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Marketing is an increasingly costly, resource-intensive, highly competitive, multichannel, and ongoing activity for most businesses today. So, anything they can do to reduce those costs and gain an advantage in the marketplace is critical.
That thinking spurred MetLife in 2015 to embark on a 12-month, data-driven process of reimagining its approach to customer segmentation and making “the most significant change to the brand in 30 years.” The insurer’s goal was to realize annual cost savings of $800 million and refresh its brand in front of customer segments that were both “strategic and tactical” so that the company could target the right customers for its business model
In this post, we’ll examine why customer segmentation is so important to companies like MetLife, and how advanced technology tools like Invoca’s artificial intelligence (AI)-powered call tracking platform are helping to make customer segmentation analysis more complete and precise.
What is customer segmentation exactly? No two customers are the same, of course, but they can share characteristics like age, gender, education, hobbies, or marital status. Marketers group customers based on certain attributes, behaviors, demographics, or other relevant criteria to create more targeted and effective marketing strategies. That’s customer segmentation in a nutshell.
Customer segmentation has been around for a long time. But the practice is especially vital to marketing success in a digital, omnichannel world where consumers expect personalized experiences. Grouping customers by shared characteristics, also known as lookalike audiences, provides businesses with greater, more meaningful insight into their customers and a greater understanding of their motivations and buying behaviors.
Segmentation can benefit many functions in a business, including sales, marketing, and product development. MetLife even uses segmentation to help drive employee engagement by uncovering employees’ preferences, motivations, personal goals, and individual circumstances. The insurer reports that its research is “helping HR leaders select their benefits, communicate product features more effectively, adjust current programs to suit their diverse employees,” and help employees understand how to get the most out of their benefits.
As for customer segmentation, what can developing or refining an approach to this method do for your business? Well, it might deliver higher open rates on email marketing campaigns, bring more customers like those already buying from you, improve overall customer service, or guide you to new markets. Creating personalized customer segments can also drive sales higher and increase customer satisfaction.
Market segmentation is reportedly used by 70% of marketers, and 80% of companies that use market segmentation report increased sales. Market segmentation takes a macro view of the customer base and, as a result, includes both customers and non-customers.
With market segmentation, businesses typically know little or nothing specific about the customer. For this reason, it’s most often used by startups or companies venturing into entirely new markets where they don’t have existing customers and need a more generalized approach to find their footing. The goal is to build a customer base.
In contrast, customer segmentation is focused firmly on known entities — the existing customer base. Because businesses have a degree of micro-knowledge of those customers, they can easily group them. Grouping allows marketers to create more personalized brand messages and offers that are likely to resonate with these different sets of customers.
Recent research found that 80% of consumers are more likely to make a purchase when brands offer them personalized experiences. (That number jumps to 86% among automotive consumers, by the way.)
How can you approach customer segmentation? In general, there are six types of segmentation that you can use alone or in combination to inform the creation of a customer segmentation model:
These are the steps you should take when planning an effective customer segmentation analysis:
The first step toward successful customer segmentation is to identify your target market. You can’t create viable customer segments if you don’t know who your potential customers are and which groups are most likely to be interested in specific offerings. (If you don’t have answers to those questions yet, try market segmentation instead.)
You need relevant and accurate data about your customers to create meaningful customer segments. Data can be collected from various sources, including market research, customer service surveys, focus groups, and interviews.
You can also use call tracking software, such as Invoca, to farm valuable first-person data from phone interactions with customers. Hearing the true voice of the customer in phone calls can help you take your customer segmentation models to another level.
Analysis of the data you’ve collected from sources like surveys, interviews, and call tracking can reveal patterns and insights that will allow you to refine your customer segments. Data analysis is a time-consuming task, but technology is easing the process.
For example, Invoca’s conversation intelligence software uses AI and machine learning to group and analyze large amounts of phone call data at scale. It can detect patterns and identify signals in the data relevant to buyer intent, buyer behavior, and other shared characteristics that enable effective customer segmentation.
Segment identification is the process of defining a distinct customer segment based on the insights gleaned from the data you’ve gathered through any of the six types of customer segmentation listed earlier, like demographic and needs-based segmentation.
