By Team ZineOne October 1, 2020
Creating a More Personalized Customer Experience
The concept of marketing segmentation emerged in the 1950s. Since its emergence, segmentation has been one of the most researched topics in marketing literature. Today, market segmentation is a common practice throughout every industrialized country. Goods and services cannot be produced and sold without first considering customer needs and recognizing the diversity of those needs.
As production processes became more flexible, and consumer wealth led to more diversified demand, companies that started identifying specific needs of groups of customers were able to develop the right offer and gained a competitive demand.
Over the years, marketing segmentation evolved into four key types of market segmentation:
- Behavioral Segmentations
Demographic segmentation is focused entirely on who the customer is. If you’re running a B2C company, for example, demographic traits you’d include would be age, education, gender, occupation, family status, and income.
Geographic market segmentation allows marketers to split their audience based on location. The core traits and segments that are considered with geographic segmentation include region, continent, country, city, and district.
The psychographic market segmentation is aimed at separating consumers based on personalities. Traits within this segmentation include lifestyle, attitudes, interests, and values.
Behavioral segmentation divides customers based on previous behavior that they’ve exhibited with your brand. Some of the primary traits within this segmentation include product knowledge, purchase patterns, digital behavior, previous purchases, awareness of your business, and product rating.
In this article, our focus is going to be on Behavioral Segmentation. Although all four types of marketing segmentation are used by today’s marketers, with the combination of AI and machine learning, companies that are investing in behavioral segmentation are finding success that was thought not possible not that long ago.
We’re going to take a deep dive into what behavioral segmentation is, benefits, types, examples, and how it’s creating a better-personalized experience that benefits both the consumer and the business.
What is Behavioral Segmentation?
Behavioral segmentation categorizes customers according to behavior patterns as they interact with a company or brand. As the name suggests, behavioral segmentation studies the behavioral traits of consumers. It looks at a consumer’s knowledge of, attitude towards, use of, likes/dislikes of, and response to a product, service, promotion, or brand.
What is the Goal of Behavioral Marketing?
Behavioral segmentation does not exist in a bubble. Demographic, geographic, and psychographic segmentation often correlate to behavioral data. This means that behavioral data is often used to confirm conclusions about other types of segmentation.
What are the Benefits of Behavioral Segmentation?
Behavioral targeting comes with four key advantages for marketers:
- Personalization: Understand how to target different groups of customers with different offers, at the best time, through a preferred channel. Personalization allows businesses to nurture customers based on their unique needs along their buying journey.
- Predict: Historical behavior patterns help predict and influence future behaviors and outcomes.
- Prioritize: Identifying high-value prospects with the greatest potential enables companies to make smarter decisions about allocating time, budget, and resources.
- Performance: Monitoring growth patterns and change in segments over time gives clarity on business health and track performance against goals.
Why is Behavioral Segmentation Important for Marketers?
Dividing the market into smaller segments, each with a common variable, allows a business to better optimize the use of time and resources. When all consumers experience the same marketing message, it only works on a small percentage. When brands better understand a particular market and divide it appropriately, they’re able to implement personalization to meet the specific needs and desires of consumers.
Types of Behavioral Segmentation Explained
It seems the “types” of behavioral segmentations keep growing the more marketers use it. In the beginning, there were four types of behavioral segmentation referenced in the literature, then six types followed. Here we’re going to look at the top eight behavioral segmentation types. It’s important to note that one or more of the types of segmentation can be utilized at the same time or combined with other types.
- Purchasing Behavior
- Benefits Sought
- Customer Journey
- Usage Behavior
- Occasion or Timing
- Customer Satisfaction
- Customer Loyalty
Purchasing Behavior Segmentation
Purchase behavior-based segmentation is about identifying trends in how different customers behave while making a purchase decision.
Purchasing behavior helps marketers understand:
- How different consumers approach a purchase decision
- The complexity of the purchasing process
- The role the consumer plays in the purchasing process
- Barriers along the path to purchase
- Behaviors that are most and least predictive of a customer making a purchase
Predictive Behavioral Segments
By implementing machine learning capabilities to analyze consumer behavior throughout the customer journey, marketers can more accurately identify important patterns over time. Companies are now building predictive segments based on the likelihood of different customers making specific purchases.
There are two ways that past behavior can predict future outcomes:
- Using past purchases to predict future ones
- Using behavior along the path-to-purchase to predict the likelihood of a customer following through to the completion of a purchase
Digital Behavior Patterns
Another approach is using patterns in digital behavior to understand the variety of ways different customers approach the buying process in order to remove common obstacles that need to be removed from the path to purchase.
