What is user segmentation?
What is user segmentation?
User segmentation is the process of dividing your customer base into defined groups so you can understand where to concentrate your efforts. Used wisely, segmentation can help tailor your product experience and marketing strategies to the characteristics of your highest-value customers.
You’ve probably heard of user segmentation. Maybe you’re even doing it already, and want to better understand how it can improve your analytics results, leading to more useful insights, greater customer loyalty, and better business goals and outcomes. In this article, we’ll look at some successful examples, and help you develop a set of potential considerations to investigate so you can better understand your target audiences.
What are the different types of user segmentation?
There are four general types of customer segmentation strategies:
Demographic: ”Who are my users?”
Demographic segmentation groups users based on gender, age, occupation, marital status, income, etc.
Example: targeting dual-income couples with two or more children.
What you could do for them: You sort through your users and realize that dual-income couples tend to buy certain products, so you isolate these customers in your database and send them special marketing campaigns.
Geographic: ”Where are my users?”
Geographic segmentation lets you group people based on their country, state, or city of residence, as well as localities like townships and counties.
Example: targeting registered voters who own homes in Lincroft, New Jersey.
What you could do for them: Your data tells you that tri-state sales slump (or skyrocket!) during an election cycle, so you budget your marketing efforts accordingly.
Psychographic: "What are my users like?"
Psychographic segmentation groups users according to their personality traits, attitudes, opinions, values, or interests.
Example: targeting bird-watchers who subscribe to the New York Times.
What you could do for them: Your data tells you that this group tends to be high-income and prefers premium offerings, so you develop a prestige version of your service and present it to them.
Behavioral: ”What do my users do?”
This is where it starts to get interesting. While the first two categories are simple, the downside is you’re basically forecasting based on assumptions and stereotypes. The results will be better than spamming your entire database, but aren’t going to be very nuanced.
Behavioral segmentation is often considered the gold standard of user segmentation, because it segments groups of users by the actions they take on your product or website. With behavioral segmentation, you can group by specific actions (or inactions), spending and consumption habits, feature use, session frequency, browsing history, average order value, and much more.
While the other types of segmentation help you guess at what different users might do in your product or site, behavioral segmentation groups users based on actual behaviors—not what you imagine they’ll do, but what they’ve already done. Because of this, behavioral segmentation is usually touted as the greatest insight-generating category in the analytics game. And as you dig deeper into how different groups behave on your site or app, you can learn more about what makes your target customers tick, their pain points, and what kinds of features, offers and upgrades will make them smile…and convert!
For example: You notice that users who take the time to write a review tend to buy more than those who don’t. And users who leave a review as well as read at least one blog post tend to convert at the highest levels.
What you could do for them: Thank blog readers and encourage them to express their opinions in a review, and steer loyal reviewers towards content they might like in your blog. Now track what happens to your retention and conversion rates!
You can see how behavioral segmentation not only creates a more engaging customer journey, it helps you optimize the path to conversion for each user. Messaging at each step of the funnel can be tailored to their behaviors, as well as to other users with similar traits. Another valuable result is getting clarity around the kinds of questions the team should be asking. (This is why the ability to perform retroactive event tracking is super important.)
How does segmenting your users produce a better digital experience?
Simply put, accurate segmentation helps you avoid wasting valuable time—your own, and that of your users. You won’t be barking up the wrong tree trying to sell dog toys to cat lovers. By segmenting, you can make your marketing and outreach efforts more engaging AND more efficient.
What are some benefits of user segmentation?
Since customer experience is becoming the leading factor in choosing products, brands and experiences, user segmentation is becoming equally critical to effective marketing and fully understanding groups of customers.
Figure out what works for whom, and then scale your findings
Like beauty, a good user experience is in the eye of the beholder. When you truly understand your different customer segments, you can speak to entire categories in the manner they best respond to.
You can analyze your user base for prior behaviors when remarketing or developing market research.
You can craft irresistible offers for first time users.
You can isolate high-value users by analyzing how different segments behave in your product, then sort for prospects who behave in similar ways.
You can perform market segmentation to experiment with new products and pricing models.
You can engage with subscribers when they are most likely to respond. Are they early birds, or night owls?*
Create marketing messaging that's personalized AND accurate
Offering personalization from the start helps potential customers feel understood and appreciated. But nothing's worse than a tone-deaf “personal” email that gets your name wrong! Segmenting helps you avoid alienating users due to misunderstandings and simple blunders.
