5 data-driven tactics to improve your product experience

Today’s most successful companies are utilizing a single mechanism to facilitate and optimize the customer journey: the product they’re selling. These companies leverage the product to drive adoption, deliver onboarding, fuel growth, and collect feedback—all the while reducing the need for human-driven tactics. This not only helps teams scale their efforts, but also drives down costs and enables more efficient growth.

As businesses use the product to do more of the heavy lifting, data becomes even more essential. Quantitative and qualitative data allows you to understand your users, so you can better meet (and exceed) their needs and ensure they continue seeing value in your product offering. 

Below, we share five ways to infuse data into how you improve your product, taken from our new e-book, “The data-driven playbook for product-led teams.” 

#1: Leverage data to target your launch announcements

Use data from a product analytics tool to understand users’ behavior, then build segments for in-app guides based on what you learn. 

While teams use multiple channels (e.g. email, blog posts, and social media) to announce new products and features, it’s most effective to deliver these announcements inside the product itself. For in-app launch announcements, make sure you’re targeting messages to users who will find the new feature most valuable or to whom the new functionality is most relevant. Remember: Your product likely serves multiple types of users, and very few features will be deeply relevant to all of them.

For example, if a new feature is meant to complement an existing feature, start by targeting users who currently use the feature to let them know about this new functionality and how it will improve their workflows.

#2: Personalize onboarding based on what you know about users

Leverage what you already know about your users to craft different in-app onboarding flows.

Whether your product serves users with different job titles, permission levels, or otherwise, in-app onboarding is most effective when it caters to an individual’s specific use case. Segmentation not only ensures your onboarding content will be as relevant as possible, but also helps avoid cluttering the UI with unnecessary information that can be distracting or frustrating for new users.

Here are some data points that can inform how you segment in-app onboarding:

• • 職名
  • Free trial start/end date
  • Time spent in the application
  • Features used

#3: Use data to identify products or features to sunset

As you track adoption data, take note of any pages or features that have low or no activity for 90+ days—these may be good candidates for a potential sunset.

Product teams are often largely focused on effectively launching and driving adoption of product and features. Sometimes, however, the most strategic move is to actually remove a feature from the user interface (UI) or retire a product entirely. 

Although low usage can prompt a sunset decision, it’s important to first consider the context since low usage levels might be a sign of poor discoverability or usability, not necessarily that users no longer find the functionality valuable. Try to determine the reasons for low usage before flagging a feature to be removed, and even consider asking users directly if (or why) they find the particular feature valuable.

#4: Segment your feedback data for deeper insights

Rather than only viewing customer feedback as a whole, segment it to learn what different types of customers are asking for. 

In order to extract valuable (and actionable) insights from customer feedback, it’s important to segment that feedback to better understand the unique needs across your user base.

Consider segmenting feedback data by things like company size, annual recurring revenue (ARR), role, industry, NPS response, assigned customer success manager (CSM), feature usage, or subscription type (if you have a free and paid version of your product). The way you segment will primarily depend on your business’ overarching goals and if you’re looking to launch or iterate on any specific initiatives.

For example, if your company is trying to move up market and target larger enterprise customers, you’ll want to look at feedback from these accounts specifically to see if there are any patterns. If your product team is looking for feedback after a recent feature launch, start by evaluating feedback from users who have engaged with the new feature.

#5: Let data inform your freemium product’s usage threshold

Look at product usage data from your paid product to identify average usage patterns, then consider what a scaled down version of certain product areas could look like. 

Freemium products give users access to part of a product for an unlimited amount of time, which requires deliberate decisions around what to include in the freemium experience. The goal is to offer enough to demonstrate the product’s value, but still leave users wanting more. 

It’s best to use data to inform your approach. For example, if your average customer creates four dashboards in your application per month, you can set the threshold at two dashboards and require free users to upgrade if they want to create any more. Additionally, if you see that a certain feature is only accessed by users with advanced skills, that might be a good feature to leave out of your freemium product.

Looking for more data-driven best practices? Check out our new e-book, “The data-driven playbook for product-led teams,” to learn how to take a data-driven approach to adoption, onboarding, growth, and more.