Giant Eagle’s myPerks Loyalty Pilot: Behind the Scenes

Loyalty programs hold a tremendous amount of value, but the ability for a retailer to unlock the power of those little plastic keychains accelerates when artificial intelligence helps create true personalized experiences. Giant Eagle’s Justin Weinstein recently provided a look behind the scenes at the grocer’s myPerks loyalty program pilot, sharing learnings as the company works to build it out. Read on to learn how Giant Eagle is pivoting myPerks to serve as a platform that provides value to consumers through reward currency, gamification, personalized savings, content and more — all while keeping their privacy concerns at the forefront. Tim Denman: Welcome, everyone, to the “How AI-Powered Analytics Is Transforming Customer Loyalty” webinar. I'm Tim Denman, editor-in-chief of CGT and RIS, and I appreciate you joining us today. As we all know, the customer journey continues to evolve across the path to purchase, and understanding what motivates shoppers has never been more important. Customers are demanding a more personalized one-to-one experience in both physical and digital interactions, and savvy retailers are adjusting their tech stacks and operations to provide it. In order to deliver this tailored experience, retailers need customers to provide vital information about themselves, which research shows they are willing to do as long as there is strong perceived value in providing that data. With me today to explore how to best entice shoppers to provide some personal information and how that information can be converted into actual insight that truly improves the shopping experience, are Giant Eagle's Justin Weinstein and Hypersonix's Todd Michaud. Justin is the senior director of customer experience at Giant Eagle. He joined the grocer in 2018 and has held a series of progressive leadership roles across digital product, personalization, loyalty, and customer experience. In his current role, he leads enterprise customer experience and is responsible for loyalty brand and business, customer product, user experience research and design, and marketing technology. Most recently, Justin and his team led the cross-function of research, design, prototyping, and build-out of the myPerks loyalty program, which is currently in pilot in Ohio. Todd is the president and chief commercial officer at Hypersonix, a fast-growing, venture-backed, Silicon Valley company specializing in AI-powered analytics. Prior to Hypersonix, he was the president and chief customer officer at Symphony Retail and had a number of successful years as the president of Retalix, a global shopping, engagement, loyalty, merchandising, and supply chain company. Thank you both for joining us today. For now, I want to hand things over to Justin who will get us started. Justin, take it away. Justin Weinstein: Thank you, I’m excited to talk about Giant Eagle, as well as what we've been doing over the last many years as it relates to customer loyalty, and more recently, the foundation that we've built that really enables us to bring our level of personalization and use of data to a new level on behalf of our customers. So, who are we? We are a multi-format food, drug, and fuel retailer that has a 90-year history in retail. We operate in five states, and do about $9.7 billion in annual sales. We have a little over 34,000 team members in almost 475 locations across our big box and small box. I'm going to talk about our formats and some of the connective tissue that we've built to bring them together in a bit. First, I want to talk about who we are as a company and the fact that we really are a values-driven organization that aims to provide our communities with life's essentials so our neighbors have the opportunity to thrive. We, as a company, especially over the last year, have had a really significant opportunity to step up on behalf of our communities through COVID — to be there in a way that we always have been, but in ways that our community has really valued — and we valued the opportunity to serve. Those things that we believe and embraced over these past many years have been accentuated over the last year, including the belief that safe healthy food and medicine is an essential human right, that everyone deserves respect, and that future generations deserve a healthy planet. Our core values sit as more than just posters on a wall. They make their way into the decisions that we make as an organization and the ways that we come to market for our customers, our team members, and our communities. We're a multi-format, omnichannel retailer, and we operate four primary banners: Giant Eagle, which is our traditional supermarket banner; GetGo Cafe + Market, which is a rapidly-growing convenience store that has a very strong made-to-order food offering as well; Market District, which is our destination for foodies in some of the largest stores that we have; and then a Giant Eagle Pharmacy business that is not only filling prescriptions but also providing essential health services to many within our community. I'm going to go through these next slides quite quickly that just speaks to each of the different banners that we have and starts to put some pictures for those of you who have not been in our stores. Giant Eagle Supermarket is about 75,000 square feet with between 20 and 60,000 items. We have a very strong own-brand penetration, and some of our stores have close to 350 team members working in them. Market District, which is more of a specialty store, ranges from anywhere from 80 to 150,000 square feet. We had 15 when this slide was written, but we have 17 of these locations across our geography and we're sourcing food internationally. Our GetGo format is smaller boxes anywhere between 4,000 and 6,500 square feet. We have over 250 locations at