Customer segmentation has always been important. But now that personalization and customer experience are make-or-break factors for business success today, effective segmentation is an absolute must.
However, according to a recent Forrester report, only 33% of companies using customer segmentation say they find it significantly impactful. According to the report, the main reason companies fail is that they are still using traditional customer segmentation approaches, without leveraging the breadth of customer data and advanced analytics techniques available today.
In other words, they are not using a modern behavioral segmentation approach.
In this post GoDigital will bring you up to speed with an in-depth overview of 10 different approaches to behavioral segmentation (including both B2B and B2C examples) that can be used to better understand your customers and maximize results at every stage of the customer journey.
What is Behavioral Segmentation?
Traditional approaches to segmentation focused mainly on who customers are and segments were based on demographic attributes such as gender or age, and firmographic attributes like company size or industry. By understanding who your customers are is not enough anymore.
Behavioral segmentation is about understanding customers not just by who they are, but by what they do, using insights derived from customers’ actions.
Behavioral Segmentation is a form of customer segmentation that is based on patterns of behavior displayed by customers as they interact with a company/brand or make a purchasing decision. It allows businesses to divide customers into groups according to their knowledge of, attitude towards, use of, or response to a product, service or brand.
The objective is to identify customer segments that enable you to understand how to address the particular needs or desires of a group of customers, discover opportunities to optimize their customer journeys, and quantify their potential value to your business.
Why Segment Customers by Behavior?
Here are four main advantages of grouping customers into different segments based on their behaviors:
Personalization. Understand how different groups of customers should be targeted with different offers, at the most appropriate times through their preferred channels, to effectively help them advance towards successful outcomes in their journeys.
Predictive. Use historical behavioral patterns to predict and influence future customer behaviors and outcomes.
Prioritization. Make smarter decisions on how to best allocate time, budget and resources by identifying high-value customer segments and initiatives with the greatest potential business impact.
Performance. Monitor growth patterns and changes in key customer segments over time to gauge business health and track performance against goals. At a high level, this means quantifying the size and value of customer segments, and tracking how “positive” and “negative” segments are growing or shrinking over time.
10 Powerful Behavioral Segmentation Methods You Can Use to Better Understand Your Customers
Traditionally, most experts site around six primary types of behavioral segmentation.
While those six “classic” types of behavioral segmentation are all still very much relevant today, they have also evolved to take on new meanings, applications and use cases.
In this post we will explore both the “traditional” and “modern” interpretations of each type, while also making some new additions to the list to include some interesting new ways some of our customers and partners are using behavioral segmentation today.
A few important items to keep in mind before we dive-in:
This list is NOT mutually exclusive.
The way you go about defining segments and using different behavioral segmentation types can vary greatly depending on your business.
One or more of these segmentation methods can be utilized at the same time or combined with other types of segments.
1. Purchasing Behaviour
How do customers behave differently throughout the path to purchase?
Purchase behavior-based segmentation is about identifying trends in how different customers behave during the process of making a purchase decision.
Purchasing behavior can help us understand:
How different customers approach the purchase decision
The complexity and difficulty of the purchasing process
The role the customer plays in the purchasing process
Important barriers along the path to purchase
Which behaviors are most and least predictive of a customer making a purchase
By leveraging machine learning capabilities to analyze customer behavior throughout the customer journey and identify patterns over time, companies are now building predictive segments based on the likelihood of different customers making a specific purchase.
There are two common ways to use past behavior to predict future outcomes:
Using past purchases to predict future purchases
Using behavior along the path-to-purchase to predict the likelihood of completing a purchase
Another modern approach uses patterns in digital behavior to understand the variety of ways different customers approach the buying process, in order to identify the key obstacles marketers need to remove from the path to purchase.
There are a variety of ways to approach this, depending on your business. Lacie Larschan shared some eCommerce examples of this method in a recent article. She characterizes buyers into six different behavioral segments with corresponding buyer personas by forming implicit assumptions based on their online interactions:
The “Price-conscious” buyer is a bargain hunter looking for the lowest possible price.
