First-Session Wins Cut Churn 2–3x – Most Onboarding Never Delivers One

01 /The Concept

Users abandon products when they fail to see the value in them. Too often, the value is actually there – users just never discover it in those critical first days after signing up.

For a user to grasp a product's value, they need to get up to speed quickly. This process is called onboarding – getting the user "on board" with the product. Some users can navigate this on their own, but many need help.

The most reliable onboarding method is one-on-one sessions with new users, walking them through how the product solves their specific problems. But this approach doesn't scale – not when hundreds of users sign up every week, and especially not in B2B where an entire enterprise team may need to be onboarded simultaneously.

The alternative is to build automated onboarding flows. The problem: different user types need different flows, and building anything takes time. Every time the onboarding scenario or the product UI changes, something has to be rebuilt.

Andoria built an AI assistant that conducts personalized onboarding for different user types – without any programming. A product team member can configure it in a single day.

From there, onboarding unfolds in three stages.

In the first stage, the AI assistant asks the new user a few simple questions to understand who they are and what they're trying to accomplish. For example: a VP of Sales with beginner-level AI skills who needs to hit a quarterly number and make sense of the pipeline their reps have stuffed into the CRM.

In the second stage, the AI:

- Searches its database for how similar users in similar roles have used the product before.

- Forms new hypotheses about how this specific user could get the most out of it.

- Builds a tailored onboarding flow.

- Maps that flow onto the product's actual UI – generating a sequence of in-product actions the user should take.

For that VP of Sales, the action list might look like this:

- Run a first query to get a live read on revenue.

- Assign a task to a digital sales rep.

- Enrich the CRM contact list to filter out leads who clearly aren't buyers.

The AI assistant then walks the user through the product screen by screen, highlighting the relevant UI elements and providing real-time guidance.

It also prompts users to take specific actions – for instance, typing a particular query into a search box and reviewing the results.

Andoria has just been accepted into Y Combinator, receiving the standard $500K investment. The company published details about its AI assistant on the Y Combinator blog two days ago.

02 /Why It Matters

The highest churn rate happens in the first days and weeks after signup – because users didn't get invested in the product quickly enough and never hit that first moment of genuine success.

Well-executed onboarding can change this dramatically.

HubSpot demonstrated on one of its own products that active onboarding of new users during the first week after signup boosted retention by 15% by the end of that week.

But that's not the headline number. The real payoff is that this seemingly modest early improvement cascaded into a 50% retention increase by week 10. When you lift the start of the retention curve, the entire curve shifts upward.

Other products have seen similar effects when developers put serious effort into early onboarding. InnerTrend, for instance, saw retention triple by week 12.

The lesson is simple. If you want fewer users to drop off, it's almost pointless to scramble for them once they've already started to leave. Prevention beats intervention every time – and smart onboarding is the prevention.

Andoria didn't invent this idea. A [related review](/review/chto-ja-tut-mogu-poleznogo-dlja-sebja-sdelat) covered Appcues, which built a platform for creating onboarding flows manually – and has raised $52.8 million in investment, including a new round after that review.

Appcues is a no-code platform: product and marketing teams can build and A/B test onboarding scenarios without engineering support. Andoria's key move is to go one step further – offloading the entire scenario-building process to AI.

03 /Opportunities

It's worth emphasizing: preventing problems is almost always more effective than solving them after they appear. AI is now capable of doing this at scale.

The broad opportunity here is building AI platforms that proactively prevent the problems users run into with products. Within that space, there are several specific paths.

Andoria's path is automating new user onboarding – introducing users to the specific product features that help them accomplish what they're trying to do right now. But it's not the only path.

Actionable ([related review](/review/nashi-dengi-jeto-ih-schaste)) raised €2 million in a seed round for an AI engine that analyzes all of a company's user communications to automatically surface the top drivers of churn – enabling sharp prioritization of what to fix first.

Subsets ([related review](/review/privychka-vazhnee-chem-polza)) graduated from Y Combinator with a platform for reducing subscriber churn at media publications. Its AI analyzes reading patterns, segments subscribers, and nudges each person back toward their established reading habit before they drift away.

The insight is that different readers have different habits: some read daily in the morning, some weekly, some once or twice a month in long sessions. Sending daily re-engagement emails to a once-a-week reader doesn't just fail – it trains them to ignore you. Subsets raised $1.7 million on top of its standard Y Combinator check.

The highest-leverage verticals are those where churn is both expensive and predictable – SaaS tools with steep learning curves, media subscriptions dependent on reading habits, and marketplace products where users need successful matching before they see value. In each case, the intervention AI can own is the one that requires real-time behavioral data to personalize: not a fixed sequence, but a dynamic response calibrated to what each user is actually doing.