this point and have a robust food offering. All of the reasons that I wanted to talk through each of the different formats is because as a customer, you're interacting with Giant Eagle in many different ways over the period of a day, a week, a month, depending upon your shopping habits. As we start to think about the role of loyalty and the role of the platform that we've built, one thing that's very important to us is to think about this business as an ecosystem. To think about how our customers interact across a whole host of different channels, both physical channels as well as digital. Loyalty has played an important part of the company's history because loyalty is the emotional connection that we have to our customers. It's also played an important part because it’s the way that we endeavor to provide personalized value to our customers. At the center of our loyalty strategy and program for a very long time has been what we call the Giant Eagle Advantage Card. It’s a cornerstone of our customer strategy because we've been providing our customers with significant value by scanning for a period of many, many years. We've gotten to a place today where a significant percentage of transactions that are happening take place on this card, which enables us to be able to analyze that data and use it to provide as much value to our customers as we can. The reason that we've been able to drive towards such a strong scan rate within our business is because we've been on a loyalty journey for the better part of 20 years — dating all the way back to the early 2000s when we launched our fuelperks! program, which now exists in many retailers across the country. In 2010 we launched a program called foodperks!, we’ve evolved it into a program that we launched in 2017 called fuelperks+, which connects food, fuel, pharmacy, and made-to-order food in a unique way, and started to build towards a loyalty currency. Then, last year, we launched the myPerks program, which is the first two-tier loyalty program in grocery retail in North America. This journey has really been about developing the right exchange of information, as well as value for our customers to create a value proposition that is sticky, enticing, and creates a lot of value for our communities. As Tim mentioned, myPerks has been an initiative that we’ve been piloting for the last several months as a response to driving more AI, more personalization, and more digital capability into our loyalty platform over time. We started on this journey around myPerks to provide our customers with three things: We wanted to provide them with more, easy, and now. It’s about more perks, more personalized savings, more bonus events and promotions. We want to create a currency that is incredibly easy for our customers to optimize and see value whether they save those up for big savings on a transaction, or walk out and not pay anything on fuel or a sandwich in our convenience store. We wanted to provide maximum flexibility. In the world that we're living in, we want to be able to provide it to the customer now, in terms of them not needing to wait in order to take advantage of those savings. One of the big pivots that we've made as we bring this myPerks platform to life, is exactly to think about it not just as a program in terms of having a set of mechanics and a set of value levers, but rather as a platform that enables us to provide significant value to our customers through simplified earning, a true reward currency, gamification in the form of personalized benefits as well as unique ways for customers to engage with the platform, embedding loyalty tiers into the offering, and personalized savings not just tied to a perks currency but also tied to offers in content and meal planning, that all come together to create a very compelling value proposition for our customers. As we think about that evolution that we have taken on, one of the big pieces that has been incredibly important to us has been driving greater digital engagement through the launch of the program. We've done that through building out a benefit set that offers customers enough value to jump through that hurdle of downloading our app — to create an account, and ultimately to be able to see the savings and engagement that they are able to have with us as a brand — that we believe full-heartedly makes their shopping experience far more rewarding and engaging. Those are things like myPerks deals, xtra perk days, and perks challenges that have come together to create this digital value proposition. This enables us to leverage AI, machine learning, and personalization in a way that drives towards a type of customer experience that's very different from not only what we've done previously, but from what many in our industry are doing. A big part of that has been building towards this multi-tier platform. Today, within myPerks, we have a member tier and a pro tier. Customers are able to earn into the pro-tier based upon the number of perks that they earn over an earning period. Our real goal is to create a program that is highly engaging for our customers, that drives increased trip intent with us versus another retailer. Then, as they do that, they're able to unlock more and more benefits that we're really excited to power in the market, and we're excited to power more and more of those benefits as being personalized to the individual customers that are taking part in them. One example of how we're doing that is through a program that we have in the market called Perks Challenges. The backbone of this program is the myPerks platform that we just talked about. This takes a journey-based approach to loyalty, taking the platform and currency that we've built, and then engaging our customers to take on various actions throughout the store — whether it's downloading our