User segmentation: A guide to understanding your customers
User segmentation is the practice of dividing all customers into segments based on characteristics they share. For example, sorting users by region, language, or behavior. The better teams are at segmenting their users, the more personably they can treat each group. But if personalization is the path to long-term product success, most companies still have a long road ahead.
Eighty-one percent of consumers today wish companies knew them better, while ninety-four percent of marketers wish they knew their customers better. Why aren’t the two just talking it out? Because communicating with users is a science-powered art, and intuiting what users want takes tools, time, and proper segmentation.
Let’s explore how you can use segmentation to build a better product.
What customer segmentation strategies can I use?
Customer segmentation can be divided into some of the following types:
- Paid vs. free users: Paid users are often more advanced, more committed, and easier to retain than free users, who are likely to treat the app as a commodity and present a higher churn risk. Segmentation allows teams to retain the former while converting the latter.
- New vs. returning users (and frequency of return): New and returning users fall into very different stages of their customer journey. Returning users have found enough value in the service to come back. Perhaps it helped them balance their finances or find great GIFs, and now they want more. New users, on the other hand, haven’t yet found the value. If teams know that all returning users watched one particular video, they can embed that video in the welcome journey to help new users get hooked.
- Time on site/app: Netflix treats its binge-watchers differently than once-per-month users. That’s because everyone’s time is valuable. If users choose to spend their time on a site or app, it’s a powerful signal that they love Stranger Things, er, the app. If they don’t spend time, that’s also a useful signal. By segmenting users by the time they spend, teams can learn which features, factors, and content are correlated with higher engagement to surface new ideas for driving increased usage.
- Conversion goals: What’s the difference between a user that has subscribed and one that hasn’t? Or between a user that has purchased once or ten times? It’s different for every business, but it’s critical to know. By analyzing users that have passed a conversion checkpoint, teams can isolate the characteristics their most prized users share and use that information to convert more of them.
- Other behaviors: Not all user behaviors are positive. Sometimes customers churn, abandon their shopping carts, or downgrade subscriptions. By analyzing the events that preceded these actions, such as a service interruption or a new feature release, companies can identify areas of friction and improve those parts of their site or app.
Any piece of data companies track on their users can be useful in segmentation, so long as those distinctions are meaningful. Customer segmentation should always tie back to a business goal such as purchases and revenue.
Every team will have its own idea of what data is most valuable to capture. An ad-driven online magazine might make money from views and be quite happy tracking viewer traffic, whereas a CRM platform might care about sales and recurring revenue. It all depends on the business model.
Here are a few types of data teams might want to capture on their users:
- Demographic data: A user’s social data, gender, birthday, language, location, marital status, or income
- Psychographic data: A user’s interests, beliefs, affiliations, or socioeconomic status
- Behavioral: A user’s actions such as visits, taps, clicks, time on site, and conversions
- Firmographic data: Like demographics, but for businesses: age, employee count, revenue, industry, location, and business model (B2B or B2C)
- Technographic data: Technologies a company uses, such as CRM, marketing system, or ERP provider
The sum of each segment’s likes, dislikes, behaviors, and characteristics offer clues as to what they want. It’s up to each team to record its users’ desires in a user profile and to make changes to their product’s features, messaging, and marketing to better meet users’ needs.
So how does a team decide how to segment its user base? Let’s explore how a user segmentation platform like robotmia can help.
How to do customer segmentation
The customer segmentation process consists of four steps:
1. Track user engagement
To segment customers, companies need a way to view all users. But getting a grasp on that entire audience isn’t always easy. Most sites and apps aren’t built to analyze themselves and it can be difficult to track individual users and the journeys they follow through the app, much less compare two different user segments.
A media site, for example, might be able to use its website’s analytics to determine how many users merely visit its publication versus how many stay to click an ad. But which users are which? What screenflow do they typically follow? How can the team send a message to just the users who subscribed but never returned? To draw meaningful distinctions, the media site needs a way to separate visitors by their characteristics and behaviors.
A user segmentation platform is often the answer. robotmia, for example, integrates into a site, an app, or both, to pull user data that the site or app was never designed to capture. It offers a user-friendly interface with which teams can create segments, save reports, track funnels, and message users, all in one platform.