Stop looking for love in all the wrong places
Messages can only be useful, relevant, and appealing when they’re to the right users for your product in the first place. The downside of personalization is that misguided marketing attempts can come across as rude, irrelevant and self-serving. You don't want to rub people the wrong way, even if they’re not in your target market.
Collect useful feedback from customers who matter
Your mom has lots of opinions about your product, but would you pay for them? Segmentation will show you who is most interested in your products— and why—so you’re better equipped to sort for valuable feedback and incorporate it into your development efforts.
Increase word of mouth marketing—the FREE force multiplier
When users feel an emotional connection and sense of ownership with your brand, they’ll tell friends, family and whoever will listen about their great discovery and the terrific value they feel they are getting. Segmenting satisfied customers increases your odds of attracting new ones like them, rather than wasting time in the wrong channels or employing tactics that aren’t relevant to the target group.
Improve your Customer Lifetime Value (CLV)
Attracting and retaining loyal customers who make frequent high-value purchases is exponentially better for your bottom line than playing catch-and-release with bargain hunters and looky-loos.
Track events regardless of when they’ve occurred
Autocapture means you have immediate access to a complete, retroactive data set, all the way back to when you installed the snippet. Having more data means you can identify and analyze trends, rather than isolated events—all without ever having to set up explicit tracking parameters in advance.
How does data quality influence user segmentation?
For proper segmentation, product, marketing and CS teams need to be able to take data on everything their users have done, so they can slice and dice that data into different potential segments. And the only way to do this is via implicit tracking, aka autocapture.
For example: You are examining a group of high-value users and trying to trying to figure out what they like best. Without autocapture, you can’t see everything they do. Teams can’t look into the past and test for patterns they did not initially suspect. You may be missing a hugely important correlation because you just don’t have that data!
Implicit tracking enables you to collect data first and ask questions later. Without this ability to make retroactive inquiries, you have to set trackable events up ahead of time. Which, when you think about it, totally kills the surprise factor of discovery. You can only confirm things you already suspect are true. An insight that you had to supply ahead of time—isn’t one!
Why do tools like Google Analytics struggle to segment users effectively?
Google Analytics and other basic web analytics tools like it were built (and a long time ago, at that) to assess page traffic and marketing spend. They’re still pretty good at doing this, but who cares? Understanding a modern customer journey today requires so many more elements—more than these simple tools can handle, particularly when it comes to integrating sensitive PII data and handling it securely.
These tools can never show you what users are actually doing—like what features they prefer, where they get stuck, and where they drop off. It’s simply not possible to see what kind of value they are receiving, let alone to predict what they might do next.
Why is automatic data capture essential for effective user segmentation?
That means there’s no need for expensive engineering time. No need to decide in advance what to track. Data science finds correlations and patterns in the data you weren’t even looking for—these are what we mean by INSIGHTS!
How are leading SaaS companies using Heap to segment users?
SaaS companies tend to rely on tools like Heap because those tools give teams a complete set of data, which means you can identify and analyze trends, rather than simply track isolated events. You can start your analysis by capturing everything automatically. Then dig deeper into events that are core to your business by attaching additional relevant data using snapshots, APIs, and custom properties. Heap’s accessible UI empowers even your non-technical teams to discover immediately actionable insights. Nobody needs to ask for (and wait for) help from engineering.
Forge, for example, used segmentation to run split tests. The result? A 17% lift in engagement! (Read the full story here!)
For example, Heap can help teams understand where potential customers get stuck, compile a list of users who are falling off, and retarget them to complete the funnel.
And with the Heap Shopify source, teams can sort by ecommerce specific metrics such as Average Order Quantity (AOQ) to understand what behaviors correlate with repeat purchases, and identify bottlenecks for low-performing products.
Don’t just take our word for it, read some customer segmentation examples and see what our own customers are saying:
Customer story: FIGS
FIGS is an online apparel retailer making scrubs that medical professionals actually want to wear.
“Heap really fits our paradigm with minimal dev ops and data engineering effort involved. With most other analytics platforms, you need a dedicated engineer to make sure events are tagged correctly.”
Customer story: REDFIN
Redfin is redefining real estate with seamless interactions and technology that’s intuitive and easy for bo clients and agents.
“Heap makes your job easier, frees up engineering and analysts’ time, and helps you be a better product manager.”
Customer story: Purple
Purple is a mattress company looking for new ways to help people access their products during the many challenges of COVID-19.
“Heap turns the whole customer journey into a story that we can analyze and use to make decisions for our business.”
Interested in a demo of Heap’s Product Analytics platform? We’d love to chat with you!