app, engaging in categories that they're both super loyal to today and might not frequent often, asking actions that might exist across multiple different banners that we have both at small box as well as big box — then being able to use machine learning and AI to drive that experience forward. The loyalty platform that we've built enables us to deliver an experience, such as Perks Challenges, that's individualized on a one-to-one level. It leverages our data science capability that we've built and partnered on, that then creates a different runway around loyalty within our business both today, as well as where we're headed in the future. I'm going to hand it off to Todd, but I would close by saying while we haven't talked a lot about technology, the foundation that we have built has been about being able to match up the customer-facing capabilities that need to be brought to market in order to bring a truly personalized experience. Then, working backwards into the capabilities from a technology perspective — whether marketing, merchandising, promotion, a data infrastructure, etc., etc. — that are needed to be able to bring that experience to life in the most one-to-one, automated way that we possibly can. With that, I'm going to hand it off to Todd. Todd Michaud: Thank you, Justin. I appreciate that you shared your vision. Obviously, many in the audience got a lot of value in seeing what a future-looking vision can be around shaping customer loyalty going forward. I'll talk a little bit more about the technology, but the technology is pointless without a great vision to apply it to. The theme for my portion of the session is how artificial intelligence is transforming customer loyalty. I'll give you some perspective on that. I'd start with something that we all know, the needs of our shoppers have changed. Understanding our shoppers and their behaviors has never been more important. Shoppers are looking for us to simplify their life, understand their product and brand preferences, understand their channel preferences, and understand when they want relevant and timely information. They're looking for ways to save money and be rewarded, and they're looking for their retail partners to keep them safe. Importantly, they're very sensitive about making sure that retailers are good custodians of their data, and of course, they want to be well-informed. This is not unique to any particular set of shoppers or retailers. It's just the state of play today in this data-driven world. So, let's talk about the fact that Loyalty programs have been around for a long time, and they need to evolve with time. But why are multi-programs struggling in some cases today? It comes down to three main challenges that we consistently see. Challenge number one is that oftentimes, retailers have difficulty bringing together customer data. It's captured in disparate systems, and it's not always clean — it's sitting in silos. Then, of course, bringing the data together requires tools, talent, orchestration, and governance. So, the data continues to be one of the headwinds that great retailers face. Second, there is a lot of muscle memory around great loyalty programs, and again, many of the programs that are out in the market today have been built around gut instinct. The decisions that drove certain program determinations weren't necessarily data-driven. How do you go from an environment or instinct that's driving decisions to one with a more data-driven approach, without necessarily having a dependency on experts to make that happen? Thirdly, as programs have evolved around fuel, points, food, and other mechanisms, coupons, etc., there's often a high degree of complexity for shoppers, and we find retailers that have conflicting and confusing programs. Maybe they're not integrated across all shopper channels, so what I'm doing when they visit my stores may not be the experience that we're giving them when they're shopping digitally. In some cases, we're well aware of circumstances where general content makes its way to our shoppers at the very time when they're looking for more personalized content that's individually relevant to them. These are the three main challenges that are out there, so how do you move forward? We all say, "Well, artificial intelligence can help." Just to make sure that we're on a level playing field, let's talk about what artificial intelligence is and then we’ll get into how it can help transform customer loyalty. I'm going to give a very generic definition, then how we apply it to our customer engagement. AI is a computer program that augments what humans would otherwise need to do, including autonomous learning, reasoning, problem solving, and language understanding. A very high-level definition, but those points are where artificial intelligence is bringing transformation. Here are three specific examples that I think of as appetizers for how we'll spend the next few minutes: First, when we talk about data being the problem, artificial intelligence can help us unify our data and tear down data silos, essentially creating a 360-view of our shoppers allowing us to truly understand them better than ever before. Second, we can provide much better actionable insights for better, faster decision-making that's data-driven without the dependency on experts. Third, and very importantly, we can automate a lot of the key capabilities that drive personalized engagement, and in the end, this is about obtaining a greater shopper wallet share, greater shopper loyalty, etc. Now, let’s elaborate on these three macro-level topics. Generally speaking, when you think about the world of loyalty, it starts with having an array of capabilities. This is quite a generic view, but if you think about it starting on the left — we've got all of our commerce platforms ranging from point-of-sale, self-service