New York-based alternative data analytics provider Apteo has rolled out Predictive Insights, a service that provides key indicators of a company’s financial performance. The vendor anticipates it will help the company to broaden its client base and expand its business to sectors outside financial services.

Predictive Insights allows financial professionals to analyze data to find the drivers of the key performance indicators (KPIs) that matter to them, without needing a data scientist on staff (officials say users can create their first artificial intelligence model within five minutes). Apteo co-founder and CEO Shanif Dhanani says the service identifies and explains the data attributes that have the biggest impact on a user’s chosen KPIs, and enables them to create machine-learning models that predict changes to those KPIs.

In addition to providing insight into the drivers of a company’s revenue and other factors, Predictive Insights also enables firms to test and validate datasets to show their effectiveness at predicting revenues.

Dhanani says the vendor developed an initial version of Predictive Insights around March, which included a visualization component, as well as statistics and machine-learning capabilities, but realized that clients really wanted analytics around specific metrics—such as the impact of individuals or behaviors—that contribute to key financial indicators, rather than a machine-learning capability that would allow them to build their own analytics.

“So we took that feedback and modified what we had done into something less geared toward technical people, and more toward non-technical people, to enable them to find and interrogate the data and metrics they’re looking at, without needing to be a data scientist,” he says. “You upload your data, and with a few clicks, you tell the system what you’re looking for.”

Apteo has built a foundation of core functionality that can be used regardless of the data type being interrogated or the industry that the client works in, and can be tailored to the needs of financial professionals or people working in other sectors.

“Whatever industry you’re in, it will work for you. You have to tell the system a bit about what you do, and you have to input the specific data,” Dhanani says. Next, users define their workflows, and can plug in data sources—ranging from a market data application to CRM platforms such as Hubspot or Salesforce—and define the key performance indicators they’re looking for. Though users must initially set up their workflows themselves, Dhanani says Apteo will create a series of templates based on users’ roles to make the onboarding process easier.

BYOD (Bring Your Own Data)

While clients can upload their chosen datasets for use in Predictive Insights, Apteo will no longer provide its own sources of traditional market data, since clients typically source this themselves, meaning the vendor was duplicating data collection efforts, which was distracting from its core aims.

“At the time, we were providing public financial data—for example, Fed interest rate reports and stock prices—but we found everyone already had their own sources of that, and we decided it was slowing everything down, because we were dedicating a lot of resources to collecting and cleaning that data, which was taking time away from creating our analytics. So when we realized it wasn’t adding anything, we removed those datasets so we could reallocate our resources to artificial intelligence and analytics … and now we let people bring their own data,” Dhanani says.

The ability to support external datasets is an important factor in the vendor’s decision to expand its target base beyond financial markets, where the company started out. For example, the KPIs that users look for insight do not have to be financial: Predictive Insights is able to identify “churn” in any area.

So, for example, marketing professionals could use it to identify which customers are at highest risk of going elsewhere, e-commerce and sales teams could use it to drive sales by understanding and monitoring a company’s web traffic, and HR staff could identify which employees are at greatest risk of leaving the company. In each instance, the platform helps users understand their key drivers of churn and identify opportunities—such as cross-selling and upselling potential, or employee incentive plans—to address each situation.