Midwest grocer turns shopper data into satisfied customers

Customer
Overview
What we did
Technologies used
Key outcomes

Customer

Schnucks, a leading Midwest supermarket retailer headquartered in St. Louis, Missouri was looking to capitalize on robust customer data insights to drive stronger decision making. Google Cloud Platform (GCP) anchors their efforts, along with data strategy, architecture support and ongoing guidance from Pythian. Since implementing Google Workspace, Chrome Enterprise, and several Google Cloud services over the years, Schnucks has relied on Pythian as a Google Cloud Managed Service provider with broad expertise.

To launch their analytics initiative, Schnucks needed to:

Organize their data. While the grocer had robust customer and purchasing data, to make it usable they needed to inventory, segment and store this information efficiently. They also needed to adopt formal data governance protocols.
Understand staffing requirements. Leadership knew emphasizing analytics would require different competencies in the internal Data & Analytics group, as well as outside expertise.
Start strong. Quick wins are key for high-visibility initiatives. A proof of concept would show stakeholders the potential within company data and create an appetite for ongoing insights.

Working together, Pythian helped the grocer to:

Set the right incentive strategy. Pythian’s data scientists helped Schnucks develop a machine-learning model to predict shopper likelihood to buy certain products. These insights drove other campaign parameters.
Launch in the cloud. The modern, scalable GCP platform gave Schnucks computing power and ease-of-use. It offered far more flexibility than creating a custom, on-premise solution.
Test and learn. The 12-week proof of concept gave the Schnucks team the opportunity to learn the platform and absorb Pythian’s analytic approach in a collaborative environment.

With the subject in place, Pythian and Schnucks worked quickly to launch the proof of concept. This included:

Building an algorithmic, machine learning model in GCP. The model predicts the likelihood a customer will buy certain products, based on their individual shopping history.
Identifying Own Brand products to recommend to each customer, based on their unique results in the model.
Distributing coupons, special offers and other promotions to the test group during each week of the 12-week campaign, via Schnucks Rewards, the company’s loyalty program. (The control group received no recommendations or incentives.)

Pythian helped Schnucks jumpstart their analytics efforts with:

Increased demand. The 12-week proof of concept clearly demonstrated the potential of data-driven programs. It generated a measurable boost in Own Brand sales and reactivated 1,200 lapsed Customers.
Improved competitive position. With GCP’s agility, scalability and robust features, Schnucks now has a platform to close the tech gap with larger grocery chains. They’re also armed with data insights to show their own customers new ways to eat well and save money.
Roadmap for the future. Going forward, Schnucks has a strategic partner in Pythian, as well as a clear plan for where to leverage this outside expertise and where to hire and train in-house talent.

The pilot quickly proved a success on multiple fronts. First and foremost, it generated a tangible improvement in Own Brand sales.

The project showed the power of targeted communications. Not only did more customers try Own Brand products, the promotions also increased the frequency of shopper visits to the store.

For Henry, even the small-scale results of the proof of concept exceeded his expectations with the caliber of information available to tailor their marketing programs and spending.

“From day one, Pythian was an extension of my team,” Henry said. “They weren’t a third-party; they were a group of teammates just as intent on project success as the Schnucks employees.”

Tom Henry

Chief Data & Deputy Chief Information Officer, Schnucks

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Midwest grocer turns shopper data into satisfied customers

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Overview

What we did

Technologies used

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Tom Henry

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