platforms, e-commerce, and of course, the mobile engagement — surrounded ideally by a common, consistent loyalty platform. Then, we want to ingest that transaction and customer information into a customer data platform, understand the segments and micro-segments that exist within our customer base, measuring and monitoring the efficacy of our loyalty programs, understanding our shopper journeys, understanding what sort of offers or rewards will in fact shape and immerse their experience, and then ultimately, personalize it to all of the various touchpoints that are in front of our shoppers, whether it be an email, text, mobile application, website, or screen on a self-checkout platform. These are all shopper touchpoints and there needs to be a consistency of that shopper experience regardless of how they choose to shop, particularly when you're dealing with shoppers that are quite multi-channel in nature. This is a high-level framework — let's kind of decompose this a little bit. We talked about the various commerce platforms that are out there, and all of them will have some loyalty wrapped around them. Artificial intelligence is helping retailers to consolidate and unify customer data. Now, this can be transaction data, customer master record information, and it can include external data that we're correlating with our shoppers. What is a unified customer data platform? Typically it's in the cloud, and adjusts data from the disparate sources governing that data, ultimately unifying it. It's very important at the end of the day that we are creating a synthesized customer 360-degree environment and we're leveraging machine learning to read signals in the data that we're consolidating if we're talking about improving the loyalty and engagement with our shopper. This is an important foundational capability, and the ingestion of data needs to be batch, real-time, and streaming data, particularly with customer listening or social feeds that we can now take advantage of. Regardless of whether you are doing this in the cloud or on-premise, make sure that data security, privacy, and data governance are handled with the highest standard of care. We all hear about the regrettable situations in the market where people penetrate a retailer or enterprise systems, so that this data that you're capturing on customers has to be protected with the highest standard of care. That's where it all starts. Moving forward, when you have all the data in one common place, you could start reading signals in the data, including understanding how your customers shop. What are their journeys? What are the different steps before they shop, during their shop, and after their shop? We tend to think of these as customer journeys, and there's a variety of vectors that one needs to look at in terms of individual shoppers or shopper classifications around lifetime value. How loyal are they? Are they a relatively new shopper? Are they infrequent? Are you their secondary shop into product-centric journeys? Are they brand loyal or product loyal? Do they only shop in certain categories? To the channels, do they behave differently when they're in the store versus when they're shopping the e-commerce site? What are their interactions like? Ultimately, understanding even their click stream as they're interacting with you in the mobile app or in your digital premise, digital capability. The idea here is to make sure that we truly understand our customer journeys, and can read these signals in the data. It's very important at the end of the day that we are creating a synthesized customer 360-degree environment and we're leveraging machine learning to read signals in the data that we're consolidating if we're talking about improving the loyalty and engagement with our shopper. This is an important foundational capability, and the ingestion of data needs to be batch, real-time, and streaming data, particularly with customer listening or social feeds that we can now take advantage of. Regardless of whether you are doing this in the cloud or on-premise, make sure that data security, privacy, and data governance are handled with the highest standard of care. We all hear about the regrettable situations in the market where people penetrate a retailer or enterprise systems, so that this data that you're capturing on customers has to be protected with the highest standard of care. That's where it all starts. Moving forward, when you have all the data in one common place, you could start reading signals in the data, including understanding how your customers shop. What are their journeys? What are the different steps before they shop, during their shop, and after their shop? We tend to think of these as customer journeys, and there's a variety of vectors that one needs to look at in terms of individual shoppers or shopper classifications around lifetime value. How loyal are they? Are they a relatively new shopper? Are they infrequent? Are you their secondary shop into product-centric journeys? Are they brand loyal or product loyal? Do they only shop in certain categories? To the channels, do they behave differently when they're in the store versus when they're shopping the e-commerce site? What are their interactions like? Ultimately, understanding even their click stream as they're interacting with you in the mobile app or in your digital premise, digital capability. The idea here is to make sure that we truly understand our customer journeys, and can read these signals in the data. Most multi-retailers have segmentations, but the segmentations have been built for a different era, for the decade past rather than the decade ahead. Retailer by retailer, you've got great leaders like Justin that have different points of view on what constitutes what the segmentations should be: Who are our primary, our