You can build the perfect funnel, invest in expensive advertising, and hire the best copywriters, but as long as you ignore user behavior, all this works at half-strength. Behavioral segmentation is not a trend, but the basis of a modern marketing strategy. And if you work in SaaS, e-commerce or digital products, it is behavior, not the user profile, that will tell you who is really ready to buy, who is just browsing, and who is already leaving for competitors.
What is Behavioral Segmentation
Behavioral segmentation is a way to group users not by who they are, but by how they behave. What do they click, how often do they come back, at what stage do they drop off when they buy, and what do they ignore? This data cannot be collected in a survey but is collected daily in the background with your analytical tools. Unlike demographic segmentation, where you rely on gender, age, or location, behavioral segmentation looks deeper: it answers the questions "What is this user doing right now" and "What action is he most likely to take next".
For SaaS, this is especially critical. For example, a user has completed onboarding but has not configured a key feature - this is already a behavioral signal that requires a personalized trigger. Or a client returns to the pricing page but does not upgrade the plan - this means hesitation that should be responded to.
In the world of AI, behavioral segmentation becomes smarter: instead of manually setting up segments, you can let algorithms find hidden patterns. For example, AI in M1-Project analyzes user behavior in the ICP generator and offers personalized tips when it sees that the user is stuck or has not completed the segmentation.
Behavior is the only type of data that cannot be faked. You can write in the profile that you are interested in business tools, but if you open TikTok instead of the demo, it will say more than any words.
Key Types of Behavioral Segmentation
When you start using behavioral segmentation in online marketing, it’s important to understand that there’s a pattern behind every click, missed email, or page view. These patterns can be categorized, and that’s what gives you the ability to build precise segments that convert better than any “personas” in a brand book.
User behavior is a multi-layered structure. It includes everything from the frequency of interactions with your product to reactions to specific triggers. Here are seven key types of behavioral segmentation that work in B2B and B2C, and are especially effective when paired with AI:
1. Purchasing Behavior
Perhaps the most obvious, but also one of the most powerful categories. This includes whether a person has purchased anything before, how many times, how much they spend, how quickly they make decisions. If you see that one customer segment often buys at the end of the quarter, this is a signal to launch campaigns with a limited validity period. If someone regularly buys only with a promo code, do not waste full-price offers on them.
SaaS example: a client activates a free trial version, but does not switch to a paid plan - this is a signal for a trigger with a personalized email series.
2. Occasion & Timing
This is about when the user interacts with the product. In B2C, this could be the holiday season, payday, or Saturday morning. In B2B, this is the end of the financial quarter, preparation for the launch of a new product, or a change in team.
AI can identify windows of increased interest and recommend launching campaigns on specific days or even hours when the probability of conversion is higher.
3. Benefits Sought
Understanding user motivation is the key to personalization. For example, one segment of customers chooses your SaaS product because of the speed of implementation, and another - because of deep customization. This behavior can be recorded at the level of the pages they read, requests in the support chat, or actions in the product.
If you use Social Media Content Generator from M1-Project, and the system notices that the user always chooses templates for LinkedIn, not Instagram, it means that business positioning is important to him, and your communication should talk about it.
4. Loyalty
The behavior of loyal customers is fundamentally different from new ones. They participate in surveys more often, are more willing to leave reviews, respond to referral programs, and read landing pages less. Do not confuse them with newcomers and do not send them a welcome series - they already trust you and are waiting for insider offers.
Example: users who returned to M1-Project after 60+ days are more likely to respond to offers of “new features” and beta access than to standard offers.
5. Buyer Journey Stage
Content and triggers that work for those who just opened your site for the first time are absolutely not suitable for those who have already participated in the demo twice. Behavior helps to understand what stage the client is at: recognition, consideration, and decision-making.
AI is especially effective here because it can dynamically update the user’s stage and change recommendations, offers, and communication channels.
6. Usage Frequency
If a user logs into the product every day, they are in the retention group. If once a month, they need to be “reactivated”. This is important both in the product and in communications. Chatbots, push notifications, emails, and even content should be adapted to the level of engagement. Google Analytics, Amplitude, and now AI modules in CDP systems help track these patterns and launch the right touches at the right time.
7. User Status
New, active, lost, reactivated, trial, paid, corporate... This is the basis for primary segmentation. Status affects the tone of communication, the type of offer, and even the duration of the nurturing campaign chain.
In B2B, where the deal cycle can last months, correctly determining the status allows marketing not to "overheat" the client with an offer too early.
