How Giant Eagle launched a multi-tier loyalty program with cross-channel personalization

Few things have a bigger impact on quality of life than access to safe, healthy food and medicine when you need it. For millions of shoppers throughout the Great Lakes and Mid-Atlantic regions, that assurance comes from Giant Eagle.

Having grown to over 470 locations throughout Pennsylvania, Ohio, Maryland, West Virginia, and Indiana since their founding in 1918, and now providing services across grocery, pharmacy, quick-service convenience, and fuel, Giant Eagle has become one of the most prominent retailers in the country. Central to the group’s success has been their mission to put purpose beyond profit, and to ensure that with every offering they are enriching their customers’ day-to-day lives.

The services that customers need today, however, are very different from those customers needed 80 years ago. With the rise of eCommerce giants, home grocery delivery, and digital integration into in-person shopping, the Giant Eagle team knew that to continue providing value to their customers, they would need to evolve their offering. For Damian Scott, Sr. Vice President and Chief Customer Officer, and Justin Weinstein, Sr. Director of Customer Experience, that evolution meant expanding the customer experience beyond the store and bringing digital personalization to their users in the form of cross-channel digital experiences, loyalty programs, and member rewards.

Like many high-growth companies, the Giant Eagle team had adopted a number of best-in-class tools, ranging from transactional databases to engagement platforms to ML-driven product recommendation applications, to support their digital initiatives. While each of these tools provided the team with essential capabilities, it was often difficult to integrate data between them. This presented several technical challenges that restricted the team’s ability to deliver the cross-channel customer experiences they envisioned.

Every database and analytics platform has its own way of identifying users, which is often helpful when you need to access user records and use this data within a single environment. If you don’t have an easy way of connecting identities and keeping them consistent across the stack, however, it can become nearly impossible to use multiple systems in unison.

The Giant Eagle team found that the data in their server-side transaction logs followed a completely different identity architecture to the user engagement data being collected from other sources, such as web. This meant that connecting purchase records with website engagement data, keeping them consistent, and using them together to deliver personalized product recommendations would require hours of manual identity resolution.

In addition to presenting identity resolution challenges, the inability to integrate data across systems also made it difficult to understand the end-to-end customer journey. While loyalty program data lived in one system, user data such as email, household, and geographic information lived in other sources. The different datasets had limited association with one another, and using them together to deliver contextual customer experiences would require hours of data cleansing and reformatting.

In addition to the challenges faced with their existing tools, the Giant Eagle team was also restricted from developing their tech stack. Implementing a new loyalty partner, data science system, or marketing engagement platform would require months of dedicated engineering support. This high adoption cost made it hard for the team to launch new initiatives and test channels. Furthermore, because implementing new systems was such a lengthy process, new additions to the stack were error-prone. For example, implementing a marketing engagement platform into the team’s stack required different engineers to take on different stages of the implementation project. This led to inconsistencies across the event instrumentations, impacting the completeness of data being collected into the engagement platform and, ultimately, the effectiveness of the downstream application.

To deliver the cross-channel, personalized customer experiences that they envisioned, the Giant Eagle customer experience team had to rely on engineering support to connect data between systems, keep it consistent throughout the pipeline, and manually create segments. The process was inefficient, error-prone, and wasn’t scalable (although not at all uncommon among growth teams today). To successfully deliver personalized experiences to their customers, the team knew that they needed a way to move faster and more efficiently.

The Giant Eagle team had selected all of the tools they needed at the application layer and the database layer, but they needed a central data infrastructure that could integrate data across tools and systems. The team selected mParticle as their Customer Data Platform, knowing that they could rely on a CDP to manage data quality, gain a single view of the customer, and automate data routing throughout their pipeline.

With a streamlined data infrastructure, it was time to begin designing the data flows needed to support the delivery of cross-channel personalized experience.