The “Smart” buyer is a thorough, meticulous researcher who wants to understand every intricate factor, before committing to any single one.
The “Risk-averse” buyer is a cautious, economically-careful shopper, who struggles to pull the trigger on a purchase without the proper insurance, such as a good, hassle-free return policy.
The “Needs-proof” buyer is a shopper who needs confirmation that the product is popular and backed up by claims of her peers.
The “I’ll get it later” buyer is a shopper who lacks urgency.
The “Persuadable” buyer is an impulse shopper that is highly susceptible to cross-sell offers.
If you can learn that much about how different customers approach a purchasing decision through behavioral data from just a single channel within a single web session, imagine how much more you could discover using customer behavior data that encompasses interactions across all channels over a longer period of time.
2. Benefits Sought
What primary benefits are different customers seeking during a purchase decision?
As customers research a product or service, their behavior can reveal valuable insights into which benefits, features, values, use cases or problems are the most important motivating factors influencing their purchase decision.
When a customer places a much higher value on one or more benefits over the others, these primary benefits sought are the defining motivating factors driving the purchase decision for that customer.
A simple example is consumers who buy toothpaste for different reasons:
This B2C toothpaste example can apply to just about any business in any industry. For B2B software, the benefits sought might be specific features or capabilities, ease-of-use, speed or accuracy-related benefits, or key integrations with other tools.
Two potential customers may look identical in terms of their demographic or firmographic traits or from a customer persona standpoint, but they could have very different values in terms of which benefits and features are the most and least important to each.
If you have four customers who are all seeking a different primary benefit and you message all of them about the same benefit, then you’re missing the mark with 75% of your communications and wasting 75% of your time and budget.
By understanding each customer’s behavior, as they interact with your brand over time, you can group customers into segments based on their desired benefits and personalize your marketing accordingly for each segment.
Which Benefits Are Most Effective for Acquiring and Retaining High-Value Customers?
In some cases, the desired benefit may also be predictive of a customer’s likelihood to purchase, potential lifetime value, or even their likelihood to churn. Here are a few examples of how benefits can be analyzed within this context:
What were the benefits sought for prospects that ended up purchasing? That did not purchase?
What benefits are most and least important for your highest lifetime value and most loyal customers?
What benefits are most and least important for low lifetime value customers or those that churn?
How do these benefits match up with your strongest value propositions and differentiators?
With this knowledge, you can increase conversion rates through more personalized journeys and also have a clearer understanding of which customers to target for acquisition and which messages to use to attract them.
3. Customer Journey Stage
Which stage of the journey is a new or existing customer currently in?
Building behavioral segments by customer journey stage allows you to align communications and personalize experiences to increase conversion at every stage. Moreover, it helps you discover stages where customers are not progressing, so you can identify the biggest obstacles and opportunities for improvement.
But segmenting your customers by journey stage is not easy.
A common misconception is that a single customer behavior or interaction is enough to accurately pinpoint which journey stage a customer is currently in.
In most cases one or two behavioral data points is are not enough to accurately identify a customer’s current journey stage.
Customers in all different stages interact and engage with content and experiences designed for all different stages, across all different channels, at all different times, and in no particular order.
The most effective way to accurately determine a customer’s current journey stage is by leveraging all of a customer’s behavioral data across channels and touchpoints, so you can build weighted algorithms based upon patterns of behavior over time.
This diagram shows the behavior of an individual prospective customer over a period of the previous fourteen days. This prospect is in the consideration stage of the customer journey, but his behaviors occur in a completely random order and do not happen in a linear fashion from stage to stage. This is a much more realistic view of what customer behavior can look like in a given timeframe as they interact with a brand.
If you were to try and identify which journey stage this prospect was in based on one or two of the behaviors, you could easily make a wrong assumption. For example, if you made your judgment by one of the first two behaviors, it seems the prospect is in the awareness or education stage. But by weighting the behaviors using algorithms built from historic patterns, you can see how it becomes much clearer that consideration is the most likely current journey stage for this prospect.
