What is audience segmentation? 

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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.

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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.

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By using customer segmentation, you can:

• Identify the best customers and focus on offering them the best treatment;
•  Improve product assortment based on your top customers’ preferences;
• Create better communication, offers, loyalty programs, experiences, and products for your power customers;
• Increase customer engagement by leveraging the best-performing channels;
• Improve and prioritize customer service team’s activity
• Target leads & accounts
• Create lookalike audiences based on your top customers and improve the results of acquisition campaigns.

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.

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Types of audience segmentation

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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:

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Demographic segmentation

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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.

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Firmographic segmentation

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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.

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Recency, frequency, monetary (RFM segmentation)

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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.

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Customer status segmentation

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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.

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Behavioral segmentation

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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.

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Psychographic segmentation

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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.

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The Three Levels of Segmentation

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In addition to there being multiple types of segmentation, there are also different levels of segmentation, which are as follows:

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Level One: Manual Segmentation

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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.

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Level Two: Rule-based Segmentation

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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.

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Level Three: Segmentation with AI/Machine Learning

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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.

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Level one use cases can work in very limited scenarios - we usually suggest focusing on rule-based and AI segmentation - Intempt supports both types.

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Segmentation use cases

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1. Use firmographic and technographic information to build a list of leads who are interested in a new integration or product feature 

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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:

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1. Existing app customer

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2. Logged in in the last 90 days

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3. Director, VP, or C-Suite title 

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You could then create a further segment with the added variable:

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4. Company size 100+ 

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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.

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2. Create segments based on engagement 

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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.

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3. Identify your ‘ideal’ customer based on MRR/CLV

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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:

• Has placed an order in the last 30 days
• The average order value of $100+
• Engagement score of 100+

You can then look at this segment to find out things like:

• Emails sent  
• Demographic and geographic information
• Time of day emails are opened
• Activities taken

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.

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4. Create segments based on geographic information and web pages viewed 

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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.

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5. Segment customers by buying behavior to identify patterns

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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:

• Typical purchase amount (e.g., buyers who never spend more than $50 on any product)
• Purchase frequency (e.g., buyers who make a purchase at least once a month)
• Seasonal buyers (e.g., buyers who only buy products during the holiday season)
• Benefit/promotional seekers (e.g., buyers who only make a purchase when there’s an offer available)

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.

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6. Segment based on psychographic data

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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:

• How customers perceive your products and services
• How to better communicate with your target audience
• What customers want – and why
• Customers’ biggest challenges
• Customers’ lifestyle
• Customers’ attitudes, motivations and values

For example, you can segment your audience based on OCEAN: a model that describes five aspects of a personality:

• Openness
• Conscientiousness
• Extroversion
• Agreeableness
• Neuroticism / Emotional Stability

Using what they know about their customers’ personalities, you can design messaging and build customer journeys that speak to those characteristics. 

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7. Segment by stages in the buying journey

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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.

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How to use segmentation with Intempt
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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:

• Rules-based segmentation
• Predictive segmentation

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Rules-based segmentation

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Step 1: Create the segment filter

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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.

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Step 2: Apply recency and frequency criteria

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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.

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Step 3: Add additional conditions

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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.

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Step 4: Adjust the operators

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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

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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.

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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) 

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Step 5: Click Save to create the segment. 

Then to view the audience navigate to Segments (in the navigation bar)

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Predictive segmentation

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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:

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•  Users that have a high likelihood to churn
•  Users that have a high likelihood to convert
•  Users that have a high likelihood of purchasing a specific product

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Step 1: Create a predictive score

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Check our other guide that in detail explains how you can create predictive scores. 

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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.

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Step 2: Add predictive score as a segment condition

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Select the predictive score attribute and opt for the filter value that matches your use case.

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Step 3: Add any additional conditions

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You can add additional behavioral or demographic/firmographic conditions to narrow the audience.

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Step 4: That’s it; save the segment!

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Wrapping up

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Properly executed segmentation allows you to group and target your audience effectively. However, the main challenges remain:

• How to group customers based on company goals
• How to get all the data required to perform cross-channel segmentation
• How to apply complex segmentation criteria to get micro-segments that matter

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.

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Join the waitlist to be the first to build your advanced segments with Growth OS.

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