Blueshift’s AI Enabling Continuous Customer Journeys

Blueshift’s AI Enabling Continuous Customer Journeys

Robert Woolliams

June 9, 2017

2 Min Read

In the midst of the huge uptake of AI in customer service functions this year, personalization platform Blueshift has launched 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.

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Feature image credit: Pexels

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