Marketers must reach the right person at the right time with the most relevant messaging. Customers now expect personalised experiences, which means generic campaigns won’t work. Audience segmentation is the key to doing this.
This isn’t an easy process because there are many types of audience segmentation. The wrong approach or poor data management can lead to irrelevant messaging or lost customer trust.
This article breaks down the most common types of audience segmentation with examples highlighting their usefulness and information on segmenting campaigns without breaking data regulations.
What is audience segmentation?
Audience segmentation involves dividing a customer base into distinct, smaller groups with similar traits or common characteristics. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.
It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.
Consider this: an urban office worker and a rural farmer have vastly different needs. Targeted marketing efforts aimed at agriculture workers in rural areas can stir up interest in farm equipment.
Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. For example, they would run a golf club ad in a golf magazine, not the national newspaper.
Now that businesses have more customer data, audience segments can be narrower and more specific.
Why audience segmentation matters
Hyken’s latest Customer Service and CX Research Study revealed that 81% of customers expect a personalised experience.
These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.
They expect relevant product recommendations — like a shoe polishing kit after buying nice leather loafers.
Without audience segmentation, customers can get frustrated with post-sale activities. For example, the same follow-up email won’t make sense for all customers because each is at a different stage of the user journey.
Some more benefits that audience segmentation offers:
- Personalised targeting is a major advantage. Tailored messaging makes customers feel valued and understood, enhancing their loyalty to the brand.
- Businesses can understand users’ unique needs, which helps in better product development. For example, a fitness brand might develop separate offerings for casual exercisers and professional athletes.
- Marketers can allocate more resources to the most promising segments. For example, a luxury skincare brand might target affluent customers with premium ads and use broader campaigns for entry-level products.
8 types of audience segmentation
There are eight types of audience segmentation: demographic, behavioural, psychographic, technographic, transactional, contextual, lifecycle and predictive segmentation.
Let’s take an in-depth look at each of them.
Demographic segmentation
Demographic segmentation divides a larger audience based on data points like location, age or other factors.
The most basic segmentation factor is location, which is critical in marketing campaigns. Geographic segmentation can use IP addresses to separate marketing efforts by country.
But more advanced demographic data points are becoming increasingly sensitive to handle, especially in Europe, where the GDPR makes advanced demographics a more tentative subject.
It’s also possible to use age, education level, and occupation to target marketing campaigns. It’s essential to navigate this terrain thoughtfully, responsibly, and strictly adhere to privacy regulations.
Potential data points:
- Location
- Age
- Marital status
- Income
- Employment
- Education
Example of effective demographic segmentation:
A clothing brand targeting diverse locations must account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, promoting lightweight or summer attire would be more effective in warmer climates.
Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections:
Each collection features differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — just single out the factors when building a campaign. And it’s unnecessary to rely on data mining to get information for segmentation.
Consider incorporating a short survey into email sign-up forms so people can self-select their interests and preferences. This is a great way to segment ethically and without the need for data-mining companies. Responses can offer valuable insights into audience preferences while enhancing engagement, decreasing bounce rates, and improving conversion rates.
Behavioural segmentation
Behavioural segmentation segments audiences based on their interaction with a website or an app.
Potential data points:
- Page visits
- Referral source
- Clicks
- Downloads
- Video plays
- Conversions (e.g., signing up for a newsletter or purchasing a product)
Example of using behavioural segmentation to improve campaign efficiency:
One effective method involves using a web analytics tool like Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, identify what can significantly enhance visitor conversions.
For example, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.
Then, set up a conditional CTA within the video player. Make it pop up after the user finishes watching the video. Use a specific form and assign it to a particular segment for each case study. This way, you can get the prospect’s ideal use case without surveying them.
This is an example of behavioural segmentation that doesn’t rely on third-party cookies.
Psychographic segmentation
Psychographic segmentation involves segmenting audiences based on interpretations of their personality or preferences.
Potential data points:
- Social media patterns
- Follows
- Hobbies
- Interests
Example of effective psychographic segmentation:
Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. However, for rugby athletes, the ad is very relevant.
Brands that want to avoid social platforms can use surveys about hobbies and interests to segment their target audience ethically.
Technographic segmentation
Technographic segmentation separates customers based on the hardware or software they use.
Potential data points:
- Type of device used
- Device model or brand
- Browser used
Example of segmenting by device type to improve user experience:
After noticing a serious influx of tablet users accessing their platform, a leading news outlet optimised their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better tablet-readability. These changes gave users a more enjoyable reading experience tailored precisely to their device.
Transactional segmentation
Transactional segmentation uses customers’ past purchases to match marketing messages with user needs.
Consumers often relate personalisation with their actual transactions rather than their social media profiles.
Potential data points:
- Average order value
- Product categories purchased within X months
- Most recent purchase date
Example of effective transactional segmentation:
Relevant product recommendations and coupons are among the best uses of transactional segmentation. These individualised marketing emails can strengthen brand loyalty and increase revenue.
A pet supply store identifies a segment of customers who consistently purchase cat food but not other pet products. To encourage repeat purchases within this segment, the store creates targeted email campaigns offering discounts or loyalty rewards for cat-related items.
Contextual segmentation
Contextual segmentation helps marketers connect with audiences based on real-time factors like time of day, weather or location. It’s like offering someone exactly what they need when they need it the most.
