Are you doing everything you can to drive product retention? We caught up with Ken Rudin, Head of User Growth at Google, to talk about the little things Google does to achieve large-scale impact.
At Google, we’re of the mind that the growth process starts and ends with analytics.
We’re big believers in using innovation loops and an approach based on the scientific method to drive product growth—where trends are observed through data to identify key product experiences or features that affect the growth of a product (which we call “growth levers”); hypotheses are generated to explore potential ways to improve those growth levers; experiments that test those hypotheses are run and the results are analyzed; and finally, learnings are used to update our understanding of the product and repeat the process.
All of this allows our teams to make data-informed decisions that benefit users with clean, streamlined experiences.
While looping through this process will surface growth levers that can be used to drive acquisition and retention, there’s one particular area that I’ve found to be valuable across a broad range of products: raising awareness of existing features in your product.
Raising feature awareness
Developers will inevitably try to highlight new features they’ve added to their product. But, it’s sometimes the case that there are existing features in the product whose usage is correlated with user retention (i.e. sticky features), but which are currently only used by a small portion of users. Identifying and increasing the adoption of these features is very often a great way to accelerate product growth.
One of the easiest ways to raise awareness for an existing feature, function, or action is to change the UI so it’s more prominent. Another effective way is to provide pop-up messages in the app highlighting the feature (often called “in-app messaging”).
While in-app messaging might seem like an easy fix, there are effective and ineffective ways of doing this. A common—but usually ineffective—way to use in-app messaging is to bombard the user with messages about a feature when they first open an app. We’ve all seen the pop-ups that appear when we open an app, describing some (usually new) feature that we’re being encouraged to use, yet analysis usually shows that this approach has little impact. That’s because users aren’t necessarily in the mind-frame to use that feature at that particular time, so it’s easily forgotten.
Promoting a feature in context to drive usage & retention
A much more effective way to increase awareness of a sticky feature is to promote it “in context”. In other words, the best time to make the user aware of the feature is in a use case where that feature would be valuable to them at that moment. That’s the time when they’re most likely to want to use the feature, and once they’ve used it, they’re much more likely to remember it and use it again.
For example, imagine you’re the Product Manager on Google Maps who was responsible for the “Pitstop” feature that was launched a few years ago (when using Google Maps to navigate, the Pitstop feature allows you to easily find and add pitstops along the way for things like food, gas, or restrooms). Also, assume that analysis shows that usage of this feature correlates with user retention, yet not many people have discovered and used it.
The typical solution would be to announce the feature in a pop-up the next time a user who hasn’t used the feature opens Google Maps. But, that’s unlikely to be effective, since many users might just be looking for a nearby pizza restaurant, or traffic during their daily commute. They have no use for that feature at that time, so they’re not likely to remember it. However, if you wait until the user asks Maps to navigate to a location that is multiple hours away, showing the pop-up at that moment and asking if they’d like to add pitstops will have much more impact on feature adoption.
Adding entry points
Another good way to drive adoption of a sticky feature is to add entry points for that feature. That is, provide multiple places either within or outside the app to make it easier for users to discover and access the feature.
Google Search has long been able to provide weather forecasts for any location, as well as scores for various sports. To access that functionality, a user just needs to search for “weather”, or for the score of a particular game on their device. Those who discover this functionality tend to continue using it—but what if you were the Product Manager of those features and wanted to help more people become aware of them? Following the approach of creating more entry points, you could add tappable icons labeled “weather” and “sports” under the Google Search bar on your device. Anyone doing a search would now have a visible icon that they could tap on that not only raises awareness of the feature, but also makes it easier to use by no longer requiring the user to type in their search.
Listening to the data
Once we create a hypothesis about ways to move our key growth levers, we run experiments as controlled A/B tests and analyze the resulting data to see if it had the intended positive impact. Unfortunately, in most cases, it won’t. In my experience, only about a third of experiments show a lot of impact on the first iteration. But, we analyze the results, identify new insights, and then use what we’ve learned to adjust the experiment and try again.
Following this growth process might seem like common sense, but it’s surprisingly not used in a lot of companies. It might be because there’s an established mindset that a product or engineering team is responsible for creating new features, and a belief that iterating on existing features won’t provide as much value as adding more new features.
Successfully growing your product isn’t about just launching new features—it’s also about making sure those features are providing the value they were intended to add.
For decades, the technology industry has been operating on a build-and-release model: build a feature, release it, and then move on to the next feature. But launching new features is only half of the equation when it comes to driving product growth. The other half requires the complementary approach of experiment-and-iterate. Use data to analyze existing features and user experiences, run experiments targeted at optimizing those features/user experiences, and then repeat the process to optimize them further.
Becoming data-informed as an organization isn’t always an easy shift, but by taking steps to better understand, engage, and interact with users, companies can position themselves to innovate, grow, and succeed.