Ultimate Guide to Behavioral Segmentation

Graphic representation of a person centered among silhouettes with a dartboard target above, signifying the focus of behavioral segmentation. The background is a digital-style matrix in shades of blue, symbolizing the collection and analysis of consumer data in marketing strategies.

In this post, you’re going to learn everything you need to know about behavioral segmentation and how it’s used in marketing. If you already know what behavioral segmentation is, you might want to skip directly to the three examples.

Behavioral segmentation means grouping customers based on their online (and offline) behaviour. Your customer’s behaviour is an important criteria to consider when creating audience segments for email marketing, advertising, and onsite marketing. Your customers behaviour is highly correlated to intent (i.e. their likelihood to buy). Here are a few examples of behavioral signals used when creating behavior-based segments:

  • Website activity
  • Visited pages
  • Traffic source
  • Purchase frequency
  • Discount usage
  • Geographic location

Data points like the ones above are combined with static data like geographic location, customer status, and demographics like gender and age in order to create customer segments.

Why is behavioural segmentation important?

  1. Focused marketing efforts: By leveraging behavioral segmentation you can exclude uninterested prospects and email subscribers who have never opened an email from you, or haven’t visited your website in months. By using behavioral data in your segmentation you can make sure that your marketing resources are allocated to your most engaged prospects. Instead of casting a wide net and hoping someone will convert, you can tailor your efforts to those individuals who you know have shown a genuine interest in your products. This not only saves money but also increases the likelihood of communicating with someone who wants communication from you.
  2. Enhanced relevance and messaging accuracy: Behavioral segmentation empowers you to create relevant messaging angles that makes your content stand out. Even if you only tailor a small part of the message, you can create marketing campaigns that speak directly to the unique motivations of each segment The result? An overall better customer experience, more effective promotions, and content that makes an impact.
  3. Proactive marketing: One of the standout advantages of behavioral segmentation is its capacity to make your marketing efforts proactive rather than reactive. Traditional marketing often relies on reacting to generic customer needs or trends. However, with behavioral segmentation, you can anticipate customer actions and schedule campaigns to be triggered in advance, based on past and future behavior. Imagine a scenario where you can identify a potential buyer before they even express their intent to purchase. This level of proactive marketing not only boosts conversion rates but you will also create a better customer experience. Several of our customers are using a strategy called “Buying intent”, which is a simple but effective email or SMS automation that is triggered when an existing customer has visited a certain product or product category 2-3 times within a fixed time frame (usually 7 days). It has a very high open rate (over 50% on average) and conversion rate (up to 30% purchase rate in some cases).

The 4 ways to segment customers

There are four main types of behavioral segmentation:

  1. Behavioral segmentation uses digital behavior data focused on how customers interact with a brand’s website or email campaigns. All trackable interactions like website visits, pages visited, email link clicks, and even visitor intent can be used to create behavioral segments.
  2. Demographic segmentation divides customers into groups based on age, gender, income, education, and occupation.
  3. Psychographic segmentation uses data about lifestyle choices, opinions and beliefs. This data is collected using quizzes or in membership profiles and is also called “Zero party data”. In other words, the user is self-submitting this data in exchange for a better experience or other benefits.
  4. Geographic segmentation is data based on a customer’s location or where they live, and regional factors which may influence their purchasing behavior. This data is collected from purchases, delivery settings, membership profiles, and so on.

The main difference between these segmentation strategies is that behavioural data is highly dynamic and changes frequently.

Demographic, psychographic, and geographic data are mostly static and remain unchanged for longer periods of time. These types of segmentation strategies lets you target very broad groups of people.

How is behavioral data collected?

Data used in behavioral segmentation is mainly collected by onsite marketing software like Triggerbee and marketing automation software like Klaviyo and Custobar.

Marketing automation tools needs behavioral data from customers browsing your website in order to trigger automated email flows. But the data they collect is often limited to certain events such as completed purchases or add to carts. Meaning, if you want to use behavioral segmentation effectively you need to collect minor events from your website as well. Otherwise you can’t accurately target your customers with relevant messages.

For example, in order for an email marketing software to be able to send abandoned cart emails, it needs both an email address (the recipient) AND behavioral data that can correctly identify customers on the website who has 1) added a product to their cart, 2) has not yet made a purchase, and 3) has abandoned the website.

Here is an example of what behavioral data is and how it looks in onsite marketing platform Triggerbee:

An example of a customer profile in Triggerbee which contains behavioral data used for behavioral segmentation.

This is a screenshot of a customer profile in onsite marketing platform Triggerbee. A customer profile is basically a visitors browsing history and a summary of important events from each visit.

