Over 90% of a typical app's users are dormant or inactive

Don't believe us? Here's the data we calculated from over 30 million users across several apps in a variety of industries:

*As you can see, new users, who typically get a disproportionate amount of attention with onboarding campaigns and user journeys, are actually an incredibly small portion of the average app's population.

What is a dormant user?

First, let's get into some definitions. Here's how we defined the different user segments in the table above:

  • New user: A user who was first seen on the app in the last seven days
  • Active user: A user who performed a conversion event (a purchase, a workout, a meditation, or however you define a conversion event) in the past seven days.
  • Recent user: A user who performed a conversion event in the last 8-14 days.
  • Lurking user: A user who visited in the app in the last 14 days, but hasn't done a conversion event in the last 14 days.
  • Inactive user: A user who hasn't visited the app in the last 14 days.

*Note: We can play with these definitions, but the results stay largely the same.

As you can see, by far the largest user segments are those lurking or inactive users — users who haven't delivered any value to your app in the past 14 days.

After them (at a measly 5.6% of the population size of the Lurking/Inactive user segment) comes the active user population, followed by new and recent users who each make up half of the size of the active user segment.

(Try it yourself and let us know what you find for your app.)

Why dormant users matter

As much as we'd like to pretend it isn't true, a lot of CRM comes down to a numbers game. Every user isn't going to convert every day, so we need to rely on a variety of users doing different things to help us reach our key KPIs.

From the raw numbers show in the table above, even getting just over 5% of those dormant or inactive users to convert would be almost equivalent as getting every single active user to convert. Getting 10% of them to convert would be more than double the conversions compared to if every single one of your active users converted.

Your dormant users are a potential powerhouse, and you don't even have to go acquire them!

Let's talk about user acquisition (UA) for a second —

According to BusinessofApps, customer acquisition costs have risen by 60% in the last five years (caused by a variety of factors including increased privacy legislation, removal of third-party cookies, the introduction of iOS 14.5, and more). BoA references Charles Nicholls, the founding director and chief strategy officer for SimplicityDX, who says the average CAC has risen to $29 for every new user (an increase of over 200% since he carried out a similar analysis back in 2013).

In addition to rising user acquisition costs, app store competition is at an all time high. With over 1.8 million apps to choose from, users have more options than ever before.

Look at just the meditation space — more than 2,500 different meditation apps have launched since 2015. Standing out is harder (and more expensive) than ever.

Meanwhile, your dormant users are just sitting there, right at your fingertips with your app already installed.

How traditional CRM creates dormant users

At best, traditional CRM represents a mis-prioritization. At worse, it actually causes customers to become dormant.

Traditional CRM prioritizes the wrong groups

Most CRM managers spend their time building, testing, and optimizing user journeys and campaigns, but we have to ask ourselves who these campaigns are focused on and how this relates to the various proportions of our user population.

For example, companies often spend a significant amount of time building their onboarding campaigns, but as we've shown above, these campaigns only apply to around 2-3% of the average app's user population. Again, if we continue to follow the data, approximately 5% of these remaining users will become active users, while the remaining 95% will join the rest of the dormant/inactive pool.

Other common CRM user journeys and campaigns like abandoned cart campaigns only apply to "Active" or "Recent" segments (a combined 7-8% of the user population).

Occasionally CRM teams will create reactivation campaigns, but these are usually more of a 'discount-led,' 'throw everything but the kitchen sink at them' attempt to keep users from churning (There are dozens of metrics for "reactivation campaign success" online. Most claim anywhere from 5-15% success with some as high as 30%. I'm guessing the average is quite a bit lower than that).

This begs the question: Should we be spending virtually all of our effort on ~2-10% of our user population, or would some of our effort be better spent trying to reengage the 90%+?

Standard CRM practices can drive customers away

So, if your dormant user segments aren't getting your carefully curated and tested user journeys, what are they receiving?

That's right: Ad hoc messages.

