But now, a growing share of the sessions, events, and interactions logged under those profiles isn’t coming from humans, but agents. If your product supports MCP integrations or API-level automation, some version of this is already reshaping your data.<\/p>\n
Moreover, unlike old scrapers and crawlers, AI agents hold real accounts, complete legitimate tasks, and leave behavioral data in your dashboard without you noticing. And since customers deploy agents to drive value from your products, you shouldn’t block them. Instead, you should analyze and understand them.<\/p>\n
So for this post, I’ll cover three things:<\/p>\n
- \n
- How user profiles are being reshaped by agentic behavior,<\/li>\n
- What data actually belongs in a user profile in 2026,<\/li>\n
- How to build a profiling process that drives decisions rather than just accumulates data.<\/li>\n<\/ul>\n
User profiles in the agentic era<\/h2>\n
A user profile is a collection of data, settings, and behavioral logs linked to a specific individual using your product. It captures everything from basic identity information like name, role, and login history, to a full record of their interactions with your app over time. Unlike a user persona<\/a> (which is based on aggregated research to represent a user type), profiles track users individually and update automatically as they interact with your product.<\/p>\n
Now, every user profile is built on the assumption that the “user” is a human. That assumption is breaking down, leading to a shift in how companies approach customer data collection.<\/p>\n
How to tell human data from agentic data in your profiles<\/h3>\n
Human and agent behavior show different patterns in your product analytics. Humans scroll, hover, pause, backtrack, and take variable paths through your product at irregular times. Agent signals are the opposite:<\/p>\n
- \n
- Deterministic event sequences executed in the same order.<\/li>\n
- Consistent speeds since there’s no idle time between steps.<\/li>\n
- Off-hours activity.<\/li>\n
- No scroll events or mouse hover trails.<\/li>\n<\/ul>\n
Agents connecting through MCP also tend to access your product’s data layer directly rather than using the frontend UI. That means they generate API-level logs rather than standard session events, which will show “sessions” with suspiciously clean navigation paths and instant step completions.<\/p>\n
![\"Human](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/05\/image2-human-vs-agent-signals.png\")
The difference between human and agent behaviors.<\/figcaption><\/figure>\n The problem isn’t solved yet, and the current solutions aren’t simple. For instance, you might have to label agentic accounts at the account level, requiring customers who connect via MCP or API to use a dedicated service account or API token, so all activity from that account is tagged as agent-generated. For us, Userpilot’s Agent Analytics<\/a> tracks MCP-powered agentic interactions in a dedicated dashboard, keeping the human behavioral data in user profiles a bit cleaner.<\/p>\n
![\"Userpilot](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/05\/AI-Agent-Analytics-General-view-Userpilot.png\")
Userpilot’s Agent Analytics separates agentic interactions from human sessions so your user profile data doesn’t conflate the two streams.<\/figcaption><\/figure>\n\ud83d\udca1 Read related blog posts: Product Usage in 2026: How It’s Shifting in the AI Agents Era<\/a><\/div>\nWhat an accurate user profile should include in 2026<\/h2>\n
First, user profiles rely on first-party and zero-party data: behavioral events, session data, survey responses, support interactions, welcome survey<\/a> responses.<\/p>\n
Third-party data is increasingly unreliable and legally risky in most markets. Under GDPR, you’re required to store only data that is necessary for your stated purpose, and regulators are interpreting “necessary” more narrowly every year. For most SaaS products, first-party and zero-party data is sufficient; that’s why 68% of businesses<\/a> are investing more in first-party data strategies.<\/p>\n
With that said, here is what a complete user profile should cover in 2026:<\/p>\n
- \n
- Demographic information:<\/strong> Name, role, location, and company size. This comes primarily from signup data or welcome surveys<\/a>.<\/li>\n
- Account information:<\/strong> Subscription plan, seat count, renewal date, and custom entitlements.<\/li>\n
- Product usage data:<\/strong> Every event, feature interaction, and session your product records.<\/li>\n
- Customer sentiment data:<\/strong> NPS scores, open-text survey responses, support ticket themes, and CSAT signals.<\/li>\n