There are a variety of ways to approach this, depending on the type of business. Using buyer personas is a great way to form assumptions based on online interactions. Some examples may include:
- The “Price-conscious” buyer – someone who is looking for the lowest price possible.
- The “Smart” buyer – a meticulous researcher who wants to understand every variable and feature before committing.
- The “Risk-averse” buyer – a cautious, economically-careful shopper, who struggles to make a decision on a purchase without insurance and a solid return policy.
- The “Needs-proof” buyer – a shopper who needs social proof from peers that the product is popular and performs well.
- The “I’ll get it later” buyer – the customer that procrastinates on purchases and lacks urgency.
- The “Persuadable” buyer – an impulse shopper who is highly susceptible to cross-sell offers.
Benefits Sought Segmentation
As a prospect researches a product or service, their behavior can reveal valuable insights into which benefits, features, values, or use cases are the best motivating factors influencing their purchase decision.
When a customer shows a higher value on one or more benefits over the others, these benefits provide valuable feedback as the biggest motivating factors that drive a purchase decision.
A simple example would be a customer who buys toothpaste for different reasons:
This example can apply to almost any business in any industry. For B2B software, the benefits may be specific features or capabilities, ease-of-use, speed or accuracy-related benefits, or key integrations with other tools.
Two prospects may look similar in demographic traits and have near-identical user personas, but they may have very different values in terms of which benefits and features are most and least important.
If you have four customers who are all seeking different primary benefits and your marketing message to all features the same benefit, then you’re effectively missing the mark with 75% of your marketing communications.
When you understand 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.
The Customer Journey
Building behavioral segments throughout the customer journey stage allow companies to align communications and personalize experiences for increased conversion at every stage. Additionally, businesses can discover stages where customers stop progressing and identify obstacles and opportunities for improvement.
Identifying where in the customer journey a prospect is currently at is not always easy. A single customer interaction is not enough to pinpoint which location within the journey stage a customer is currently in.
Customers in all different stages interact and engage with content and experiences designed for every stage, across all channels, and at different times, 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 and building weighted algorithms based on patterns of behavior over time.
The diagram above shows the behavior of a prospective customer over a period of 14 days. The prospect is in the consideration stage of the customer journey, but his behaviors occur in random order and do not happen in a linear way from stage to stage. This is a realistic view of what customer behavior may look like in a given timeframe as they interact with a brand.
As you can see, attempting to identify which journey stage a prospect is in based on one or two behaviors can result in easily making a wrong assumption. However, when you weigh 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 the prospect in the chart above.
Knowing how, how often, and how much a customer is using your product or service is extremely valuable.
If you’re a B2B SaaS company, it would be valuable to know how frequently customers are logging-in and using your software. You’d also want to know how much time they use it, how they use it, the features they use most, and how many users from the company are using it.
Usage behavior is a strong predictive indicator of loyalty and lifetime value.
Segments Based on Frequency of Usage
- Heavy Users – customers that spend the most time using your solution or product and make the most frequent purchases. They are a company’s most avid and engaged customers.
- Average Users – customers that semi-regularly use or purchase, but not frequently. Use is often time or event-based.
- Light Users – customers that use or purchase much less compared to other customers. Depending on your business, they could even be one-time users.
Usage-based behavioral segments are valuable for understanding why certain types of customers become heavy or light users. When companies segment this way, they can test different actions and approaches to increase usage from existing customers and attract new customers that are likely to match the same usage behavior patterns as heavy users.
Segments Based on Quality of Usage
While the frequency of usage is valuable, high usage does not always translate into the most value delivered. For example, a SaaS customer might have a lot of product usage behavior, but might not be as satisfied as high use data may indicate. They may be:
- Not using the product as effectively as they could be
- Only leveraging a fraction of the most important features
- Only using the product because they have to, but are unhappy and looking for an alternative
This is just a warning that you must take a diversified approach to behavioral segmentation and not only rely on one type.
Occasion or Timing-Based Behavior
When are customers most likely to make a purchase or engage with a brand?
Occasion and timing-based segments refer to both universal and personal occasions.
- Universal occasions – Holidays and seasonal events are good examples, where consumers are more likely to make certain purchases based on the holiday season or time of year.
- Rare-personal occasions – are related to individual customers, but are more spontaneous and more difficult to predict. Attending a friend’s wedding would be an example of a rare-personal occasion.
- Recurring-personal occasions – are purchasing patterns for a customer that consistently repeats over a period of time. Think of birthdays, anniversaries, or daily purchases like a cup of coffee each morning.
