When it comes to ecommerce engagement, a brand’s ability to connect with customers meaningfully is only as good as its segmentation strategy.
Consider an online apparel shop that wants to microsegment its customers based on ZIP code. This could come close to treating each and every customer as a separate segment, a one-to-one marketing strategy that’s very difficult to scale.
On the other end of the spectrum, a global retail giant that segments customers on country alone misses out on the opportunity to use richer customer-level data, like first-purchase category or acquisition channel, to establish a deeper connection with its customers.
Both of these examples are a bit exaggerated, but the reality is that today’s online retailers have such a wealth of data at their fingertips that it can be difficult to decide what the correct level of segmentation should be.
The conventional argument for segmentation is that it makes marketing more efficient by breaking up a diverse market into groups of similar customers: subsets that will respond in a similar way to marketing messages and gravitate towards the same kinds of products.
Traditional approaches have divided markets based on everything from geography to psychography.
So, how should today’s retailers be thinking about segmentation? A good rule of thumb is to get real with your segmentation strategy:
- Relevant. Are you paying attention to the segmentation dimensions that actually move the needle? The typical retailer has dozens of choices from customer demographic data to purchase data. It’s important to identify the variables that explain the most about differences in your customers’ behavior.
- Efficient. Are your segments broad enough that a handful capture the majority of your customers? Or are you getting so granular (e.g., “customers who live at 100 Brown Lane”) that you miss the opportunity to identify common behaviors and preferences?
- Actionable. Do you have any way of measuring and acting on your segmentation dimensions? An online car rental site might know that taller-than-average customers are more likely to choose sedans with extra legroom over compacts, but unless it can gather the information it needs to make personalized product recommendations, segmenting on customer height probably doesn’t make sense.
Lasting. Choose segmentation dimensions that will remain relatively stable over time. Segmenting your customers based on how they responded to a one-time promotion or advertising campaign isn’t too helpful in categorizing new customers who joined after that date.
The specific values in a segmentation dimension may change — for example, a company segmenting on acquisition channel may add Facebook and Twitter as it branches into social media — but the categories themselves should be flexible enough to remain applicable over time.
So, the next time you sit down to draw up your segmentation strategy, remember the Goldilocks principle (not too broad, not too narrow) and focus on identifying the dimensions that drive the biggest differences in your customers’ behavior.