How Segmentation Can Improve Your Predictive Modeling EffortsReading time: 3 minutes
If you build predictive models with your organization’s data, you’ve probably run into situations where it seemed to make sense to build different models for subgroups of your sample population. This is called “segmentation,” and it can dramatically improve how well you capture the unique behaviors of your population. But segmentation requires the additional work of building a separate model, which can be hard to justify after building a model that works well enough to get the job done.
Let’s explore why you should segment your models and what roadblocks may stand in your way.
Why You Should Segment Your Model Populations
Models are incredibly helpful in exploring what drives your business or organization’s key outcomes, but your product lines, customers, and prospective buyers aren’t all identical. Different groups have different motivations, behaviors, and constraints. A single model may not capture the subtlety in your target population.
These four questions are great guide-posts as you consider this approach.
How well can one model explain my entire population?
What groups might be misrepresented (or sub-optimally represented)?
Could I build models for each group?
Should I build models for each group?
Once you finish a predictive model, you might not be eager to go through the whole process again. Segmenting your modeling data necessarily means building additional models. But the insights gained from this extra effort can be significant. You work with data and build models because you care about improving processes at your organization, so in cases where segmentation makes sense, it’s worth the extra effort.
So What’s Stopping You From Segmenting Your Model Populations?
Now we know why data segmentation in modeling is important. So why isn’t it always done?
Put simply: modeling takes time, and for many, it’s not feasible to build multiple models on the same outcome.
Additionally, it might not be worth it if your population is too homogenous. To justify splitting your modeling population, you’d have to see evidence that certain members of your target population respond differently to the same factors.
For example: if you’re analyzing customer shopping habits, factors like “distance from closest retail location” might be relevant to the physical store sales figures, but not to online sales. Even more interestingly, a larger distance from your physical locations might encourage online shopping and discourage physical sales. A single model applied to all customers can’t capture this subtlety.
In that instance, you’d have plenty of reason to segment your model. But the dividing lines between your subpopulations won’t always be as clear-cut as in the example above, so you might not be aware that segmentation is warranted.
The Easy Way to Segment Your Model Populations
There are heaps of benefits to creating multiple models for your population segments. So what’s stopping you? Likely, it’s the time it takes to build new models and/or the lack of an easy way to identify when segmentation is warranted.
But what if segmentation didn’t take much time and there was an easy way to identify variables that indicate when segmentation is worthwhile?
You might have guessed by now that there is a way. Rapid Insight’s Predict modeling software makes creating new models a fast and easy task, requiring just a single click. Predict also identifies statistically significant variables to advise you when segmentation makes the most sense.
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