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Bigabid Launches Precondition Targeting

New Proprietary Mechanism Helps App Developers Understand User Behavior for Ad Targeting

Bigabid, a second-generation optimized DSP that helps app developers with user acquisition & re-engagement, is launching a new, proprietary mechanism called Precondition Targeting. Precondition Targeting is designed to help developers send in-app ads to relevant users based on the timing of their actions, as it measures the current state of users when they are taking an action, aligning that action to the appropriate message.

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Precondition Targeting allows app developers to analyze user behavior from the moment they interact with an app (e.g., download, purchase), as Bigabid’s platform registers the complex composition of the moment that preceded it. The platform focuses on the user’s:

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  1. Long-term history (within an allowed time period as indicated by law, often around three months):
  2. Immediate history (past 48 hours)
  3. Present actions
  4. User patterns and behavior: Using a proprietary tool Bigabid launched earlier this year called “Deep Categories,” the platform can understand users through a high-resolution categorization of the apps they engage with. Deep Categories reorganizes the app store using 1000 categories/sub-categories, which helps determine which apps are utilized by high lifetime value users (LTV)
  5. Fixed features: age group, geography, etc.

Once these patterns from a user’s pre action state are accounted for, the mechanism scours the web’s billions of users and millions of moments in search of recurring and similar patterns. The AI algorithm at the heart of Precondition Targeting then uses this knowledge to predict the ideal time serve a relevant ad to a mobile app user.

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“Since the founding of the company, Bigabid has been developing a platform that takes into consideration the complete scope of an app user’s activity. With the launch of Precondition Targeting, this objective is fully realized,” said Ido Raz, President and Co-Founder of Bigabid.  “For example, You might need a sofa, but you’re less likely to buy one made of leather if, yesterday, you downloaded a vegan-recipes app. Precondition Targeting solves this issue, enabling clients to reach their LTV users at the right timing with a message they can relate to.”

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