Each of these types of behavior does not work in isolation. The strongest segments are obtained at the intersection: for example, “loyal customers who rarely access the product and missed the last three emails” is already an entry point for personalized reactivation. And with the help of AI, you can not just build such segments manually, but allow the algorithm to find links that you would not even have time to formulate.
AI in Behavioral Segmentation
What exactly does AI do in behavioral segmentation? First of all, it automates the analysis of large volumes of user behavior, identifying patterns that are difficult to notice manually. Instead of manually setting up rules and segments, machine learning algorithms themselves find clusters with a high degree of behavioral similarity. This allows you to quickly adapt marketing actions to real signals from users.
One of the key scenarios is predictive segmentation. Algorithms evaluate behavior on the site, in the product, and in email campaigns, and create a forecast: who is likely to buy, refuse, upgrade, or drop out. This allows you not only to personalize touches but also to optimize the moment, channel,l and content of communication.
For example, if AI detects that users who spend more than 90 seconds on the pricing page but do not register leave forever in 60% of cases, you can set an automatic trigger: show a custom pop-up with a minimum price or offer a demo with an expert. This approach is not based on assumptions — it is based on data collected and interpreted in real-time.
At M1-Project, we use behavioral analysis as the basis for Elsa AI, an assistant built into the ICP Generator. Elsa analyzes user actions inside the tool: how much time they spend at the segmentation stage, which industries they choose more often, which fields they skip, and which prompts they ignore. This allows not only the improvement of UX but also the creation of dynamic segmentation of users with different barriers. One receives an invitation to a consultation, another — a ready-made selection of ICP templates, and the third — a reminder with an unlocked function.
It is important that AI can combine signals from different sources: CRM, website, email, advertising campaigns, and behavior in the product. This creates an end-to-end behavioral profile, on the basis of which you can build highly accurate marketing chains. For example, a user who opened two emails with cases did not click, but then went to the “Customer Stories” page and read it to the end is a clear candidate for a personal demo meeting offer.
A separate area is identifying anomalies. AI can record deviations in the usual behavior pattern: if an active user suddenly stops opening emails and does not enter the product, this may be a sign of dissatisfaction, a technical problem, or leaving for a competitor. Marketing can react instantly, and not a month later based on the results of manual analysis.
AI also allows you to build multivariate tests on large segments and switch creatives, offers, or messages in real time depending on the response. This eliminates weeks of waiting for A/B test results and allows you to conduct dozens of hypotheses simultaneously.
The implementation of behavioral segmentation using AI provides not only an increase in conversion, but also a sustainable decrease in CAC, an increase in LTV, and an increase in engagement without increasing the marketing budget. Companies that apply this approach systematically gain an advantage not at the campaign level, but at the level of their entire business model.
SaaS Case Studies of Behavioral Segmentation
Below are real examples where the behavioral approach played a key role.
Duolingo. One of the benchmark cases. The platform actively uses in-app behavior to personalize push notifications and email series. If the user, for example, takes breaks from studying, the system offers them an “easy win” lesson — short and as simple as possible to restore motivation. For the segment that consistently opens the app in the evening, the push is triggered closer to 20:00, and not in the morning. Behavioral data determines at what level of difficulty to offer tasks in order to prevent churn.
Notion. The product team integrated behavioral analytics into onboarding, which allowed them to reduce churn among new users by 27%. The system tracked the actions of newcomers in the first 15 minutes after registration: whether they created a new page, duplicated a template, or invited someone to the workspace. Depending on the behavior, different email chains were activated with video instructions, tips, or an offer of a demo session.
Headspace. The level of personalization in content depends on usage patterns: some regularly log in before bed — and receive recommendations for sleep packs, others use the app in the morning — and receive short practices to start the day. Behavior determines not only the type of content but also the format: push, email, internal recommendation, and timing — all based on the analysis of a specific user’s behavior.
Intercom. One of the pioneers in automating customer messaging through behavioral segmentation. Their client, a SaaS platform for HR, used Intercom to launch automatic messages based on certain actions: if a user creates the first job post, the system sends advice on promoting the vacancy; if no applications are received within 48 hours — an automatic recommendation to change the description or try another channel. This approach increased the conversion of vacancies by 19%.
M1-Project: Behavioral approach inside ICP Generator and Social Media Content Generator. At the M1 team, we don’t just observe behavior, we give an automatic response to it. For example, if a user enters the ICP generator, selects "B2B SaaS" as the company type, but does not fill in the other parameters, Elsa AI records this pattern as a “stop at the segmentation stage” and offers specific templates relevant to the selected category. If the user returns several times but does not save the result, an email with cases and a CTA “finish creating an ICP to test the strategy” is activated. This helps turn a behavioral pattern into a specific marketing action that leads to activation and retention.