A famous example of this type of behavioral segmentation was when Target made headlines after using point-of-sale data to figure out when to market diapers and other baby products to women based on when they had previously purchased pregnancy tests. While this example is accompanied by the uncertainty of the test result, another scenario could be when an expecting mom signs up for baby registry. The business can then market baby products based on the expected birth date.
Surveys and customer feedback mechanism are valuable methods for helping companies gauge customer satisfaction, but alone, they are not enough because:
- Only a fraction of customers participate.
- There is too much time between data collection points.
- They do not accurately reflect the customer’s changing needs and experiences at different stages of the customer journey.
Understanding customer behavior is a much more accurate and reliable way for measuring satisfaction, especially with data that can be captured in real-time and at every stage of the customer journey.
There are many data sources available through which customer behavior can analyze to measure a customer’s satisfaction at any given time. Call centers, support portals, help forums, billing, and CRM systems, and social media are just some examples of where valuable data about customer satisfaction lives.
When you segment customers by satisfaction, you can decide on the appropriate set of actions for each segment and quantify and prioritize them by their potential business impact.
When you know who your most loyal customers are, you can maximize their value and find more customers like them.
Loyal customers are extremely valuable. They cost less to retain and can become a brand’s biggest advocate. Using behavioral data, companies can segment customer loyalty and identify the most loyal customers and better understand their needs.
Loyal customers are great candidates for programs that offer special treatment and privileges like exclusive reward programs. Classic examples include the frequent flier program airlines offer, platinum credit card programs, and preferred guests at hotels and casinos.
Using customer loyalty, behavioral segmentation can yield answers to valuable questions like:
- What factors and behaviors along the customer journey lead to loyalty?
- Which customers are the best candidates for loyalty programs?
- How can companies keep the most loyal customers happy and maximize the value they get from them?
What are your customers interested in?
Understanding customers’ personal and professional interests is key for personalization, customer engagement, and delivering value.
Interest-based behavioral segmentation delivers personalized experiences that keep customers coming back.
Netflix, Amazon, and Spotify use recommendation engines for suggesting content and products based on behavioral interests. A big advantage of using interest behavior is the ability to connect specific interests with other potential related interests.
Machine learning helps to scale the process. As more customers engage and interact, more interest-based behaviors become discoverable over time.
Behavioral Segmentation Use Cases
At this point, you have a good understanding of the power of behavioral segmentation and why so many companies are using it. You’re familiar with the top eight types of behavioral segmentation. Let’s take a look at some very successful companies and see how they use behavioral segmentation in their marketing efforts.
Coca-Cola Behavioral Segmentation Marketing
Everyone has heard of the largest soft drink company in the world. The global giant has been marketing for a long time, but they still have to keep up with the latest and best ways to market in order to stay competitive.
So how is Coca-Cola using behavioral segmentation?
- Loyalty Status – The company is constantly measuring loyalty using social data and data gathered from the activity on Coke’s website.
- Occasions – Coke puts a lot of focus on understanding the most popular occasions for consumers to drink coke. Typically this is related to seasons, events, or types of meals.
- Benefits Sought – Coke focuses on what their customers are looking for when they purchase a Coke. It could be refreshing taste, product uniqueness, the brand look, or even promotional benefits.
Netflix Behavioral Segmentation Marketing
The world’s largest streaming service is perhaps the most proficient brand when it comes to behavioral segmentation. Personalization begins as soon as a user creates an account and streams their first show. Once a user engages, Netflix’s behavioral segmentation efforts kick in.
Netflix uses an algorithm that allows them to consistently and accurately A/B test and experiment with viewer preferences. The algorithm dictates the homepage layout, recommended content, and the visuals for each recommendation. Netflix actually personalizes the image you see based on the actors, actresses, or genres that it thinks you like.
Airbnb Behavioral Segmentation Marketing
Airbnb is the world’s largest accommodation-sharing site. Airbnb uses machine learning to generate insights from user reviews, which are then displayed at the top of their webpage. Airbnb also uses consumer behavior data and preferences to pair hosts and guests. They’re able to do this by using a specialized search algorithm. The algorithm analyzes data from both Airbnb and guests and offers matches based on similarities.
Behavioral Segmentation Recap
We’ve come a long way since the early days of marketing segmentation. Today, grouping users by their behaviors is the clearest way to understand how you can meet their needs and desires consistently. Using the power of AI and machine learning to predict what consumers are looking for, and where they’re at in the customer journey is the only way to stay competitive in today’s in-the-moment marketing paradigm. The most competitive companies today deliver a personalized experience to each customer every step of the way.
Create Memorable Consumer Engagement through predictive intelligence!
Request a Demo
We look forward to getting to know your business!