Home Mobile Google’s App Ads Chief On Why Less Is More When It Comes To App Campaigns

Google’s App Ads Chief On Why Less Is More When It Comes To App Campaigns

SHARE:

AdWords, search, display, the Play Store, YouTube, AdMob, the Google Display Network…

App advertisers used to have to run six or more campaigns at once with Google to try and achieve a single objective.

But, as of November, there’s been only one way to promote apps across all Google properties: Universal App Campaigns.

Google first tested the offering in 2015 and now it’s the default campaign type for app advertisers.

Advertisers provide Google with text, a few creative assets, the marketing objective, a starting bid and a budget, and the algorithm takes it from there. The whole process is automated. Google creates the ads, serves them across the Google network and uses machine learning to dynamically adjust keywords along the way.

“You tell us the business outcomes and we find the people we believe are in-market using all of the signals Google has at its disposal,” said Lee Jones, Google’s director of app ads commercialization.

Google is even gaining market share from Facebook, the undisputed king of the app-install space, growing its slice of the pie by 40% over the course of 2017, according to AppsFlyer’s most recent performance index report.

But there are still some kinks to work out, namely reporting and more control. Advertisers can’t specify where their ads show up in Google’s ecosystem and don’t have insight into why certain placements were chosen.

More transparency is on the agenda, Jones said, with plans to gives app developers a peek inside the black box.

AdExchanger spoke with Jones.

AdExchanger: What was the motivation behind overhauling app promotion?

LEE JONES: With the millions of signals you get on mobile, it’s very hard to do manual optimization – not to mention having to do separate tracking for mobile web and in-app. What’s good about Universal App Campaign is that we’ve leveled the playing field so that smaller and medium-sized customers can afford to run app-install campaigns.

I have heard grumbles from some developers that they’d like more control over placements.

It’s funny to hear people give that feedback now. The reason we did this is because people were saying it was too complicated before. But we’re dedicated to listening to the market. That’s what drives our product strategy.

We’re working on providing a value distribution curve that gives more details on what happened. For example, we could tell someone: “Well, we got this many search conversions for you, then we tapped out on search, so we graduated you to YouTube and it was a little more expensive, but you also got more volume.” People want to know what the machine is doing.

How does machine learning come into play with Universal App Campaigns?

We can look at user attributes agnostic of the channel. It’s like being able to enter five auctions at once. Say someone just downloaded three games in a row and we know that person is about to get on an airplane. We can use that information to show an ad on YouTube we think is really relevant for that moment. It’s not something you could do manually and it’s efficient for the advertiser.

For example, the Mayweather fight was really big last year. The fight happened over the weekend, but we were able to automatically start showing related keywords for a sports app we were working with in the UK. It wasn’t even something they were planning to capitalize on, but it drove a ton of value for them.

What’s next on the road map?

A big area we’re focused on is showing which creatives worked, why they worked and who are the people being reached – because that has implications not just for campaigns, but also for content within an app.

For example, I recently met with a grocery delivery company that was mainly targeting millennials. But when we showed them the data, it turned out that 25% of their most valuable customers were over 50.

A revelation like that doesn’t just impact what they do with Google, it impacts their entire marketing and how they tailor their paths. Because of what they learned, this delivery app is going to start experimenting with different creatives.

These are the types of tools and insights we’re investing in because, increasingly, it’s becoming much less about what someone might do in, say, AdWords, and more about helping advertisers map their user journey. That’s where smart marketers can get ahead.

Must Read

Who Will Stand Up For The Open Web?

The open web is done, stick a fork in it. Banner blindness is near universal, search traffic has run dry and publishers are struggling for oxygen. But what if that’s … not true?

A comic showing lab techs as stand-ins for legislators experimenting with provisions for US state privacy laws, including restrictions on collecting sensitive data.

What Publishers Don't Know About New Jersey’s Data Broker Law Could Cost Them

Attention, publishers: Although you might not think of yourself as a data broker, in the great state of New Jersey, that’s not really your call anymore.

Predict Bowl Icon. Magician Element, Forecasting Symbol – Vector.

Why This Marketing Measurement Company Just Open-Sourced Its Forecasting Engine

MMM can tell marketers what worked, but Lifesight’s open-sourced forecasting tool aims to tell them what to do next.

Privacy! Commerce! Connected TV! Read all about it. Subscribe to AdExchanger Newsletters

Podcasts Are Becoming More Programmatic. But Now Advertisers Have To Keep The Ad Load In Check

As programmatic buying becomes more common in audio, marketers and platforms fight the temptation to cram in as many placements as possible.

PubMatic Jumps On The Show-Level CTV Targeting Bandwagon

Connected TV advertisers are still pining after show-level control. And PubMatic announced contextual targeting at the episode level is available to media buyers accessing CTV inventory through its platform.

SQREEM Touts The Large Behavioral Model – Not The LLM – As The Winning Predictive Engine

Rather than relying on machine learning, SQREEM uses a mathematical AI model to track how systems change over time and predict audience behavior.