Segment (now part of Twilio). They implemented behavioral segmentation not only for clients but also for their own marketing teams. Using user behavior in content (what articles they read, what videos they watch, what pages they spend more time on), they launched highly targeted drip campaigns adapted to the current interests of the client. This gave an increase in the open rate of email series by 32% and increased the share of SQL by 21%.
What do all these cases have in common? None of the companies segment based on demographics. The focus is on user actions, not their “portrait.” Behavior is the basis for predictable growth in SaaS.
How to Activate Segments and Drive Conversions
For behavioral segmentation to start producing results, dividing the audience is not enough. Segments need to be activated — that is, building precise chains of interactions, where each trigger, channel, and offer is related to the user's actions, and not to their profile in the CRM. The main mistake is to build segmentation, but continue to use template communication. Below is a structured approach to activating behavioral segments, applicable in SaaS and digital products.
1. Preparation: linking data, triggers, and channels
The first stage is synchronizing data sources. Behavioral information can come from a product, website, CRM, email platform, and analytical systems. All of them should flow into a single point — be it a CDP, custom analytics, or AI integration. If you do not see the full picture of behavior, it is impossible to activate a segment.
At this stage, it is important to set:
Behavior labels: viewing the pricing page, returning after 7+ days of inactivity, unfinished onboarding, etc.
Response channels: email, in-app message, push, chat, personalized blocks on the site.
2. Selecting activation scenarios for each segment
Behavioral patterns form the basis for scenarios. For example:
- Visitors to the pricing page 2+ times without registration - email series explaining the benefits of the plans, cases, and demo CTA.
- Users who have not completed the creation of the ICP - in-app pop-up with a template offer.
- Customers who have reduced the frequency of using the product → reactivation scenario with an offer of a new feature or an individual session.
M1-Project in its tools (for example, in Social Media Content Generator) records which templates are selected more often and which are ignored. This allows you to run scenarios for specific interests: if a user constantly selects templates for LinkedIn, but does not publish, we activate suggestions with ideas for posts based on their industry.
3. Personalization of offers and content
The power of behavioral segmentation is not only in the moment but also in the content. The user's behavior suggests what is important to them:
- Does the user visit documentation? - offer a technical deep-dive.
- Compare with competitors? - send a comparison of functionality or a review from a company with a similar profile.
- Uses the same function, ignoring others? - show how other functions can complement its use case.
AI can dynamically change the content of an email, web page or pop-up block based on the user's current status. This works especially well when there is too much data for manual adjustment.
4. The right touch time
One of the most underestimated parameters. Behavioral analytics allows you not only to segment users but also to determine at what moment they are ready to interact. For example:
- The user returns to the site after 3 weeks - activate the "what's new you might have missed" scenario.
- The user actively interacts with the content, but does not leave a request - enable a CTA for a consultation with elements of urgency.
- AI can predict the moment with the highest probability of conversion, which is especially effective in high-ticket SaaS with a long decision-making cycle.
5. A/B testing of segments and touches
Each behavioral segment is a hypothesis. Efficiency can and should be measured:
- Conversions for each scenario
- Engagement within the scenario (opens, clicks, actions)
- The path after activation (transition to another segment, repeated inactivity, etc.)
It is important to test not only offers but also the segments themselves: perhaps the "repeat visits to pricing" segment should be supplemented with the "visit to the documentation page" criterion to filter out non-targeted transitions.
Mobile user behavior is a separate type of data that requires its own segmentation logic. The behavior here differs not in nuances, but in the structure itself: short sessions, touches in different contexts, frequent distractions, and a strong dependence on the time of day. If segmentation does not take these features into account, part of the audience remains under-reached or receives irrelevant touches.
Usage context. Users open applications in a queue, on public transport, during meetings, and during breaks between tasks. AI records patterns: at what hours logins occur most often, in what situations key functions are launched, and how long a session lasts. This makes it possible to select the time and format of interaction. For example, a push at 7:15 with a reminder of a daily task shows a 30% higher CTR than notifications sent according to a template at 10:00.
Session frequency and depth. The same user can log in five times a day for 20 seconds or once every two days but for a long time. The behavioral profile will be different. The first type may indicate a search for a missing function or navigation problems. The second is a persistent habit. In the first case, it makes sense to launch triggers with hints, and in the second - to offer a function to increase efficiency.
