Ad networks for mobile apps make it possible to target your ideal app audience—not just in the form of awareness campaigns, but by allowing potential users to download your mobile app directly from the publisher or sub-publisher. Not only can this be a boon for user growth, but it also provides robust marketing attribution data.
These ad networks often operate on a cost-per-install (CPI) model, allowing you to only pay for the download milestones, as well as segment out installs on a network-by-network and campaign-by-campaign basis.
This model helps cut out some of the fat in the marketing attribution process, making the movement from awareness to acquisition a much simpler journey for both businesses and consumers.
With robust analytics tools and the ease of use of self-attributing ad networkings with built-in reporting tools, attribution data is highly accessible and ripe for analysis, allowing companies to identify where ad spend is yielding the most fruit, and where to cut ties.
But beyond allowing companies to see how their creative assets perform, ad network data also allows companies to review the performance of the ad networks themselves. Companies can determine if ad-serving configurations are not just optimizing exposure and installs, but also generating installs among users who have a high propensity to engage and remain loyal to an app.
Once companies understand the fundamentals of campaign attribution, they can dive deeper into their ad network data and optimize ad spend by refining their attribution windows, measuring install quality based on engagement, and then blacklist ineffective sub-publishers that may be costing installs without any significant return on investment
Calculated metrics that expose install quality, mapped against acquisition metrics, can offer insights into wasted ad spend, fraudulent downloads or incent spend, and, of course, a rich supply of growth opportunities.
Grant Simmons, Director of Client Analytics at Kochava, recently said, “Your data holds a wealth of insight and potential cost savings, if you know where to look. Reviewing dozens of client accounts has revealed that most advertisers can expect to save between 10% and 35% on their mobile ad spend, simply by analyzing the data they are already tracking.”
Simmons will be presenting “Focused Analysis for Better Marketing Decisions” at the upcoming Mobile Analytics Summit, an event dedicated to mobile marketers, analysts and product owners dedicated to optimizing experiences and mobile marketing processes.
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