The team began by collecting data from across channels into mParticle. To ingest web engagement data, they implemented mParticle’s Web SDK, tagging events such as account creation and sessions. To ingest additional user attribute data, such as customer store location preference, email subscriptions, and account info, as well as customer coupon savings, transactional records, and loyalty information from the server-side, the team connected mParticle’s server-to-server API and several CSV Feeds to their internal systems. Finally, to collect loyalty and rewards information from their AI-powered personalization engine, the team connected one of mParticle’s prebuilt Feed integrations. 

As data is collected into mParticle from across channels, the team leverages mParticle’s data quality toolset, Data Master, and identity framework, IDSync, to ensure their customer data is clean, consistent, and tied to persistent profiles before it is forwarded downstream tools and systems. With mParticle in place as the central infrastructure of their growth stack, the Giant Eagle customer experience team is able to monitor data collection, enforce a data schema, and query customer profiles in an accessible UI, without heavy engineering work. 

Once data is validated against the established data plan and has been resolved to customer profiles, the team leverages mParticle’s Audience Builder to construct segments of users. By defining segments based on attributes such as product recommendations (ingested from their AI-powered personalization tool), loyalty and reward data (ingested S2S and through a productized feed integration), and loyalty program status. As mParticle Real-time Audience memberships stay up-to-date dynamically, the customer experience team simply defined Audience conditions once with confidence that memberships would remain accurate as new data continued to flow into mParticle.

Once defined, the team was able to connect Audiences to be forwarded to downstream tools by activating Audience forwarding integrations within the mParticle UI. Specifically, they connected audience segments to a Data Enrichment Platform for advertising and their promotional messaging application.

In addition to Audience forwarding, the Giant Eagle team also set up automated event forwarding from mParticle to their customer engagement tool using mParticle’s server-side kit integration with that tool. With the connection in place, they were able to forward customer engagement events as well as rewards and loyalty data collected from their AI-powered personalization tool to their customer engagement tool, where it could be activated for personalized email, push, and in-app messaging, without having to manage the engagement tool’s SDK. This data was also forwarded to their internal systems to enrich historical records.

Finally, the customer experience team also utilized mParticle’s Raw Data Export capabilities to connect the data being collected into mParticle to their data warehouse, where it is accessed by teams across the organization.

Implementing mParticle as the central infrastructure of their growth stack has provided the Giant Eagle customer experience and growth and retention teams with greater control over how data flows between systems. The ability to tie data from multiple sources to deterministic customer profiles made it possible for them to understand their customer journey, build dynamic audiences, and coordinate experiences across multiple tools. With an agile growth infrastructure in place, the team was ready to release digital experiences that would truly amaze their customers. 

Over the past 8 months, Giant Eagle has been moving to scale across several loyalty and personalization initiatives. In November 2020, Giant Eagle launched myPerks loyalty program, beginning with a beta launch limited to the Canton, Ohio community. The myPerks program provides dollar rewards to Giant Eagle customers based on purchases at Giant Eagle and Market District supermarkets, as well as GetGo convenience stores. Furthermore, myPerks rewards customers who earn a certain level of perks within a six-month period PRO loyalty status, which allows them to earn 50% more perks on groceries, retailer gift cards, gasoline, prescriptions and more, making myPerks the first multi-tier loyalty program introduced by a major U.S. supermarket. Additionally, Giant Eagle has been expanding its Perks Challenges program, which delivers 1:1 personalized promotions to customers leveraging its dynamic reward currency.

With the ability to integrate data across transactional logs, recommendation tools, loyalty systems and more, tie all of that data to a single profile, and activate that data strategically across channels, the Giant Eagle team was able to deliver an end-to-end personalized rewards program.

Throughout the pilot launch of myPerks and the scale out of Perks Challenges, the team drove:

All while saving thousands of engineering hours in vendor SDK maintenance, data quality management, and identity resolution.

If you’re interested in learning more about mParticle, you can explore connecting customer data, building audiences, and managing data quality yourself in our platform demo here.