Product Messaging and In-App for Successful Onboarding
User onboarding is a major growth lever, especially in the SaaS segment. According to Userpilot:
“Often, SaaS companies don’t treat User Onboarding as a priority. But this attitude can backfire:
- User onboarding has a direct impact on user activation. When you think about it, this is one of the very first touchpoints your users have with your product. If they don’t have a good time here, they’ll likely leave, never to return.
- If your users don’t activate, they won’t experience value from your product and will churn within the first few weeks of signing up. This will directly affect your trial-to-paid conversion rate.”
With the shift from sales-led growth strategy to product-led growth (PLG), many companies adopt smarter user onboarding. It is no longer about filling out necessary details post-sales; PLG user onboarding is the sales process in itself. Product usage analytics is central to what onboarding should be like. Data analysts dig product usage data for key “aha” moments, and ensure every new user experiences them through onboarding.
Yet, with higher-stakes PLG onboarding, ensuring onboarding completion requires a smarter approach to these two key elements: product messaging and in-app communication. They replace sales-rep-led onboarding to guide a user through the process. In this blog post, we’ll break down the intricacies of successful user onboarding, focusing on product messaging and in-app communication to drive a startup’s success.
Table of contents
Onboarding: Sales-led Growth vs Product-led Growth
In short, the key distinction is as follows: in sales-led growth companies, onboarding often happens after the sale is made. It takes the form of a one-to-one session with the company representative or documentation/tutorial for self-exploration. However, in product-led growth, onboarding is the sales process in itself.
User Onboarding in Sales-Led Companies
In sales-led growth businesses, onboarding sessions are basically a tool to set up the software. They can be either done via a one-on-one session or self-managed. However, the intent is the same – to set up the app for an already paid user. Because of that, the onboarding flow is rather something a user goes through out of necessity, post-factum. This is why some companies can even send a dozen-page PDF manual instead of onboarding.
Moreover, in sales-led growth companies, onboarding serves the purpose of collecting demographic data, information about the client’s company, and any other necessary information for setting up the profile, structuring the account, and so on. This type of onboarding can often be standardized and lengthy, while product usage issues are customarily handled through customer success representatives, documentation, or tutorials.
Overall, the key here is that post-sales behavior (that includes onboarding) does not focus on time-to-value. Users go through onboarding at their own discretion. Later on, a customer success specialist might reconnect for a one-on-one call to make sure users enjoy using the product and derive value from it.
Product-led Onboarding
In contrast, product-led growth (PLG) companies must treat onboarding as a sales process, converting visitors to paid users. To be scalable, this has to be a self-service interaction. As such, the product experience that represents core value becomes the single most important focus of user onboarding. The shift is drastic: it is no longer an administrative tool but a value-demonstrating experience. The gold standard of user onboarding in PLG is the one that promotes user action, feature activation, and engagement over data collection.
Case-in-point: Kommunicate.io
The data-first approach is responsible for the shift towards product-led growth. Once companies set up analytics and dive deep into how real users actually use their product, common issues that product usage data reveals are:
- users use only a few features out of the entire variety, if that;
- users submit feature requests for features that are already in the product.
This is how it was for Kommunicate.io. They discovered that up to 70% of their users used only a narrow subset of the available features. Moreover, the company would often receive requests for features that were already in the product. The company solved the issue with the changes in their onboarding process. And the results were immediate:
- Feature adoption increased from 28% to 41%.
- There was a 3% increase in paid feature sign-ups.
- Improved user onboarding ultimately increased its Monthly Recurring Revenue (MRR) and Customer Lifetime Value (LTV).
How does Product Messaging Impact User Onboarding?
Consider a simplified example. Imagine a SaaS tool for novice designers to manage their open-ended projects, let’s call it PlanIt. So, the visitor lands on the landing page, and its title announces “The best tool to organize your life”. Well, for most, it might reasonably create an expectation of a productivity tool. The kind to manage appointments, grocery lists, calendars, and such. However, after hitting the sign-up, the onboarding asks to connect to GitHub and invite 2 teammates. A confused user is likely to leave.
