KELLOGG'S ACCELERATES NEXT-GENERATION ANALYTICS

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“With every challenge, there’s?always?an opportunity,” says Garrett Byrne, Global Data and Analytics at Kellogg’s. Byrne joined CGT’s webinar, “Accelerating Next-Generation Analytics at Kellogg’s,” to discuss next-generation analytics alongside Albert Guffanti, Vice President and Group Publisher of?CGT?and?RIS News, and of course, yours truly!

Prior to the Kellogg Company, Byrne worked for companies across numerous industries, primarily in financial services and consumer goods, where he delivered transformation programs. It wasn’t until working at Allied Irish Banks (AIB), a pillar bank in Ireland, that Byrne discovered his passion for data and analytics. At AIB, Byrne helped the company invest in and deliver new digital products, like self-service machines and mobile apps, and he even introduced Apple and Google Pay to the Irish market. In fact, seeing these projects come to light is what inspired Byrne to seek out more data-and-analytics opportunities in his career.

Leaning into innovation concepts

According to Byrne, next-gen analytics (NGA) refers to the use of big data, machine learning (ML), artificial intelligence (AI), and visualization to easily digest large amounts of data, gain near-real-time insights, and use these insights to predict outcomes. NGA is a form of predictive and prescriptive analysis, as Byrne describes it: an enterprise-wide program Kellogg’s has mobilized to inform and support business decisions across the company.

Focused on becoming a data- and insight-driven company, Kellogg’s sees the data-and-analytics journey as a key enabler to delivering its business strategy, which is why the company is investing in its new cloud-based analytics platform, Kortex. Part of the larger NGA program, Kortex has four key capabilities:

• Improving analytics performance
• Increased capacity to manage big data
• Enhanced speed to insight
• Decreased total cost of ownership

With immense volumes of data, Kellogg’s is focused on making the right data available to the right people at the right time, from operational data to syndicated and point-of-sale data. Kellogg’s knows just how important consumer data is to its AI and ML efforts. As Byrne says, “When you start to add first-party data with second- and third-party data, the value is prodigious. Marrying these data sources, you start to see real transformation opportunities as related to both customer and consumer engagement. After all, that’s what the Kellogg Company is all about: turning insights into growth opportunities and over-delivering on the consumer brand promise.”

A prime example Byrne points out of how Kellogg’s has applied Machine Learning (ML) to solve consumer issues was its analytics work during the COVID-19 pandemic. Seeing the impacts that the pandemic had on schools and businesses pushed Kellogg’s to look at the data that it had on its consumers and synthesize it with the information that the company already had on buying patterns, the economy, and consumer sentiment. Kellogg’s also looked at different pandemic scenarios, generating early hypotheses as to how the pandemic would impact consumer and retailer behavior. One of the biggest insights that the company gained was how customers’ emphasis on surface cleanliness was changing. Thinking critically about how the COVID-19 pandemic impacted consumers, Kellogg’s responded by reducing the size of its packaging, setting the stage for more permanent changes to Kellogg’s relationship with packaging and cleanliness.

Business and technology collaboration

Fortunately for Kellogg’s, the company has approached analytics from both a technology and a business-process perspective, focusing first on business use cases that might be supported by better access to data and insights. The Kellogg’s analytics team’s close partnership with business stakeholders is key to the success of its next-generation analytics program. “We’re in the lucky position where we’ve developed an industry-leading platform and that means that we have stakeholders from across the organization knocking on our door to see how we can partner together to deliver great solutions,” Byrne says.

But it’s not just the technology, nor the partnerships, that will help Kellogg’s achieve its goal. To become a data-driven and insights-fueled organization, Kellogg’s knows it needs to use Kortex as the foundation for other business transformation initiatives. After all, every business process generates data—and needs data-facilitated insights to power decisions. “We need to provide Kortex with the right data in the right format and at the right time and make it available to our business stakeholders. This involves migrating our current data from our constrained and expensive on-premises solution to our new AWS cloud-based platform,” shares Byrne. Migrating to Kellogg’s new cloud-based platform promises the company a breadth of benefits. This includes virtually unlimited storage, faster performance, easy test-and-learn trial opportunities, and an advanced suite of tools and technologies at a low cost with a market-leading provider, AWS. As Kellogg’s moves forward, using these capabilities is ultimately what will help the company accelerate the way that it uses data.

Lessons learned

For anyone embarking on the journey to implement next-generation analytics, Byrne advises to keep things simple, work early and often with business stakeholders, and stay incredibly focused on the customer—in this case internal customers, then external retail customers, and the end consumer. Byrne shares that there are four things Kellogg’s is doing right now to be successful in its NGA program: investing in business and people, changing management early in the process, having a crystal-clear communication strategy, and building a network of champions and advocates across the organization to personalize the program and facilitate adoption.

View the Consumer Goods Technology (CGT)?Accelerating Next-Generation Analytics at Kellogg’s?on-demand session.