Customer segmentation is a technique where existing customers are split into groups based on similar attributes or affinities. These customer groups are beneficial in identifying potentially profitable customers amongst your target audience , and in developing customer loyalty.
Companies use customer segmentation to message and market to each group differently, with the goal of improving relevance and effectiveness.
The customer segmentation model makes it easier for customer experience (CX) professionals in marketing, sales, product, and service teams to organize and scalably manage more tailored interactions and relationships with similar groupings of customers.
Typical Customer Segmentation Techniques
Customer data is at the heart of segmentation. A successful customer segmentation strategy requires a customer database that can be queried by CX professionals.
The database must contain descriptive attributes about each customer on which to create segments. As the breadth of customer attributes becomes richer, so too does the potential for more sophisticated and targeted segmentation.
In business-to-consumer (B2C) marketing, some of the attributes along which customers might be segmented include:
- Demographic segmentation – Age, gender, marital status, location
- Psychographic segmentation – Personality, values, opinions, attitudes, lifestyles
- Behavioral segmentation – Frequency of visit, pages viewed, # purchases, content consumed
In business-to-business marketing, because communications are directed at individuals within a business, B2C techniques are often applicable. But additional attributes may come into play when working with business customers, such as:
- Firmographic segmentation – Annual revenue, location, business model, # of employees
- Needs-based segmentation – Performance, efficiency, risk aversion, speed-to-market
- Tier-based segmentation – Classification as tier 1, tier 2, tier 3 and so on, based on revenue potential
By leveraging all of the existing granular customer data, marketers can create comprehensive customer segments through advanced technical analyses, like RFM analysis, to increase the effectiveness of their campaigns.
The Value of Customer Segmentation
Every customer is different—and companies recognize that tailoring communications and offerings to the individualities of customers improves relevance, strengthens relationships, and boosts conversion and lifetime value.
Segmenting customers also helps companies gain a deeper understanding of customers’ affinities, wants and needs by allowing research and analysis (and the actions based on those findings) to be tailored per segment.
Effective application of segmentation techniques can deliver a range of benefits to a brand including:
- Increased conversion on offers
- Boosted transaction size through optimized cross-selling and upselling
- Better return on marketing investments
- Improved customer retention and greater LTV
- Higher regard for the brand
The Rise of 1:1 Personalization
The overwhelming value of customer segmentation has been proven time and again over the course of many decades. As a result, processes and technologies for segmentation have continually matured. With the emergence of big data analytics, sophisticated marketing automation and machine learning/AI, the ability to tailor your marketing message and communications has now gone beyond conventional segmentation.
The aim is now to achieve 1:1 personalization, where communications and offers are unique down to the level of each individual customer. Customer-facing teams are going beyond designing one-off, departmentally siloed initiatives or campaigns, and extending their thinking to end-to-end, personalized customer journeys that are executed with coordination across marketing, service, product, sales and more.
In fact, consumers now expect this level of personalized service from the brands they favor. They want to know their brand understands the full context and history of their previous purchases and interactions. They want communications and offers to be made through the channels they prefer, and they want them to anticipate their preferences, wants and needs. They want authentic, contextual, cohesive and relevant experiences across the entire customer lifecycle and through every mode of interaction whether in-person, on the phone, through web, app, email, direct mail or otherwise.
Is Customer Segmentation Still Meaningful in a 1:1 World?
The level of granular personalization required for 1:1 interaction goes beyond what can be managed using conventional customer segmentation techniques. In fact, it goes beyond what can be practicably managed by humans alone when done at scale. On the data side of the equation, it means harnessing and synthesising customer intelligence from massive volumes of fully granular customer data that comes from dozens to hundreds of disparate sources. On the activation and orchestration side of the equation, it means deploying an individualized experience for each customer across the multiple channels through which they interact with the brand.
For many, this begs the question of whether customer segmentation is still a meaningful and useful approach in a 1:1 world where automation and augmented intelligence play such a core role. This answer is yes — but CX pros must now think about segmentation differently than they did in the past.
Redefining Buyer Segmentation
In the past, customer segmentation was essentially a “list pull” from your current customer base. Marketers would group customers based on a set criteria such as demographics, whether someone had visited the site or store in the last 30 days, time of last purchase, etc., and then pull a list of customers who met the criteria. The members of the list would all receive the same point-in-time campaign or experience, and results would be measured. In this segmentation model, the segment is married to the execution—everyone in the segment experiences the exact same thing. There’s no 1:1 personalization.
Customer Segmentation as Applied to 1:1 Marketing
In a 1:1 world, customer segments are separate from the execution. Rather, they are used as context setting for the final execution of the experience. This is best explained by example. As a marketer, for instance, I want to know which products to next offer to a customer based on their history of purchases.
An analysis of my customers tells me I have some customers who typically purchase my “good” products, some who buy my “better” products and others who tend to buy my “best” products—each of which are at different price points. I’ll create good, better and best segments so that customers in each segment are always offered products at (and sometimes above) their current preference.
But when it comes to which specific product will be offered, and over which channel that offer will be made, I’ll relinquish control to the model that runs my automation and machine learning algorithms to do the final 1:1 personalization. They’ll examine each individual customer’s purchase history and find out what other similar customers bought next. They’ll look at the channels in which each customer tends to be most active and responsive and at what times of day. They’ll even look at how that customer is interacting on my site, app, or in a brick-and-mortar location right now. Then, they’ll optimize which product to present, how to present it, and when. The offer will be customized with relevant details of the customer’s history and current needs and interests.
In modern customer experience, the segment guides the context of the customer’s experience, while the data, the model, and machine intelligence drive the final stretch of 1:1 personalization.
CDPs: the Modern Approach to Customer Segmentation
If you currently use conventional customer segmentation techniques to build your customer journey, creating 1:1 personalized like the example above might seem difficult or impossible using your current processes and technology stack. You’re not alone. Most marketing stacks are still siloed in one way or another—often by department and/or channel. They can’t pull in all the data needed to comprehensively understand each customer from a 360-degree vantage point. They don’t have the automation or AI to personalize 1:1, and can’t orchestrate experiences across all your channels.
But achieving those things doesn’t require a rip and replace of your stack. More and more customer experience initiatives have been leaning on customer data platforms (CDPs) that take a smart hub approach. Smart hub CDPs transform the way you can build customer experiences, leveraging your existing tech stack, so you can get up and running creating authentic, 1:1 personalized customer experiences quickly. Smart hub CDPs are designed to:
- Unify customer data across all your channels and systems, both online and offline
- Provide business users with self-service access to generate customer insights
- Orchestrate experiences across every online and offline customer touchpoint
Because they also take advantage of the most modern, scalable enterprise-class infrastructure, smart hub CDPs are also a foundation for your CX to evolve and grow into the future. By modernizing your segmentation approach to support 1:1 personalization, with the help of a CDP, your entire organization becomes more agile and empowered. You will be positioned to further grow LTV, loyalty, retention, and other key metrics beyond the limits of what traditional customer segmentation can offer.