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May 31, 2017

A Data-Driven Approach to Mobile App Advertising

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In 2017, the average consumer spends more time every day using mobile apps (198 minutes) than they do watching TV (168 minutes). Given the surge in Smartphone adoption in recent years, it’s easy to see why marketers view mobile apps as a goldmine.

However, revenue doesn’t come as a direct result of building an awesome app. If you want to generate money, you need to learn how to market your app effectively.

Facebook ads can drive highly targeted traffic to your app’s product page, and if you’ve optimized the page appropriately, this will result in a high conversion rate and profitable ROI.

This is easier said than done.

When marketing a mobile app, you may find yourself basing strategic decisions on assumptions. Unfortunately, an assumption-driven approach to marketing is like taking stabs in the dark and praying for success.

By putting your assumptions to the test and letting the data guide your decision-making process, you’ll stand a much better chance of finding the right audience for your app, creating a powerful value proposition, driving revenue and getting existing customers to re-engage with your app.

Empirical data helps to guide marketing decisions at every stage of the customer journey, from awareness to loyalty.

Key Performance Indicators (KPIs)

I recommend tracking the following KPIs in order to inform your advertising strategy.

  • Total installs – This is the most crucial KPI that all developers must track. If you’re not getting installs, then you’re not getting revenue. You should watch this metric very closely for 72 hours after a launch.
  • Cost per install (CPI) – You need to know how much it costs, per user, for an app installation to take place. If you’re not tracking this metric, then it’s impossible to allocate a budget for your marketing campaign.
  • Session time – The amount of time each user spends in-app indicates how engaging your app is. If you’re getting plenty of installs but people aren’t sticking around for long, then the problem is with your app, not your marketing.
  • Add to carts – Although not as useful as total installs, add to carts are still an indicator of interest. You should always track this data so you can remarket to audiences who have added the app to their cart but who haven’t purchased. These users typically convert at a much higher rate than cold traffic.
  • Retention rate – Sadly, many app marketers focus all their energy on getting initial customers through the door rather than retaining existing customers. Your retention rate is a great indicator of how engaging your app is.

Acquiring Users

Buying data is an essential process in rolling out a mobile app campaign. If you’re going to start pumping big money into a campaign, you need to fine-tune your advertising at the lower end of the scale first.
This is achieved via split testing.

For instance, you may want to try targeting different geographic regions to see how they respond to your advertising. If you receive a low CPI (cost per install) for your Scandinavian targeting, but an unacceptably high CPI when targeting the United Kingdom, this could be because consumer prices are higher in Scandinavia than they are in the United Kingdom, so your pricing needs to be adjusted.
You may find that females respond particularly well to your app that you initially designed with a male buyer in mind. You may also find that certain age groups convert, while others don’t.

All of these vital insights need to be purchased by running ads at a low cost and analyzing their effectiveness.

By running ads at a low cost ($5 per day, or thereabouts), you can put all of your audience assumptions to the test and then allow the empirical data to guide your ad strategy moving forward.

Post-Install Campaigns

Smart marketers know that customer retention is the key to long-term revenue.

Fortunately, Facebook allows you to target customers who have already installed your app. You can send this audience specific, high value content which encourages them to re-engage with the app if they’ve drifted away from it.
People aren’t always keen to open marketing newsletters. But when a remarketing ad pops up in their Facebook newsfeed, reminding them of the awesome mobile game they played several months ago, with an incentive to rejoin the action – these types of ads convert particularly well.

Cohort Analysis

In order to evaluate the way users engage with your app, I recommend performing a cohort analysis.
Cohort reports allow you to group users together and analyze each group’s collective KPIs (in comparison to one another). For example, you may want to analyze a cohort group that installed the app via a Facebook ad targeted at men and compare their KPIs to a cohort group of women.

If you see that one group has a higher amount of sessions and spends more time in-app on average, you can make deductions about where your highest quality users come from, as well as what you need to modify in-app to engage underperforming users.

A cohort analysis enables you to track user segments for specific time durations. This is crucial, because new users won’t engage with the app in the same way that users in an advanced stage of their customer lifetime will.

With this data, you can determine where users are losing interest in your app and then make modifications at this juncture to re-engage them.
The more data you have to draw from, the more you can improve your advertising strategy and user experience – ultimately resulting in more app installs and a lower CPI.

How else can data be utilized to improve mobile app advertising? Please let me know in the comments below.


avatar

Aaron Agius, CEO of worldwide digital agency Louder Online is, according to Forbes, among the world's leading digital marketers. Working with clients such as Salesforce, Coca-Cola, IBM, Intel, and scores of stellar brands, Aaron is a growth marketer - a fusion between search, content, social, and PR. Find him on Twitter, LinkedIn, or on the Louder Online blog.

5 Responses to “A Data-Driven Approach to Mobile App Advertising

    Nice points, i will follow up your approach.

    avatar Aaron Agius says:

    Thanks! I’m sure you’ll find a lot of success with a data-driven approach.

    avatar Oliver says:

    Interesting information & combination of helpful stats.

    avatar Aaron Agius says:

    Thanks, Oliver. Glad to hear it was useful for you.

    avatar Jenny says:

    This Data Driven approach seems to be resulting. I will try it. 🙂

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