secondary, or our tertiary shoppers? What are the factors that we're going to look at to consider our best shoppers and maybe our less loyal shoppers? Instead of prefabricated segmentations, this is much more dynamic and retailers are able to put a lot more of their fingerprints on what the definition of segmentations is all about. Getting beyond the macro-segmentations around recency, frequency, and monetary spend, the more exciting place for artificial intelligence can bring real value to the table, particularly around personalized shopper engagement. This is what I call data-driven micro-segments, the ability to understand smaller audience groups within a broader array of shoppers, understanding who you're turning and lapsing customers are. Who are your shoppers that are price sensitive versus quality focused? Who are your shoppers that resonate with everyday low prices versus those that are promotionally driven? One thing most retailers understand is that so much of our promotional investments today, particularly mass promotions, are consumed by cherry pickers or pantry loaders, and not our best customers. They're the customers that are the least loyal to us, but are consuming a large portion of our promotional investment — understand their channel preferences, category buying preferences, or what we call lifestyle preferences. We can understand, based upon buying patterns, whether someone has dietary restrictions, is nutritionally driven, natural, organic, vegetarian, maybe they're wine lovers. Maybe they’re pet owners, we can tell that the pet owner buys dog food and might also be interested in dog treats. This is something we can tell from a data-driven perspective. In the past, we always had to have a thesis. Today, data can guide our understanding of the broad needs or the array of our shopper and micro-segments, including brand and product affinities — a very important thing that artificial intelligence brings to the table. Empowered with the shopper journey awareness and the segment awareness, we can create and target customer cohorts or audience groups and then try to influence or shape their shopper journeys for the future. One of the key factors that drives most common loyalty programs today is rewarding shoppers or concentrating their spend with us. This is limited by the offer content that we have available. One of the things that artificial intelligence can bring to the table is that there is always — every single week, and between mass promotions or personalized promotions — a content deficit or content limitations that need to be satisfied. Let's start by making sure that we recognize we're already making investments in our ads, circulars, and/or TPR that we're investing in at store level, so that's part of the content equation. The coupons that are out in the market are part of the equation, but helping retailers to identify where they've got offer deficiencies or a lack of content, and then rectifying that to make sure that all of our micro-segments and segments are being satisfied with sufficient content that again, engaged them this week. I'd love to say that this is something you do once and it's done. The reality is that content needs to evolve week in and week out, and we need to continue to give our shoppers reasons to come back. The offers and personalized content associated with the offers are central to that ongoing, long stream of perceptions that they'll have about your business. It's as Justin said, "At the end of the day, retailers will have thousands of offers out there," but those thousands of offers aren't what's relevant to me as a shopper or to whoever the shopper may be. How do we simplify their experience and put mass and personalized content in front of them? It doesn't just have to be offers, it can be content like recipes or other factors that shape their experience. The point is, by the time it gets to a shopper, it's personalized down to the segment, the micro-segment, and even in some cases, down to the one-to-one level. That sounds very nice to stay at a high level, but it's hard to do that with just human hands and human minds making those determinations. The application of using artificial intelligence to drive the personalization process is one of the places where innovation is helping to solve this problem and provide more relevancy to our shoppers. Engaging the shoppers with a personalized experience — this really means we need to recognize how our customers shop. If they're omnichannel shoppers, let's make sure that we're delivering engagement with them in an omnichannel fashion. Let's also make sure one of the best practices is this idea of immediacy, time and place awareness about and engaging with our shoppers. It's one thing if we know that they're on Aisle 3, which is the cereal aisle, and that they happen to like Cheerios. That is an example of time and place awareness where maybe sending them an offer immediately or a reward for something we know they like will shape building a basket. But engaging with customers and helping them in the planning phase before they shop, during the shop with that immediacy, time and place awareness, and then of course, reinforcing after the shop and understanding that they're going to choose to engage with you based upon their preferences. What they want to see is that you're sending them the right offers. If the right offers are being driven towards the right shoppers, then you'll get the right outcomes — greater loyalty, greater frequency, bigger baskets, more profitable baskets — all of the things that we as retailers really care about. Doing this customer engagement requires you to measure and monitor the efficacy of what you're doing and learning. One of the key tenets of artificial intelligence is this learning process that occurs. We tend to think of all of these