The rise of social media in recent years has resulted in more and more marketers turning to influencer campaigns in order to raise brand awareness. Their aim? To tap into an influencer’s audience and use the power of their… well… influence.
However, there are many variables to consider if you’re thinking about trying this out. Though it’s easy to pay for someone with a social media following to promote your app or game, the success of that promotion hinges on some key details. Two of the most obvious are:
- Making sure that the influencer’s audience is one that would actually want to engage with your app. Using a children’s entertainer Youtube channel might not relate best to your finance app.
- Ensuring that your plug is well placed. Appearing halfway through a long, 20-minute video could cause you to miss out on a number of potential users who dropped early in the video – which most do. Ideally, your influencer would open with the sponsorship details and plug your app at the beginning of their video.
Now, let’s say you’ve passed the initial stages of preparation and covered the dos and don’ts. You’re ready with an influencer who’s aligned with your product and offer, and now it’s time to ask yourself the pivotal question:
“How can I measure the immeasurable?” (Is it a word? Computer says yes.)
The biggest challenge that comes with using influencer campaigns is how to measure them. Many YouTube videos or Instagram stories have links or swipe-up functions that allow the audience to quickly access a product page and download the app/game. Ace. But there’s a problem. Many users aren’t actually clicking on those links or choosing to swipe up, but they do remember the promo and may in fact, download because of it. Thus, mobile marketers face the same measurement challenge presented by any offline campaign (TV, Billboard etc.)
- How do we quantify the real number of users the campaign attracted?
- What is the real cost-per-install (CPI) for our efforts?
- Essentially – how do we measure view-through attribution?
How to Measure View-Through Attribution
Let’s take a moment to examine the user journey.
Arriving at their favorite YouTube channel to watch the review of the latest MCU movie release, a user sees an influencer pushing a game instead of relaying their reasoning as to why Thor isn’t authentic to the original comic book. The user likes the look of the game because it’s similar to others they have, and they want to download. It’s highly unlikely that they’re going to hit the “Link in the comments” at that moment. Firstly because they might be watching on their TV or computer so they can’t, but also because downloading an app or game probably wasn’t the reason they showed up to watch this particular video. What’s more likely is that the user will commit the name of the app to memory to search for it a bit later (or if they’re watching on their TV, they may search in parallel.)
The moment the user decides to search as opposed to clicking on the link, we lose the ability to track them.
So what’s the solution?
We have to start looking at the trends in our data and understanding the uplifts in metrics such as organic searches created by such campaigns. We know two things:
- More people will be searching for our app with an effective campaign.
- They will be searching specifically using the branded name of our app.
Using a tool like Polarbeam can help us understand this.
In the image below, Polarbeam shows App X’s daily organic search downloads (Apple doesn’t surface this metric, it is actually synthetically calculated by Polarbeam). It’s clear to see the large increase in traffic once the customer launched the YouTube campaign. Using the pre and post-periods, we can compare the two periods and ascertain the uplift achieved via the campaign.
Alternatively, we could use forecasting tools that estimate what we might have received had we not run the campaign. Polarbeam’s AI uses historical data to predict the alternative outcome.
This data would also be supported by any Apple Search Ads (ASA) campaigns run at the time. If the uplift is indeed caused by the influencer, then we would expect to see a correlated uplift in branded paid search downloads.
In this example, the other half of the puzzle would be to look at the Google Store results. Influencer campaigns are device-agnostic after all and therefore any uplift that was seen in Apple should be replicated in Google (and vice versa.)
Organics in Google are a more complicated affair when compared to Apple. However, they do provide a detailed breakdown of search and acquisition terms allowing us to pinpoint the exact number of users that searched for any single term and consequently hit the download button. In the below image, the dashed line represents the number of users who searched and downloaded via the very specific app branded term. We see the trend matching that of Apple’s organic uplift.
Looking at the data through this lens allows us to derive the impact of our chosen influencer/influencers more accurately and ultimately reassess the CPI (it will likely be far less than first thought.)
What if I use multiple influencers at the same time?
This is a question I often get. The truth is, while we can have the tools to measure intent, it is far harder to identify inspiration. My counter-question is always “Why is it important?” If you chose to cluster multiple influencers together, you should be interested in the total uplift created by that cluster. I would answer the question “What if we want to know which one to reuse” with a straightforward ”You don’t!”. Once you’ve saturated the audience of a particular influencer, the results of readvertising with them will be far less. It could be six months before you decide to reuse their services and the social media landscape can change a lot in that time.
What if UA increases at the same time. How do I know that the search uplift was created by my influencers?
Any time we add additional marketing efforts, we create ambiguity in terms of the cause of our organic uplift. In these scenarios, tools such as Polabeam are imperative to help us establish baselines and direct the specific reasons we see for an uplift in our organic KPI’s.
Main takeaways
Correctly executed influencer campaigns can certainly bring more users to your app and result in an increase in downloads. However, there are still measurement challenges to overcome like quantifying the users that were attracted by a campaign and CPI.
Polarbeam can help you navigate the tricky influencer campaign terrain. To get a feel for the product or to speak to one of our experts, get in touch.