In the sales-led growth model, this kind of broad statement often worked. Mainly, because during onboarding, a sales representative conducting a live demo would smooth out the gap between the expectations and the real use case.
In the PLG model, the human intervention is gone. The product itself should reconcile the user expectations and the actual product use case. Moreover, a broad messaging like this will attract a very broad audience, most of it with no intent for the actual use case. But, analytics would indicate connecting to GitHub or inviting peers as drop-off points. Whereas the real issue is the acquisition.
Now, change this message to “An organization tool for designers to co-create together”. The expectations are set correctly. As a result, GitHub integration and connecting to peers feel logical and even motivating.
Case-in-Point: Siebel CRM vs Salesforce
Siebel CRM is one of the most cited examples of a sales-led growth model company where overpromising was compensated via human agency. The CRM market in the 1990ies to early 2000ies was dominated by many vendors for CRM solutions, and overpromising was the norm. Siebel CRM, like many at that time, promised a single CRM, a fully-integrated, ready-to-use solution for sales, service, and analytics. Clients were under the impression that you get it and start using it. However, in reality, it had to undergo heavy customization. So, their promise would eventually become true after a couple of years of implementation and hefty consulting fees.
While Siebel CRM was acquired by Oracle for $5.6 billion in 2006, today’s industry behemoth is Salesforce, a cloud-native CRM solution. Salesforce is cheaper in monthly fees and does not require heavy consulting. It is the model of self-service onboarding. While today Salesforce also does consulting and requires coders for advanced, specialized features, most businesses can use it out-of-the-box with minimal customization.
Product Messaging & Product Usage Analytics
Product messaging is the ‘outside’ content that prospective users find in ads, marketing materials, and landing pages. Inthe PLG model, it should align with product usage analytics as closely as possible. It must be noted, though, that the best practice is to inform product messaging with analytics as well as user interviews. You can read about those in our article “User Feedback: Getting Honest Insights Via Customer Interviews”
Table: 4 Product Messaging Questions
| Question | Product Messaging | Product Analytics |
| What is your product? | Often aligns well with the North Star metric, e.g,. a completed design with collaborators on Figma, a booked stay on Airbnb, a completed ride on Uber, etc | Often aligns well with the North Star metric, e.g. a completed design with collaborators on Figma, a booked stay on Airbnb, a completed ride on Uber, etc |
| Who is it for? | Here, this is already the point of experimenting with onboarding for different segments. Below you can see the welcome screens of ClearCalcs to experiment with early user segmentation to deal with premature churn: role-based, goal-based, and size-based. This early segmentation helped to create follow-up contextual experiences during their onboarding and improve activation. | This reflects whether your product messaging targets the right segment(s) by measuring early drop-off rates. In addition, products often have several target segments, and cohort analytics starts here. |
| What does it replace? | Any novel tool replaces something, be it a competitor’s app, a manual workflow, or a combination of existing tools. The marketing campaign of Monday.com explicitly focused on how it is better than Jira and is a textbook example of this point. This product communication sets up user expectations on product use, onboarding, and setup. | The efficiency of this product communication directly impacts time-to-value, completion of setup, and drop-off during integration steps. |
| Why is it better? | This should explicitly describe the tangible benefits of the product or its features, such as what immediate payoff the user gets. | This forms a basis for quick wins and user milestones of the onboarding to the core Aha moment. |
In-App Messaging for User Onboarding Completion
While product messaging ensures the right expectations, in-app messaging manages the reality. After all, in SaaS companies, user onboarding aligned with the North Star metric can rarely be achieved immediately. Why? Imagine Figma. The North Star metric is likely to be “a completed design with collaborators”. This takes time, user effort, and implies some product knowledge. So, most of B2B and SaaS user onboarding is set to occur over a period of time and can rarely be completed within the first visit.