capabilities being deployed in the form of campaigns (weekly campaigns, daily campaigns, longer-term campaigns) that are all sent with a purpose in mind. Get that and you serve the needs of those shoppers that we talked about, but it also helps us to achieve our objectives as a retail enterprise. One of the benefits to thinking this way is that campaigns can be forecasted. Can we anticipate how big the audience universe will be? How many redemptions do we think that they will consume? So projecting the future-looking aspects of the campaign, forecasting impact, and then measuring those capabilities both in-flight and post-campaign. Again, making sure that we're always calibrating and tuning our learnings from how shoppers are reacting to the activities that we're putting in front of them. Doing this customer engagement requires you to measure and monitor the efficacy of what you're doing and learning. One of the key tenets of artificial intelligence is this learning process that occurs. We tend to think of all of these capabilities being deployed in the form of campaigns (weekly campaigns, daily campaigns, longer-term campaigns) that are all sent with a purpose in mind. Get that and you serve the needs of those shoppers that we talked about, but it also helps us to achieve our objectives as a retail enterprise. One of the benefits to thinking this way is that campaigns can be forecasted. Can we anticipate how big the audience universe will be? How many redemptions do we think that they will consume? So projecting the future-looking aspects of the campaign, forecasting impact, and then measuring those capabilities both in-flight and post-campaign. Again, making sure that we're always calibrating and tuning our learnings from how shoppers are reacting to the activities that we're putting in front of them. So, to summarize where I started. That is one of the things that I talked about — let's get the data right and be more data-driven in how we're shaping our customer journeys and customer experiences. It's about the orchestration of this whole life cycle and making sure that we're benefiting from a closed loop experience in all of these interactions. Hopefully, this has shined some light on places where artificial intelligence is bringing value to the table. I'd love to turn it back over to Tim, thank you. Denman: Thanks, Todd. Thanks, Justin. Great presentations from both of you. Justin, we have a question about data privacy. Obviously, to pull off AI-based personalization, you're going to need to capture a lot of data, so how is that handled and with the idea of customer privacy? Weinstein: It's a great question, and it's an area that we're quite focused on. The way that we think about it from a philosophical perspective is that there is an exchange of data for a value, and the value has to ultimately outweigh the data. That exchange needs to be genuinely valuable for the customer in order for that to take place. Our real goal is to put the control in the customers' hands as to whether they want the benefits that come with scanning their card or becoming a part of a program like myPerks, or whether they would prefer not to. As we think about myPerks, our strategy in terms of getting more customers into the program is to invite them into it. Again, that decision sits in the customers' hands, and as we've taken that approach, seeing an outpouring of customer support for wanting to take part in the benefits that we're offering, both in the form of monetary benefits around gaining perks in our case that are exchanged for dollar rewards, but at the same time there's other benefits in terms of being able to personalize a shopping list or provide content recommendations. We really see it as that exchange of value and that the choice sits in the customers' hands as to what they want to take part in and what they don't. Denman: Obviously, loyalty programs were around for a long time. You'd go back 10-15 years, we all had a bunch of things on our keychain, or our wallet was overflowing with these cards. A question here about the idea of cards versus using an app. How do you work with customers that are kind of old school that prefer to use a card rather than having an app? Is there a way to integrate the two experiences today? Weinstein: Tim, you hit the nail on the head as you closed that question, which is the key — to create that right bridge between the card that sits on the keychain and using the app. Again, it comes back to the same paradigm that we just talked about. You have to convince the customer that there is enough value to take the space on the phone and to take the time to create an account, download an app, get set up, and get acquainted. Our goal is that customers are able to continue to use that keychain to unlock value, but there's more value that we can provide when we're able to touch them through their phone, which is a medium that enables the type of personalization and offer strategy that Todd talked about. That sits in the customers' hands in terms of whether they want to take part in that. However, the onus sits on us as a retailer to be able to both create that value for the customer, and tell that story in a compelling enough way that the customer will actually take action, whether that's in our store, at home, or consuming digital media. Denman: Great, thank you. Todd, personalization has been a big buzzword, and it can mean a lot of different things in different contexts. If you were to advise a retailer just starting out on their personalization journey, would you recommend starting with simple business rules, or would you jump in with an AI-powered machine learning application? Michaud: Well, AI will enable simple business rules to start with, so getting to a desired end-state is a journey. There are definitely mechanisms for crawling