Also, do not make the mistake of assuming customers will just naturally transition to the next stage in their journey as time passes.
If you have an annual subscription business and you make the assumption that a customer has moved from the adoption to retention stage over the course of the year, you may be in for a rude awakening when it comes time for renewal. Once again, behavioral data is the only way to get the truth, or at least as close to it as possible.
How often (and how much) are customers using your product or service? How are they using it?
Product or service usage is another common way to segment customers by behavior, based on the frequency at which a customer purchases from or interacts with a product or service.
How often do customers travel with Airbnb? How often do customers buy products from Amazon?
For a B2B SaaS company, how frequently are customers actually logging-in and using your software? How much time do they spend? How are they using it? What features are they using? How many users from the same account or company are using it?
Usage behavior can be a strong predictive indicator of loyalty or churn and, therefore, lifetime value.
One example is how Netflix leverages customer usage data to build behavioral segments based on users’ monthly content consumption, which ultimately allowed them to reduce their churn rate and increase customer lifetime value to the point where executives estimate saves the company $1Billion every year.
This Netflix use case is a good example of quantity-based usage segmentation.Segments Based on Quantity or Frequency of Usage
Heavy Users (or “Super Users”) are customers that spend the most time using your solution and/or purchase most frequently. These tend to be your most avid and engaged customers, that can also often rely most on your product/service.
Average or Medium Users are customers that semi-regularly use or purchase, but not very frequently. Often these can be time or event-based.
Light Users are customers that use or purchase much less in proportion to other customers. Depending on your business, this could even mean one-time users, but again, it depends on the relative usage to the rest of your customer base.
These usage-based behavioral segments are invaluable for understanding why certain types of customers become heavy or light users. By segmenting in this way, you can test different actions and approaches to increase usage from existing customers and attract more new customers with a higher likelihood of following the same usage behavior patterns as your super users.
Over time, it’s critical to monitor changes in customer usage behavior. This way you can identify problems and opportunities at both an aggregate level (to gauge overall business performance) and at the individual customer level (to identify, for example, if a customer might be at high risk of churning).
Hand-picked related content: How to Reduce Churn with Customer Journey AnalyticsSegments Based on Quality of Usage
While quantity and frequency of usage can certainly be valuable behavioral segments, high usage does not always translate into most value delivered, both to the customer and ultimately to your business
For example, a SaaS customer might have a ton of product usage behavior, but in reality things might not be as peachy as they appear on the surface. Perhaps they are:
not using the product as effectively as they could be,
only leveraging a fraction of the most important features or capabilities in the solution,
only using the product now because they have to, but are unhappy and looking to switch to a competitor in the long-term.
In all three examples, the quantity of usage is not reflective of the value they are actually receiving.
While this customer might fit the criteria of a “heavy user” segment, in reality they aren’t getting enough value and will likely have a high risk of churning in the future (if not already.)
5. Occasion or Timing-Based
When are customers most likely to make a purchase or engage with a brand?
Traditionally, occasion and timing-based behavioral segments refer to both universal and personal occasions.
Universal occasions apply to the majority of your customers or target audience. Holidays and seasonal events are a typical example, where consumers are more likely to make certain purchases around the holiday season or at certain times of the year.
Recurring-personal occasions are purchasing patterns for an individual customer that consistently repeat over a period of time, which could range from annual occasions such as birthdays, anniversaries or vacations, monthly purchases such as business travel or even daily rituals such as stopping for a cup of coffee on the way to work every morning.
Rare-personal occasions are also related to individual customers, but are more irregular and spontaneous, and thus more difficult to predict, such as attending a friend’s wedding.
While these can be very difficult to predict, it is however possible (you might recall the headlines from a few years ago where Target famously used point-of-sale data to figure out when to market diapers and other baby products to women based upon when they had previously purchased pregnancy tests. )Targeting Segments by Time of Day, Day of Week, etc.
Another more modern application of timing-based behavioral segmentation has to do with times when a customer has higher propensity to engage with a brand or be more receptive to offers.
