The emergence of programmatic advertising has created a huge opportunity for digital marketers to more effectively reach their ideal customers, enabling an advertiser to bid on placements within milliseconds. Although the automation and efficiency benefits are clear, programmatic can present new challenges when it comes to measuring whether an ad is creating its desired impact.
Tackling core verification challenges
Campaigns are often plagued with core verification challenges: invalid, non-human traffic, placements served on inappropriate or offensive content, or ads that are completely out of view from users. To increase transparency, IAS monitoring solutions analyze viewability, invalid traffic, and brand safety across ad campaigns. IAS Optimization takes transparency a step further, allowing advertisers to avoid these same risks before placing a bid programmatically. But demands have grown; advertisers now also seek mechanisms to guide demand-side platforms (DSPs) to approach viewability in a more nuanced way — and IAS has expanded capabilities to meet these needs. In addition to approaching viewability as a binary question of whether an ad is viewable (an effective way to handle viewability in many cases), IAS offers Real-Time Signals (RTS) as another way to tackle the challenge. RTS considers viewability as one piece of a more sophisticated algorithm, while expanding the variety of metrics an advertiser can reference.
To maximize return on investment, DSPs need to assess the monetary value of each impression served, on behalf of the advertiser, allowing advertisers to bid on placements in real-time. When it comes to fraud and brand safety, it can seem obvious whether an impression is worth the spend or not. An impression served to non-human traffic — whether actually fraudulent or served to non-malicious bots — is worth nothing to the advertiser (after all, it’s difficult to convince a bot to buy products). Similarly, if the buyer serves an impression on content that is inappropriate, offensive, or inconsistent with brand values, brand reputation and consumer loyalty could be significantly impacted. Standards for viewability, however, are often not so black and white.
Digital ads can be classified as out-of-view if the user has not scrolled all the way through the webpage to reach the ad, or if the ad was not on screen (time-in-view) long enough to qualify as in view. Once a bid is placed, neither the advertiser nor the DSP has any insight into how the end user will interact with the page. For many digital placements, it is impossible to say for certain if the ad will or will not be in view — only whether it is probable, based on historical data.
Traditional pre-bid filtering and other rule-based approaches are a good, straightforward way for advertisers to avoid buying impressions that are unlikely to be in view, or to focus on buying impressions with a good chance of being in view. But what if an impression has a 40% chance of being seen? Should they avoid bidding on that placement entirely — or simply bid less to account for this lower in-view probability? If viewability is one of many KPIs that you wish to optimize, taking these probabilities into account might be worthwhile. Rather than the binary, viewable-or-not approach of standard pre-bid solutions, a more robust product that considers the variability of viewability could be a better way to reach your campaign objectives.
Use Real-Time Signals to inform bidding
Real-Time Signals (RTS) enables viewability measurements to inform bidding algorithms instantaneously while balancing them with other factors. Unlike the binary, buy-or-don’t-buy approach, RTS offers a solution that takes into account the gradations between viewable and not viewable.
For example, consider a campaign to help sell a 20-speed mountain bike with two options for targeting:
- Option A: a 25-year-old in Colorado, (known for its mountains and outdoorsy appeal) with only a 30% probability of being in view
- Option B: an 80-year-old in Kansas, (not known for its mountainous terrain) with a 90% likelihood of being in view.
Which placement is most likely to help drive sales for the mountain bike? Chances are that targeting the male in Colorado will yield a more favorable return on ad spend, despite the likelihood of lower viewability.
Further complicating in-view probability, is the need to define viewability as a metric that will make an actual impact on the end user. Industry guidelines for viewability set an excellent baseline — typically considering a placement with one second for display ads or two seconds for video placements as “in view”. However, studies show that consumers are not always swayed by scanning a display ad for one second. For video ads, the brand name may not even appear until it’s played for 10 seconds or more. So standard viewability optimization may be fine for many campaigns, but they can prove to be suboptimal for others. Programmatic campaigns will not all have the same optimal time-in-view standards, and thus, true viewability optimization must be tailored to each advertiser, campaign, and piece of creative.
The complexity of these questions surrounding viewability has motivated IAS to offer real-time custom viewability signals for programmatic optimization. This approach enables optimization for any time-in-view and percentage requested by the advertiser — or better yet, recommended by IAS. Using RTS, IAS sends signals to a DSP, indicating that the placement has reached its viewability threshold in real-time, permitting instantaneous updates to bidding models. That data can be used much like traditional IAS pre-bid for any desired time-in-view preference or can be fed into the bidder directly, allowing viewability to serve as a component of a more complex KPI-based algorithm. This solution takes into account the complexities of predicting viewability and is poised to reshape how marketers consider viewability in the future.
To learn more about how IAS can help you, check out our Programmatic Solutions.