Ad hoc messages are often far from our best work. Not only do they take much more of our team's time and effort to create (as they're not as "set it and forget it" as user journeys), but they often don't benefit from the testing and learnings from our established journeys, and they present less of a cohesive "journey" or experience than our user journey messages do.

Don't get me wrong, one-off ad hoc messages can produce great results, but those results typically aren't as consistent. More often than not, these notifications are thought of as intrusive (CleverTap found that 28% of users uninstall apps because of excessive notifications).

So, how can we resurrect dormant users, and keep our new and active users from becoming dormant?

After all, you only have one CRM team. How can they best spend their time without letting any of your current segments slip?

Propensity modeling is the answer

Don't tune out on me yet. You're likely more familiar with propensity modeling than you think, and it's actually easier to implement than you'd expect. This simple shift in thinking and practice can produce sizeable results in reversing the damaging effects of traditional CRM practices and engaging your dormant users.

What is propensity modeling?

Propensities are essentially predictions that come from statistical models that predict whether or not a user will do something in the future (e.g., purchase, subscribe, churn, etc.) based on what they, and other users, did in the past.

Propensities are constructed by evaluating large collections of data (e.g. an app's event stream) and then drawing connections between the various events users interact with and the typical outcomes that result after a user does those interactions.

In some ways, propensity modeling can be thought of as very complex behavioral segmentation.

How to use propensity modeling to engage dormant users

We typically use user propensities in three main areas:

1. Providing product and content recommendations

Did you know that product and content recommendations used propensity modeling?

If you think about it, we're essentially saying "out of all of the products or content that your app has available, which is your user most likely to interact with?" ...and that's what we send them.

Note: You have to be careful when selecting recommender systems. Most types of content-based and collaborative filtering recommenders quickly devolve into popularity contests (showing users the products which are most viewed...not necessarily the ones that are most likely to lead to conversions). Our push notification/SMS integrated product recommender system is optimized for conversions and based on consumption sequences, which keeps it lightweight and adaptable.

Why product recommendations trump abandoned cart messages

Ok, by the numbers, abandoned cart messages outperform product recommendation messages, but there's a critical difference that gives product recommendation messages an edge: A user doesn't have to abandon a cart to receive one.

This critical distinction makes all the difference, as this means product recommendation messages — which are still 289% more effective at driving conversions than standard lifecycle messages — are applicable to your dormant/inactive group.

In other words, although the abandoned cart messaging observed in our study was 308% more effective at driving purchases (“Revenue events”) compared to general or “standard” notifications (driving 526 additional conversions from 86,948 messages sent), this app was able to send almost 6x the volume of product recommendation messages, driving just under 3,000 purchase events—almost 1,000 more purchase events than the 1.2 million general notifications sent over this same time period.

(Want to learn how to integrate product recommendations into your push notifications? Click here.)

2. Understanding event propensities to guide user behaviors

Another incredibly useful tactic is running a propensity model over all of an app's events to look for patterns in user behavior that can lead to particular outcomes (e.g. churn, purchase, subscription, etc.)

Here's an example output of the top 20 and bottom 20 events (out of 363 events total) that lead to retention or churn for an eCommerce app with almost 13 million users. The model then predicts the probability that doing an event will influence next-month retention (whether a user will still be on the app 30 days after the time of measurement):

Darker blue means the event corresponds to increased retention, while darker red means it corresponds to increased churn.

Notice a few things:

  • Many events that have a high impact on retention for brand new users have a very different impact for returning users (in fact, many of the strongest retention propensities for new users were actually moderately strong churn propensities for users who’d been on the app more than a month).
  • The 8th event from the bottom (right-hand list) is actually a placeholder for the user simply not showing up at all. That’s only 8th from the bottom. There are things users can do on the app that actually have a higher impact on churn than not showing up at all.
  • Some of the churn propensities suggest ways you might want to segment your users from the very start. For example, using a gift card on your first day on the app has a strong relationship with churn. That suggests there are some users who come to the app purely because they’ve been given a financial incentive to do so. Counting those users in your overall churn numbers might not make sense - you might want to track those users separately and message them differently.
  • Submitting a rating on the first day - and actually, submitting a rating no matter how long you’ve been on the app, indicates a strong propensity for retention.
  • Only one of the events in the top 20 has to do with making a purchase and it was number 20, and it wasn’t the actual completing of the purchase, but a particular optional step users could take in the step out process. It often doesn’t make sense to push-push-push users to buy early and often. That can push them away. There are other things you can encourage them to do.

