The Complete Guide to Contextual Advertising

By Vungle | May 6, 2022

How knowing where your audience is right now can help you identify where they’ll be in the future.

In 2021, iOS users across the globe demonstrated just how much they value their privacy by opting out of data trackers en masse — but it’s left many advertisers in the lurch. Without the data they used to rely on, many mobile advertisers are searching for how to effectively place the right ads in front of the right eyes. And the answer lies in contextual advertising.

At a minimum, contextual advertising can serve ads that are relevant to a user based on topics they’re interested in, the time zones they’re active in, the app categories they’re most likely to favor, and more. When augmented by creative approaches like GameRefinery’s, a Liftoff company, Creative Intelligence tool, it can connect advertisers with the right audiences at an unprecedented scale. All without ever falling afoul of privacy rules.

But let’s dig in, and get more specific about the pieces of contextual advertising, and how they work.

What is contextual traffic?
What is contextual marketing?
What is a contextual ad network?
Examples of effective contextual advertising
How contextual targeting works in programmatic advertising
Contextual targeting strategies for the post-IDFA world

What is contextual traffic?

Contextual traffic refers to the device sessions originating from users/devices that allow for contextual targeting, but typically not behavioral targeting. Contextual traffic allows for data to be gathered from users at a level removed from historical behavior or the personally detailed information of IDFA and other similar identifiers. Also known as Limit Ad Tracking (LAT) traffic in the mobile world, it covers all users who have opted out of having their movements tracked online — which is the substantial majority when given the option. LAT refers primarily to the app activity of untracked mobile users and covers details like what app they’re using, what sort of device they own, and how the user is interacting with both. Without detailed historical data points of IDFA or AAID, advertisers use data from contextual traffic to accurately target app users by information that is still available. For example, contextual traffic data could include:

App-level data: App store category and subcategory, who published and developed the app, and what version of the app the user is running.
User-level data: Session-specific information about how the user interacts with an app, like how long the session has been active.
Device-level data: What type of device does the user have, how much battery life do they have left, how much storage is available on their phone, and what language keyboard are they using.

While all these data points may seem vague individually, they can come together to create an accurate view of a user and allow for effective targeting.

Contextual traffic has been a thing for a long time — users have been able to opt out of ad tracking for years. But now it’s suddenly much more important — and the number of users it applies to has grown exponentially. For advertisers that means being sure to use relevant data, and analyze it properly to target ads and grow user bases.

Be sure to read “What Is Contextual Traffic on Mobile?” for a more complete view of this topic.

What is contextual marketing?

Within the strictures of the information that can be gained from contextual traffic, contextual marketing is using that traffic information to accurately target an engaged audience. While we may not know quite as much about each user as we did before the changes to IDFA, you can still gain a tremendous amount of insight from what we do know.

One of the most common avenues of contextual marketing is linking app characteristics between what app the user is currently engaged with, and those within an advertising campaign. You can advertise a racing game to someone who already plays racing games or a new productivity app to someone using a to-do list. Likewise, device-level data like timezones means that you can be sure to advertise at times of day that are appropriate to the user, and knowing the precise device info for what phone or smart device they’re using ensures that any app they get pointed to will work on their system.

All these data points can come together to create an effective contextual targeting strategy. If a person is in the Pacific Time Zone, is actively using a productivity app, and has the newest and best version of a phone? Then it’s reasonable to assume that person possibly works in, or is at least interested in, the world of tech and startups, and you can zero in on that.

Want to start marketing using contextual traffic? Find out more in “What Is Contextual Marketing?”.

What is a contextual ad network?

Contextual ad networks are those specifically set up to deal with contextual marketing and traffic. They already have established tools to use the contextual traffic data to accurately serve up ads that will attract a user’s attention, and be suited to them individually. Ad networks connect demand sources (the advertisers) with the supply sources (the apps with ad space, looking to display ads to users), and those that specialize in contextual ads use the data from contextual traffic to ensure the appropriate ads are served in the appropriate places to drive campaign performance.

With many ad networks still trying to re-establish themselves after the deprecation of IDFA, going with an established contextual ad network means using a service that already knows how to navigate under the current regulations, and quickly and accurately serve the ads that you need, where and how you need them. With the global contextual ad market passing $150 billion in 2020, and that number is expected to more than double by 2026, this is a huge industry and one that’s only growing. For both demand and supply sources, working with ad networks that know how to tap into that enormous market will be crucial to maximizing the dollars spent on campaigns. With that in mind, these are some of the top mobile in-app contextual networks include:

On the web side of contextual advertising, the major players include:

For more information on these networks, and where they specifically excel, check out “What Is a Contextual Ad Network?.”

