For all of the talk around one-to-one marketing, human beings still need frameworks to understand their world, and marketers are no exception. The word “segmentation” might be ubiquitous among marketers, but it’s difficult to find two people who can agree on what segmentation really means and how they become actionable segmentations. That being said, sales executives and marketers can probably all agree on what a bad segmentation looks like:
- Bad segmentations are hard to describe in plain English. In other words, only the lead researcher can really describe what the segmentation does, and who the members of the segments are. Another dead giveaway is that the names of the segments or personas don’t really align with what makes them unique. If you can’t name something, it’s probably not really much of a thing.
- Bad segmentations can’t be used by the business. This kind of segmentation might be brilliant academically, but when you sit down and start to try to implement it, you can’t really do anything meaningful. This is always a challenge for the PhDs who create brilliant latent models of beliefs and wants among buyers—can we actually find these people?
- Bad segmentations become obsolete quickly. A segmentation that only lasts for a year or two is frankly wasted money. The costs to do the research and analytics, and the opportunity costs of training and implementation across the organization, make it imperative that segmentation is long-lasting. I’ve found that longevity is rarely stated as an upfront goal at the start of segmentation work.
- Bad segmentations are too broad or too specific. In some cases, the segmentation is too broad. For example, a segmentation might attempt to describe all of the behaviors of a customer, from media usage to IT behaviors to channel preferences to leisure choices. In other cases, a segmentation might only be useful in describing such a small set of behaviors that the use cases are just too limited.
So, what does a successful, actionable segmentation look like? A successful segmentation effort is first and foremost one that is adopted. Fortunately, there is a checklist that marketers and market researchers can follow to protect against the risk of a six-month segmentation effort ending with a thud (literally, the 100-page PowerPoint hitting the bottom of the shredder bin). These are divided into pre-analysis and post-analysis checks.
Set clear descriptive/predictive objectives upfront.
Before any work is started, before a question is written, or before a database query is made, everyone needs to be in agreement on what the segmentation will be used to predict/discriminate, and what it won’t. For example, a segmentation might be great for understanding technology buyers’ wants and needs when it comes to the technology itself, but lousy in predicting where they shop. Of course, segmentation can serve many purposes, but realistically, a segmentation with 3-10 segments can really only be used to predict a few major themes. One way to do this is with a simple table, for example, this table for a printer/copier buyer segmentation:
Will Be Used to Predict
Won’t Be Used to Predict
If everyone signs off on this upfront before anything else is done, it’ll be much clearer to researchers and stakeholders why this work is being done, and what it’ll be used for.
Set actionability requirements upfront.
Actionability is the ability for a segmentation to be used to actually contact or describe specific customers. In other words, “Can the model be handed off to field sales, channel partners, digital, my advertising agency, my database team, etc?” If a segmentation model needs to be useful in tagging every customer in the database, the design must take this into account, by ensuring that “knowable” variables in various databases are included in the research (if primary research is actually being done.)
Concretely, as illustrated in the table below, create a list of knowable variables by customer interaction point, and include these in requirements for the research.
Known Data to Include in Segmentation for Assignment
|1. Website Registration||IP, OS (from browser); previous website traffic (from cookie); age, gender, ZIP code, SIC code, title|
|2. E-Commerce Purchase||All from (2) plus SKU(s) purchased, shipping chosen|
|3. Retail Channel||Retailer, location; firm, title, age, gender, SIC code from loyalty card|
Another option is actually conducting the research from the database, ensuring a 1:1 tie. Email is a great way to do this. An approach that can work well is doing a split of 50/50 existing customers and unknowns. It goes without saying that the sample would need to be reweighted to account for the true population after the research is completed.
Decide where to be on the actionability/insights spectrum.
This is kind of an addendum to the second to-do on pre-building actionability, but leaders should think about whether or not to do any primary research at all. If there is a lot of behavioral data out there—think Amazon—it’s probably possible to create an extremely powerful and descriptive segmentation based on database information alone. However, for a CPG company whose primary concern is deep-seated psychological constructs and how they impact attitudes about cleaning one’s home, a database segmentation won’t cut it. There is no magic rule here, but again, it pays to have these open conversations among stakeholders up-front.
Think of quota as the population you are describing.
After setting objectives and being clear on use cases, setting the quota is a critical, no-going-back decision that has to happen very early in the process. When you think about what setting quota actually means, you’re defining the population that you want to segment. In other words, by setting guidelines like those in this table…
…you are literally defining the population you are describing. You are also defining who you are not describing. In other words, if you want to be able to use this segmentation to describe DevOps Directors, you can’t be sure that it’s going to work—you’ll be extrapolating. So, spend a lot of time on the quota, and think about it as a description of the buyers you want to describe, rather than just cells to fill out.
