employees do not have a separate HR benefits manager. If one is marketing HR benefits (health insurance, 401k plans, legal assistance), the 25-employee company will probably be seen as more similar to a five-employee company.

Number of employees is a fairly objective topic. When the segmentation topics are intangible attributes, such as attitudes or preferences, defining similarity can get even messier. Fortunately, multivariate algorithms can automatically investigate the covariance among dozens of input variables to see where they clump together and to find bumps or piles in a multidimensional plane.

2. Differences across segments

The second goal of a good segmentation is to find groups that are clearly distinct from  each other. Groups with fuzzy boundaries are the blight of good segmentation models, at least as they are usually conceived. Finding differences across segments is connected to the first goal of finding similarity within segments. The two goals are corollaries. In statistical terms, it is sometimes even said that the variance (distance) across groups should be maximized while the variance within groups should be minimized. In other words, people within a group will look similar, but people in other groups will look different.

3. Interpretability of segments

Once groups have been found, it is common to interpret and name the groups. Sometimes interpreting a segment is easy. Consider, for example, a group of individuals whose data shows that they love all types of food, both diet and regular foods. They frequently visit restaurants, buy upscale kitchen appliances and watch cooking shows. The interpretation of this group is straightforward. Thus, marketers are free to wordsmith on a catchy name for the group, such as Yummies, Food Lovers or Foodies. Whatever its final name, this segment gets an A+ on interpretability. It is internally consistent, coherent and compelling.

Unfortunately, statistical algorithms can also come up with segments that don’t make sense. Segments may be non-interpretable. For example, consider a hypothetical Adventurers segment. Their data show that they like to take risks on outdoor hobbies, drive fast and play Lotto more than other segments. So far, so good. However, elsewhere their data say that they are low on watching dramas and fright shows, they will not experiment with new products, and they index higher on wanting airbags than on wanting horsepower. One can slap the name Adventurers on this group, but the overall interpretation of the group is suspect. This segment gets a C- on interpretability.

Technically, a segment does not need a coherent interpretation to be valuable. A portfolio of stocks, for example, can lack a consistent theme and be poorly named, yet it may still be profitable. For most marketers, however, it is difficult to accept any segment that lacks a common-sense interpretation, let alone a catchy name.

4. Measurability of segments

Segments differ on how well they can be measured. Sometimes, segments can be identified with relatively objective measures such as gross revenue, vehicle ownership, shoe size or type of hospitalization (e.g., acute vs. rehab). These are cut-and-dried topics that are fairly easy to measure. Other times, however, segments can only be identified with subjective topics such as new technology attitudes or computer brand loyalty or health care services knowledge. The latter topics are more difficult to measure.

Of course, questions about such attitudes, loyalty and knowledge can always be asked. But respondents may struggle with these questions. The same respondent may even give different answers to the same question, just because the question is ambiguous. Ideally, you will pilot-test and refine any ambiguous questions before you build a segmentation model on those questions. Alas, time does not always permit such pilot testing and refinement. Time or not, a good segmentation still requires good measurement.

5. Stability of segments

There are many types of cluster analysis. Most cluster analysis techniques will always make segments. When there are clear and natural breaks (real divisions) in the data, most techniques tend to get the same answer. But watch out: when the data are flat, and when there are few real divisions in the data, the various techniques will still make segments. The problem is that the resulting segments are arbitrary. They are neither reliable nor stable.

Methodologically, there are a couple of ways to assess segment stability. Neither method is perfect. Some experts track the number of weakly-classified respondents - that is, the number of respondents who sit on the border between segments. If the number of borderline respondents is too high, the segmentation solution is deemed instable. Other experts prefer to use cross-validation techniques to index stability. Here, for example, one may split the sample into odd- versus even-numbered records (or ID numbers). Do the split samples share the same cluster solution? Sample sizes are often small in B2B research, potentially ruling out the split-sample approach. Fortunately, both methods of measuring stability are acceptable. In fact, either method would be an improvement for most segmentation studies.

Five applied-marketing guidelines

6. Size of segments

Size matters when it comes to segments. Your key segments must be large enough to support revenue generation. By default, junior marketers often look for segments that are at least 20 percent of consumers, hoping that their 20 percent target segment will provide 80 percent of the available profits. They are following an idealized Pareto rule, which indexes at 400 percent. There is no harm in following this expectation, but it rarely works.

More often, marketers are lucky enough to find that 15 percent of consumers generate 45 percent of profits (a 300 percent index on a smaller base). In hyper-segmented markets, one’s target segments are often much smaller. For example, one may have to settle for a segment that accounts for 10 percent of consumers, 16 percent of profits, and thus, indexes at 160 percent. Obviously, the hypothetical segment sizes and index scores illustrated above are not benchmarks. Rather, the acceptable size for segments depends on your business model and industry.

