Customer segmentation and personalization go hand in hand for marketers.
Success with the former (creating buyer groups based on various criteria and information from your data sources) leads to success with the latter (more opportunities for better personalized and individualized marketing and messaging that converts your target customers into paying customers).
Many brands have implemented segmentation marketing strategies today, with many CMOs pouring marketing spend into personalization based on distinct segments their teams build.
And yet, despite the growing ROI from segmentation, many companies have yet to invest in the proper martech that can help bucket customers and, in turn, better understand and market to them.
In short, customer segmentation is a giant piece of the marketing puzzle that can help companies of all kinds — from B2C retailers to B2B manufacturers — as it helps them:
- Identify buying and browsing trends among their customers and leads
- Capitalize on those patterns with targeted messaging to high-value groups
- Grow their customer base and customer retention strategies over time
With that in mind, let’s explore some customer segmentation examples and how grouping buyers based on various factors (purchase patterns, household demographics, etc.) can improve your most important metrics — including and especially conversion — and unlock even more ROI for you.
What is customer segmentation?
The basic customer segmentation definition is distinguishing groups of customers (or buckets or clusters — whatever nomenclature you prefer) with shared attributes. These shared traits are typically based on demographics, brand engagement (online and offline), and buying behavior.
(Psychographic traits are technically another customer data type you could collect. For the sake of this blog post, though, we’ll focus on these characteristics).
In BlueConic, these attributes are called profile properties. Our customers select the profile properties they want to consider and set specific conditions for each one to create unique segments:
Profile properties range from basic (personally identifiably information integrated from a database, like a CRM, into BlueConic) to complex (hyper-specific customer actions or traits):
- Gender, name, email address, location, phone number, language
- Browser name, browser version, device brand, device version
- Job title, job focus, company, company size, company industry (more applicable to B2B)
- City, ZIP code, county, state, region, country, and continent codes
- Page views, content downloads, demo requests, newsletter signups
- Behavioral profile properties featuring scores based on activity
What’s more, you can use these profile properties along with group properties: household, company, or account. This allows you to easily develop segments based on automatically updated profile information for given prospects and buyers with the same grouping IDs in BlueConic.
Of course, that’s just a sample of the first-party customer data you can collect and types of customer segments you can build using our pure-play CDP — but I digress.
Determining which customer segments to build depends on your business as well as what you want to learn about your niche audience and what drives their buyer’s journeys.
The ultimate goal is to use these attributes to build segments that reveal customers’ behavior: how, when, and where they interact with you and what compels them to buy.
Customer segmentation and martech
Before we share specific types of customer segmentation, let’s eliminate any concerns you may have about building segments without help from marketing operations or IT colleagues.
Martech, including marketing automation, has advanced to the point that several solutions, like CDPs, leverage AI and machine learning to make customer segmentation modeling — and the accompanying messaging that ensues segment creation — a cinch for even the most non-technical of marketers.
What does this mean for you? In short, if you’re not a tech-savvy marketer, but you still understand the distinct benefits of data-driven approaches, you can master segmentation-based marketing and, in turn, advance your business strategy, marketing metrics, and bottom line at large.
Certain predictive segmentation models may require a helping hand from your resident data scientist. However, given you’re reading a blog post outlining the basic customer segmentation definition with some examples, we’ll hold off on discussing those in greater detail another day.
Customer segmentation examples
With that cleared up, here are some hyper-specific customer segmentation examples (and we mean really specific — the best segments are) you can build in BlueConic in minutes, not hours or days.
One, some, or all of these examples may not apply to your products and services whatsoever. The point of sharing these is simply to give you an idea of what’s possible with segmentation.
Example #1: Online retailer trying to reengage shoppers
With a customer data platform like ours, though, an e-tailer facing this problem could identify segments who left one or more items in their carts over a given period and develop a reengagement plan.
Types of customer segmentation models this brand could build:
- Basic: Potential customers who added a specific product to their carts without purchasing
- Intermediate: Potential customers who added a specific product to their carts in the last 7 days without purchasing
- Advanced: Potential customers from a particular city or state who added a specific product to their carts in the last 7 days without purchasing and have visited 5 or more website pages
Example #2: News publication focused on subscriptions
Many publications today are (rightfully) focused on building revenue through digital subscriptions. After all, ads can only help so much these days. Embracing other revenue models is now a must.
