Despite the fact that customer segmentation has always been an important part of the marketing department's work, effective segmentation is an absolute must today, as the widespread use of CJM allows you to study the customer experience, and not just his interaction with the company at the last point of contact.
Next, we'll look at 10 different behavioral segmentation approaches that can be used to better understand your customers and their goals, to maximize CJM quality and achieve your business goals.
What is behavioral segmentation?
Traditional approaches to segmentation focus on identifying who your customers are. They were based on socio-demographic, geographic, psychographic and other criteria. But just understanding who your customers are is no longer enough.
Behavioral segmentation is about understanding customers not only by who they are, but also by what they are doing using data derived from customer actions.
Behavioral segmentation is a form of customer segmentation that is based on the behaviors exhibited by customers when interacting with a company (brand) or making a purchase decision. It allows companies to divide customers into groups according to their knowledge, attitude to a product, service or brand, their use or reaction to them.
The goal is to identify customer segments that will enable you to understand how to meet the specific needs or wants of a consumer group, discover opportunities to optimize their customer journey, and quantify their potential value to your business.
Why segment customers by behavior?
The main advantages of dividing customers into different segments depending on their behavior:
- Personalization. Understanding how different customer groups should target different offers at the most appropriate time through their preferred channels to help them along their customer journey.
- Forecasting. Using typical patterns of behavior to predict and influence future customer behavior.
- Prioritization. The ability to make smarter decisions about how to best allocate time, budget and resources by identifying important customer segments and initiatives with the greatest potential business impact.
- Performance. Tracking growth dynamics and changes in key customer segments in order to be able to assess the state of the business and track the achievement of the set goals. This means quantifying the size and value of customer segments, and tracking how the “positive” and “negative” segments grow or contract over time.
Traditionally, most experts identify six main types of behavioral segmentation:
- Seeking benefits;
- Attitude towards the product;
- Consumption intensity;
- The degree of commitment;
- User status;
- Readiness stage.
While all of these six "classic" types of behavioral segmentation are still relevant today, they have evolved with new meanings and uses.
Behavioral segmentation techniques to better understand your customers
The way you define segments and use different types of behavioral segmentation can vary greatly from business to business. One or more of these segmentation methods can be used simultaneously or in combination with other types of segments.
1. Segmentation based on buying behavior
Segmentation based on buying behavior is the identification of trends in the behavior of different customers in the process of making a purchase decision.
Shopping behavior can help you understand:
- How different customers approach their purchasing decision;
- Complexity of the buying process;
- The role played by the customer in the purchase process;
- Important barriers to buying;
- Which behaviors most or least predict a customer's purchase.
By leveraging machine learning to analyze customer behavior along the customer journey and identify patterns, companies are now building predictive models based on the likelihood that different customers will make a particular purchase.
There are two common ways to use empirical data to predict future customer behavior:
- Using past purchases to predict future purchases;
- Using Buying Path Behavior to predict the likelihood of a purchase.
There are different approaches to segmentation depending on the line of business. For example, in e-commerce, six different shopper behavioral segments are distinguished with corresponding shopper avatars based on their online interactions. The following customer segments can be distinguished based on digital behavior:
- The "conscious" shopper is a bargain hunter looking for the lowest possible price;
- A smart shopper is a thorough, meticulous researcher who wants to understand all the details before making a decision;
- A “proof-of-mind” buyer is a buyer who needs confirmation that a product is popular and backed up by reviews from his peers;
- A persuaded shopper is an impulsive shopper who is highly susceptible to cross-bids;
- A “risk averse” shopper is a cautious, thrifty shopper who hesitates to purchase without adequate insurance, such as a hassle-free return policy;
- Buyer “I'll buy it later” is a buyer who lacks urgency.
This approach uses digital behaviors to understand the diversity of how different customers approach the buying process to identify key barriers that marketers need to address in the buying journey.
If you can learn so much about how different shoppers approach buying decisions using behavioral data from only one feed in a single web session, imagine how much more you can learn from shopper behavior data that cover interactions across all channels over a longer period of time
2. Segmentation based benefits
What are the main benefits different customers are looking for when making a purchasing decision? When shoppers are exploring a product or service, their behavior can provide valuable insights into which benefits, features, values, use cases, or issues are the most important factors in influencing a buying decision.
If a shopper places much greater emphasis on one or more benefits than others, these primary benefits sought are the determining motivating factors that determine the buying decision for that shopper.
For example, consumers who buy toothpaste are guided by different reasons: whitening, sensitive teeth, taste, price. And for B2B software, the benefits you seek can be specific features or capabilities, ease of use, speed or accuracy benefits, or key integrations with other tools.
