User adoption, sometimes called onboarding, is the process by which new users become acclimated to a product or service and decide to keep using it. Users only adopt a product if it helps them achieve a goal of theirs—whether it’s ordering food or launching a business.
The goal of a user adoption strategy is to help users reach their goal. That means conducting research to understand users’ motivations, needs, environments, beliefs, complaints, and, most important, all the reasons why users don’t adopt.
Users often churn for reasons teams don’t expect, such as the perception that the product is too expensive, the feeling that it’s too complex, or the impression that a competitor’s product is better suited to their needs.
Teams can think about improving their user adoption rate in terms of this equation:
If the value of the service outweighs the costs, adoption is positive and users are likely to remain customers. If the effort outweighs the value, they abandon it. To improve adoption, the team can either increase the value, decrease the effort, or, ideally, both.
For a more unbiased view of a feature and its value, tap into your most valuable resource: your customers. Before launching a new feature, conduct user tests to get a feel for how the feature works for your customers Users might find some kinks in the user experience (UX) that you can work out before you release the feature into the wild.
Once you feel like the feature is ready to go, and you make it available to everyone, get into some cohort analysis, and conduct user surveys to discover how a wider audience feels about it. This customer feedback helps you gauge how good the UX is for customers and what benefits users find in the feature. User surveys are also a great way to find out if your customers turn to a competitor’s product to address the pain point the feature in question is meant to solve.
When you have a complex product and it's complicated to explain it, think of user flows and use cases that can lift the value. Dedicated training sessions to showcase these flows are really helpful. This offers you more time to refocus your UX, UI strategy in order to decrease educational sessions in the onboarding and develop a self explanatory product.
Teams can make changes to their marketing or product features to give users more of what they want. Marketing teams can offer discounts, promotions, and freebies that increase the perceived value of the product, such as access to more advanced features.
Product teams can launch new features that make the product more useful. For example, an e-commerce retailer could launch a more powerful recommendations feature powered by a machine learning algorithm, or a streaming video service could allow users to add multiple profiles for different members of their family.
The mere perception of value is value itself. Amazon’s famous Prime membership program offsets it’s allegedly “free” shipping with an annual membership fee, but the positive perception around its shipping policy has attracted over 80 million members.
Nonetheless the value of your product or feature decreases if the learning curve isn't as exponential as expected. If you train a loved family member of yours, your dog, to follow certain rules, like sit, the call back or that he should not attack other dogs, it's essential to underline these rules with a positive connotation. Like food or praises. You can establish praises during the adoption process to keep adoption friction as low as possible.
One of the biggest barriers to adoption across industries is that users must learn something new. Whether it’s a new workflow, app interface, or dashboard, learning takes mental effort. Users can get fatigued easily, and, following the law of inertia, they tend to stick to services they already know.
To help users adopt something new, teams can lower barriers by adhering to the generally accepted design conventions that users have come to expect, such as navigation menus, transition animations, and progress bars. If the service feels familiar at the outset, it requires less learning.
With product analytics, teams can review the new user onboarding funnel to identify areas where users are running into trouble. Any areas with large drop-offs, or many users exiting the app, may be areas where something is going wrong. When one international peer-to-peer shopping app looked at its new user referral funnel, it found high drop-offs right as users reached a particular landing page. By rewriting the copy, the team doubled the number of successful referrals.
Teams can also conduct first-party customer research via surveys, interviews, journey mapping, and task analyses, where UX researchers study customers using the products as they go about their day.
If the entire adoption sequence seems to frustrate users, consider offering:
In combination with the key adoption metrics, the friction measurement and customers perception analysis, tracking feature adoption is key to be ahead of your developments and achievements.
Getting users to stick around past the first login boils down to how engaged they are and how much value they find in your product. The more features people use, the more value they get from the product. The fewer features people use, the less value they get for their money.
When customers feel as though they aren’t getting their money’s worth from a product, they’re on the fast track to Churn City. To retain users, you need to keep an eye on how every one of your product’s features performs.
In the SaaS world, where subscription-based services are the norm, tracking feature adoption is especially crucial. Every time a company sends over monthly bills to subscribers, those subscribers consider whether that product is still beneficial. This setup puts pressure on SaaS businesses to regularly show how their product meets users’ needs and adds value.