Behavioral patterns in individual customers’ preferences for reading email, browsing social networks, researching products and consuming content are all examples that can be leveraged to help marketers understand the best days and times to target different customers with offers.
Netflix, Dominos, Open Table and Hotel Tonight all send me emails on Fridays more than any other day of the week. Why? Content, pizza delivery, and last minute restaurant and hotel reservations are all things I am more likely to consume or purchase on the weekend.Segments by Time Elapsed Since Prior Purchase or Action
Another time-based approach is predicting when customers are most likely to make a purchase based upon the amount of time that has elapsed since a previous purchase or action.
For example, a customer could be much more likely to purchase again within the weeks or months following an initial purchase, or conversely, much less likely to make an up-sell or cross-sell purchase until a certain amount of time has passed since an initial purchase or renewal. The aforementioned Target pregnancy test case would be another example of this.
6. Customer Satisfaction
How satisfied are your customers, REALLY?
NPS® surveys and other similar customer feedback mechanisms are certainly valuable methods for helping to gauge customer satisfaction, but you can’t rely on these alone.
Here are three reasons why:
Typically only a fraction of customers participate.
Whether you are running surveys annually, bi-annually, quarterly, or even monthly or weekly, this leaves a significant amount of time in between data collection points, leaving you in the dark for extended periods of time during which a customer’s satisfaction level can drastically change.
As Swati Sahai pointed out in her recent post on how to measure customer experience, relying solely on NPS as a customer experience metric is an ineffective approach because it does not accurately reflect customers’ changing needs and experiences at different stages of the customer journey.
A customer’s behaviors can be a much more accurate and reliable source for measuring satisfaction, especially with data that can be captured and updated in real-time, and at every stage of the customer journey.
There are many data sources available through which customer behavior can be tapped to measure a customer’s true satisfaction at any given time. Evidence of negative customer experiences can be found in many places, and detected through many different channels, systems and tools across your organization. The same is true, of course, for positive customer experiences.
Call centers, support portals, help forums, billing and CRM systems, and social media are just a few out of a long list of examples of where this data might live.
By first segmenting your customers by satisfaction—as with all segmentation—you can decide on the appropriate set of actions for each segment and then quantify and prioritize them by their potential business impact.
High Satisfaction SegmentLow Satisfaction Segment
Target with up-sell or cross-sell opportunities
Reach out to for references or case studies
Eligible for loyalty program
Analyze customers in this segment to identify patterns that might lead to high satisfaction
Suppress from up-sell, cross-sell and other promotional offers
Target with retention campaign
Prioritize personal reach out from customer service or success team
Analyze customers in this segment to identify potential root causes of low satisfaction
By segmenting your customers by satisfaction you can determine the answers to questions such as: Which of your customers are most and least satisfied at any given time?
Which factors have the biggest impact on customer satisfaction?
7. Customer Loyalty
Who are your most loyal customers? How can you maximize their value and find more customers like them?
Your most loyal customers are the most valuable assets to any company (arguably with the exception of its employees.) They are cheaper to retain, usually have the highest lifetime value, and most importantly, can become your biggest brand advocates; the ultimate goal of every customer relationship.
Through behavioral data, customers can be segmented by their level of loyalty to help you identify your most loyal customers and understand their needs to make sure you are satisfying them.
Loyal customers can make perfect candidates for programs that offer special treatment and privileges such as exclusive rewards programs to nurture and strengthen the customer relationship and incentivize continued future business.
A few classic B2C examples of such programs include airlines’ frequent flier programs, “platinum” credit card members, or preferred guests at hotels and casinos.
In addition to maximizing revenue from loyal customers, there are many other potential benefits that can increase the lifetime value of the relationship, such as referrals, references, endorsements and testimonials, participation in case studies, providing product feedback and, most importantly, sharing positive word-of-mouth with their peers.
Use customer loyalty behavioral segmentation to yield valuable answers to important questions such as:
What are the key factors and behaviors along the customer journey that lead to loyalty?
Which customers are the best candidates for loyalty or advocate programs?
