How We Built Our Machine Learning Model for Predictive Send Time Optimization

The (Mostly Manual) Ways Brands Have Been Trying To Predict Best Time To Send

While building this model we polled customers about their general engagement strategies to determine how they decide the best time to message their users.

Machine Learning To The Rescue

We created a model that:

  • Selects optimal engagement time personalized to each individual user
  • Is localized to each user’s timezone
  • Is extensible to dormant users, and
  • Is both actionable when building campaigns and explorable in our data products

Testing & Results

To test the model we compared predicted best times to true open times for open events that occured after model generation. We compared the best time model to both a baseline model (e.g. everyone’s best hour is set to 12pm) and to the general best hour (e.g. everyone’s best hour is set to the general model best hour).

Making It Easy To Put the Model To Work for You

We want to make deriving insights and taking action with the Predictive Send Time Optimization model simple for the marketer and their analytics teams:

  • As with our Predictive Churn model, we ship weekly predictions as user tags to facilitate integration between predictive model output and our data solutions.
  • We make it easy to schedule messages with Predictive Send Time Optimization
  • We offer simple ways to visualize the user-level predictions
  • Analytics teams can ingest predictive tags into their own systems with real-time data streaming.
  • Through our customer analytics solution Insight we provide a deeper dive into the model, surfacing the cross-audience best hour as it relates to each day of the week and for each app operating system.

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Urban Airship is now Airship! Leading brands trust us to help them achieve digital growth. We make mobile moments magic. www.airship.com