Tracking feature adoption empowers you to optimize your product at very specific touchpoints, feature by feature. When you figure out which features people use frequently, you can investigate what makes those features so sticky and use that insight to make other features more attractive.
Tracking feature adoption also shines a spotlight on unpopular features and nudges you to dive into product analytics to understand why those features aren’t so hot. Equipped with information about your less-utilized features, you can head back to the drawing board and think of how to improve them or toss them if the demand just isn’t there.
This metric is similar to our first product access model, but feature usage is more about determining actionable next steps whereas product adoption rate is best for identifying overall business trends. You can measure for the number of times a feature is accessed per day/week/month or for the number of times it is accessed per login. Each offers a separate insight into customer behavior and should guide your customer success efforts. For example, if a user is regularly accessing your product but not a specific feature, then they may benefit from an educational campaign about the benefits of said feature.
For the latter model, your equation would look like this:
So, the feature adoption rate would be 12.5% if the customer uses a certain feature 5 times and logs in 40 times a week.
Overall, the findings of these metrics should guide your customer success efforts toward successful customer adoption. Now feature adoption is just a snapshot in the Customer Lifecycle. To reduce churn and keep customers its key to keep up the good work and maintain a positive customer lifetime value.
Successful adoption leads to increased customer lifetime value (CLV). If your product usage and adoption rate calculations are telling you that a customer is losing interest, it’s time to implement some CLV boosting strategies, such as:
This is how you calculate the CLV. ARPA is the Average Revenue Per Account ( Customer) with a fixed time variable. Monthly, Weekly, Quarterly, Yearly.
As mentioned above, some features of your product provide greater value than others. Time-to-value measures the time it takes for users to obtain their desired results from a given feature.
Naturally, you want this time to be as short as possible, because adopters who derive value from your product more quickly are more likely to become regular users.
You should prioritize TTV assessments surrounding your key feature and provide a path of least resistance for your users. The shorter you can get this value to be, the more likely you are to encourage an adopter to take the next step in the user journey.
Determine your product's core features and value opportunities, then use the above metrics to understand if you provide the best user experience in your product.
Generally, you need to measure these within a time period to obtain more specific guidance. Focus on the key actions that support your product's user journey, then evaluate whether users are taking those actions.
As you have seen, product adoption is a key factor to your business success. With each feature release it is important to maintain a consistent database and track each key metrics and northstar metrics that you have set for yourself.
I can’t emphasize more on the importance of measuring your product adoption. It helps to define your product roadmap, identifies Persona Gaps, new Use Cases and furthermore if the equation between Value and price is right. In these cases you need a platform that helps you to identify which customers are healthy and which are at risk. With journy.io you will get a better sense of how your business is doing, not only monitoring the leads or customers that may be at risk of bad health, but also its corresponding business value. Encounter the Health score for any segment, feature, campaign or content. See not only how healthy each customer is, also uncover which interacted features, channels, campaigns, web content is producing healthy or unhealthy segments of customers.
To conduct a correct B2B PLG motion, you need to act on both accounts and users. First, proper product-qualified accounts (PQAs) must be identified, to then engage with the best possible users (PQLs) from within these specific PQAs. We're today introducing relatioship properties to improve that process.
While product-led growth (PLG) and sales-led growth (SLG) may seem like they are at odds with each other, they can actually work together to capture full business' potential.
Changing the way you do business, case by case.
Detect which signups are most likely to buy. Sell more with less effort.
Automatically surface product qualified leads.
Prioritize PQLs call lists and engage with quick actions.
Add tasks and full PQL context to existing CRM and other engagement tools.
Automated sales playbooks and collaborative inbox.
Onboard. Monitor. Get expansion signals. Reduce churn, proactively.
Automatically detect churn & expansion candidates.
Accelerate onboarding and product adoption.
Align activities around 360° customer view, with health and onboarding scores.
Automated CS playbooks and collaborative inbox.
Build revenue workflows, based on how people use your product.
Use machine learning to uncover new sales opps.
Add slow accounts to nurturing campaigns.
Optimize engagement playbooks for maximum conversion.
Leverage any data without needing engineering.
See which impact your product features have on revenue and churn.
Analyse feature importance, usage and impact.
Build key product metrics without SQL, nor coding.
Easily create customer segments based on any product interaction.
Comply to GDPR and CCPA.
Create your free account and start driving a product-led growth strategy with the tools you're already using.