As such, the right user onboarding often has a structure of a series of steps, supported by quick wins, finally leading to a big one representing the core value. To achieve that, in-app messaging can take different forms, such as:
- tooltips,
- checklists,
- modals,
- banners,
- walkthroughs.
In addition, most SaaS products aim for habitual use occurring daily or a couple of times per week, so one-step onboarding will not make sense anyways. This is where in-app messaging steps in. It is often contextual, follows the user’s in-app behavior, and supports user progression inside the product over an extended time-period.
Product-Led Growth: In-App Messaging for Continuous Onboarding
The issue of how long the user onboarding should stretch depends on the product type, complexity, and update frequency. For some products, the value constantly evolves and becomes feature-rich. For instance, LinkedIn and Slack have continuous onboarding. There is a distinct activation milestone, yet after that, there is a sense of evolving product value. In-app messaging constantly nudges users to try new features, explore integrations, and engage deeper with toolkits.
On LinkedIn, it used to be an explicit Profile Strength meter once a user completes basic activation. Now, the interface has changed: there is in-app communication in the form of suggestions and banners.
From the user standpoint, the new version seems more beneficial than the previous one. After all, with a never-ending progress bar of profile strength, it felt like whatever you did was never enough to have a strong profile. In contrast, banners and actionable labels, here and there, present an opportunity to discover the value. Overall, the dispersed UI/UX design of onboarding over contextual in-app messages works better.
On Slack, Slackbot used to guide onboarding and offer rule-based tips and suggestions about the product use. It used to respond to fixed triggers. Now, it has evolved into an AI assistant and offers a much more proactive and action-based help.
In the PLG model, onboarding and in-app messaging are among the fastest-evolving fields. The design process of onboarding experiences is one of the most investable processes and offers the highest ROI. After all, in sales-led growth organizations, the customer success representative is expected to lead the customer throughout their lifetime with the company. It involves regular syncs and updates. “Eternal” onboarding through in-app messaging in PLG companies replaces engagement with sales reps by offering automated guidance and engagement prompting. It is almost single-handedly responsible for sales and upselling.
Final Words
- Product-led growth focuses on defining key aha moments that allow users experience product value. They are often translated into an onboarding checklist.
- Two major elements are responsible for driving its completion: product messaging and contextual in-app communication.
- Improving user onboarding through these elements is a key driver of the company’s revenue as well as feature adoption, user engagement, trial-to-paid conversion, MRR, LTV, and North Star metric.
- In PLG, the user onboarding has to replace a person who will showcase functionality, documentation, tutorials, and whatnot. As such, contextual in-app messaging emerged as a key conversion tool while clear product messaging filters out high-intent visitors and sets the right expectations.
FAQ: Product Messaging and In-App for Successful Onboarding
Effective onboarding increases activation rates by guiding users toward key “aha” moments. When users experience value early, they are more likely to continue using the product and convert from trial to paid plans.
Product messaging sets user expectations before sign-up. If messaging aligns with actual product experience, users are more likely to complete onboarding steps and engage with relevant features instead of dropping off early.
An “aha” moment is the point at which a user clearly understands and experiences the product’s value. This moment often aligns with the company’s North Star metric. For example, it may be completing a collaborative project, publishing content, or automating a workflow. Designing onboarding around leading users toward this milestone increases activation and long-term retention.
Contextual guidance reduces uncertainty and cognitive load. When users understand what to do next and how to extract value, frustration decreases. In-app messaging also encourages feature discovery and re-engagement, which increases stickiness. By supporting progression over time, it helps prevent early abandonment and improves long-term retention metrics.
In product-led growth models, value delivery should come before extensive data collection. Asking users to complete long forms or provide excessive information before they see product value creates friction. Progressive data collection, where additional details are requested after users engage meaningfully, tends to improve completion and reduce drop-off.