Get your screener right.
This is a close cousin to #4, but different. The screener is a magical piece of the equation because small changes to questions can have drastic implications on the results. For example, say you want to talk to decision-makers who have a say in what group health insurance plan a company should choose. You could say “Describe your role in the decision-making process for choosing group health insurance for your company,” and only allow in those who say they make the decision or are part of a committee. But what if you also added an option I have veto power (the executive who needs his Doctor to be in the plan), or the person who makes the decision reports to me. You will get different populations for each of these screeners, and consequently, different results. Again, there’s no right or wrong answer—just be very choiceful, and make sure the executives who will choose to adopt or trash the segmentation are well aware of the decisions being made.
Do qualitative first to ask better questions.
There seems to be an aversion among analytical marketers to qualitative research; maybe they think it isn’t actionable, or they don’t understand it. However, qualitative research is critical in setting the scope of analysis and asking the right questions in the quantitative phase.
Qualitative can be done before and/or after a segmentation, but doing it before makes a lot of sense, simply because you’ll end up asking better questions when it comes to quantitatively describing your universe. Of course, qualitative research is an art in and of itself. Focus groups can be effective, but in a business-to-business, considered purchase environment, I’ve found that in-depth interviews or ethnographies (where you literally go into a company and watch them work, peppered with questions) are even better. When you’re writing your discussion guide, take the time to brainstorm all of the “why” questions that might dig up insights for the buyer group. Pick a seasoned moderator who understands the buyers you are talking to. Finally, have the executives, and the people actually doing the analysis, actually attend the research. It will make them much better at driving the questionnaire/analytics effort. They’ll be building the intuition they need to make better decisions when it comes to the questionnaire, analyses to perform, etc.
Spend a ridiculous amount of time naming your segments.
Naming things doesn’t sound very analytical, but it’s incredibly important when it comes to segmentation. If you can’t name a segment clearly, it will never stand the test of time. The naming exercise is best done by a group of people, sitting in front of the data, over several hours. Ask questions about why a name is applicable, or not. Does it just “sound good”, or is what the name describes actually seen in the data? If you can’t name the segment, there’s something wrong with the solution. You’ll know when you have good names—they’ll describe the segments perfectly, and the data will line up with the names across every crosstab you look at.
Check that your solution can actually do its job.
Thought experiments can be done in a few hours, but will save a lot of time down the road. There are three listed here, but the point is to put the model through its paces before it’s actually put through its paces.
- Prima facie validity.
If you spent the right amount of time naming the segments, this one should be easy. Can you describe, in one or two sentences each, how each segment is unique, and how they should be treated differently by your company? If not, you’re in trouble.
- Segment difference.
The segments should have clear differences in all of the variables/factors that matter. What are the variables that matter, you ask? Those should have been defined back in step one. If you see a lot of grey when looking at crosstabs, you have a problem. Go back and look at the algorithms used for clustering, the number of clusters, etc., to ensure that truly different segments exist.
- Go-to-market use cases.
Do a thought experiment and see if different sales and marketing actions can now be taken with the new information. Is there a different campaign apparent for Segment A? Can we find enough of Segment B in the database to build a meaningful campaign? Could a sales rep build a pitch for Segment C that makes sense and will drive results?
Check for reproducibility.
What if a segmentation solution doesn’t hold together when presented with new data? One way to address this is to use the classic machine learning technique of train-test or train-test-validate splits with data. The problem here is that primary research records are more expensive, so a little bit of extra budget will need to be burned, but it’s worth the peace of mind knowing that a solution repeated itself on a 300-n holdout sample. So, do your modeling using train/test, and then score the validation set. Cross-tabs of descriptive statistics should be very close to the train and test data.
Get the outputs right.
A PowerPoint deck isn’t enough. For a segmentation solution to be adopted far and wide, two sets of outputs need to be created; communications outputs and operational outputs. The below table lists some frequently used outputs in both categories, but marketers should, by all means, get creative—it’s only through rich, well-thought-out communication that segments will be used and addressed appropriately throughout the organization.
|Communications Output||Operational Output|
As I reflect back over my career and think about “segmentations I’ve known”, it’s shocking to me how few of these steps were followed, or even articulated, by huge organizations and their consultants and research providers. If you follow these ten steps, the chances of a catastrophically bad segmentation effort—which happens more often than anyone in marketing would like to admit—will be really low. More importantly, the chances that the segmentation will be robustly adopted—which is, after all, the most important measure of success, will be better.
Ten Steps for Building Foolproof Customer Segmentations
Access the framework for 10 steps to avoid bad segmentations–ones that are hard to describe, unactionable in business processes, too broad or too specific, and become obsolete quickly.