7. Availability (accessibility) of segments

Just because a segment is easy to measure (per Guideline 4) does not guarantee that the segment is easy to find. Segments can be inaccessible simply because they are defined on non-public topics - that is, on topics that are not available in syndicated databases. A shoe company can easily define consumer groups by shoe width, but it will be hard to find a database that provides access to double-E-width consumers. Likewise, C-level executives (CEO, CFO, CMO) are easy to define as a segment. However, C-level executives are notoriously inaccessible. They often hide from  marketers, let alone from marketing researchers.

The two guidelines of measurability and availability interact in the future recruitment of segments. After making a successful segmentation model, marketers often want to create a short survey (or segmentation screener) to find more segment members, either for future marketing research or for sales calls. The fewer the questions, the easier it is to implement this segmentation screener. In some organizations, the segmentation screener takes on a life of its own. It becomes the de facto segmentation for years to come, long after researchers have forgotten the original segmentation study. In such cases, the screener must work well with all the normal and easily accessible sample sources, whether phone, mail or Internet sample sources.

8. Brandability of segments

By brandability, I simply mean that a brand does well in a key segment. By now, if you have made it as far as Guideline 8, it is possible to evaluate whether a specific segment can be adopted as a priority or target segment for your brand.

Ideally, your brand will score high in your proposed target segment. Actually, your brand does not need to score high in absolute terms. Rather, your brand just needs to index higher in its target segment. For example, if your brand has 14 percent purchase interest across the whole sample, you may be satisfied with a target segment that has 22 percent purchase interest in your brand.

All the better if your main competitor indexes poorly in the target segment. Besides scoring well on purchase interest, your brand should also score well on brand metrics such as awareness, uniqueness, favorability, loyalty, etc. Brandability can also require strong performance on brand imagery ratings such as quality, dependability, safety, efficacy, luxury, friendship, fun, etc. This is a matter of brand positioning. Within your target segment, your brand’s imagery ratings should lean toward your brand’s prior stated positioning.

9. Profitability of segments

In the past, researchers seldom attempted to estimate the profit of segments. Nowadays, it is becoming common to estimate segment profitability, even with survey data. Segment profitability can be calculated many ways. Here is a simple method.

Eq. 1:   Relative Profit Index = “Size” x “Income” x “Brand A Purchase Consideration” where, for each segment, there is a measure of:

a) Size = Size (of the segment)
b) Income = Average income
c) Brand A Purchase Consideration = Definitely Will Buy Brand A

Profitability indices differ on their degree of complexity and completeness. Equation 1 is admittedly simplistic and limited. It should not be used to forecast sales volume. It can be used, however, for a topline financial analysis of segments. Even better indices are available, if one includes terms or adjustments for d) disposable income or purchasing power, e) brand loyalty, f) willingness to switch out of a competitive brand. To develop a ROI analysis, it is necessary to include terms for g) marginal costs of production and marketing and h) marginal gains from  incremental sales, that is, sales beyond one’s current portfolio of products.

10. Communicability of segments

Even if all nine guidelines above are met, a good segmentation still needs one more asset. A good segmentation must be easy to communicate. Even the best segmentation scheme can fall flat, if it cannot be easily understood and communicated within a company. The name of a segment (e.g., Foodies, Adventurers) is the main way that a segment gets communicated. Obviously, care must be taken to select a name that is descriptive. Beyond the segment name, the main way to increase communication is with eye-popping visuals. Here, it is important to create and use multiple pictures or collages. No one visual should be allowed to represent a whole segment, lest the future interpretation of that segment become too pigeonholed by a single photo.

To optimize communication, researchers may have to sub-optimize other guidelines. For example, to improve communication a researcher may select a five-cluster solution even though a 12-cluster solution performs better on some of the guidelines mentioned above.

Meet all 10

I have shared 10 guidelines for a good segmentation. These guidelines are balanced between methodological guidelines and applied marketing guidelines.

Few segmentation schemes can meet all 10 guidelines without encountering some degree of trade-off. To improve the communication of the segmentation (Guideline 10), one may have to select segments that are slightly harder to measure (Guideline 4). To improve the brandability of segments (Guideline 8), you may have to adopt segments that are slightly less differentiated across segments (Guideline 2). Such trade-offs are endemic to segmentation research. Assuming that the segmentation scheme performs satisfactorily on both guidelines, researchers will have to decide which guideline gets priority. Some companies will seek maximally profitable segments (Guideline 9), sacrificing a bit of interpretative clarity (Guideline 3). Other companies will need crystal-clear interpretations, knowing that extra profits will never be tapped unless the segments sound compelling. Just as long as performance on both guidelines stays above acceptable levels, you are free to optimize the guideline that best meets your business needs.

Trade-offs among the guidelines will exist, regardless of whether researchers acknowledge the trade-offs. A novice researcher can sometimes make good trade-offs just by intuition or luck. Most often, however, the best trade-offs are made by experienced researchers who are conscious of the 10 guidelines and their implicit trade-offs. A bad segmentation will ignore and stumble through the 10 guidelines. A good segmentation will acknowledge and balance all 10 guidelines. A great segmentation will foresee and optimize the 10 guidelines for your exact marketing needs.