first, then walking, then running, and so no one is suggesting that you need to get to the end-state to begin with. You can start with just tuning your existing offer content, primary, secondary segments within your business to get started, and measure that efficacy. Then as you become more sophisticated around reading the data signals, elevate the depth of your shopper engagement from there. Denman: Good points. Justin, what has been the most effective way to drive new signups and engagement with your loyalty programs? Weinstein: The audience is going to get tired of hearing me say this: the most effective way that we've seen is to do two things: One, be able to very simply articulate what the value is of signing up for a program like myPerks. That comes back to the benefits, and it comes back to a benefit set that is simple enough such that anyone within our stores, whether they've been with us for a day or for 10, 20, or 30 years, is able to articulate to a customer why they would want to take part in the programs that we're offering. Then, the second real way is to show the customer the value that they specifically are going to get by taking part in the programs that are launched in the market. That means being able to take an omnichannel and cross-channel approach to communicating with the customer and saying, "If you would have taken this action, you could have saved this," as well as, "If you were in the new program, you would actually sit in this tier," that in our case we're calling pro, "and these are the benefits that you could be receiving." Again, putting that option in front of the customer in a language that resonates with them, both through the simplicity as well as through those personalized elements, is the package that we've seen as being able to move the needle for our business and break through the clutter for our customers. Denman: Thanks, Justin. I don't think people are going to get tired of hearing that because that's the key, right? How do you get people to engage in it? You can't push that message through quite enough. Now, Todd, you talked about a customer data platform. Can you share your view on the advantage of putting that in the cloud versus using a traditional, on-premise network? Michaud: The cloud brings a lot of value to the table in terms of being cheaper, faster, better. The real consideration on whether you're using the cloud or something on-premise is your stance around how you're going to manage privacy, data security, and data governance. In our view, the cloud provides maximum flexibility as long as the partner that the retailer’s engaged with adheres to the best practices around leveraging the scale, performance, and elasticity, if you will, of what the product could bring to the table. This is about how we isolate shopper data, bagging it for the benefit of that retailer. Are we protecting it with the best practices around data governance, compliant with CCPA for our friends overseas, GDPR standards? Are we adhering to SOC 2 compliance? Are we meeting the high standard of care associated with HIPAA-related data? These are the key considerations, and so as a solution provider in this space, we put a really significant level of effort around making sure that we will provide a higher standard of care than the retailer would have otherwise done on their own. At least that's our ambition, but I think that the data privacy, data security is the biggest consideration, and scale and affordability is one of the reasons why one would consider a cloud-deployed platform versus something that was managed in-house that required infrastructure expertise etc. Denman: Thanks, Todd. This is probably a good way to put a bow on this whole presentation. Justin, how did you come to the realization that artificial intelligence needs to be part of your loyalty initiatives? Weinstein: The biggest driver is around customer expectation, and as we think about who our customers are spending the majority of their time with, particularly over the last year, it's many companies who have embraced a very strong data infrastructure and have built their businesses on being able to provide personalized benefits and experiences. Take all of the streaming services that customers are taking part in, all of the music services that many of us and our customers are using — thinking about personalization that exists on other platforms, whether they're social platforms or meal-planning platforms. So, the onus from our perspective sits on us to be able to meet the customer where they are and to be able to provide that level of relevancy to them. In order to close that gap we have to be able to develop a greater understanding of customers. We take an approach that says: there's understanding the customer in terms of the data that they are sharing with us implicitly through their behavior, and then there's also the old school way to do this which is to talk to our customers — that's through our team members, primary research, ethnographic research — and really understanding them. When you bring those two things together, that's where the magic happens in terms of understanding the why and the intent behind your customer. Then being able to match that all the way through to being able to provide that incremental value to them because you actually know who they are. That relationship building is at the root of why AI, machine learning, and personalization becomes such an imperative for us within the business. Denman: Well said. Well, thank you, Justin, for your time. Todd, thank you, as well. Unfortunately, we have come to the conclusion of the webinar. I'd like to thank everyone again for listening, Todd and Justin for presenting, and Hypersonix for sponsoring this event. Thank you, everyone, and have a nice day.

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