- Technographic data:<\/strong> Operating system, browser, device type, and the third-party tools they use alongside your product.<\/li>\n
- Transactional information:<\/strong> Purchase history, billing events, upgrades, and downgrades. This layer is useful for understanding the commercial lifecycle of individual accounts and for predicting expansion.<\/li>\n
- Marketing engagement:<\/strong> Email open rates, webinar attendance, and content downloads. This layer is useful for identifying users who are engaged with your brand but not yet active in your product.<\/li>\n
- Agent usage:<\/strong> MCP connections, automated workflow runs, and API call volumes attributed to service accounts. This is the new category we should start thinking about capturing now.<\/li>\n<\/ul>\n
![\"What](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/05\/image3-user-profile-categories-2.png\")
What user profiles should include in 2026.<\/figcaption><\/figure>\n How to build and leverage user profiles to drive product decisions<\/h2>\n
User profiles are only as useful as the quality of the data behind them, and with AI agents increasingly skewing behavioral metrics, getting that quality right requires new strategies.<\/p>\n
The process I follow has five steps, and I’ll walk you through each, with some examples.<\/p>\n
Define the data you want to track<\/h3>\n
The most common mistake I see with user profiles is analyzing too much data. Teams that try to analyze everything usually end up acting on nothing.<\/p>\n
I recommend starting with the decisions you actually need to make. Every data point in your profile should map to a specific product question you plan to act on, and you should be able to trace a direct line from the data you’re storing to the intervention it enables.<\/p>\n
For example, Userpilot’s customer Attention Insight had a specific goal: improve user activation by 47% in Q1 by implementing interactive onboarding. That goal told the team which user data to track, such as their funnel behaviors or task completion rates. Goal-setting frameworks<\/a> like OKR and SMART are useful here, as they’ll help you be more mindful about the data you’ll need for making decisions.<\/p>\n
Another aspect is the regulations. Under GDPR’s data minimization principle<\/a>, you’re only permitted to store data that is adequate, relevant, and limited to what is necessary for your stated purpose. At the same time, your industry may add further constraints: fintech products typically must collect personal identification data for KYC verification regardless of preference, while healthcare products face HIPAA rules that restrict what behavioral data can be stored and how. Knowing your regulatory environment before you define your profile schema prevents significant rework later, and often adds clarity about what you actually need.<\/p>\n
Connect the data with the right tech stack<\/h3>\n
At this stage, it’s essential to consolidate your customer data sources in one place.<\/p>\n
The data you need for a useful user profile might already exist, but in disconnected tools. This means behavioral events in one platform, survey responses in another, support tickets in a third. When those streams aren’t connected, the profiles you build are partial at best and actively misleading at worst.<\/p>\n
For teams building profiles across marketing, sales, and product touchpoints, I recommend using a CDP like Segment, Tealium, mParticle, etc. They provide a unified layer for person-level data across channels and feed downstream tools like email, ads, and CRM consistently.<\/p>\n
For product teams, connecting in-app behavior, user sentiment,\u00a0 session replays,<\/a> and CRM data can be enough. Userpilot, for instance, can consolidate all three in one single profile per user, allowing you to make quick decisions. For example, one time I noticed a sharp drop-off at domain verification in our email feature, then I built a checklist and a targeted tooltip in a few hours that walked users through the correct steps. Activation improved within days without a single engineering ticket.<\/p>\n
![\"User](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/user-profile-gif_2f8a5003817627fece0f3255c4ededcc.gif\")
How a user profile looks like in Userpilot.<\/figcaption><\/figure>\n \ud83d\udca1 Read related blog posts: Macro and Micro Segmentation in B2B: How to Segment Your Users<\/a><\/div>\nAnalyze user profiles with AI<\/h3>\n