The first box (top) named “Onsite campaign activity” refers to all the campaigns this user has interacted with. You can see that this visitor has clicked on the campaign “Membership offer #14124” and have seen at least 3 more campaigns.

The box in the middle named “Goals” highlights important events that was triggered during a certain visit, along with a datestamp.

And at the bottom, you can see each visit with data about their traffic source and how many pages were viewed. This box is a summary of the most recent visits, and if you were to click on a visit you will see the specific activity from that particular session.

All of this data is automatically connected to a person and an email address. Triggerbee remembers this visitor, so whenever they visit the website in the future, Triggerbee knows their browsing history and can target them with new offers and promotions.

This data is also synced to the marketing automation platform this company is using, so they can behavioral data to segment their email campaigns.

The main benefit of behavioral data is that it is collected individually. Because this allows for individual targeting and is the foundation of personalization on the website, in emails, and more.

Example of how behavioral data is collected and used

Behavioral data is collected from every interaction throughout the complete customer journey.

Let’s say you’re shopping for a new bicycle. You already know which type of bike you want, so you go to Google and search:

Mountain bike for sale near me

You take a look at the results and click on one of the shopping ads. This is the first point of behavioral data that you leave to the business whose ad you clicked on.

Mountain bike google ads

On the website, you see that the bicycle you clicked on can only be picked up in-store. So you attempt to leave the website…

Product page with a bicycle for sale. Button says "Available for pickup in store"

Suddenly a popup appears that says “15% OFF ON MTB – VISIT OUR STOCKHOLM STORE TO USE YOUR COUPON”.

Popup appears: "Get 15% off on MTB"

Awesome. You sign up to get the coupon code, and the business can notify you about upcoming promotions via text messages.

Behavioral data was both collected and used in this example.

The first data point is your traffic source. In the example above, performed a search on google and clicked on one of the ads in the search results. This means the traffic source will be recorded as “Search ad”.

Once you land on the website, the onsite marketing software will check if it can match you with one of the customer profiles that already exists.

If you cannot be identified, the on-site marketing platform might trigger a popup to capture your information and identify you.

However, this popup might not be the same popup as another visitor gets. In this example, the popup appeared only when you attempted to leave the website. This is called “Exit intent” and is a behavioral-based trigger. Geotargeting was also used in this example, to make sure that only visitors living in Stockholm received the message “Visit our Stockholm Store to use your coupon”.

Once you sign up in the form, you will be become identified and assigned a “customer profile” where your website activity will be recorded and saved. All of the data in the customer profile is synced to a marketing automation system where the segmentation takes place.

The next time you return to this website, the company’s on-site marketing software will remember you and your behavior. Instead of showing you the same popup again, you might see a message like “Welcome back”.

And if you had bought that bicycle in the store, you might have seen a message asking you to leave a review of your latest purchase.

For future emails, the company can create segments of people with similar behavior to yours. Let’s say that the above popup gets 5000 signups per month. Only a fraction of those will use the discount code.

The company can then create a segment based on their visitors behavior and target people who:

  • Visited their website in the last 30 days
  • Signed up in the exit intent popup
  • Has received the discount code
  • Did not use the discount code

Where in the customer journey can you collect behavioral data?

Most retail and e-commerce brands have three main “sources” where behavioral data is collected.

  1. Self-reported data (from account registration forms, membership profiles, preference centers, etc)
  2. Purchase data (Address, name, location, product categories, coupons used, etc)
  3. Indirect tracking (Visited pages, time on page, traffic source, etc)

These sources are all connected to some type of behavior, whether it’s a customer who visited a certain page or bought a certain product during a promotion. Every important online interaction is correlated with behavioral data, digital footprints.

When a customer buys something in a store, the cashier might ask for their membership ID. And when the cashier scans your membership card, the customer’s receipt is synced and stored in your membership and customer profile that is usually in the marketing automation software and on-site personalization software.

The same thing happens online. If you are logged in on a website and make a purchase, your purchase is synced and stored on your membership and customer profile.

And when you’re not logged in, the receipt is still stored on your customer profile because it’s connected to your email in the marketing automation software.

Memberships are the main data source, because every purchase creates new data. For example, purchase frequency (how long was it since last time you made a purchase), where you shop (online or in-store), how many of your purchases are made with discount codes or during promotions, what type of products you buy, and how much you spend, are all important behavioral data points.

All of this data is then combined, filtered, and used to create behavioral segments that are used for targeting ads, email campaigns, or controlling dynamic content on the website.

