You can adjust all of your cookie settings by navigating the tabs in this window.
We’ve all heard the buzz words – “big data,” “ad tech,” “personalization,” “programmatic,” and “mobile” have been discussed in digital marketing articles and on industry event panels ad nauseum for the past couple of years. Why is everyone so fixated on these terms?
Just because consumers are spending increasing amounts of time online and on mobile, that doesn’t mean they aren’t still watching TV or using desktop. In 2015, advertisers have a ton of channels to monitor and data streams to digest in order to keep a handle on customer behavior. Ad tech is paving the way for programmatic media buying, freeing paid marketing managers from the need to manually test ad creative and run campaign analysis in Excel. The ability to identify top performing audiences and iterate quickly on strong creative is easier than ever, but the option to get granular with targeting is still critical. As mobile continues to eat up time spent online and digital advertisers keep allocating more budget to app install ads, aligning messaging with the right customers will only become more crucial.
We often find that our new mobile gaming and online retail customers follow a similar trajectory of campaign management – they start their ads out with an eye on CPA and focus on hitting user acquisition goals. As they become more familiar with what Nanigans software can do and more confident in the data they’ve collected, they tend to switch their campaign goals over to revenue optimization. With yield as a focus, the ability to set variables at the most granular level becomes a competitive advantage – and that’s where Audience Override Tables come in.
Audience Override Tables enable digital advertisers who really understand the mechanics of app performance and monetization to take further advantage of higher performing pockets of audiences by setting individual conversion rates (yield or CPI) for geo, age, location, and gender. This override of current Strategy Group settings can lead to more aggressive goals for certain segments that they know from experience perform better. For example, an online retailer could upload a CSV file that would allow them to change bids dynamically by country — so, prospective shoppers from Brazil would be treated differently than their more expensive counterparts in Canada.
This approach is also great for mobile gaming customers who pull insights directly from app activity. If an app developer knows that males age 18-24 that are interested in Candy Crush are worth more than those aged 24+, they can target that audience segment for a greater percentage of yield. The reverse is also true – with lower performing segments, campaign managers can set more efficient goals to trim the fat.
Being able to set separate yield or CPI goals at the audience level allows digital advertisers to reap further benefits from a performance standpoint, driving an increase in revenue. And no matter how much digital advertising evolves over time, that will always be a critical target.