Retention Science Releases Adaptive Lifecycle for Marketers
Marketing platform provider Retention Science today launched Adaptive Lifecycle, a solution that uses artificial intelligence and predictive analytics to automate the email conversations organizations have with existing customers. According to Jerry Jao, CEO of Retention Science, the goal is to reduce the time marketers spend manually designing campaigns and free them up for other tasks.
"There are a lot of tools out there that are designed to help marketers create multichannel [email drip] campaigns," Jao says, highlighting solutions such as Salesforce.com's ExactTarget. But to use these effectively, "marketers have to [spend a lot of time] scheduling, configuring, creating templates, and deciding when the campaigns should go out. It's not an easy effort, and typically takes a couple of people to coordinate and work out the logistics."
To ease the workload for marketers, Adaptive Lifecycle aims to makes sense of online customer interactions and use intelligent decision making to outline the next best steps. The solution processes customer signals—including engagement rates and browsing patterns—and applies them to machine learning models. Customers are then segmented, according to their health, as "At Risk of Churning," "Completely Churned," or "Ready for a Promotion." The solution predicts customer behaviors—including when a person is most likely to complete a purchase or turn away from the company altogether—and determines in real time the best messages to send each individual.
For Retention Science, the release is in line with its goal of enabling companies to get the most out of existing customers. "Most businesses spend a lot of money and resources on acquiring customers, but few invest time in existing customers," Jao says. "If you've already spent so much in getting customers in the first place, why not invest in retaining them?"
Jao notes that the system takes into account pertinent data regarding demographics—including household incomes, the average length of a daily commute, or the kind of house a person is likely to live in—and puts it into the mix to predict what messaging strategies will lead to conversions. "The goal is to really understand what different customer attributes mean," Jao says.
Tracking offline data points is one of the product's current blind spots, but Jao suggests that Retention Science is working on bolstering those capabilities for future releases.
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