Examples of effective contextual advertising

There are several ways that contextual data can provide you with a surprising amount of information on a user, which can be used to accurately serve them ads. The most straightforward of these are app category and subgenre. A user who is already invested in an app is much more likely to interact with an ad about a similar app rather than something completely different. This can be as simple as serving an ad for a photography app when they’re using another.

Thankfully, the current ecosystem also leaves room to be more creative by layering on additional contextual data points — for instance, GameRefinery, a Liftoff company, uses more than 700 category-specific variables, which allows for linking games based on more than just app category, but also gameplay specifics, game style, aesthetics, and more. So just as a user of one photography app might be interested in another, someone who likes one game with a cozy vibe might be interested in another, even if they’re in totally different genres. By burrowing deep into these more granular variables, GameRefinery, a Liftoff Company, can better understand contextual traffic in a unique way that drives campaign performance.

Focusing on device specifics can be useful as an extra layer of thoughtfulness on top of app-level data. Timezone information means you’re not sending a user information about a lunch special at midnight. Keyboard language lets you know the languages they’re comfortable in, and when combined with timezone provides a way to estimate their location.

Gathering OS version information and hardware specifics means a user will never be presented with an ad for an app they can’t use — and you can even direct them to ones that make use of specific features of their phone, like a folding screen, stylus, or new camera.

Device information can even be gathered on the fly to serve ads that are best suited to the way that person is using their device at that very second. If their audio is muted, you may not want to give them an ad that relies on spoken dialogue. Or if their screen is turned all the way down and their battery is almost dead, they probably don’t want an ad that will further eat at their remaining power.

Looking for more examples of contextual advertising that works? Read “4 Examples of Effective Contextual Advertising Tactics” for more information.

How contextual targeting works in programmatic advertising

On the demand side, programmatic advertising takes the detailed requirements of an advertising campaign and uses them as the basis for finding the best inventory opportunities to slot them into.

On the supply side, rather than following the traditional method of “waterfalling”, where a game or app cycles through a list of ad sources one after the other using primarily historical performance data, a programmatic approach has all demand sources on an equal footing, allowing them to bid dynamically on inventory as it’s needed. Ads can be automatically bought and sold according to the needs of both buyer and seller — and if it’s a highly sought-after ad slot, then the price can increase automatically via a transparent bidding system.

So if a publishing app has space on a user’s screen, a programmatic advertising service with contextual targeting will receive the necessary information as part of a bid request. If it meets the criteria an advertiser has set up previously, it will decide if this matches the advertiser’s needs, and then bid on the slot up to a certain limit. The auction process is also done entirely in the open, with the price paid for each ad impression known by everyone involved — which helps maintain a free and fair market, and prevents pricing disparities.

Since inventory is moved as it’s needed rather than in big blocks, and because the contextual data means that the ads are targeted specifically and efficiently, buyers can ensure the relevancy of their ads to a viewer.

Contextual ad targeting is an old technology that’s been reborn in this new, more private, world. Originally crafted for web ads where they would be paired with relevant web content, these techniques are now being used and updated for contextual traffic.

Ready to start setting up programmatic advertising for your content? Learn more details in “How Contextual Targeting Works in Mobile Programmatic Advertising”

Contextual targeting strategies for the post-IDFA world

Contextual targeting is the practice of defining an audience using non-IDFA data and can take several forms. One option is for publishers to leverage the Identifier for Vendors (IDFV) as part of a cross-promotion strategy. For a publisher with a broad app catalog, this portfolio-based strategy gives them access to a similar granularity of data as they had before — it just has to be used internally rather than bought and sold. A publisher with a dozen games in their portfolio can know a lot about how an individual user interacts with these apps, and what content resonates with them from app to app — and what doesn’t.

It’s not always possible for a developer to invest in a large portfolio of apps, so instead, it might make sense for them to link with an existing ad network that is experienced in this realm, and have the tools and technology to run a campaign. Partnering with experienced contextual ad networks can also make all the difference, as these companies know how to take the contextual targeting data and turn it into an ad campaign that will do the work of finding users who are likely to interact with an ad.

For more detailed examples of winning strategies, have a look at “4 Contextual Targeting Strategies for the Post-IDFA World”

The move away from IDFA has some advertisers worried about the future of targeted advertising, and if there will still be effective ways to generate awareness, actions, or installs from interested users. Contextual advertising shows that while we might not have exactly as much information as we once did, there’s still more than enough to effectively identify user groups, and craft campaigns to target them, even without knowing quite the level of granularity that we did before.

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