How Brands Should Measure Ad Engagement Levels
by Freddy Friedman, Chief Product Officer
App marketing used to be all about scale: getting as many users into an application as possible at the lowest cost. But technology has improved and competition has exploded. With more than 3 million apps across app stores, attitudes are evolving to favor quality over quantity. Consumers are using fewer apps regularly. A Millward Brown Digital survey found that while the average user has more than 40 apps installed on his or her device, only 28 percent use more than four to six apps per day. Consumers are also demanding better app experiences. They, likewise, expect advertising to be a better experience as well.
Beyond the target
The Nielsen Connected Device Report for the first quarter found that 65 percent of consumers surveyed currently find mobile ads annoying and intrusive, particularly if the ads are irrelevant. Advertisers and app developers both have an interest in getting relevant ads in front of the right users — and that means improved targeting. Advertisers are seeking high lifetime value (LTV) users. Developers want ads relevant to their app’s users, so that they convert. While targeting is the best it has ever been, developers and advertisers should look beyond targeting to other powerful, if underused, options to gain an advantage.
Many advertisers and developers make extensive use of targeting criteria such as age, gender and location. But while this method might narrow the audience, it does not necessarily improve the quality of those targeted. This is where understanding engagement levels is invaluable.
So how to measure engagement levels? Here are three ways:
Clicks and installs
Engagement levels for advertising campaigns are primarily measured by clicks and installs. The more users click advertisers’ ads and install the advertised app or product, the more engaged they are perceived to be with that specific campaign or product vertical. Those users are actively responding to the ads in one of the following ways:
● By clicking on a banner, a user signals that he or she is interested in either the advertised product, or similar products from that category
● Installing an app after seeing an ad implies that the user is interested in what the app offers. Further, it implies that the user may also be interested in complementary products that might increase the user’s engagement with the installed app
We can also measure user engagement by looking at how users are behaving within an app. This does not require further ad clicks or installs. The fact that the device is requesting ads signifies their engagement by triggering ad impressions as they navigate through the app. Additionally, post-install information reveals additional user engagement metrics related to in-app purchases or other actions. Examples include leveling up in a game, adding products to a shopping cart, or user journey analysis.
This activity data combines to create a nuanced persona for each device, resulting in a detailed source of targeting information for both advertisers and developers. This helps identify and connect with high-value customers, boosting app monetization and revenue generation.
Targeting by user engagement levels
Collecting the right data over time is key to translating it into actionable picture of engagement levels.
Demographic attributes such as gender, age group and location change infrequently, if at all. This information is, for our purposes, static. But the user’s engagement level is dynamic: it is built over time and will vary. A sophisticated data management platform (DMP) is key to both understanding this data, and revalidating it over time. Advertisers can use engagement level as an additional targeting criterion to assess the potential quality of a newly acquired user.
For example, a fashion app might target young women. But with suitable engagement data, it could also target those who specifically demonstrate an interest in fashion by frequently clicking on fashion ads or engaging with other fashion-related apps.
In addition to targeting the right type of users, developers can target their highest-paying ads to their most engaged users, maximizing their app’s monetization potential. By intelligently augmenting existing targeting methods, engagement levels can also help determine other indicators, such as churn probability. Metrics such as churn probability allow developers to understand an app’s user lifecycle and better plan acquisition strategies to ensure new users are being added at the correct time.
Data management platforms, along with dynamic data, such as engagement levels, have become an indispensable tool in enabling the current transition to precisely targeted advertising. Developers and advertisers are realizing that quantity is out and quality is in. Most successful apps already make extensive use of data management platforms to target users as specifically as possible. The ones that do not will find themselves playing catch-up or flying blind.
This article previously appeared in Mobile Marketer.