The most influential event for first-day users was to click into a loyalty savings program that incentivizes users to stick around for a month. For first-week users, it was reviewing an order (so, yes, actually buying things does impact retention). For first-month users, it was reviewing a schedule page that listed upcoming events. And for users more than a month on the app, is was starting a review - not completing a review, just starting it.

Running this analysis may sound intimidating, but it's actually pretty simple to set up. Most customers are up and running in less than an hour without having to involve engineering resources. If you're interested in having us run this for you, please reach out.

We ran the same analysis on a health and wellness subscription app and discovered things like:

  • Pressing "share workout" was the single most important event indicating that a user would stick around
  • Connecting a wearable (like an Apple watch) had an incredibly strong influence on user retention
  • Deleting workout data was a stronger indicator than actually completing a workout
  • Logging in with Facebook or Google (instead of making a dedicated user ID and password for the app) indicated a user who was much more likely to churn

Having key insights like this can help you come up with new outreach or product updates that highlight your "stickiest" features, while steering users away from those features that are less likely to contribute to retention.

3. Using propensities to trigger messaging, instead of events

As mentioned, what makes users dormant or inactive is the fact that they haven't completed any events in a while. Therefore, they're typically not eligible for any of your triggered campaigns (except for the odd reactivation campaign that's triggered by something generic like "user hasn't visited the app in > 14 days).

But, while this user may not have been active in your app, they are still active in their daily lives and, therefore, their various propensities are always changing. If you can capture and build upon those propensities, you can have much more meaningful and effective outreach.

For sake of example, pretend that you sell printer paper and your residential customers have been inactive since the beginning of June. You sent a reactivation campaign after 30 days of inactivity, but there was little response. If you were using events, these users would now be at the mercy of your ad hoc messaging.

On the other hand, if you were using propensity modeling, your system would start detecting an uptick in propensity to purchase around the end of August or early September (e.g. Back-to-School season), and automatically start ramping up your messaging volume and engaging users.

Instead of triggering on events, trigger on propensities.

Triggering on propensities also allows you to respond in a much more effective and personal way. For example, not every user who is "ready to buy," is equally "ready to buy."

  • A discount might be really effective at converting a user who is 60-80% ready to buy, but it would be giving away margin to send a discount to a user who is 95% likely to buy.
  • Similarly, sending a discount to a user who's only 25% likely to buy would be much less effective than sending them a product recommendation message.

Here's more detail:

Finally, by truly monitoring and adapting to changing user propensities, we can build entire AI-powered user journeys that fit your user's natural ebb and flow.

For example, here's a journey for a dormant user who was resurrected (yellow, red, and brown dots indicate activity. Blue dots indicate messages sent by our system.):

Note that the messaging cadence slowed down when the user was dormant and then sped up as the user started to be more active.

Contrast that with an experience for a very active user over the same timeframe:

78 messages vs. 15 messages 😲 What a difference!

If these users were both getting standard ad-hoc and user journey messages, the first user would have likely churned and the second user would have been greatly underserved. (Here's more detail on AI-driven user journeys.)

Traditional CRM leaves dormant users in the dust

To effectively engage this large, reachable, but mostly untapped audience segment, we need different tools and strategies.

Thankfully, using propensities doesn't take a substantial additional time or resource investment. It just takes a slight change in thought and the use of tools that support these new workflows.

Every Aampe customer uses at least four elements of propensity in their messaging (messaging timing, frequency, content, and copy) out of the box and gets substantial results over traditional user journeys.

To find out if Aampe can help you revive your dormant users, reach out.

Cover image credit: rawpixel.com on Freepik