11/18/2014 12:00:40 PM
Kahuna Introduces RevIQ Tool Using Data and Behavioral Analysis to Automatically Optimize Push Notifications
Push Notification Campaigns,Conversion Campaign,A/B Testing,Push Campaign,In-App Messaging
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App Developer Magazine

Kahuna Introduces RevIQ Tool Using Data and Behavioral Analysis to Automatically Optimize Push Notifications



Richard Harris Richard Harris in Monetize Tuesday, November 18, 2014
2,223

Kahuna has introduced Kahuna RevIQ, a tool that helps companies maximize sales by telling them when and how to send push messages to their customers. RevIQ is a new product capability that uses collected data and behavioral analysis to automatically optimize push notification campaigns.

There are two components of RevIQ. The first component optimizes send time delay for conversion/trigger campaigns, and the second component optimizes message selection for all recurring campaign types. RevIQ optimizes send time delay by starting with a set of send time delays for a conversion campaign and then measuring the campaign goal achievement rates for those times. The optimizer is updated with the collected data as the campaign is run and progressively adjusts the parameters for the campaign.

A use case example would be after users put an item in their virtual carts but then don’t check out. Here, Kahuna could send a message to groups of users at different times - 5 minutes, 30 minutes, etc. - to find the time delay to which people respond most positively. Once the results become significant, RevIQ automatically stops user testing and only sends the message after the amount of time that has the best performance.

After establishing the highest performing time delay, Kahuna’s then tests five different messages to determine the highest level of engagement and conversion to understand which message content brings in the most revenue, automatically optimizing to the best message once significance has been established. The algorithm uses sophisticated AI, analytics and A/B testing technology to automate the process and select the best type of notification and the best time to send it.

RevIQ works by using response data collected in a push campaign to update parameters for a Bayesian network that models population parameters associated with the conversion fraction for the send time delay or the campaign message. Initially, all messages and send delay times are considered equal and push messages are sent to each with the same proportion. As the population estimates become better defined, more confidence is gained in the estimates of the best selection of message and send delay time. The proportion of messages sent are adjusted to the combination of message and send delay time for which receive the best responses.  When the estimate confidences reach a high threshold score, a “winner” is picked and all future messages will use the best parameter selections.

RevIQ optimizer is currently only available for push notification campaigns. In the near future, Kahuna will expand the functionality to optimize email campaigns, in-app messaging, web messaging and additional mobile marketing channels.



Read more: https://www.kahuna.com/

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