Alteryx Adds the Promote Component to Its Analytics Platform

Alteryx, a self-service data analytics software provider, today upgraded its Analytics Platform with Alteryx Promote, a component that aims to help business users implement prebuilt predictive data models on their customer-facing applications. The announcement comes as a result of Alteryx’s June acquisition of Yhat, whose data science platform enables companies to develop, deploy, and manage real-time decisions via APIs.

“Essentially, what Alteryx is is an end-to-end platform for analytics,” explains Matt Madden, director of product marketing at Alteryx. “We allow users and organizations to discover and share information via our Alteryx Connect product.” The vendor’s Designer product enables users to prepare, blend, model, and analyze data. “At end of the analytics journey is where Promote comes in,” he says. With this component, users can “deploy, manage, and share predictive analytics at scale.”

Alteryx Promote gives trained and untrained data scientists the ability to quickly implement and alter their predictive models without having to burden IT. Users can embed their predictive models into apps that can respond to REST API requests without additional code. The system can then administer the models on-premises, behind a firewall, or in the cloud, in a manner that is both “flexible and scalable,” according to the company.

Developing a model in Alteryx’s “code-friendly environment” and deploying it in a secure cloud environment can drastically reduce the time a company typically spends on such processes, the company claims.

Austin Ogilvie, director of product management at Alteryx, says that a common use case for Promote is in e-commerce companies that want to prevent shopping cart abandonment and have created models to determine that a customer will not check out within a designated time frame. With the predictive model in place, the organization can push actions to drive purchases. That’s just “one example of a direct revenue-generating integration that a marketing team would work on,” he says.

Another example of an employee-facing application that could be improved are lead scoring tools in Salesforce.com. A sales organization can integrate a predictive model to determine what email script is most appropriate to send a prospect when following up. Lead development reps might use a model that helps them determine what rep they should route a lead to.

In a statement, Mark Thompson, director of data services at The General, noted that Alteryx has been a “game changer” for the car insurance company:

“Our use of Alteryx has rapidly spread across the company as our department heads continue to apply the technology to new use cases and expose opportunities to save significant time and costs, while improving operational efficiencies. As an Alteryx platform customer, we’re excited about Promote and its ability to help data scientists develop and manage applications to help solve critical business problems.”

Ashley Kramer, vice president of product management at Alteryx, said in a statement that the vendor aims to help companies "operationalize" predictive analytics:

“With the Alteryx platform, we have been helping organizations learn how to do predictive analytics without coding; the next level of maturity is operationalizing those models for consumption. This convergence of machine learning, business intelligence, and data science is essential for modern organizations to realize the full potential of their data assets.”

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