Here are the most common variables used in behavioral segmentation sorted by channel type:


  • Open rate
  • Click-through rate
  • Conversion rate
  • Time of day opened/clicked


  • Identification rate
  • Logged in / out status
  • Returning visits
  • Pages visited
  • Time spent on site
  • Product categories viewed
  • Search terms used
  • Items added to cart
  • Abandoned cart items
  • Location
  • Device used
  • Traffic source
  • Buttons clicked


  • Time since last purchase
  • Purchase frequency
  • Average order value
  • Discount usage
  • Date and time
  • Location of the purchase (in-store, online)
  • Device used


  • Type of support request (e.g., technical, billing, product)
  • Frequency of support requests
  • Channel used for support (e.g., phone, email, chat)
  • Ticket resolution time


  • Store location
  • Purchase history (per store)
  • Product purchased
  • Time since last purchase

Mobile app:

  • App usage frequency
  • Time spent on app
  • Features used
  • In-app purchases
  • Push notification interaction
  • Location data

Social media:

  • Location of followers (aggregate)
  • Age of followers (aggregate)
  • Content engagement

Real examples of behavioral segmentation

Behavioral segments are created to target certain individuals with certain content. A common example of behavioral segmentation in action is retargeting ads. Retargeting ads are ads shown to people who have visited a website and then left. Sometimes the retargeting ads are only shown to people with a certain cart value or who have spent X amount of time browsing key pages. This ensures that a business only spends money showing ads to visitors which they deem have a high intent of buying.

But behavioral segmentation can also be used to create segments for onsite promotions, email campaigns, and dynamic website content. Here are three real-life examples:

Zalando uses behavioral segmentation to display size recommendations on product pages

Zalando, one of the largest fashion retailers in Europe, takes shopping online to a new level. They use behavioral segmentation on their website to create a personalized customer experience for every visitor. If you’ve bought from them before, they remember what you bought and how you rated the fit. Next time you’re visiting their website and if you are logged in, they suggest the right size for you. But if you’re logged out or an anonymous visitor, they keep it simple without size recommendations until you’ve made some choices. This way, Zalando’s site changes based on who’s shopping.

Screenshot showing two scenarios of Zalando’s product pages. Top: For logged-in users, a recommended size for a pair of jeans is displayed, based on past purchases. Bottom: For new or not-logged-in users, no size recommendation appears. Text explains Zalando personalizes size suggestions for returning users but not for anonymous visitors.


Amazon uses behavioral segmentation to send out browse abandonment emails

Amazon is the king of website personalization and delivering a great customer experience. Their website might not win any design awards, but they have a superior customer experience strategy that few can compete with. They use behavioral segmentation for a lot of things, and one of those things are sending out browse abandonment emails. Amazon tracks your behavior and which products you have visited. If you haven’t bought any of the products you visited, they will send you a browse abandonment email to win you back and get you back into shopping mode.

Amazon uses behavioral segmentation to send out browse abandonment emails.

CLN Athletics uses behavioral segmentation to recover abandoned carts 

CLN Athletics is a small athletics apparel brand in Sweden. They use behavioral segmentation to recover abandoned carts. By tracking important events on their website such as the “Add to cart”-event, allows them to trigger email automations and onsite campaigns based on their visitors activity. If you add a product to your cart, visit the checkout, and attempt to leave the website before purchasing… they trigger an exit intent popup with a 20% offer to recover the potentially lost cart.

How CLN Athletics uses behavioral segmentation to recover lost carts

Use cases for behavioral segmentation:

Creating email segments

Behavior segmentation is very useful for creating email segments, both for individual campaigns and when setting up triggers for automated email flows. You can target people who have recently been active on your website or has performed a specific action on your website. For example, if you have a loyalty program you want to have an audience segment that consists of “all members” which have a bunch of sub-segments like “Gold members”, “Inactive members”, “Recently purchased”, “Discount buyers”, etc. Some of these segments should definitely be based on behavior since it will help you uncover new profitable segments.

In our experience having some of the largest retailers in the Nordics as our customers, one of the most profitable segments is Existing members who have visited a product or product category several times within 7 days. A high frequency of visits to one or two products usually indicates a strong interest and intent to buy.

Creating audiences for ads

There are two main approaches to advertising within ecom and retail. 1) Segment based, which means you build an audience in your advertising tool whether it be Meta ads or TikTok ads, and then run ads to a specific segment. Ads running to these audiences will most likely be retargeting ads. 2) Creative segmentation. As the algorithms get better, a lot of brands that rely heavily on Meta ads use their creative to segment their audience. The idea is that a person interested in a certain topic wants to see relevant content for these topics – which your ad is in.

