As app marketers, we pride ourselves on being data-driven in all we do, yet when it comes to launching a new app we often start our marketing and promotional activities in an ad-hoc fashion only to course-correct once we have enough of our own analytics data. Usually, this is too-little-too-late and we’ve missed the opportunity to establish our app in the critical early phases. This article provides you with guidance on how to be data-driven from day one.
A New App
Your team is getting excited, your app is almost finished, and there is an overall sense of accomplishment floating through the office. You are preparing to launch your new app to the world and reap your newfound fame and wealth. The responsibility passes from your development team to marketing and as you sit down to plan out the overall promotional strategy, you quickly realize that what you have is an ad-hoc approach and no idea how to allocate resources (time and money) across the myriad of promotional options.
Basically, you’re flying blind. You’ve read a few how-to articles about marketing your app through social, ASO (app store optimization), and paid user acquisition is getting too expensive. You know you need a mix all of these but have no sense of proportionality and how much effort and resources to devote to any specific promotional tactic. You know that once some time passes and you have real analytics data to work with, you can optimize your mix to isolate the tactics that are working and eliminate those that aren’t. Great, but you might have just wasted a few weeks or a whole month and in ‘App-Land’, that’s a lifetime.
Unfortunately this scenario is all too common and it’s interesting because as ‘growth hackers’, we like to think we’re data-driven in everything we do. However, often when it comes to allocating resources before and during the launch phases of our apps, we end up trying to do a little bit of everything and we have no idea if we’re spending our time and money wisely.
If you’ve ever been in a situation where you started with a promotional mix that changed dramatically once you applied your own analytics data, it’s safe to say that the decisions you made up front were the incorrect ones. The sweet spot is when analytics data helps you optimize and tweak but if you have had to make major course corrections, then you need to evaluate the decision making process you had at the beginning.
Normally, what we’d like to do is survey our target audience ahead of time to learn about their app discovery habits. From this insight we could better construct the right promotional mix that matches with how our audience typically discovers apps. While it’s probably not practical for us to do this kind of intensive surveying beforehand, we can fall back on the work of the good folks at Forrester Research who have done it for us.
The chart below shows the app discovery habits of 4,315 European adults. They were asked to “Think[ing] about the applications that you have downloaded and now use on your mobile phone, how did you initially learn about those applications?”
You’ll notice that since multiple selections were allowed, the percentages will not add up to 100% but the important takeaway for a marketer is the proportionality between the various answers.
Let’s consider the following stats for IOS users:
• 63% of users find apps through general browsing in the app store
• 19% of users find apps through social networks
• 6% of users find apps through clicking mobile ads
What these consumers are telling us is that for every app they find and download through social, they will likely download three from browsing the app store. For every install from an ad clicked on mobile, they will download three apps from social media sources.
Applying The Data
There are really two ways to use this data effectively. Firstly, you can use it to help set your initial marketing / promotional mix and resource allocation for launching a new app. For example, prior to looking at this chart you may have thought you could launch a massively successful app with just some paid advertisements and social. Perhaps you fancy yourself a social media ninja and thought clever tweets, social media engagement, and community building could carry your app to success. Hopefully this myth has been dispelled and you can now make better decisions up front.
It’s worth repeating here that it’s the proportionality that matters. For every dollars worth of resources (including time) you plan to devote to paid advertising, you should devote an equal amount of resources to blogger outreach. For every dollar of you devote to social media, devote three to app store optimization, and so on.
Consumers are telling us how and where they discover new apps, and we ignore them at our own peril. The point isn’t to set your tactics in stone but only to help you get started with an appropriate mix during your pre-launch, launch, and short-term post launch phases. From this starting point, your own analytics over time will reveal opportunities for optimization.
Clearly, there are some items from the chart that may not apply to your circumstances and you can safely ignore them.
Finally, the data serves as an analysis tool to benchmark your own results over time. For example, I’ve often heard app developers say that organic social media wasn’t working for them, so they cut back on their social effort. While this can be perfectly reasonable, it doesn’t change the fact that roughly 20% of consumers find apps through social. So the question to ask yourself is:
• Why are my results different?
If you’re not getting results from organic social, does it mean you should scrap social altogether? Not likely, and perhaps the cause is simply: you’re just not doing it right. If you can’t reasonably assess your audience as being different from the wide swath of surveyed consumers in the Forrester report, then you’d be better served by reassessing your social strategy and making changes rather than severely cutting back your efforts.
If your experience over time begins to vary widely from the benchmarked data in any broad category, it’s likely worth digging a little deeper to uncover potential problem areas or opportunities.
A Note About The Data
I have several issues with the data from this survey. At a high level I don’t believe the questions asked by the survey in many cases are specific enough. For example, would a consumer really be able to differentiate between general app store browsing vs. browsing the ‘top-rated’ apps? Secondly, I have serious questions about an individual’s ability to accurately recall the sources and proportionality of what drove their own download behavior.
The study also indicates word of mouth from friends and family as a huge driver of app discoverability; however, we have no idea about the deeper sources of those downloads. If I discover an app through social and subsequently tell a friend who also downloads the app, the value of social is being understated. I could go on. The survey is far from perfect and not granular enough; however, it can still serve as a valuable place to start.
In a time when the rate of change is accelerating and the ultimate success or failure of an app is often determined in the first few weeks of its release, we are best served by being data-driven from day one.
Too often have I seen app developers ‘throw spaghetti at the wall to see what sticks’ only to radically change course once their own analytics reveal problem areas. Whether you are a startup or major app publisher, your resources are limited and the window of success is short. Spend time upfront to determine your initial resource allocation / promotional mix and adjust as necessary.
Photo (c) jfraissi via Flickr
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