If you’re working in customer service, then you know your customers better than anyone else at your organization—better than marketing, better than sales, and better than leadership. It’s kind of like having a superpower.
Unfortunately, all too often companies fail to take advantage of their customer service team’s knowledge, and it goes to waste. But you can harness this superpower by running a customer segmentation analysis to inform your company’s strategy and develop a tailored approach to customer service, based on the needs of each individual segment.
If you’re running a market segmentation analysis and need to find actionable insight—ones that can improve the customer journey—read on.
What is customer segmentation analysis?
Don’t be intimidated by the term—it’s simply a name for how businesses identify and group customers based on certain characteristics. These factors can range from behavior and age to purchase histories and physical location. Think of it like this: when your business is just starting out, you’ll likely have a pretty solid handle on who you’re serving. But as your business grows, that can be harder to pinpoint—and if you lose track of who your customers are, it’s tough knowing how best to serve them. This is where customer segmentation analysis shines.
Step 1: Simplify your segmentation variables
Simplifying your market segmentation requires a lot of prioritization. This is where your superpower comes in.
You talk to customers all day. You know their traits, you know their needs, and you know their pain points. In the back of your mind, you might know which variables to start with already.
When you’re segmenting a customer list, the obvious answer is usually the right answer. Keep your variables simple and let the numbers speak for themselves.
Getting stuck in the customer analysis phase is pretty common. If you’re looking at segments but not making changes based on your data, the problem might be the way you’re segmenting in the first place.
It’s tempting to measure too many things at once. A simple, well-organized approach to customer analytics is often the best way to coax a story out of a bunch of numbers. Take a step back and look at one consumer segmentation variable at a time.
A simple, well-organized approach is the best way to coax a story out of a bunch of numbers.
Start with simple baseline metrics to create a few groups that all use the same segmentation variable. Your variable might be age, company size, or geographic area, and the metrics might be customer happiness rating or Net Promoter Score (NPS).
Make sure to look at one variable at a time. When you’re controlling for one variable at a time, your groupings are distinct, and you can pull insights more reliably. Your goal here should be a Venn diagram with no overlaps. If one of your segments is “millennials,” and the other is “people who live in big cities,” you’ll have a lot of overlap. Overlap means muddy data, and muddy data means you can’t trust your insights.
And be sure to think about what kind of customer segmentation model will best serve your needs. That could mean using cluster analysis—which employs mathematical models to identify similar customers based on minute variations among segment customers—or an RFM model, which focuses on recency, frequency, and monetary factors.
Step 2: Organize your segments into subsegments
If your single-variable segment approach isn’t giving you anything actionable, it’s time to add a subsegment.
Try dividing segments with variables that you didn’t choose in the first stage of market segmentation, like geographic area or company size. Also, consider variables unique to customer support teams, like means of first contact and type of request.
Here’s a segment analysis example: say you chose age as your top-level variable. Your segments might be Baby Boomers, Gen X, Millennials, and Gen Z. If the next variable you’re looking at is city size, subsegments for the “millennials” segment might be:
- Millennials who live in big cities
- Millennials who live in small-to-medium cities
- Millennials who live in rural areas
Two best practices to keep in mind:
- Don’t mix your variables when comparing KPIs.
- Always compare a subsegment’s performance to the segment as a whole.
Remember the Venn diagram? There will be overlap between millennials who live in big cities and millennials who called support, so comparing happiness scores between those two subsegments isn’t going to tell you much. Always compare metrics within one variable: big cities versus small, phone support versus email.
Compare that number to the average for your entire segment, too. By comparing the happiness score of millennials who used phone support versus the average happiness score of all millennials, you’ll be able to spot if phone support is bringing up the average or pulling it down. Big differences in performance between a subsegment and a segment will show you what your customers love and what they don’t.
Use Zendesk Explore’s customer segmentation analytics to pull insights from your subsegments. Explore can turn your scattered customer data into customizable charts and dashboards you can act on.
Step 3: Assign KPIs and compare them to your goals
Key performance indicators (KPIs) provide a framework to measure performance between segments and subsegments. KPIs without context won’t be helpful, so make sure to assign them based on your goals, which will inevitably vary by customer segment. What does success look like for a customer service team for particular segments?
Let’s use means of contact as an example. When measuring time to first reply, your benchmark for phone support should be much faster than email to meet that segment’s expectations.
Why? In the Zendesk report, Customer Experience Trends 2021, we found that 51 percent of respondents expect a response in less than five minutes when they’re using phone support. Only 7 percent of respondents expect the same when they’re using email to contact support, though more than half expect a response within an hour.
Likewise, if your team has an overall NPS goal for all customers of 85, but you discover your baby boomer segment has an NPS of 70 and it’s bringing down the average, that’s evidence that you either need to adjust your goal or adjust your approach to this segment.
Step 4: Iterate with data, not anecdotes
You should not make generalizations about a customer segment based on anecdotes alone. An effective customer segmentation analysis uses trends, not outliers.
Consider the case of an angry customer. Cognitive biases can trick you into thinking that this person is experiencing a common pain point, even if they’re the only one complaining. But while you should take angry customers seriously, you shouldn’t build your strategy around them. Just because someone is the loudest person in the room, doesn’t mean they speak for everyone.
An effective customer segmentation analysis uses trends, not outliers.
A good customer segmentation tool should remove anecdotal bias from the analysis. That one vocal customer might represent a bigger trend, but you won’t know that without looking at the bigger picture. Zendesk Explore’s powerful reporting helps you see the nuances of your data and investigate the nature of customer issues.
Along with identifying larger customer trends, your goal is to understand the unique needs of each segment. What does this group of segment customers require that you’re not providing?
Sometimes benchmarks give you insights into why certain groups expect different standards. In our 2020 Customer Experience Trends Report, we found that younger demographics tend to use faster-response channels, like in-app messaging, at a higher rate than older age groups. If you’re looking at the relationship between response time and customer happiness with age-based demographic segmentation, that might add clarity to some known unknowns.
A customer segmentation analysis goes beyond support
A segment analysis is a goldmine of data that an entire company can use. Your analysis can help product teams prioritize features, marketing teams write resonant copy, and sales teams tailor demos. And even better, customer segmentation analysis can lead to more coherent marketing efforts, improved customer loyalty, and a deeper understanding of customer needs.
Segmentation insights should inform every team’s work, but that’s impossible to do without the right system. When you use the right tools to analyze your customer data, you’re building a sharable knowledge hub that multiple teams can reference. When you pass it along to the right teams, you’re giving the customer a voice to impact your company.