Aurelie Guerrieri is vice-president of global marketing solutions at Cheetah Ad Platform
Mobile advertising has reached a point where big data is beginning to line up with the big idea: native ads and content exquisitely targeted to the mobile user’s data signals, screen and circumstances.
The key to success is a focus on in-app behavioral signals to determine if the user is a candidate to make a mobile purchase, and of what type, and when. But sorting out which of the mobile native’s millions of data signals have the potential to yield the greatest return, then using technology to action that intelligence at scale, is tricky.
The haystacks are large, the needles are few and unique
At the time of writing, there are about 2 billion mobile internet users on the planet. As you can imagine, all that activity produces an enormous amount of data, a lot of it from inside of apps where data signals contain behavioural information of tremendous value to advertisers. The problem is that mobile users generate so much data that it is impossible for anyone to make sense of it all.
For advertisers to get down to what matters in engaging the mobile native, they first need to adopt a less is more approach and commit themselves to concentrating only on the signals that are indicative of what moves me down the path towards a transaction. Let me emphasize the word ‘me’ in the last sentence, because even if 10,000 other women around the world are interested in buying the same Burberry Brit trench coat as me, if you want my business, you’re going to have to find a message and a moment that are just right to get me to buy, not just repurpose a generic ad and drop it on my screen whenever you feel like it.
It takes good quality data to get good results
First-party data is essential – there is zero point attempting to create targeted native content from third-party data with no behavioural value that relies on ‘bucket think’ approaches to segmentation.
It’s also imperative that the data advertisers use is fresh. You don’t want to put someone in a category as a ‘great potential buyer’ based on data that’s old and may not really be indicative of anything about the me of today. So what if I downloaded Candy Crush six months ago? Maybe I downloaded it to entertain one of my kids on a long car trip. Don’t make a mistake of painting me with the label of ‘casual gamer’ just because that fits some easy marketing definition.
Algorithms tend to come in a black box
As I addressed above, marketers have little ability to parse how good somebody else’s data is. In the same vein, it is virtually impossible to know how good another person’s algorithms for targeting or latching are.
A quick analysis of my personal data would reveal I like to play online poker games. Typically, that’s an activity that marketers associate with men. So, if you were using an algorithm to select targets for your new poker app, the technology would probably assume that I’m a man and serve me up an ad with a buxom girl, or colour and font combinations that I’d find garish, to try and get me to download, and that will fail miserably.
There are data verification tools and strategies out there to help brands validate third party data and algorithms, but those solutions are nascent and unproven.
Moments matter, context is king
In order to meet each of these challenges, it’s important for brands that want to succeed in mobile—and in m-commerce in particular—to develop the skills to leverage in-app data. Because, even more importantly than raw behavioural data, in-app data provides the advertiser with the context required to understand my total experience as a mobile native.
I did play a poker app for 25 minutes last night before bedtime. I did open and close the app twice while responding to messages from friends on Facebook, and I did click on an ad for the elusive trench coat that I’m looking for while I was on Facebook. If the advertiser isn’t working from a position of having my fresh, in-app mobile data signals in hand, how would they ever know any of that?
Amazon Prime has the right idea
Recently, Amazon introduced Prime Now, an app that combines shopping, entertainment and everything else all-in-one. Anything that you ever see on Amazon—on desktop, mobile web or in-app—is accessible from Prime Now.
It’s nice that I don’t need to go back to my desktop or another app to shop, but it’s even nicer that I don’t need to make a trip to the store to buy groceries. I don’t even need a shopping list. I just open my Prime Now, drop things in my shopping cart and it’s at my house an hour later. This is a real-life use case of a brand with a heavy stake in the future of m-commerce using a combination of data signals, algorithms and slick native calls to action to make my life better and earn a bit of my money in the process.
The big idea in mobile native advertising boils down to using small parts of user’s mobile data signals to provide calls to action that are delivered at just the right moment, in just the right context, to inspire an m-commerce transaction.
Where before this was just an idea or wishful thinking, the confluence of trillions of first party in-app data signals, the technology to interpret what they mean, and the commitment to treat each mobile native like the unique individual we are is starting to make advertiser’s big ideas for big data a reality.