A newspaper or magazine with intent on growing their bottom line through subscriptions could define segments of customers who have the potential to churn (i.e., not renew their subscriptions).
Moreover, a publication in this situation could pinpoint subscribers who have signed up for free trials but not purchased long-term plans or even leads who maxed out their metered paywall windows.
A few specific customer segmentation models this company could build:
- Basic: Paying subscribers whose subscriptions will end in the next 30 days
- Intermediate: Paying subscribers whose subscriptions will end in the next 30 days and have visited your website at least 10 times
- Advanced: Paying subscribers whose subscriptions will end in the next 30 days and have visited your website at least 10 times in the past 7 days and landed on a “renew” landing page
Example #3: DMO aiming to convert interested travelers
Let’s say you’re a destination marketing organization that wants prospective vacationers to finally book hotel and travel via your website. You’ve seen many begin reservations, then jump ship, so to speak.
In this instance, you can build segments that bucket users who checked out a certain number and type of reservation pages (lodging, flights, train, etc.) during a certain period.
With this customer data in hand, you could develop retargeting ads that promote deals to the destinations of interest to them and travel arrangements for those trips elsewhere online.
Three kinds of customer segmentation models for this organization:
- Basic: Leads who visited hotel- and flight-booking pages without buying
- Intermediate: Leads who visited hotel- and flight-booking pages for a specific city without buying
- Advanced: Leads who visited hotel- and flight-booking pages for a specific city at least 3 times without buying and who have reserved lodging and travel with the DMO before
There are countless ways to segment customers. The key to success with segmentation marketing is to test early and often and constantly evaluate the effectiveness of your bucketing and messaging.
Eventually, you’ll learn which segments are worth developing — in other words, ones that provide beneficial customer insights and lead to conversions and which aren’t: ones that generate little to no ROI.
Analyzing (and improving) your customer segmentation strategy over time
Once you’ve gotten going with segmentation and used the approach to bolster your marketing campaigns and real-time messaging, you can discover key customer trends — ones that will tell you if your clustering efforts are improving your digital marketing metrics.
Routine customer segmentation analysis can help you unearth new and potentially eye-opening patterns about customers’ behavior. This, in turn, can inform how you market to certain segments.
You may even notice nuances with your segmentation strategy that would’ve remained unknown without categorizing your customers into various groups. For instance, you may discover:
- Smaller segments grew considerably over time or large segments diminished in size: This could be a case of dynamic profiling at work: the automatic updating of customers’ profiles based on actions they take (or don’t take) with your marketing messaging. In other words, as customers’ activity with your brand change (increase or decrease), they may enter or fall out of certain segments.
- Certain segments respond better to one type of marketing communication than other kinds: You could have the same drip campaign set up for multiple segments and find only one actually engages with your emails. It happens — and simply means you ought to refine your messaging approach for the buckets that aren’t helping your customer acquisition and retention numbers.
- Directing segments to specific product pages doesn’t lead to more purchases: Sending segments to pages of interest, like for products they’ve investigated before, via targeted emails and ads is certainly ideal. But A/B testing what you promote to segments is also ideal to you see if your hypothesis is correct (i.e., if they do, in fact, want to learn more about or buy a particular product).
As with the types of customer segments you can build, there are just as many insights you can potentially glean from the marketing efforts you implement for those segments.
And all without relying on IT or marketing ops. (Pretty great, right?)
Segmentation marketing: Best realized with a leading customer data platform
Your customer segmentation models — at least when run with the ideal tech, like a CDP — are something you can operate entirely on your own, meaning without the need for tech assistance.
At the end of the day, your customer segmentation — the consolidation of customers into a single source of truth, the bucketing of them into distinct categories, the targeted marketing to each segment — is something you control exclusively, not something you have to wait for IT to handle.
How many other facets of your data-driven marketing strategy can you say that about?
As long as you have the right customer segmentation software in place, you’re capable of gaining invaluable insights into your customers’ behavior and marketing to them in real time accordingly.
Download our eBook to learn how you can develop a business case for a CDP and outline the benefits of segmentation marketing to your leadership team.