Data science drives big decisions at Kohl’s – CIO

Long before the advent of customer data platforms (CDPs), Kohl’s business model centered on collecting and cultivating customer data.

“We’ve had a homegrown customer data environment for decades,” says Paul Gaffney, CTO and supply chain officer at the $19.4 billion American department store chain. “And we’re quite happy with our custom implementation.”

The Milwaukee, Wis.-based retailer originally built its homegrown on-premises CDP on Netezza, creating robust customer profiles based on the chain’s large credit card portfolio and “a historical approach to cultivating customer loyalty and attachment that is very personalized,” Gaffney says.

But for the past several years, Kohl’s has made a big push to the cloud as part of a “technology modernization” that Gaffney says makes the most of machine learning, personalization, enhanced demographic data sets, and “hyper-localization” insights to deliver the most relevant merchandise to local stores.

The transformation sees the retailer, which is currently up for sale, running workloads on Google Cloud Platform and on private on-premises Google Cloud servers running VMware, as well as some utility workloads on Amazon Web Services, the CTO says. While the company’s current on-premises cloud uses a comprehensive suite of tools, including Qlik for advanced analytics and data visualization, Kohl’s long-term plan for data is all about Google BigQuery, Gaffney says.

“Four years ago, we started focusing on BigQuery as our primary data environment,” a decision Gaffney says he inherited. Kohl’s has since built a sophisticated data science practice around the Google platform, with most of the retailer’s critical data, including customer, product, and business performance views, now residing in that modernized data environment.

But Gaffney is far from finished.

“We’ve got about two more years to go to get to a place where I would describe us as a fully data-native organization, using automated decision processes instead of using data just augmenting human decision processes,” says Gaffney.

Key to that push is a strategy to make the most of machine learning and third-party data in service of customer personalization and the “hyper-localization” of merchandising decisions, Gaffney says.

The power of third-party data

Kohl’s, which employs 1,000 people in its IT organization, including 50 data scientists, started its data automation push 18 months ago. Currently, the chain’s ample collection of first-party customer data as well as licensed third-party data sets are being migrated to BigQuery to apply advanced machine learning models and enhanced personalization technology to bolster sales, Gaffney says.

Like many retailers, Kohl’s also uses publicly available machine learning models on the Google platform and has used Google’s Vertex AI platform. The retailer also licensed a data set called Demand Brain from Deloitte focused on consumer demand, comprehension, and forecasting, says Gaffney, explaining that all the big consulting firms have data subscription products and ML engines available for licensing.

Gartner analyst Erick Brethenoux says use of consultant data and ML models is gaining steam, especially among retailers.

“Many organizations employ third parties to build models for them,” Brethenoux says, noting that consulting firms also use third-party data sets to pre-build models to embed in client systems or, in rare cases, use both their own technology and their own data to build models for retailers and other clients.

Kohls, for example, has licensed a platform from Deloitte called InSightIQ and is working with another partner, Axiom, to enhance its first-party data with other data sets. …….


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