In-app, pop-up, IAM, interstitial – call it whatever you like, but the definition is the same. It’s a messaging channel characterised as a window displaying over a webpage or an app screen. Its goal is to grab users attention and to direct the user to do the desired action – subscribe to a newsletter list, shop the new sale, watch the new show, or whatever else you can imagine.

Image: Examples of web and mobile in-app messages

With in-apps, as well as all the other marketing campaigns, we always focus on counting conversions, increasing conversion rate, and revenue. However, few take a closer look at the flip side – the negative impact of in-app messages on engagement metrics.

Why do in-app messages cause negative effects?

All owned marketing channels like email, push notifications, SMS and in-app messages can negatively impact metrics to some extent, but in-app messages are particularly notable because they are disruptive to the user experience.Users can ignore emails, push notifications and SMS messages, but they can’t ignore in-apps.

In-apps interrupt whatever the user is doing and compel them to either take the desired action or close the message. While these actions may seem simple, they add extra steps to the user journey, which means additional friction, which means worse user experience – annoyance due to the pop-up, inability to find a close button, losing track of the original purpose for visiting the website or app.

How do negative effects of in-apps manifest?

The most noticeable negative effects of in-app messages show up in the following metrics:

• Bounce Rate – Users leave websites or apps due to intrusive pop-ups.
• Exit Rate – Higher exits on pages with pop-ups.
• Session Duration – Less time on site and fewer pages viewed.
• Conversion Rate – Interruptions can cause drop-offs in conversions.
• Subscription Churn – Annoying pop-ups can tip users into cancelling subscriptions.
• App Uninstalls – Users delete apps due to persistent interruptions.
• CSAT/NPS Scores – Pop-ups lower satisfaction and loyalty scores.

Let’s analyze some recent in-app campaigns I’ve run to assess their positive effects and explore whether there are indeed any negative outcomes.

In-app campaign analysis

Let’s use a push primer in-app message. This mobile in-app message aims to increase the push notification opt-in rate for iOS and Android users. Eligible users receive it every three months if they have push disabled.

Image: Push primer in-app message examples. *Illustrative only and do not represent actual brand messaging.

The primary goal of this in-app is to increase push opt-ins, and it was fairly successful, achieving a 2.5% increase, translating to 54,100 additional users with push enabled.

Furthermore, engaging additional users through push notifications improved their 30-day retention rate (based on sessions) by 1.8%.

On the surface, the in-app message appears successful—we gained more users in our push audience, resulting in greater long-term engagement. However, let’s examine whether there were any negative effects.

The product in the video streaming industry, so we consider a session successful if the user has watched at least one video. For this metric, there is 1.4% increase in users closing the app without watching anything.

Additionally, there is a 3.5% increase in subscription cancellations, which is more than 3,200 lost subscriptions! For users who received the in-app two times, the increase in cancellations goes up to 6%.

This is concerning. Should we stop this in-app?

The answer isn’t to stop but to optimize by reducing or mitigating these negative effects.

How to mitigate the negative effects of in-app messages?

Reducing the negative impact of in-app messages can be achieved through global and local optimization.

Global optimization implies implementing a sunsetting policy or a suppression rule that will stop users from receiving in-apps if they haven’t engaged with any recently. For example, do not send more in-apps to users who have not clicked on any of the last 5 in-apps. From my analysis, this threshold should be fairly low – between 4 to 6.

In most cases, the suppression will offset the majority of negative effects, but if they still persist, local optimization is the way to go.

Local optimization is tailored to specific in-apps or categories with similar characteristics. In our example, we could identify the user types most likely to cancel their subscription and exclude them from receiving the in-app. Additionally, we can limit views to one per user to further reduce cancellations. Local optimization will vary based on your in-app settings, triggers, audience, and other factors.

The Conclusion

In my option, in-apps messaging is the channel with the most power to influence conversions out of all commonly used messaging channels. Email, push and the others can be hit or a miss, while it’s almost unreal how consistently in-apps drive positive incremental lift. 

My goal with this article was to raise awareness of these potential negative effects and encourage you to give them a thought the next time you’ll be counting those sweet conversions. Getting a hundred conversions may seem fantastic, but it loses appeal if 120 users cancel due to excessive pop-ups.

To conclude, it’s essential to acknowledge that behind every conversion uplift, there could be a decline in other metrics. Making well-rounded decisions means looking beyond just conversions to weigh the net balance between positive and negative effects.