Kochava announces the industry’s first Configurable View-Through Attribution service for mobile marketers. Configurable View-Through Attribution allows advertisers to track the true reach of their campaigns including the influencing power of every impression served to users.
This new capability from Kochava enables advertisers to identify the networks and publishers who drive the (unclicked) ad impressions that influence app installs and other activities. This insight changes the industry’s paradigm of last-click attribution, where the source that drove the final click receives all of the credit.
Not all campaigns are created equal. Different traffic sources, targeting options, and campaign goals require a fitting attribution configuration. The measurement platform you choose for attribution should give you the ability to configure your measurement methods in accordance with all of these variables.
Due to the lack of widely adopted and enforced impression standards, configuration is critical. For example, the definition of a “view” on a video ad varies broadly. It may be counted upon serving the video, after watching a portion of the video, or even upon completion. Comparing a 5-second view with a video-completion view will present a skewed perspective on campaign value.
Choose a measurement platform that is flexible enough to track all your campaigns across thousands of networks and configure your attribution to your specifications. Kochava is the first measurement company to offer a completely configurable View-Through Attribution tool that enables advertisers to specifically define an impression, and then measure and capture data against that definition.
Adding View-Through Attribution to your UA strategy allows you to slide the
scale between attributed and unattributed installs and balance ROAS with
Hundreds of advertisers are taking advantage of Kochava’s Configurable View-Through Attribution. The typical lifts associated with adding view-through attribution to a campaign have averaged 5-25% depending upon the campaign and network.
Here is an example of a campaign where a 11% lift was found. The app measures it’s LTV with a 7 Day RPU (Revenue Per User). The new user’s LTV averages $10 each and the advertiser is willing to acquire users at this cost. The app generated 8,000 installs during the campaign time period. Therefore the app is generating $80,000 in LTV per day.
The advertiser’s campaigns are driving 100,000 clicks of which 3,000 generate installs, identified through typical last click attribution, which is equal to $30,000 in LTV.
This is the appropriate typical last-click attribution scale for this advertiser, and normally this is the level to which the advertiser could optimize. But add view-through attribution and the advertiser is able to look at the impact of their campaigns beyond click attribution and see the value of their campaigns that were only viewed.
In this example, the campaign measurement takes into account the 1 million impressions that were served and now an extra 900 installs are attributed to the campaign and the campaign is allocated an extra $9,000 in revenue. This allows the advertiser to see a more complete view of the campaign and they now know their campaigns are driving $39,000 in revenue, not $30,000. With this extra insight they can now adjust their future campaign budgets accordingly.