Email segmentation: definition, types, and best practices

Personalise, personalise, personalise. We all know it’s the key to great email marketing, but it’s one thing to know it and another to do it. That’s where email segmentation comes in. By grouping your email list properly, you set yourself up for targeting the right people with the right content. But what exactly is segmentation in practice, what email marketing software do you need, and how do you make sure you’re doing it correctly? Let’s break it down.

Email segmentation is when you organise your email recipients into smaller groups with the purpose of sending them relevant information. These targeted groups are typically based on behavioural or demographic data, such as geographical location, previous purchases, or specific actions taken on your website.

Let’s say I’m an avid fitness enthusiast living in California who recently subscribed to a fitness newsletter. The segmentation possibilities are numerous: California residents, new subscribers, fitness enthusiasts, and perhaps even those who have shown interest in specific types of workouts.

As someone in this segmented group, I could receive emails offering exclusive California-based fitness events, personalised workout plans for beginners (considering my recent subscription), or promotions on fitness gear tailored to my interests. This targeted approach creates a more engaging and relevant experience for me.

Segments can also be broad; for example, all the people who purchased a product through your business’s Instagram site. Either way, segments allow for more personalised and targeted content. Getting it right means more conversions and ultimately, a more satisfied customer base.

Imagine email segmentation as your marketing GPS — it guides your message directly to the right destination. By tailoring content to specific segments, you’re ensuring that your audience receives information that aligns with their interests, behaviours, and preferences.

If you’re a large fashion retailer with an upcoming sale on sneakers, you can send that announcement exclusively to the segment of your audience that has shown interest in athletic footwear. But don’t discount small businesses – they are players, too.

My local bakery, for instance, recognised that I loved their cinnamon rolls. What I noticed was that instead of inundating my inbox with irrelevant promos, they sent me a coupon for a free treat during my birthday month. Whereas I used to be a casual customer, now I’m a loyal one, and if I’m honest, it’s mostly because I felt as though they cared about who I am and what I’m into.

Segmentation allows you to speak the language of your customers. Different segments might respond better to various tones, styles, or even promotional offers. Loyal customers might appreciate an exclusive early access sale, while new subscribers might appreciate a welcome discount. Have you ever had an appointment reminder from your healthcare clinic? That’s segmentation. Shopping tips and other relevant content arrives in your inbox or via text message because of your history and preferences.

Email segmentation is about having a meaningful conversation with each member of your audience. And in the world of digital marketing, meaningful conversations translate to lasting connections and business growth.

There are a few different types of email segmentations to get familiar with as you get started.

Demographic segmentation is sorting your email list based on attributes like age, income, gender identity, language, ethnicity, and more. Let’s take travel as an example. While it might sound like an oversimplification, one strategy is to start segmenting your list based on data about age groups. Younger subscribers could receive emails highlighting adventurous backpacking trips, while more mature subscribers could receive offers for relaxing cruises.

Behavioural segmentation is when you categorise your email recipients based on their actions and interactions – purchase history, website interactions, and engagement with previous emails. Let’s say your customer is a frequent purchaser of running shoes on your website. You’d segment them in a “Running Enthusiasts” category and then send emails featuring the latest running gear or exclusive promotions for the kinds of athletic shoes they like.

Now let’s say another subscriber is the opposite. They haven’t opened an email or made a purchase in a while. Once you’ve identified them as inactive, you have a chance to re-engage them through special offers aimed at reigniting interest. Now, if a customer consistently opens emails, clicks on links, and makes regular purchases, they might be placed in a “VIP” segment. This means you can send them exclusive access to sales, early product launches, or special discounts.

Geographic segmentation is when you tailor your email based on the location of your audience so that the content is (hopefully) relevant to local interests and events. If it’s wintertime, a clothing retailer might segment their audience by sending promotions to customers in extra cold regions. Shoppers in Alaska during November are likely more interested in down coats than shoppers in Miami, right? These types of targeted campaigns can lead to greater customer engagement and inspire recipients to take action.

Psychographic segmentation is all about lifestyle, interests, values, attitudes. If you’re a tech sales company and you’ve discovered a segment of gamers and another segment of professionals, you’ll want to tailor emails promoting the latest devices and accessories relevant to the gaming enthusiasts while sending updates on productivity tools and software to the segment interested in business applications. Each group feels “seen” as the content they receive resonates with their specific technology-related interests.

Customer lifecycle segmentation is about recognising where your subscribers are in the customer journey. For new subscribers, the focus is on welcoming and onboarding, providing information about your products. First-time buyers may receive targeted emails about products similar to their purchase — they view items within their price range but with encouragement to explore.

For loyal customers, the emphasis shifts to appreciation and retention, offering exclusive rewards, early access to sales, or personalised content, all of which strengthens the buyer-seller bond by rewarding their loyalty and making them feel like VIPs.

Waterfall segmentation prioritises segments based on importance, placing customers in one segment at a time. To avoid annoying customers with too many messages, individuals move through segments sequentially, starting with the highest-priority segment.