Two potential customers may look the same in terms of their socio-demographic or any other traditional characteristics, but they may have very different values in terms of which benefits and opportunities are most and least important to each of them.
If you have four customer groups looking for different top benefits, and you write to all of them about the same benefit, then 75% of your messages miss the mark and you are wasting 75% of your time and budget.
By understanding each customer's behavior, how they interact with your brand over time, you can segment customers based on their desired benefits and personalize marketing for each segment.
What are the most effective benefits for acquiring and retaining high-value customers?
In some cases, a desired advantage can also predict the likelihood of a purchase, potential lifetime value, or even the likelihood of customer churn. Here are some examples of how you can analyze benefits in this context:
- What benefits were the potential customers looking for, who eventually made a purchase? Who hasn't made a purchase?
- What are the most and least important benefits for the most loyal customers with the most lifetime value?
- What are the most and least important benefits for low lifetime value customers?
- How do these benefits compare to your strongest value propositions and differentiators?
With this knowledge, you can improve your conversion rates through more personalized travel, as well as gain a clearer understanding of which customers should be targeted for acquisition and which messages to use to attract them.
3. Segmentation based stages of the customer journey
At what stage of the journey is the new or existing customer? Building behavioral models along the stages of the customer journey allows you to build communications and personalize the experience to increase conversions at each stage. Moreover, it helps to identify the stages where customers are not moving forward, which allows you to identify the biggest obstacles and opportunities for improvement.
A common misconception is that behavior or customer interaction alone is enough to determine exactly where the customer is on the journey. In most cases, one or two points of behavioral data are not enough to accurately determine where the customer is on the journey.
Customers at different stages interact and interact with content and experiences designed for different stages, through different channels, at different times and in no particular order.
The most effective way to accurately determine the current stage of the customer journey is to use all the customer's behavioral data across all channels and points of contact, which allows you to build weighted algorithms based on behavior patterns.
The following diagram shows the behavior of an individual prospect during the previous period. This potential buyer is under consideration, but his behavior occurs in a completely random order, and not linearly from stage to stage. This is a much more realistic representation of what a customer's behavior might look like over a period of time when they interact with a brand.
If you try to determine where a given prospect is at based on one or two behaviors, it’s easy to make the wrong assumption. For example, if one of the first two behaviors is guided, it may seem that the potential buyer is at the stage of awareness or study. But if you analyze all the behaviors using algorithms that are based on typical models, you can see that the review stage is the most likely current stage for a given potential client.
It is a mistake to assume that customers just naturally move to the next stage over time.
4. Segmentation based frequency of use
How often do customers use your product or service? How do they use it? Product or service usage is another common way to segment customers by behavior based on the frequency with which a customer purchases or interacts with a product or service. Customer behavior can be a strong predictor of loyalty or churn and therefore lifetime value.
Segments based on quantity or frequency of use:
- Superusers are the customers who shop or use the product the most. These tend to be your most inveterate and engaged customers who also often rely on your product the most.
- Average users are customers who buy or use regularly, but not very often. They can often be tied to a time or event.
- Small users are customers who use or buy much less frequently than other customers. Depending on your business, this can even mean one-time customers, but it depends on the relative relationship to the rest of your customer base.
These usage-based behavioral segments are invaluable in understanding why certain types of customers become superuser or lightweight. By segmenting in this way, you can test different actions and approaches to increase the use of existing customers and attract more new customers who are more likely to follow the same behaviors as your superusers.
It is very important to track changes in customer behavior over time. This way, you can identify issues and opportunities both at the aggregate level and at the individual customer level.
While the number and frequency of use can certainly be valuable behavioral segments, high use does not always mean great value, both to the customer and to the business.
For example, a customer may have a ton of behavioral metrics for product use, but the reality may not be as good as it first appears. Perhaps they:
- are not using the product as efficiently as they could,
- use only a part of the most important functions or capabilities of the solution,
- use the product only now because they have to, but in the long run are unhappy and want to go to a competitor.
While this customer may meet the criteria for the “active user” segment, in reality they are not getting enough value and have a high risk of churn in the future.
5. Segmentation based on engagement rate
While segmenting customers by engagement level can include segmenting by frequency of use, it also encompasses a broader range of customer interactions with a brand that can be just as valuable in assessing the strength of customer relationships.
If a customer has a positive experience with a brand and, as a result, is willing to interact with it more often and spend more time interacting with it, then this is usually a good sign.
The more time a customer spends interacting with the brand and having a positive experience, the more likely they are to:
- Trust will grow;
- Positive brand perception will develop;
- Relationship with the brand will be strengthened;
- Purchase will be considered.
Engagement is a valuable metric, both before and after the purchase.
