Kexin Xie's guest appearances

Kexin Xie

B2BiQ

The AI Network Podcast

Edda Blumenstein

Julie Seitz

Alexander Hubel

Rida Mustafa

Pelle Braengaard

Kenneth Farrugia

Clara Durodie

Roland Haefs

Ravi Rao

Deena John

Tyrone Grandison

Jack Cheng

Rohit Amberker

Paul Bao

Brian Mikkelsen

OwlTail

Kexin Xie is a data scientist for Salesforce. Among other things, Kexin is responsible for building the engineering infrastructure, services, and frameworks that makes the Salesforce marketing cloud so powerful. In this conversation, Kexin discusses how the power of predictive analytics is leveraged in businesses for “next-best-action” interactions. While these tools drive more engagement and revenue from the customer side, which Kexin touches on, he dives deep on how data science makes this possible. Kexin makes the intangibles tangible by describing the process by which algorithms are designed—including some pitfalls to be aware of—and recognizing “valuable noise.” Kexin is a realist, though, saying, “There's the use case part, there's the data part, and we're trying to build a bridge between the end solution and how we get there from the initial raw form. Eventually, we’ll start to understand the raw data more and start to build even more pipelines and then see how they connect with each other; their commonalities. And eventually, I think the bridge will be formed, and then building new things will be much easier than the initial use case.”

<p>Kexin Xie is a data scientist for Salesforce. Among other things, Kexin is responsible for building the engineering infrastructure, services, and frameworks that makes the Salesforce marketing cloud so powerful. In this conversation, Kexin discusses how the power of predictive analytics is leveraged in businesses for “next-best-action” interactions. While these tools drive more engagement and revenue from the customer side, which Kexin touches on, he dives deep on how data science makes this possible. Kexin makes the intangibles tangible by describing the process by which algorithms are designed—including some pitfalls to be aware of—and recognizing “valuable noise.” Kexin is a realist, though, saying, “There's the use case part, there's the data part, and we're trying to build a bridge between the end solution and how we get there from the initial raw form. Eventually, we’ll start to understand the raw data more and start to build even more pipelines and then see how they connect with each other; their commonalities. And eventually, I think the bridge will be formed, and then building new things will be much easier than the initial use case.”</p>

AIIA: Kexin Xie, Salesforce

Ep. 134: Kexin Xie, Salesforce