Blueshift’s AI helps platform focus on individuals and continuous journeys

The personalization platform is employing its AI to better respond to specific customers and would-be customers.

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Personalization platform Blueshift is today launching AI-powered customer journeys that move its targeting from user segments to individuals, and its focus from single campaign responses to continuous customer journeys.

Blueshift provides personalized marketing through content recommendations, email marketing, and, for mobile devices, push notifications and SMS.

The company’s AI has previously been employed to provide capabilities like Predictive Scores for evaluating such things as which customers are likely to bolt, or to make the most appropriate product or content recommendations to site visitors. The Score might look at data showing, for instance, that certain telco customers are rarely using their data services.

Now, the AI is being used to continually optimize customer journeys. While the Predictive Scores were previously a point-in-time, resulting in a specific campaign effort to a group of users, like sending a discount offer via email, now the scores are continually read so that users can be placed into a customer journey as soon as the individual Score exceeds a threshold.

The AI determines at what point in a continuous series of marketing responses — the customer journey — to place the particular individual. A journey can also be triggered by a specific event or user behavior.

Co-founder and CEO Vijay Chittoor told me the “big takeaway is that marketers plan customer journeys, but the solutions have [largely] been manual, such as when to start customers on a specific journey.” Now, he says, AI is helping Blueshift automatically place a customer on the journey as soon as predictive scoring shows a flag.

The platform’s AI is also being summoned so that A/B testing of content recommendations can look at recommendation logic. While there was A/B testing of content recommendations before, Chittoor said, it wasn’t tuned to determine if, say, recommendation logic based on previous content you chose was better than logic based on recommending content because of what others like you liked.

Blueshift is also adding an ability to determine which step in a journey had the biggest impact, compared to a prior ability to only evaluate an entire journey. Chittoor said that, although AI is not powering this enhancement, AI can be used to optimize the journey once this step-by-step attribution is completed.

Here’s Blueshift’s visualization of these enhancements:Blueshift Image Z0k63t

Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.

About the author

Barry Levine
Barry Levine covers marketing technology for Third Door Media. Previously, he covered this space as a Senior Writer for VentureBeat, and he has written about these and other tech subjects for such publications as CMSWire and NewsFactor. He founded and led the web site/unit at PBS station Thirteen/WNET; worked as an online Senior Producer/writer for Viacom; created a successful interactive game, PLAY IT BY EAR: The First CD Game; founded and led an independent film showcase, CENTER SCREEN, based at Harvard and M.I.T.; and served over five years as a consultant to the M.I.T. Media Lab. You can find him at LinkedIn, and on Twitter at xBarryLevine.

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