For instance, a software company might prioritise segments such as “Free Trial Users,” “Active Subscribers,” and “Upcoming Renewals.” By taking one step at a time, you won’t overwhelm anyone, and your messaging stays on point for the people who matter the most.

A marketer gathers, analyses, and acts on data. Customer relationship management (CRM) systems play a crucial role in storing and managing customer data, while analytics tools help in extracting meaningful insights.

Email marketing platforms equipped with advanced segmentation features allow for the creation of targeted campaigns, and marketing automation tools streamline workflows based on segmentation criteria. Integration between these tools ensures a seamless flow of information, empowering us to execute precise and personalised segmentation strategies.

I can’t talk about segmentation without highlighting the power of marketing artificial intelligence (AI). With its capabilities for predictive analytics and machine learning, their algorithms can analyse vast datasets to identify patterns and predict customer behaviours. This means we can get an even deeper understanding of individual customer preferences and engagement patterns.

I’ve witnessed segmentation evolve fast. What was once a complex process involving the manual conversion of campaign objectives into SQL code has transformed into user-friendly interfaces where segmentation is as simple as drag-and-drop. No more code required. And now, with generative AI driven by natural language prompts, we can create segments without the previous technical complexity.

Building a strategy can be overwhelming and you don’t want your team to get stuck. First establish clear goals. What do you want to do? Boost engagement? Increase conversions? Retain loyal customers?

When you know that and it’s time to implement your strategy, you’ll want to adopt a customer relationship management (CRM) system and use an analytics platform for gathering and organising data. (I like to start with broader segments and refine them gradually based on specific criteria.)

Sending surveys asking for feedback helps me review and update criteria regularly as customer behaviours change. And using CRM systems allows for centralised data storage and management. I also monitor social media interactions and track email engagement metrics.

Drawing insights from multiple touchpoints — marketing, commerce, sales, service, web, loyalty programs, apps, and connected devices — helps build a nuanced dataset. Having a customer data platform (CDP) connects interactions and consolidates data into unified customer profiles. It’s like having a centralised hub where I can segment based on multifaceted criteria.

For instance, I might target engaged customers who visited my site this month, made a certain number of annual purchases, have no open service cases, and are either loyalty members or non-members. The CDP’s ability to synthesise and leverage multiple data points helps me build more personalised and relevant customer interactions. This is the most modern approach out there.

Which begs the question: How do you craft email segments based on customer characteristics? I always consider demographics, purchasing behaviours, engagement patterns, and other unique identifiers specific to the business. After that, I create segments that align with my marketing goals. Sometimes that means re-engaging dormant customers, and sometimes it means targeting high-value purchasers.

AI for email marketing and machine learning is already helping identify and categorise customer segments – accelerating the process with automation and precision. It’s also opening the door to more dynamic and responsive strategies, so you’re going to want to make the transition to using it now.

AI can analyse vast datasets to predict future behaviours. The fact that there is more data science in segmentation has made it so that instead of relying solely on broad demographic data, AI-driven segmentation drills down into intricate customer behaviours, preferences, and interactions.

Propensity models leverage machine learning to predict the likelihood of specific actions, such as making a purchase or churning. For instance, a customer’s past behaviours, engagement patterns, and responses to previous campaigns become inputs for advanced algorithms. And the incorporation of lifetime value calculations and product propensity scoring utilises AI to assess the long-term value a customer brings and predict their interest in specific products.

AI helps us gain insights by tracking metrics such as open and conversion rates. By analysing key performance indicators, we can better identify trends and patterns within segments. We can experiment with variations through A/B testing.

AI allows us to move forward as we learn more about changing customer preferences. After all, you can only optimise and refine your strategies when you continuously measure its success. With AI, you can adapt to changing trends, refine audience segments, and ensure your campaigns represent relevancy.

You might’ve heard about the “cookieless future,” as privacy regulations across the globe start to make cookies obsolete. Great email segmentation can help you gain valuable first-party data — information that customers offer you directly.

By crafting personalised, relevant content based on willingly shared information, we can navigate the cookieless future while building trust and comfort with customers. It’s our job to make them more receptive to our outreach and more willing to share their data in a privacy-centric era.

AI marketing tools allow us to intuitively create segments without complex technical expertise. For instance, we can use natural language queries to instruct the system to create segments, prompting it to “Identify users who engaged with recent product launches but haven’t made a purchase.” It sounds simple, and it can be once you start playing around.

Predictive AI analyses datasets to forecast customer behaviours. We can use those predictive models to identify segments with a high likelihood of making a purchase. Think of this as proactive targeting. Both are improving continuously, and both have the power to reshape how we “do” email segmentation, especially in our data-centric, privacy-aware environment.

Email segmentation amplifies the impact of personalisation, first-party data, and the customer journey. By tailoring content based on nuanced customer segments, we enhance engagement and navigate the digital landscape with precision and relevance. Email segmentation is a potent force multiplier and should be treated as such. Use it and watch your brand build more meaningful audience relationships.