Potential data points:
- GPS location
- Browsing activity
- Device type
Examples of contextual segmentation:
A ride-hailing app might promote discounted rides during rush hour in busy cities or suggest carpooling options on rainy days. Similarly, an outdoor gear retailer could target users in snowy regions with ads for winter jackets or snow boots.
The key is relevance. Messages that align with what someone needs at that moment feel helpful rather than intrusive. Businesses need tools like geolocation tracking and real-time analytics to make this work.
Also, keep it subtle and respectful. While personalisation is powerful, being overly intrusive can backfire. For example, instead of bombarding someone with notifications every time they pass a store, focus on moments when an offer truly adds value — like during bad weather or peak commute times.
Lifecycle segmentation
Lifecycle segmentation is about crafting interactions based on where customers are in their journey with a brand.
Lifecycle segmentation isn’t just about selling; it’s about building relationships. After a big purchase like furniture, sending care tips instead of another sales pitch shows customers that the brand cares about their experience beyond just the sale.
This approach helps brands avoid generic messaging that might alienate customers. By understanding the customer’s lifecycle stage, businesses can tailor their communications to meet specific needs, whether nurturing new relationships or rewarding long-term loyalty.
Potential data points:
- Purchase history
- Sign-up dates
Examples of effective lifecycle segmentation:
An online clothing store might send first-time buyers a discount code to encourage repeat purchases. On the other hand, if someone hasn’t shopped in months, they might get an email with « We miss you » messaging and a special deal to bring them back.
Predictive segmentation
Predictive segmentation uses past behaviour and preferences to understand or predict what customers might want next. Its real power lies in its ability to make customers feel understood without them having to ask for anything.
Potential data points:
- Purchase patterns
- Browsing history
- Interaction frequency
Examples of effective predictive segmentation:
Streaming platforms are great examples — they analyse what shows and genres users watch to recommend related content they might enjoy. Similarly, grocery delivery apps can analyse past orders to suggest when to reorder essentials like milk or bread.
B2B-specific: Firmographic segmentation
Beyond the eight main segmentation types, B2B marketers often use firmographic factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.
Potential data points:
- Annual revenue
- Number of employees
- Industry
- Geographic location (main office)
Example of effective firmographic segmentation:
Startups and well-established companies will not need the same solution, so segmenting leads by size is one of the most common and effective examples of B2B audience segmentation.
The difference here is that B2B campaigns involve more manual research. With an account-based marketing approach, you start by researching potential customers. Then, you separate the target audience into smaller segments (or even a one-to-one campaign).
Audience segmentation challenges (+ how to overcome them)
Below, we explore audience segmentation challenges organisations can face and practical ways to overcome them.
Data privacy
Regulations like GDPR and CCPA require businesses to handle customer data responsibly. Ignoring these rules can lead to hefty fines and harm a brand’s reputation. Customers are also more aware of and sensitive to how their data is used, making transparency essential.
Businesses should adopt clear data policies and provide opt-out options to build trust and demonstrate respect for user preferences.
Privacy-focused analytics tools can help businesses handle these requirements effectively. For example, Matomo allows businesses to anonymise user data and offers features that give users control over their tracking preferences.
Data quality
Inconsistent, outdated or duplicate data can result in irrelevant messaging that frustrates customers instead of engaging them.
This is why businesses should regularly audit their data sources for accuracy and completeness.
Integrate multiple data sources into a unified platform for a more in-depth customer view. Implement data cleansing processes to remove duplicates, outdated records, and errors.
Segment management
Managing too many segments can become overwhelming, especially for businesses with limited resources. Creating and maintaining numerous audience groups requires significant time and effort, which may not always be feasible.
Automated tools and analytics platforms can help. Matomo Segments can analyse reports on specific audience groups based on criteria such as visit patterns, interactions, campaign sources, ecommerce behaviour, demographics and technology usage for more targeted analysis.
Detailed reporting of each segment’s characteristics can further simplify the process. By prioritising high-impact segments — those that offer the best potential return on investment — businesses can focus their efforts where they matter most.
Behaviour shifts
Customer behaviour constantly evolves due to changing trends, new technology and shifting social and economic conditions.
Segmentation strategies that worked in the past can quickly become outdated.
Businesses need to monitor market trends and adjust their strategies accordingly. Flexibility is key here — segmentation should never be static.
For example, if a sudden spike in mobile traffic is detected, campaigns can be optimised for mobile-first users.
Tools and technologies that help
Here are some key segmentation tools to support your efforts:
- Analytics platforms: Get insights into audience behaviour with Matomo. Track user interactions, such as website visits, clicks and time spent on pages, to identify patterns and segment users based on their online activity.
- CRM systems: Utilize customer records to create meaningful segments based on characteristics like purchase history or engagement levels.
- Marketing automation platforms: Streamline personalised messages by automating emails, social media posts or SMS campaigns for specific audience segments.
- Consent management tools: Collect and manage user consent, implement transparent data tracking and provide users with opt-out options.
- Survey tools: Gather first-party data directly from customers.
- Social listening solutions: Monitor conversations and brand mentions across social media to gauge audience sentiment.
Start segmenting and analysing audiences more deeply with Matomo
Modern consumers expect to get relevant content, and segmentation can make this possible.
But doing so in a privacy-sensitive way is not always easy. Organisations need to adopt an approach that doesn’t break regulations while still allowing them to segment their audiences.
That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. It provides features for privacy control, enables cookieless configurations, and supports compliance with GDPR and other regulations — all without compromising user privacy.
Take advantage of Matomo’s 21-day free trial to explore its capabilities firsthand — no credit card required.
