App Conversion Rate - The Best Conversion Rate for Your Mobile App

The Best Conversion Rate for Your Mobile App

Intro to Mobile App Conversion Rate

Most Mobile marketers work tirelessly to increase their app’s conversation rate. The conversation rates they’re chasing could apply to app store installs, in-app purchases, subscriptions, etc., but the idea is the same… get your prospect to complete an action that converts them to a paying customer.

But what action should you be measuring? And how can you increase the likelihood of that action? As conversion rate optimization gets increasingly sophisticated with machine learning technologies, click-to-install conversion rates are no longer enough. This article will unfold the best practices in measuring conversion rates, the importance of segmentation, and how to optimize conversion rates for your app.

Let’s Start with A Definition of Conversion Rate

Basically, conversion rate measures the percentage of people who take a desired action against those who don’t take that action but could have. According to the app stores, conversion rate is measured as the ratio of total downloads to unique Impressions, where unique impression is counted when a customer views your app in the store.

How is Conversion Rate Calculated?

To calculate a conversion rate you divide the number of impressions by the conversion action or event: (Conversion Action / Impressions) x 100 = Conversion Rate

Here’s a simple example:

Let’s say you run a mobile ad campaign on a given platform. After the campaign, data shows that your ads reached 100,000 people on that platform. In other words, you achieved 100,000 impressions.

Now let’s say the action (conversion event) you measured is how many people installed your app. Let’s say there were 50 installs.

With the equation, (Conversion Action ÷ Installs) x 100 = Conversion Rate, your conversion rate would look like this: (50 ÷ 100,000) x 100 = 5%. So your mobile ad campaign had an install conversion rate of 5%, not bad.

Installs are an important milestone in your app’s growth, but what really matters is your long-term ROAS. You need to make your app commercially viable. The most important conversion rate metric for your app should be your install to “value” rate.

The Most Important Conversion Rate: Install to Value

Today, app retention, engagement, and LTV (Life-Time Value) are the critical challenges for mobile app marketers. Considering how much competition there is, it’s no surprise that roughly 20% of users churn after a single use and roughly 70% churn within 90 days. So which value-driven conversion rates should you be measuring and how can you optimize them? It all starts with effective user segmentation.

What is user segmentation?

User segmentation is the marketing technique of dividing up (segmenting) your user base into segments based on diverse parameters and then delivering the most relevant ads to each segment for more accurate retargeting. One could say, segments make up the secret sauce for successful mobile app retargeting activity.

Examples of segments could include nationality, app version, player’s age, level reached in-app, paying, non-paying, and much more.

Tips and Definitions of Useful User Segments

Paying Users – Paying users are those who make in-app purchases. Since you are already monetizing them, it is important to not also inundate them with ads. Though that doesn’t mean not showing them anything. You could show them the same number of ads but with different rewards. We also suggest creating sub-segments based on different levels of spending e.g., Highest LTV Users, High LTV Users, and Medium LTV Users. This way, you can serve different ads, frequencies, and pacing based on how much revenue a given user is devoting to your app.

Learn more about Targeting High LTV Users.

Non-Paying Users – Yes, even non-paying users who don’t generate revenue with in-app purchases (or are predicted not to) deserve a certain segment and treatment. It makes the most sense to show non-paying users system-initiated ads like banners or interstitials, as they don’t require users to opt-in and can be worth the lighter spend.

Check out 5 Tips for Successful Retargeting Ads in Gaming.

Geographic – It can be helpful to segment your users by the country, city, state, etc. that they reside in or use a certain app in. For example, the spending routines of players in Japan versus those in Mexico can be very different. This can be due to overall higher incomes in certain geographies, cultural differences, availability of competitive apps, etc. You can’t assume it’s just about spending, so delineating segments by geography can improve targeting and your user’s experience.

Dynamic Segmentation – Dynamic Segmentation is when certain segments can be updated during a user’s session. Let’s say a user was segmented as a non-paying user prior to their session, but during the session, they make an in-app purchase. With real-time ML technologies, the paying user segment will be automatically updated to include that user during their session. Dynamic Segmentation ensures more accurate targeting and a better overall user experience.

Read more about Real-Time Data Analysis.

Demographic Segmentation – Demographic segmentation divides your user base into things like gender, age, income, marital status, etc.. Demographic segmentation is a great starting point, but not very useful on its own as it naturally creates very broad segments.

Psychological Segmentation – It isn’t enough to know deterministic facts about your users like geography or demographic. Psychological Segmentation helps you segment your users based on values, personal attitudes, interests, and other personality traits.

Check out Creative Personalization for more on the phycological factors in creatives.

Behavioral Segmentation – Behavioral segmentation takes Psychographic Segmentation one step further by not asking who your users are but by asking what your users are doing. Behavioral segmentation is based on actions or inactions, spending or usage habits, session frequency, shared search history, etc. Another type of Behavioral segmentation is User Status Segmentation.

User Status Segmentation – User Status Segmentation is the process of dividing user segments based on their relationship to a mobile app e.g., segmenting users as prospective users, active users, engaged users, ex-users, etc.

Motivational Segmentation – Motivational Segmentation is the “why” users act a certain way and segment them accordingly. By segmenting based on motivations you can align a deeper level of creative strategy with ads that motivate your users and ensure a smooth ad experience.

Technological Segmentation – Technological Segmentation divides users into segments based on their preferences for mobile phones, tablets, computers, software, etc. For example, even if you know a user is on an iPhone it might be useful that they happen to use Windows/PC as their primary CPU as it affects their exposure to different apps, games, etc.

What In-App Events Matter to You?

With retention, engagement, and LTV (Life-Time Value) as your goals, segmentation allows you to measure various in-app events for a value-driven conversion rate. The trick is deciding which segments you value, which often depends on the genre.

For example, you are growing a role-playing gaming app, and you have learned that users who build a unique avatar vs using a stock one are much more likely to be retained and make in-app purchases over time. Or you have a casino app and find that users who grow their bets from small to larger vs diminish from large to small tend to spend more money over time.

Depending on your genre, there are all kinds of segments and corresponding conversion rates that will improve your ROAS. It’s all a matter of testing, testing, and more testing, and that’s why a modern, AI-driven, transparent Gen 2 DSPs are essential. The deeper you go, the better results.

Read more about how important it is to work with Second-Generation DSPs.

Mobile App Conversion Rate – Conclusion

We’ve learned that conversion rate and its optimization in mobile app marketing needs to be focused on install-to-value rate more than the click-to-install rate. And to determine value, it’s crucial that you are measuring the right segments. In an app economy dominated by low retention, free apps, and giant spends, it’s crucial to work with the right experts to help you measure and optimize to find users with the highest LTV.

Bigabid prides itself on using sophisticated machine learning technologies to both accurately measure and optimize your campaigns, and also transparent partnering to share insights to ensure your app’s success over time. To learn more about how to grow your app, reach out!