Once the data is connected, you can start analyzing user profiles. This is when you need to focus on investigating data points relevant to your goals. So if your focus is to optimize activation rates, then you should analyze user profiles of new accounts, look at their in-app behaviors (e.g., using funnel or path reports), watch some sessions, and maybe look at the feedback you’ve sent surveys in the past.<\/p>\n
Now, I’ve been there. Reading through individual user profiles or segments manually does work, but it doesn’t scale well when you’re looking at a segment of 500 accounts or a cohort of trial users. This is where AI genuinely helps with the analysis in multiple ways, including:<\/p>\n
- \n
- Predictive churn scoring:<\/strong> AI models assign risk scores based on signals like session frequency drops, feature abandonment, or declining NPS. This means a model will flag an at-risk account before the user files a cancellation request, giving your team a window to intervene.<\/li>\n
- Anomaly detection:<\/strong> AI surfaces users whose behavior deviates sharply from their segment baseline. For instance, a trial user hitting API limits every day could mean they’re ready to expand their account.<\/li>\n
- NLP on open-text feedback:<\/strong> AI can process survey responses and support tickets at scale to find recurring themes across hundreds of responses.<\/li>\n<\/ul>\n
With Lia (Userpilot’s AI agent), you define a project, such as improving trial-to-paid conversion, and she builds the reports, segments the cohort, surfaces drop-off points, and returns actionable recommendations. The time you previously spent pulling data and forming hypotheses evolves into reviewing the output and deciding what to act on first.<\/p>\n
Take action on what the profiles tell you<\/h3>\n
The most well-structured user profile is worthless if it doesn’t influence your decisions and strategies.<\/p>\n
In my experience, the actions that follow user profile analyses can go from individual interventions to whole roadmap changes. For example, if a user who completed onboarding two weeks ago has gone completely inactive, you can reach out with a targeted in-app message or automated email that addresses the specific feature they got stuck on.<\/p>\n
Another example is when I noticed users who connected our email feature but never completed domain verification, leading to a much higher 90-day churn rate. With that finding, I built an in-app checklist that walked users through the domain verification step by step, reducing the number of stuck users dramatically.<\/p>\n
Additionally, user profiles also make it possible to create additional personalization<\/a> based on individual data. You can show a DevOps user an API documentation shortcut on their first login, and not show it to a marketer. With Userpilot, for instance, you can directly target specific experiences using profile properties you’ve already collected, without any engineering involvement.<\/p>\n
![\"\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/a0f7b9e7-3f8f-4e90-a20c-1ca0ab0a41cd.webp\")
Segmentation can be used for hyper-personalized targeting.<\/figcaption><\/figure>\n One more step: Keep your profile data secure<\/h3>\n
Many times, user profiles store sensitive data: names, roles, behavioral histories, payment information, etc.<\/p>\n
This means security and compliance shouldn’t be taken for granted. Recently, Dutch telecom Odido had a breach of 62 million customer records<\/a>, and it wasn’t detected until days later, hurting the trust of their customers. This can happen even with end-to-end encryption, role-based access controls, and secure authentication for any API or MCP connection.<\/p>\n
So when collecting a lot of sensitive data for your user profiles, I highly recommend auditing your database regularly and pruning sensitive data that doesn’t serve a purpose. Additionally, you can set data retention windows and automate the deletion of records after a period of time.<\/p>\n
User profiles are worth building, even when agents are in the mix<\/h2>\n
The arrival of AI agents makes building accurate user profiles harder. And at the same time, agents are making many parts of the job faster and easier.<\/p>\n
But the underlying purpose is the same: user profiles exist to help you understand behavior well enough to improve it.<\/p>\n
- Anomaly detection:<\/strong> AI surfaces users whose behavior deviates sharply from their segment baseline. For instance, a trial user hitting API limits every day could mean they’re ready to expand their account.<\/li>\n
- Account information:<\/strong> Subscription plan, seat count, renewal date, and custom entitlements.<\/li>\n
- Demographic information:<\/strong> Name, role, location, and company size. This comes primarily from signup data or welcome surveys<\/a>.<\/li>\n