How Cloud & AI Are Shaping Grocery, Drug, and Convenience Retail With Scott Langdoc Of AWS

  Joining us now for five insightful minutes is Scott Langdock. Scott is the global head of all grocery chain drug and convenience retail strategy at Amazon Web Services. Scott, what is taking place in the grocery drug and convenience space right now in terms of What is also top of mind for retailers and how do their concerns pertain to the cloud?

I'd say, you know, kind of top of the list, uh, for what's, you know, a challenge for grocery and fast moving consumer, good retailers is changing consumer behaviors. I mean, that hasn't necessarily been a new, uh, concern, but I think, uh, you know, both pre and post COVID, you know, buying patterns have changed.

Demographics are shifting and so trying, uh, to stay ahead of that, trying to adapt selling models, channel strategies, product availability. Product assortment, you know, to match those changing buying behaviors is has really been a challenge. And while retailers have been spending a lot of energy, both operationally and technically on customer facing and data driven projects, what we've seen over the last 18 to 24 months is a real focus on cost reduction.

Um, and so a lot more emphasis on, you know, areas of the business where they want to drive cost out. Things like loss prevention, labor optimization, um, you know, reducing the cost of fulfilling online orders. So, while there's still the focus on a number of those, uh, those revenue generating projects, that cost reduction focus has been kind of key, uh, of late.

Um, in terms of the cloud, I think retailers are realizing just the cost advantages of shifting a number of their workloads to the cloud, and now they're kind of looking for those additional solution capabilities that will, you know, drive both. Efficiency and top line performance. So Scott, you bring up a really important point because within the drug and grocery and convenience segment, we know that the margins are thinner than any other industry.

So as retailers are thinking about AI investments that are really going to drive, you know, those costs reductions that are going to boost profitability. Like you were talking about what factors they need to consider. Margins are thinner, but one of the things that we're always, uh, intrigued to see. Is a really measurable wedge between segment leaders and segment followers.

Um, driven by the fact that we see leaders investing in both top line innovations, those that help to drive top line revenue, drop, drop, draw, uh, drive top line growth and a focus on those optimization and cost reduction, you know, bottom line initiatives. Um, examples of like the top line initiatives we're seeing a lot of focus on.

Is in the area of like using demand forecasting to drive proper pricing recommendations To see that same demand forecast and customer intelligence Drive a new level of personalization in terms of the um the offers being generated uh to customers We have a large grocery customer that in implementing that hyper personalization Saw 98 percent growth in market basket size, some overall growth of two and a half times in offer related revenue.

While at the same time, the offers were intriguing enough to increase, uh, the conversion, meaning those opened and reacted to offers well over a hundred percent. So we've, you know, when you see those kinds of benefits, that really helps to improve margins from both the top line and the bottom line, uh, perspective.

Before we let you go, Scott, I want to take a longer term view. I want to get your opinion on, on the longterm. So as we consider that, what would you suggest retailers need to take action on today to drive more value in the future? In support of a lot of the innovation initiatives that I talked about earlier on the top line and the bottom line, there's still that need to focus on, um, the, the infrastructure, right?

Making sure that you've got the proper level of maturity in your data, your product data, your customer data, your operational data. Because that's the information and insight that's going to be necessary to drive the kind of efficient and accurate insights that come out of these AI driven applications.

So focus on the infrastructure is important. Another area is when you're building out new capabilities like personalization. Asking yourself the question, am I making investments that truly influence purchase behavior? Am I helping drive a customer's trip frequency, their basket size? You know, those kind of important metrics.

That we all know are related to the fact that they're responding to kind of the offers and engagements that a customer is making. Um, and, and one of, that's one of the big areas that we see generative AI having an opportunity to help with. Um, at, at Amazon Web Services, one of our approaches is to give a wide variety of large language models to customers so that they can choose The right LLM for the right use case.

Um, we embed kind of the appropriate guardrails for security and privacy into the building of those applications. So that's something that we want to continue to contribute to as these retailers are identifying the critical generative AI use cases that they have. And then lastly, Um, with all of this new insight and all of these new business signals, how does it, how do we make sure that that retailer is tying together that analysis with the required action?

What levels of improvement and automation are they doing to ensure that when they get a business signal that that business signal is Reacted to measured and responded to in the most agile way possible because ultimately those retailers who can stay agile and stay respondent to the different market market signals and customer signals, I think, are going to be the ones that win in this, uh, in this hyper competitive world.

Wow. Great stuff. Thank you, Scott. Scott.