Kochava Adapter Framework Offers Developers an Omniview Glance at Marketing

The Kochava mobile attribution analytics and optimization platform will now offer a new class of integration through the Kochava Adapter Framework, integrating push and in-app messaging insight from Localytics and other providers. Kochava has also introduced a new Audience Attribution service that provides the ability to view and correctly attribute installs to ads served on one device that also drive incremental installs across various devices in a household.

Kochava’s Adapter Framework offers an omnichannel view of user acquisition and user re-engagement from a single dashboard. Kochava provides mobile advertisers and publishers with audience-specific, real-time visualization of campaign data that spans from initial launch through conversion and lifetime value (LTV) reporting, including comprehensive post-install event tracking.

With the addition of push and in-app messaging integrations, the Kochava Adapter Framework provides the opportunity for users to automate mobile marketing campaigns and analytics through these new Kochava partners and track and understand how their messaging engagement and re-engagement efforts are attributable to their overall mobile media spend.

The Kochava platform upgrades offer the ability to create, track and optimize a variety of new campaigns beyond their initial user acquisition campaign to increase the overall lifetime value (LTV) of their existing users. With the addition of push and in-app messaging integrations, this class of campaign can now be tracked and attributed alongside all other campaign data, including email, mobile web and app media spend. 

Kochava’s metrics allow users to visualize the true impact of all of campaigns via real-time attribution and analytics. This addition to the Kochava Adapter Framework brings push and in-app messaging reporting so that events such as uninstalls and app deletes can now be used as event triggers to launch ad campaigns in real-time.

Audience Attribution offers a new way for advertisers to understand their customers’ behavior across devices and platforms. Audience Attribution provides cross-device attribution and targeting to reveal incremental attribution lift. Where other attribution models may use a single-device identifier to represent a person who is served an ad, with today’s reality of multi-screen, multi-platform usage, users are not accurately represented by a single-device ID. Kochava created the audience attribution model to overcome this limitation.

Audience Attribution offers audience-specific data which allows advertisers to attribute the whole household, exposing the real reach of an ad, versus a single attributed user and several “organic” installs. With Audience Attribution, installs that were historically considered “organic” will now be accurately attributed as a result of an originating advertisement.

Kochava’s Audience Attribution exposes a new segment of influence-based installs and new KPIs, like household LTV. Kochava’s Audience Attribution is achieved through the combination of Kochava IdentityLink and Proximity Identification. 

IdentityLink provides the ability to associate an internal identifier with a Kochava device ID to manage an audience in new ways. By associating an internal customer identifier with a Kochava device ID, advertisers can correlate attribution and analytics information within Kochava to the customer’s behavior on different devices and platforms, as well as cross-compare data from internal or 3rd-party business intelligence tools in a meaningful way. IdentityLink gives intelligence on all of their behavioral user data, regardless of where it exists, delivering a unified picture for more effective targeting, re-targeting, and optimization.

Proximity Identification offers new insights into how users interact and influence one another. Using new proprietary algorithms, Kochava’s Proximity Identification facilitates the grouping of people and their multiple devices based on location. Proximity Identification can be used for deeper levels of targeting to increase ad spend ROI by identifying the primary influencer in a household, or reduce wasted ad spend through negative targeting.