The value of customer segmentation can lend to everything from decreasing CAC to increasing customer loyalty all while building better, more personalized customer experiences. In our comprehensive guide, we break down what customer segmentation is, the business value, and the different ways you can automate segmentation using Intempt.
Audience segmentation, also known as consumer or market segmentation, is the process of grouping customers in your target audience to create a more tailored marketing message. Segmentation aims to analyze the highest-value users to divide potential customers into groups based on similar characteristics.
Rather than sending the same email, ad, campaign to everyone in your database, segmentation allows you to pick and choose who receives these messages. By segmenting your audience and engaging a specific subset of customers, your business can serve customers more targeted, personalized content. This can not only increase customer conversions but improve the ROI of your marketing and advertising efforts.
By using customer segmentation, you can:
Identifying the best customer segmentation model for your business is one of the most important steps you can take for the healthy development of your company.
Common customer segmentation models have different approach ranges that vary between simple and complex. 7 of the most important segmentation types will be included here:
Gender is usually identified to create and deliver content based specifically on that segment. Parental status is also important. It can be derived directly from purchase details, or by intentionally asking for more information from customers.
Company-related data that define their target market and includes industry, number of employees, legal status, company size, financial standing, and other business-related variables.
Recency, frequency, monetary (RFM segmentation)
RFM is a method used to identify and analyze customers based on the Recency of their last purchase, the Frequency of purchases, and the Monetary amount spent. This is useful for identifying your High-Value Customers.
Customer status segmentation
An indication regarding when was the last time one of your customers made a purchase is also necessary. Active customers are usually those who have made their previous purchases within the last 12 months. Lapsed customers haven’t bought anything for over 12 months.
Past behaviors tend to indicate future behaviors, such as spending more or less during special events, trusting some specific brands more than others, or any other rather personal reasons.
This segmentation type involves paying attention to your customer’s attitudes, beliefs, or personalities. All of these should be considered very important for the communication of your brand.
In addition to there being multiple types of segmentation, there are also different levels of segmentation, which are as follows:
Level One: Manual Segmentation
Relationships managers are responsible for maintaining personal relationships with clients and prospects. Due to the unique level of insight that these relationships offer, relationships managers will oftentimes choose to manually segment customers, or even provide additional context to rule- or query-driven segments.
Level Two: Rule-based Segmentation
By identifying priority segments as an organization, business leaders can establish rules that will automatically segment customers and prospects based on the nature of their relationship with the organization. This method enables the strategic development of prioritized segments in order to identify revenue opportunities, increase wallet share, and identify the right product for the right customer at the right time.
Level Three: Segmentation with AI/Machine Learning
Once it has established rules-based segments, an organization can take segmentation to the next level by supplementing them with artificial intelligence (AI) and machine learning (ML). Organizations that leverage AI and ML models can identify and predict a customer’s needs and next actions based on the combined history of all previous interactions with every customer and prospect. A well-executed AI/ML model will contextualize data and signals across multiple different systems.
Level one use cases can work in very limited scenarios - we usually suggest focusing on rule-based and AI segmentation - Intempt supports both types.
1. Use firmographic and technographic information to build a list of leads who are interested in a new integration or product feature
You can target customers who will be interested in a new integration or product feature by segmenting your audience based on firmographic and technographic information.
For example, let’s say you're a B2C app like and you're about to launch a B2B arm, giving employers the opportunity to give their employees access to the app’s features. You could use segmentation to build a list of B2B leads from your database using a set of variables like:
1. Existing app customer
2. Logged in in the last 90 days
3. Director, VP, or C-Suite title
You could then create a further segment with the added variable:
4. Company size 100+
This would give you one list of qualified leads to announce the new offering to, and offer them the opportunity to be part of the beta test or give them a founding member discount. Plus you'll have a smaller segment of high-value, qualified leads for the sales team to reach out to directly.
2. Create segments based on engagement
Another way to slice your customer data is by looking at engagement. With Intempt, you can measure engagement by creating engagement scores and then setting them in the segment builder as a conditional filter.
For example, you may use the condition ‘Last seen more than 30 days ago’ and ‘Engagement score of less than 50’ and save it as”‘Churning users”. You may also make an audience of ‘Highly engaged’ users, and analyze what those highly-engaged users have in common.
3. Identify your ‘ideal’ customer based on MRR/CLV
SaaS businesses can create segments based on monthly recurring revenue (MRR), and eCommerce businesses can look at customer lifetime value (CLV). Doing so means you can start to identify common attributes of your top-performing customers to develop an ‘ideal’ buyer persona.
For example, let’s say you’re a DTC-exclusive chocolate brand. You can create a segment of your best customers using filters like:
You can then look at this segment to find out things like:
Or you can create a custom dashboard to track metrics that are important to your business. Over time, this will give you a deeper understanding of what your ideal customers look like.
4. Create segments based on geographic information and web pages viewed
Look at geographic location and product activity when creating segments to prioritize customers depending on their location. Intempt uses autotrack and IP enrichment to automatically assign location to any website visitor so you can do this out of the box with Intempt.
5. Segment customers by buying behavior to identify patterns
Segmenting your customers by buying behavior is a great way to understand better their purchasing habits, their needs, and why they shop with you.
Different buying behaviors to look for include:
By looking at buying behaviors, you can identify and capitalize on changes in patterns. For example, if you know that a customer usually purchases from your store regularly, but over the last few months, they have dropped off the radar, you may choose to re-engage them using a win-back email campaign.