How can you keep your most loyal customers happy and maximize the value you get from them?
What are different customers interested in?
Understanding your customers’ personal and professional interests is key for personalization, customer engagement and delivering value.
Interest-based behavioral segmentation can be instrumental to delivering personalized experiences that keep customers engaged and coming back for more. This is true regardless of whether your goal is to increase product usage, target customers with cross-sell or upsell offers, or deliver the right content and communications to nurture customers and help move them along the path to purchase, or path to advocacy.
Netflix, Amazon, and Spotify use recommendation engines for suggesting content and products entirely based on customers’ behavioral interests.
One of the great advantages of interest behavior is the ability to implicitly connect specific interests with other potential related interests.
In doing so, each time you capture a customer interest behavior, you are not only weighting a customer’s level of interest in a particular topic, you are also multiplying the number of additional potential interests/topics that might be effective for engaging that customer.
Machine learning can help scale the process. As an increasing number of customers engage and interact, there will be more interest-based behaviors to discover, infer, and weigh over time.
9. Engagement Level
How engaged are your customers? Who are your most and least engaged customers?
Earlier in this article we talked about usage-based behavioral segmentation, which specifically relates to customer interactions with your product or service. While segmenting customers by their level of engagement can include usage, it also encompasses a broader spectrum of customer interactions with your brand that can be equally valuable for gauging the strength of the customer relationship.
How you define “engagement” will vary based on your company and your role, but we think we can all agree that generally speaking, engagement is good.
If a customer has positive experiences with your brand, and as a result is willing to interact more frequently and spend more time engaging with your brand, this is usually a good sign of positive outcomes to follow.
The more time a customer spends engaging with your brand and having positive experiences, the more likely that:
Trust is increasing.
A positive perception of the brand is developing.
Their brand relationship is strengthening.
They are considering making a purchase.
Engagement is a valuable metric in both pre-and-post-purchase realms of the customer journey.
For example, you might use engagement-based segmentation to understand how engaged different prospects are in your pre-purchase funnel, or how active existing customers are in your user community.
You can measure engagement on the individual customer/contact level, on the overall company or account level, or both. In either case, segmenting your customers by their level of engagement is hugely valuable for understanding which customers are most and least engaged with your brand at any given time and why and, most importantly, figuring out what you’re going to do about it.
Below is an example from Engagio, a leading Account-Based Marketing platform, which considers engagement one of its “Big 5” ABM metrics. Engagio’s software enables users to measure behavioral engagement in minutes for each individual role on a prospect account as well as each account overall:
10. User Status
User status is another way to behaviorally classify different customers by their relationship to your business.
Below are a few of the most common examples of user status:
Defectors(ex-customers who have switched to a competitor)
But there are many different possible user statuses you might have depending on your business.
For example, a company with a free to pro model or free trial model might have a status for “freemium users” or “free trial” users.
Leverage the Right Technology
Finally, without the right technology in place, it is incredibly difficult (bordering impossible) to be truly successful with behavioral segmentation today.
Google analytics, advertising platforms such as google adwords and facebook, and marketing automation systems are all examples of tools you can (and should) leverage for analyzing, segmenting and targeting customers based upon behaviors.
However, these tools can only deliver a fraction of the value and capabilities covered in this post. They will not provide the customer data integration and unification, machine learning, or predictive analytics capabilities discussed previously.
Customer journey analytics is a far more robust approach. A customer journey analytics platformallows you to easily classify your customers into behavioral segments, so you can determine the optimal engagement strategy for each customer and assess the impact of each customer segment on your key metrics and KPIs.
Now It’s Your Turn
Behavioral segmentation is a technique for segmenting customers by their behavior, so you can better understand them and engage with them in a more optimal way along their journeys.
Using the ten behavioral segmentation methods described above, you can make your marketing campaigns more effective, maximize ROI, increase customer lifetime value and build a deeper knowledge of your customer base.
How do you segment your customers? Which behavioral segmentations methods do you use? Join the discussion by leaving your comments below.
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