Creating segments for onsite personalization

On-site personalization needs behavioral audience segments to work. Each individual person who visits a website has their own customer profile, and a retailer can show certain types of promotions or dynamic content to visitors within a specific audience.

This is an extremely effective way to promote any type of campaign for a high-intent audience, or make returning visitors pick up where they left off the last time.

It means that the top section of a website can show different content for different visitors. If visitor A is a member in your rewards program, they might see a promotion for the latest membership offers. And if visitor B is a first time visitor they might be offered 10% off for signing up as a new member.

Behavioral Segmentation and privacy

Do not forget that all the data you collect about your customers and visitors needs to be specified in your privacy policy and cookie policy.

The introduction of the General Data Protection Regulation (GDPR) in the EU has had a major effect on behavioral segmentation practices.

GDPR, established in 2018, sets strict guidelines for collecting and processing personal information from individuals in the EU and dictates how customer data should be handled.

Behavioral segmentation often collects sensitive data so ensure that you are complying with local laws and regulations.

Here are some general guidelines, however these could change at any time and should not be considered legal advice.

  • Consent and Clarity: GDPR mandates that explicit consent must be obtained from individuals before their personal data is used for behavioral segmentation. This consent should be informed, meaning that individuals must be clearly aware of what data is being collected and its intended use. Companies must provide clear, understandable privacy notices.
  • Limiting Data Collection: The regulation emphasizes collecting only the data that is necessary for a specified purpose. For behavioral segmentation, this means gathering only the essential data needed for creating customer segments and not using it for other unrelated purposes.
  • Rights of Individuals: Individuals have more control over their data, including rights to access, amend, and delete their data. In behavioral segmentation, this means customers can see the collected data on them and can choose to opt-out or request its deletion, impacting the data available for segmentation.
  • Securing Data: GDPR requires companies to implement suitable measures to secure personal data. For behavioral segmentation, this means robust security measures must be in place to protect the data used in creating customer segments.
  • Data Transfer Restrictions: The regulation restricts transferring data outside the EU to maintain the protection level set by GDPR. For companies practicing behavioral segmentation, compliance is crucial when transferring data internationally.
  • Caution with US-Based Software Companies: When transferring data to US-based software companies, it’s essential to ensure they have the necessary permissions under GDPR. The Data Privacy Framework website (https://www.dataprivacyframework.gov/s/participant-search) offers a search tool to check if a company is compliant.
  • Demonstrating Compliance: Companies must show their adherence to GDPR, which includes maintaining detailed records of data processing activities, like those for behavioral segmentation, and conducting impact assessments where necessary.

Impact on Marketing Practices:

  • Strategy Adjustment: You need to align your behavioral segmentation strategies with GDPR. This involves securing clear consent, being transparent about data usage, and respecting user choices and rights.
  • Emphasis on Data Quality: The focus shifts from the quantity to the quality of data due to pretty harsh consent requirements. A smaller, GDPR-compliant dataset can lead to more precise and effective segmentation.
  • Building Trust and Customer Relations: Adhering to GDPR can strengthen trust between companies and customers. Ethical data handling can enhance customer loyalty and improve the company’s reputation.

Aligning behavioral segmentation with GDPR regulations is not only about avoiding fines but also about cultivating a trusting and engaged customer base.


Behavioral segmentation offers marketers a dynamic and data-driven approach to understand customer behavior and tailor marketing efforts effectively, resulting in more personalized and engaging customer experiences. Unlike demographic or geographic segmentation, behavioral segmentation is dynamic and frequently changing. Data for behavioral segmentation is collected through web analytics and marketing automation software, allowing businesses to target the right audience with personalized messages.

First-party data, such as web activity and email interactions, plays a crucial role in this process. So make sure you are collecting first-party data rather than just looking at analytics. Because that is what will let you target your audience when the Google’s and Meta’s of the world will keep this information from you.


  • Our own experience working with 400+ of the largest online retailers in Scandinavia (KICKS, Bubbleroom, Jula, K-Rauta, iDeal of Sweden, Caia, etc.).
  • https://ischolar.sscldl.in/index.php/IJCSIT/article/view/186146
  • https://www.emerald.com/insight/content/doi/10.1108/03090560610702830/full/html
  • https://www.sciencedirect.com/science/article/abs/pii/S0167923612001327
  • https://hrcak.srce.hr/file/196731
Felix Langlet
Felix Langlet

Felix is a self-taught marketer and the head of marketing at Triggerbee. He is specialized in SEO, content marketing, and copywriting. Outside of work, you can find him spending time with his family, listening to podcasts, or watching documentaries.

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