Or, say you know that a customer who usually doesn’t spend more than $50 on a product is now spending a lot more; you could target them with products that are in a higher price bracket.
6. Segment based on psychographic data
Psychographic data can tell us a lot about our customers, and using it correctly can do wonders for engagement. By surveying your users you can uncover psychographic information to understand things like:
For example, you can segment your audience based on OCEAN: a model that describes five aspects of a personality:
Using what they know about their customers’ personalities, you can design messaging and build customer journeys that speak to those characteristics.
7. Segment by stages in the buying journey
Segment customers by what stage of the buying journey they are at to guide them down the sales funnel effectively.
Those at the top of the funnel (prospects) should receive content that enhances brand awareness, such as blog posts, podcasts, social media posts, etc.
Middle-of-the-funnel leads are at the evaluation stage and want to see how your product and/or service fixes their problem. Therefore, you should send them educational resources, webinar/event invites, surveys/quizzes, free trials/discounts/offers, etc.
The bottom of the funnel leads are close to converting. Send them content that will help them to make an informed purchasing decision: testimonials, case studies, demos, product comparison pages, etc.
Intempt makes segmentation easy - whether you want to create simple audience segments or more complicated segments using multiple variables (e.g. technographic, demographic)
Intempt gets customer data from marketing, finance, sales, and product tools into a single customer profile, meaning you can better understand your customer and their journey to create more targeted audience segments.
We will go through the two different ways to create a new audience (segment your audience) using Intempt.
You can create a new audience from either:
Step 1: Create the segment filter
Select an event, attribute, or another segment as a condition for users or accounts to enter the segment. All users that match your defined conditions will be assigned to the segment.
It’s important to note that users who meet this filter criteria will be added to the segment immediately, and anyone that matches in the future will also enter.
Step 2: Apply recency and frequency criteria
You can also define when and how frequently the selected event has been performed. You can define any fixed (like May 15 -31, 2023) or relative timeframe (like the last 30 days) - this depends on the use case.
Step 3: Add additional conditions
The main power of the segment builder is that you can add limitless conditions to build precisely the segment you need. You can combine several different events and attributes to refine your targeting.
Step 4: Adjust the operators
If you have more than one condition in the segment editor, you need to consider how each condition relates to each other. These relations are reflected with AND/OR operators
Operators in the segment editor allow you to define your segment by using complex conditions. By clicking on AND/OR operators, you can specify how each segment filter relates to the other. For example, if you have selected purchase and add-to-cart events, you can create relations with an AND operator that will require both segment filtering conditions to be valid. If you put the OR operator here, it would mean that at least one filtering condition is sufficient to assign the user to the segment.
Different from other segment builders in the industry, Intempt’s operators are not AND either OR for all conditions, instead you can use complex logic to work with bracket like (A and B) or (C and D)
Step 5: Click Save to create the segment.
Then to view the audience navigate to Segments (in the navigation bar)
Predictive segmentation is similar to rule-based, but it has a unique component - a predictive score that is used as a segmentation criterion. With predictive segmentation, you predict which users have a high likelihood of performing a specific action or reaching a lifecycle stage like:
Step 1: Create a predictive score
Check our other guide that in detail explains how you can create predictive scores.
Once you have the score generated, you can use its threshold values (like Low, Medium, and High) to use as filters in the segment editor.
Step 2: Add predictive score as a segment condition
Select the predictive score attribute and opt for the filter value that matches your use case.
Step 3: Add any additional conditions
You can add additional behavioral or demographic/firmographic conditions to narrow the audience.
Step 4: That’s it; save the segment!
Properly executed segmentation allows you to group and target your audience effectively. However, the main challenges remain:
Intempt’s data model is based on CDP, which allows you to get data from any data source. Combined with advanced rules-based segment builder and ML scoring capabilities, you can use optimize your segmentation to any level of granularity.
Join the waitlist to be the first to build your advanced segments with Growth OS.
Discover marketing workspace where you turn audiences into revenue.Learn about Intempt
From the myth of full automation to the risks of data overload and creative complacency, we tackle key challenges and misconceptions. Discover how AI can amplify, not replace, human ingenuity in marketing strategies, and learn to navigate potential biases.
When we look at lead & account data, we’re focused on the past. How did we do last quarter? What happened in H1 2023? And how does that compare to H1 2022? How fast did accounts X, Y, and Z convert? Data becomes more valuable when we use it to predict the future instead of just analyzing the past. That’s where propensity scoring comes in.
A fit & engagement score is a single metric that gives you a method of evaluating customer qualification & engagement on an individual or account level. In this guide, we’ll explain how fit & engagement scores are calculated in Intempt, why they’re so powerful, and five ways you can use them to reach your goals.
Get our newest weekly use cases and resources for
Marketing & Sales workflows
Use front-end A/B and multi-page experimentation product built for any business size. Run multiple experiments on the same page.
Deliver targeted messaging, personalized offers and recommend the most relevant content for your users.
Empower your team to collaborate with an easy-to-use visual editor without using developer time to get things up and running.
Gain insight into the impact of your experiments with statistically valid results. No complex statistical analysis required.
Go beyond client-side testing and deploy experiments on the server-side. Test and optimize content changes and even complete re-brands.
Select the right goal for each personalization and experimentation campaign to ensure you’re optimizing towards the right objectives.