2. Identify segments based on business priorities
As we mentioned before, segments are only useful if they’re tied back to business priorities such as revenue. If teams know what they want their users to do, they already know their first few segments.
If the goal is for users to make a purchase, one segment could be for users who have purchased. Another could be for users who haven’t. A third could be for those who have purchased repeatedly. If the goal is to increase app usage, teams could segment by time spent in-app to isolate their power users within the sea of occasional visitors.
Here are metrics teams might use to define segments:
- Engagement / usage
- Acquisition source
- Customer lifetime value (CLV)
- Average visits per user
- Customer journey milestones
Analytics platforms allow teams to create segments more quickly. robotmia, for example, tracks interactions that occur on a site or within an app, highlights the most frequently occurring events, and suggests metrics that teams should track.
3. Use analytics to generate reports
Using either an Excel spreadsheet or a user analytics platform, teams can create, save, and share reports for different user segments. Teams might generate reports to:
- Compare the usage rates of paid and free users
- Measure the retention rate for users acquired from social media
- Determine which actions taken during a free trial make users more likely to purchase
- See whether usage increased after a new feature release
4. Make changes based on segments
By comparing a segment to another segment or the total user population, teams can identify meaningful differences. This provides teams with a roadmap for building a better product.
For a mobile fitness app, for example, a team might find that its most valuable users are more likely than the average user to finish setting up their profile. The team can then rearrange its onboarding flow to make sure that every new user completes their profile.
An enterprise financial services app might find that users that came from sales-generated leads have a greater lifetime value than self-provisioned ones, and decide to focus more marketing dollars on sales enablement. A social media app might find that users that download their mobile app are twice as sticky, and create in-app notifications on desktop to drive more mobile users. Segments lead to insights which often allow teams to build better products.
Why segment customers
Customer segmentation helps teams discover what’s true about their most valuable users to attract more of them. It lets teams focus on those power users, those high-rollers, those shopaholics, and those gaming enthusiasts to give them more of what they want.
Without segmentation, product development is a matter of intuition and guesswork and teams can’t know whether their efforts paid off. Was a spike in usage due to the new feature release or the weather? Without data, it’s hard to know. The team may have a hunch, but without certainty, they’re just likely to backslide as to move forward.
When companies learn from their segments, they can personalize the user experience. Customers today expect intuitive and user-friendly interfaces. They prefer that sites and apps “just work.” This means product teams need to learn as much as they can about users and make sure their app isn’t trying to be everything to everyone.
Just picture a classic newspaper with no segmentation. It’d be a disorganized jumble of culture, sports, and foreign affairs articles. A baseball fan wouldn’t be able to skip to the recap of yesterday’s game and she’d probably abandon the newspaper in favor of a one that’s more focused. It’s a similar experience for any site or app. Users want to find the section they love, quickly.
The most meaningful segmentations are often discovered through research. It’s only through observation that an app’s product team can know why certain features or pages resonate with users. A/B testing, or presenting several variants of a feature or message, can help teams speed up their research.
A/B testing helps app creators scientifically test their hypotheses. For example, a bank might notice that users aged 35-50 rarely touch the bill pay feature. By A/B testing the placement of the “Pay Bill” button, the team could make it more visible and prove that the placement of the button was the deciding factor.
Customer segmentation also allows teams to develop customer profiles, also known as cohorts or consumer profiles, which list the characteristics, behaviors, and interests of each segment. Profiles allow product and marketing teams to develop or improve products with the user clearly in mind.
Here’s a consumer profile example for a fictional fitness app:
- Demographics: Female, aged 25-45
- Psychographics: Loves cycling classes
- Behaviors: Uses the app more than 5 times per week
Within a customer segmentation platform, the fitness app team can use boolean search terms to save Fiona as a cohort so they can check back frequently. Whenever the team thinks about new features, they can think about Fiona, and they can measure how the Fiona cohort responds to new releases. They can even assign scores and weights to different cohorts to determine their lifetime value and measure the ROI of marketing campaigns.
Personalization is key to long-term product success, and segmentation is one of the best tools for giving groups of users what they need. So who are your users, and are they getting what they want?