Reaction to pushes. Behavior analysis should cover not only actions within the application but also reactions to external triggers. Users who ignore three push messages in a row are highly likely to miss the fourth. However, if AI records that the user returns to the app at the same time when the messages were sent, it is worth testing a change in format, not time.
Navigation structure and patterns within the interface. Which screens are opened more often, from which one does the session begin, and on which one it end? Some users consistently open only two sections. This is the basis for a hypothesis: they did not find other functions useful. The trigger may contain a recommendation to use a specific feature based on similar users.
Transitions between channels. Behavior in mobile is often associated with other touchpoints. For example, after an email newsletter, the user clicks on the link and continues to act in the application. Or vice versa — starts sessions in the application, but completes registration via the desktop. Cross-device chains can be assembled if data from CRM, push systems, email, and the product are connected. This is the basis for building multi-channel campaigns.
User intent by behavioral patterns. AI is able to determine the user's intent based on small signals: scroll depth, speed of switching between screens, and cyclicality of actions. A repeating pattern of "opening onboarding → closing → re-entering in a day" can be a signal that the scenario is too complex. Changing the order of steps or auto-filling key data will help improve activation.
Using offline functionality. Behavioral analysis shows who systematically uses the application without an internet connection. This is often an overlooked segment that can be offered offline packages, pre-loaded content, or an optimized mode. Such users show high retention if they are offered the appropriate interaction logic.
The Tech Stack You Actually Need
Without the right tech stack, behavioral segmentation becomes a theoretical construct. The data is there, the patterns are visible, but nothing is triggered. The stack must provide for the collection, aggregation, analysis, and activation of behavioral signals — in real-time, at scale, and without the constant involvement of the tech team. Below are the key categories of tools that allow you to not only record behavior but act on it.
1. Customer Data Platform (CDP)
The basis. CDP aggregates behavioral signals from different sources — websites, mobile apps, email, CRM, and products — and collects them into a single user profile. Platforms like Segment, mParticle, or RudderStack allow you to sync data with dozens of other tools. If you don’t see the full history of user actions, you can’t build accurate segmentation.
2. Product analytics
Tools like Mixpanel, Amplitude, and PostHog provide detailed behavioral insights into the product: which screens are opened, what actions are performed, at what steps churn occurs. This data is critical for building behavioral segments by activation stage, engagement level, and functional scenario.
3. Email & Messaging Platforms with support for behavior-based triggers
Services like Customer.io, Braze, and Vero allow you to set up touches not according to a schedule, but based on behavioral events: “did not complete setup”, “reopened the pricing page”, “returned after 3 days of inactivity”. These tools integrate with CDP and product analytics to launch personalized chains based on specific actions.
4. Push and in-app communications
Pushwoosh, OneSignal, and Airship are tools for launching mobile communications tied to behavior. They allow not only sending notifications on events but also segmenting the audience by frequency of interaction, response time, device type ,and other behavioral characteristics.
5. AI modules and behavioral recommendation engines
Here, ML tools are connected that analyze user patterns, predict the likelihood of churn, upgrade, or return, and generate personalized offers. Some CDPs and email platforms already include built-in AI tools. In M1-Project, for example, Elsa AI analyzes user behavior within the Ideal Customer Profile Generator and offers recommendations based on the level of engagement, delays at individual stages, and incomplete actions.
6. Web analytics with behavioral signals
Google Analytics 4 (GA4), Hotjar, and Smartlook provide heat maps, scroll depth, frustration points, and sequence of actions. This data complements product analytics and allows you to create triggers at the site level: for example, leaving the cart, staying too long on one section, or clicking on a non-working button.
7. CRM with dynamic segmentation
HubSpot, Salesforce, and similar systems make it possible to update the user segment depending on their actions. In conjunction with CDP and AI, these CRMs become the center of scenario routing: if a user moves from the MQL status to PQL, not only the tag changes but also the entire logic of communication, offers, and targeting.
8. BI and dashboard tools
Visualization and monitoring are needed to evaluate the effectiveness of behavioral scenarios. Activation, churn, and conversion metrics by segments and scenarios - all this can be tracked in Looker, Tableau, Metabase, and Superset. It is especially important to see the relationships: for example, a change in behavior in the product caused by a new email chain, and its impact on the transition to a paid tariff.
Conclusion
User behavior says more than their position, industry, or demographics. It shows at what stage the customer actually makes a decision, what keeps them in the product, and where they lose interest. Behavioral segmentation allows you to build marketing not on assumptions, but on specific actions. When AI is involved, the speed of analysis and accuracy of response reaches a new level. This is no longer about sending out mailings to segments, but about managing every step of the client in real time.
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