Predictive App Marketing in Action

Apr 7

Predictive App Marketing in Action

The StreetHawk mission has always been to “add value” to marketers, developers and App users in real-time. With my background at Surfcontrol and ThreatMetrix its in my DNA to build companies that use big-data and real-time action to add value to customers.

At ThreatMetrix our goal was to score fraud decisions in milliseconds when a user is in a shopping cart or banking login.

It is this very intersection – the point where App usage, customer data and real-time action intersect – is where predictive app marketing lives. Specifically there is a convergence happening right now that gives marketers great power and great simplification – the converging 3 elements are:

  • big data
  • mobile analytics
  • cloud computing

Examples of this “value-add” is that the StreetHawk Cloud allows you to set simple trigger-based actions for conditions – for example combining a user’s personal preferences with their usage of your App allows you to:

  • target power users to share your App
  • trigger offers to dis-engaged users if they haven’t opened the App for a week or two

Its about “right-time”

Simplifying timing decisions is also a valuable tool – for example, a user might match the above conditions but when is the best time to reach them?

StreetHawk has always provided a simple way to reach users:

  • at lunch time
  • in the afternoon
  • on weekends
  • anytime you wish!

The key thing about this is that you can reach them in their timezone.

Just because a user might match the conditions, you want to make sure its THEIR LUNCHTIME or THEIR AFTERNOON – you just set it up and stop worrying.

Making “right time” easier

But a cool example of the intersection of big data and action is a tiny checkbox in the StreetHawk Console. In our old console “Let StreetHawk choose the best time to send a message” and in our new console it looks like below.
Let StreetHawk choose in the new consoleThis simple humble little checkbox means that we deliver to the user your chosen message at the time when they are most amenable to using your App – this is a big data calculation based on their usage history.

For example – if the user has typically opened your App whilst they are on the train going to work – that is when they are likely to get your notification – neat huh!

We’ll will have a lot more to explain about predictive app marketing coming up in future posts.

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