For example, you can use engagement-based segmentation to understand how different potential customers are engaged in your pre-purchase funnel, or how active existing customers are.
Engagement can be measured at the individual customer level, at the entire company level, or at both levels. Either way, segmenting customers by level of engagement has tremendous value in understanding which customers are most and least engaged with a brand at any given time and why to understand what needs to be done about it.
6. Segmentation based cases and times
When are customers most likely to purchase or interact with a brand? Behavioral segments based on cases and time refer to both universal and personal cases. Generic cases apply to most of your clients or target audience. Holidays are a common example where consumers are more likely to make certain purchases. Repetitive personal experiences are purchase patterns for an individual customer that repeat continuously over a period of time, such as birthdays, anniversaries, or even stopping for a cup of coffee on the way to work every morning. Rare personal events are also associated with individual clients, but they are more irregular and spontaneous, which means they are more difficult to predict, such as a friend's wedding.
Another use of time-based behavioral segmentation is when a customer is more likely to engage with a brand or be more receptive to suggestions.
Behavioral patterns of individual customer preferences for reading email, browsing social media, exploring products, and consuming content are all examples that can be used to help marketers understand what days and times are best to target different customers.
Another time-based approach is to predict when customers are most likely to make a purchase based on the amount of time that has elapsed since a previous purchase or action.
For example, a customer may be more likely to make a repeat purchase within a few weeks or months after the original purchase, or, conversely, be less likely to make a repeat or cross-purchase until a certain amount of time has passed since the original purchase.
7. Segmentation based customer satisfaction
How satisfied are your customers?
NPS (Net Promoter Score) surveys and other customer feedback mechanisms are certainly valuable methods of measuring customer satisfaction, but they cannot be relied upon for several reasons:
- As a rule, only a small proportion of customers participate in surveys,
- The approach does not accurately reflect the changing needs and experiences of customers at different stages of their journey.
Customer behavior can be a much more accurate and reliable source for measuring satisfaction, especially when there is data that can be collected and updated in real time and at every stage of the customer journey.
There are many data sources that can be used to study customer behavior to measure true customer satisfaction at any given time. Evidence of negative customer experiences can be found in many places and discovered through a variety of channels, systems and tools. The same is, of course, true for positive customer experiences. Information centers, support portals, help forums, billing and CRM systems, social media are just a few of a long list of examples of where this data might be.
By first segmenting your customers by satisfaction, you can determine the appropriate set of actions for each segment, and then quantify and prioritize their potential business impact.
8. Segmentation based customer loyalty
Who are the most loyal customers? The most loyal customers are the most valuable assets for any company. They are cheaper to hold, usually have the highest lifetime value, and most importantly, they can be the biggest advocates for your brand, which is the ultimate goal of any customer relationship.
Using behavioral data, you can segment customers by loyalty level, which can help you identify the most loyal customers and understand their needs to make sure they are satisfied.
Loyal customers can be ideal candidates for programs that offer special treatment and perks, such as exclusive reward programs, to develop and strengthen customer relationships and drive continued business in the future.
In addition to maximizing return on repeat customers, there are many other potential benefits that can increase lifetime value, such as recommendations and testimonials, engaging in case studies, and most importantly, sharing positive reviews with your peers.
Use behavioral segmentation of customer loyalty to get valuable answers to important questions such as:
- What are the key factors and behaviors in the customer journey that lead to loyalty?
- Which clients are the best candidates for loyalty or promotional programs?
- How do you keep your most loyal customers happy and maximize the value you get from them?
9. Interest-based segmentation
What are different clients interested in? Understanding your clients' personal and professional interests is key to personalization, customer engagement, and value delivery.
Interest-based behavioral segmentation can play an important role in delivering a personalized experience that allows customers to stay engaged and come back for new services. This is true whether your goal is to increase product usage, target customers for cross-offer or additional offers, and deliver the right content.
One of the main benefits of interest-based behavior is the ability to implicitly associate specific interests with other potential related interests.
Moreover, every time the behavior of the client's interests is recorded, not only the level of the client's interest in a particular topic is assessed, but also the number of additional potential interests that can be effective to attract this client increases.
10. Segmentation based on user status
User status is another way of behaviorally categorizing different customers in relation to their relationship to the business.
The most common examples of user status are:
- Prospective users,
- First buyers,
- Regular users,
- Former clients who went to competitors.
There are many different user statuses depending on the business.
Behavioral segmentation is a method of segmenting customers by their behavior so that you can better understand and interact with them in a more optimal way throughout their journey. Using the behavioral segmentation techniques described above, you can help clients achieve their unique goals, maximize ROI, increase customer lifetime value, and gain deeper knowledge of their customer base.