Customer segmentation and personalization go hand in hand for marketers and other technology users charged with engaging their target audience today.
Success with the former (segmenting individuals in your database ecosystem) leads to long-term success with the latter (more opportunities for personalized marketing).
Many (if not most) organizations have comprehensive segmentation strategies today. And many CMOs continue to pour marketing spend into personalization to take advantage of the distinct, high-value customer segments their teams build.
And yet, despite the strong ROI from their segmentation strategies, many businesses don’t have the right tech that enables them to both efficiently ‘group’ their customers and deliver timely, relevant experiences to them as they move through their distinct customer journeys.
In short, customer segmentation is a big piece of the engagement puzzle that is invaluable for companies of all kinds — from retailers to publishers — as it helps them to:
- Identify browsing and buying trends among their prospects and customers
- Capitalize on those patterns with targeted messaging to high-value segments
- Grow their customer base and improve their retention rates over time
- Accelerate business growth and streamline operations across the business
With that in mind, let’s explore some common customer segmentation examples from BlueConic customers and their use of our multi-dimensional segmentation capability.
A breakdown of modern customer segmentation for growth-focused pros
Disclaimer: This isn’t a “What is customer segmentation?” guide.
You already know the basic customer segmentation definition: the grouping of individuals in your database with common characteristics and attributes — a process that enables more efficient and effective analysis, modeling, and marketing across channels.
What you may not know is the most efficient means of segmenting one’s audience — and how to orchestrate the optimal messaging to each segment across customer lifecycle stages to engage (or reengage) them and gradually increase their loyalty and lifetime value.
Companies that use BlueConic, for instance, select the customer attributes (called profile properties) they want to set specific conditions for so they can create multi-dimensional segments in minutes — not hours, days, or weeks.
(And all without IT or data science help).
These profile properties range from basic (e.g., personally identifiably information integrated from a CRM) to complex (e.g., hyper-specific customer actions or traits):
- Gender, name, email address, location, phone number, language, family size
- 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
With this data stored in our CDP, BlueConic customers can easily build segments based on dynamically updated profile data for customers with the same grouping IDs in BlueConic.
Now, determining which types of customer segments to build depends entirely on your business, revenue model and goals, and what you want to learn about your audience.
The point is having a robust, streamlined segmentation strategy that helps marketing, CX, and other tech users better understand individuals and deliver the most compelling messaging and experiences to them is a must for customer engagement success today.
Customer segmentation examples: How companies across industries use our CDP
With that cleared up, here are some specific customer segmentation examples you can build in BlueConic’s customer data platform (CDP) in a matter of minutes.
(Note: These examples may not apply to your products and services or unique business model whatsoever. Having said that, we just want to give you an idea of what’s possible with a multi-dimensional segmentation approach — and a customer data platform — today.)
Example #1: Online retailer trying to reengage shoppers
Shopping cart abandonment is a major issue facing ecommerce companies today. What exacerbates things is when online retailers neglect would-be shoppers altogether.
With a customer data platform like ours, though, an ecommerce business facing this problem could identify segments who left one or more items in their carts over a given period and develop a reengagement plan to entice individuals to complete purchasing.
Types of customer segmentation models this company could build:
- Basic: Prospects who added an item to a cart without purchasing
- Intermediate: Prospects who added an item to a cart in the last week without buying
- Advanced: Prospects from a particular city or state who added an item to a cart in the last week without buying and visited five or more website pages
Example #2: News publication focused on subscriptions
Many publications today are (rightfully) focused on building revenue through digital subscriptions. Embracing other revenue models is now a must to accelerate growth.
A newspaper or magazine intent on growing their bottom line through subscriptions could define segments of customers who have the potential to churn (i.e., not renew).
Similarly, they could pinpoint subscribers who signed up for free trials but have not purchased long-term plans or even leads who maxed-out metered paywall windows.
A few specific audience 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, have visited your website at least 10 times in the past week, and landed on a “renew” page
Example #3: DMO aiming to convert interested travelers
Let’s say you’re a destination marketing organization (DMO) that wants prospective vacationers to finally book their hotel and travel accommodations via your website.
You could 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 retarget individuals with deals to the destinations of interest to them and travel arrangements for those trips elsewhere online.
Three kinds of customer segmentation models for this company include:
- Basic: Leads who visited booking pages without buying
- Intermediate: Leads who visited booking pages for a specific city without buying
- Advanced: Leads who visited booking pages for a specific city at least 3 times without buying and who have reserved lodging and travel with the DMO before
The point here is there are countless (innumerable, really) ways to segment your customers.
And the types of return on investment you can realize from segmentation efforts like these — from both a financial (greater revenue, money saved on external agencies) and efficiency (more streamlined, scalable, in-house approach) — are many.
(Just ask VF Corp, which saved thousands of dollars and sped up its time to market considerably by investing in BlueConic and using our segmentation capability).
The key to success with segmentation today is to test early and often and constantly evaluate the effectiveness of your segments and the messaging delivered to each segment.
Eventually, you’ll learn which segments are worth keeping and connecting with (e.g., those whose CLV continues to grow) and which to adjust or eliminate outright (e.g., ones that don’t convert as much or provide as much value to the organization from an ROI perspective).
Tips to optimize and improve 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 engagement efforts.
Routine customer segmentation analysis can help you unearth eye-opening patterns about customers’ behavior. This, in turn, can inform how you market to them across channels: from targeted ads on search and social media to on-site dialogues you serve them.
You may even notice nuances with your segmentation strategy that would’ve remained unknown without categorizing your customers into various distinct 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 data science. (Pretty great, right?)
Customer segmentation: Best executed with a pure-play customer data platform
Your company’s customer segmentation strategy is something you and other day-to-day technology users — including and especially those without technical expertise — can operate entirely on your own today. As long as you have a pure-play CDP like BlueConic.
It’s no longer ideal to have to wait on IT or data science (or, worse, a costly external agency) to spend days or weeks pulling segments for you to (eventually) use for activation, analysis, and modeling. With a CDP, access to real-time, actionable segments is a reality.
By using these customer segmentation examples as a starting point for your multi-dimensional segmentation efforts — and with a CDP at the center of your tech stack — you can get on your way to better understanding and interacting with your customers.
Request a demo of BlueConic today to learn all about our pure-play customer data platform’s multi-dimensional segmentation capability.