When MetLife analyzed its customer base, its marketers identified five distinct segments which they named — Young Achievers, Concerned Moms, Financially Mature, Ho Hum, and Solo Content.
After identifying the five segments, MetLife’s marketers then set about creating a detailed buyer persona for each customer segment to understand their buyer motivations, needs, and preferences. The buyer persona for the intriguingly named Ho Hum, for example, was a risk-averse middle-aged woman who was not a primary decision-maker in the household and wasn’t likely to buy insurance.
Creating customer personas involves developing detailed profiles of each segment to humanize and understand their motivations, needs, and preferences.
Once you have clearly defined each buyer persona and narrowed down your commitment to the segments most likely to make a purchase or some other desired action, you can now reach out!
Use the insight you’ve gained about your various customer sets to create personalized campaigns and tailored strategies and messages that are likely to resonate with each target segment based on their characteristics, interests, and preferences.
After you’ve launched your campaign or campaigns, you need to focus on validating and refining those efforts. Analyze and measure responses and engagement rates to determine if the message(s) you developed resonated with the customer segment you targeted.
If you discover that it missed the mark, guess what? You now have the insight to refine the message for strategic retargeting of customers.
The insights you gain from validating your results and listening to the voice of the customer in phone calls, emails, and other communications will allow you to fine-tune your customer segmentation strategy. But this refinement is not a one-time activity; it must be continuous.
Interative improvement in marketing involves making incremental changes over time to optimize the performance of campaigns to achieve better outcomes. You need to constantly adapt your approaches to meet the evolving needs and expectations of your target audiences.
Warning: It’s easy to make mistakes when segmenting customers. If that happens, it can seriously skew customer segmentation analysis. So, you need to be careful. Here are five common pitfalls to avoid:
Nothing will mess up data analysis, and customer segmentation analysis, like incorrect or incomplete data. You can avoid this issue by having a clear goal for the process and defining your target market.
Don’t make data collection too complicated. Also, be open to different methods for data collection, such as call tracking. Above all, document the process so that you can quickly identify where issues arise, fix problems, and more easily replicate the things you’ve done well.
Incomplete data has a damaging domino effect, leading to inaccurate customer segments. Manual analysis is often the culprit. Lean on advanced technologies, such as conversation intelligence, to accurately retrieve data from phone calls quickly and at scale, whenever you can.
Change is constant, and shifting consumer preferences and behavior are evidence of that. Here’s a case in point: In 2010, less than 5% of U.S. retail sales occurred online. By 2020, this consumer buying activity had leaped to 18%.
Customer behaviors can evolve fast, so make a point to stay attuned to trends and refine your customer segments accordingly.
Customer segmentation models can quickly become worthless if they aren’t consistent across the various market channels used by a company.
Take care to integrate segmentation strategies thoughtfully and deploy them consistently across every marketing channel. For example, maintain the same messaging across your display ads and social media ads for each customer segment that you are targeting. And when you make refinements to your strategy, make sure to apply those changes across channels, too.
Your customer segments may be perfectly set, at least for the moment. But if you haven’t accurately matched those segments to your offerings, misalignment can occur.
In MetLife’s case, after digging into its data, the company successfully identified two customer segments most likely to purchase insurance: Young Achievers and Concerned Moms. If they had targeted Ho Hum, they would have been misaligned . . . and wasted their marketing budget.
Technology is a critical component of customer segmentation. Tools such as data visualization, machine learning, and other AI-based solutions can collect and sift through huge volumes of data far faster than humans or even prior technologies.
The ability to collect and analyze data at scale empowers marketers to achieve spot-on segmentation and reach customers with highly personalized messages quickly and efficiently — thereby maximizing their overall marketing spend.
Conversation intelligence is one advanced technology solution for marketers to consider using to support a customer segmentation strategy. It provides rich insights at scale, from tens or hundreds of thousands of actual customer conversations, to enrich customer segmentation analysis.
Invoca’s call tracking and conversation intelligence solution seamlessly integrates with other elements of the tech stack, such as customer relationship management (CRM) systems, customer data platforms (CDPs), ad platforms, data analytics and attribution solutions, and digital experience platforms, to deliver actionable data in real time.
Want to learn more about how Invoca can help you enhance your customer segmentation strategy? Check out these resources:
