In part two of Bart Waldeck’s discussion with Tango CEO Pranav Tyagi, they talk about the three stages of retail recovery – reorient, recalibrate, reposition – as well as the reimagining of brick and mortar’s role in the post-pandemic world.
Contributors: Pranav Tyagi, Bart Waldeck
In part two of Bart Waldeck’s discussion with Tango CEO Pranav Tyagi, they talk about the three stages of retail recovery – reorient, recalibrate, reposition – as well as the reimagining of brick and mortar’s role in the post-pandemic world.
Hi everyone, and welcome to Location Is Everything, the Tango Store Lifecycle Management podcast series about the evolving role of brick and mortar stores in an ever-changing retail world. I’m Bart Waldeck, your host for this journey. The pandemic has changed almost every facet of our daily lives, including how, where, and when we shop and eat. As we emerge from this previously unthinkable scenario, it’s safe to say that the retail and restaurant game has changed forever. So what does the future hold for the industry? That’s what we’re here to discuss, with a specific focus on the brick and mortar side of the business, the location.
We can’t do this alone, so we’ll be sitting down with industry experts and other thought leaders to bring you as much information as possible so you can stay one step ahead. The pandemic has pressed the fast forward button on the evolution of the store and successful retailers are already rethinking the role of the store, which is more important than ever because location is everything.
Hi, and welcome to part two of episode one of Location is Everything, Tango store lifecycle management podcast. I’m Bart Waldeck, your host. I’m happy to have Pranav Tyagi, our President and CEO, join me again to continue our discussion and really lay the foundation for Location is Everything. In episode one, we really reviewed the impact of COVID-19 on the retail and restaurant industry and specifically how that pandemic has shifted consumer buying habits and accelerated the trend towards omnichannel retail, to which the store ironically, as we discussed is the key to success.
Now in part two, we’re going to pivot that discussion and talk about kind of the three stages of retail recovery as we see them in working with many retailers and restaurant concepts out there in re-imagining kind of the brick and mortar’s role in this post pandemic world. Those three stages are reorient, recalibrate and reposition. Lots of R’s. We’ll go through each of those. I’m going to have Pranav here to kind of help me articulate what we mean by these different things.
It’s clear, as we’ve said before, that retail is changing. It’s become more omnichannel every day. And we can really see that from the stats that you see here on the screen here. During the recent holiday period, Target saw it’s curbside drive up service balloon nearly 500% to over a 150 million items sold through that channel alone. If we look at Walmart, they had a similar staggering statistic as of October 31st, that period ending in 2020, their online sales grew a whopping 79% over the previous time last year. And then in the second quarter of this year, drive thru orders accounted for 90% of McDonald’s US sales versus the normal 70%. I think those stats are really good example of how things are changing and how they have changed in 2020.
And as we’ve mentioned before, Target is one of the shining examples of a company that really was well down the path of investing in omnichannel capabilities prior to the pandemic and that positioned them really well to do better than most and kind of lean in during the chaos and excel in a really, really tough environment. Let’s go through a couple of their stats they recently published, because I think it’s a very telling a story. As we mentioned before, in December alone, guests purchased a 150 million items using their drive up and pickup capabilities. And that’s almost four times more than during the same timeframe last year. And on a single day in December, their teams fulfilled 6.5 million items at drive up or order pickup. And that’s the most ever in a single day. In their cyber week, which is the black Monday type of concept in 2020 was their biggest ever record highs for digital orders, site traffic and order fulfillment via drive up services.
And interestingly, and we’ll get into why this is important as it relates to the store, 95% of their sales were fulfilled by stores in November and December last year. Think about that. The inventory for in store delivery and pickup were 95% of the time came from a store itself. And lastly, the guests ordered for pickup, 5.1 million fresh grocery orders as they prepared for their holiday meals. Those are some really crazy stats, Pranav, it’s crazy, but not everybody’s a Target, a Walmart, a Panera, Chipotle who were well into the digital transformation and investing in these types of capabilities. My question to you is what can the rest of the industry do to adjust to these new shopping realities?
Well that’s the key question, Bart, isn’t it? The people can’t invent a time machine and go back and change things in the past. I think to help retailers think through the evolution of their brick and mortar stores in this ever increasing, omnichannel environment, we have built a framework. We’ve created a framework. We call it the three R’s of retail recovery. We believe this represents the stages that all retailers must go through, reorient, recalibrate and reposition. Let me dig into this a little bit and explain what each of these actually means.
Reorient, this involves the development of a holistic cross channel view of the evolving pandemic and post pandemic customer in an effort to develop and execute what we call a seamless buying experience across all channels, in store, online and delivery. Put another way, a single organizational lens on the customer across real estate marketing, supply chain merchandising, any other interested parties and organizations.
Recalibrate, this focuses on re-architecting the brick and mortar sales forecasting and optimization models, which would result in location strategies. And in the re-architecting of these models here, it needs to be done to account for a rapidly changing consumer and the store’s role in facilitating the various channels that the consumer interacts with, in store, online, pickup and delivery.
And then the third R in this approach is about repositioning. This is about executing on that strategy that we just talked about. Retailers need to open, close and relocate stores and equally importantly, change formats of stores. All of this in an effort to optimize revenue and profit. Location strategy is no longer just about the in store customer, but increasingly about the pickup and the delivery customer as well.
Yeah, that’s a lot. Thanks for the overview of the framework. In each of these segments, what I’d like to do is kind of unpack it and go a little bit deeper and explain kind of what we mean and what are some of the dynamics that need to occur in each of these different areas. We talked a lot about how, where and when people shop is changing and we don’t think it will go back to pre-pandemic levels. It’s going to settle somewhere in the middle. The pendulum will come back, but not all the way. And that customer expectations are higher than ever. And because they’re behaving differently, they have higher expectations and they honestly, the data shows they don’t have a problem switching brands if they have a subpar experience. The stakes are even higher to kind of quote unquote, get it right. And it really, as you were mentioning, it really kind of hinges on understanding that customer and how they’ve changed and how they’ve evolved.
That’s really the challenge. In most retail organizations, the view of a customer is fragmented across functional silos. Each of those silos having a different lens with which it views customers. The three of the most important functions we come across that are involved in an omnichannel view of the customer are real estate, marketing and merchandising. As people would think through this, they’ll realize real estate typically looks at customers through a location centric view. Where can I put stores in a market that maximizes revenue and profitability based on in store customer shopping behavior? Marketing on the other hand, focuses on customer awareness and promotions. While the merchandising department wants to identify and sell the right products at the right price to the target customer. Surprisingly, these three organizations utilize different people, different tools and radically different statistical models to understand their customers and the same customer actually. And there’s no unified view across the enterprise. This actually has to change if retailers actually hope to successfully deliver across channels to their customers.
That makes sense. And I think the other element that really relates to reorienting is the role of the store itself in an omnichannel world. The typical knee jerk reaction is, well, the store is less important because more shopping is happening online. But when you really unpack that, that’s not the case, it’s actually just the opposite. To kind of illustrate that, let’s take a look at kind of three key elements that I think a lot of people don’t frame the problem with when they think of this. One is where does the purchase occur? Is that online? Is that over the phone? Or is it in the store? Second is, where is the inventory coming from? Is it coming from the store? Or is it coming from a warehouse? An example we just saw with Target, 95% was coming from the store. And then lastly, where does the customer take possession? Where’s it fulfilled? Is it fulfilled at the store? Is it a delivery? Is it picked up at the store?
All these different permutations you see on the screen here, we’ve got a number of different scenarios of where purchase can occur, where the inventory comes from and where the customer takes possession of the product or the food or whatever it may be. And when you lay those all out, you can see from this graphic that the store is central to two and sometimes three of these different stages. And what’s even more reinforcing of this is when you actually laid dollar transactions that go through each of these scenarios, the store is still, that top row is still the number one way that most people buy today. It was nine out of every $10 prior to the pandemic. It may shift the $7 out of every 10, but still the majority goes through that.
And then when you look at the inventory source and fulfillment elements, you really start to paint the picture of how critical the store is to a successful omnichannel strategy. And we’re kind of shifting, as you said, Pranav, from a market, kind of an optimization how I can draw customers in, to more of a market coverage, how can I best cover in the market, my in store, my delivery and my pickup customers? And that kind of relates to the modeling aspect you were talking about. Once we have a single lens of who that customer is and how they’re behaving, we need to model that. They’ve changed, the models need to change. Why don’t you take us through what we mean by recalibrate.
As I mentioned earlier in this podcast, the recalibrate stage is all about rethinking store location strategy. And that’s by shifting the calculus from in sales forecast and optimization approach, which is typically focused on the in store shopping, to one that considers the emergence of pickup and delivery. In other words, an omnichannel perspective. Let me explain why. Given the undeniable shift in consumer shopping behavior, whether it’s temporary or permanent, and the lack of clarity as to how these behaviors will remain as the pandemic subsides, we now know conclusively that modeling customers based on historical data is nonstop. What happened in 2019, it was not a predictor of how customers behaved in 2020. How 2020 went is not going to be how customers behave this year in 2021. That much is certain because everything is changing. Traditional sales forecasting models that primarily use historical observations to determine future performance, which is we like to call a rear view mirror approach to forecasting, those are likely to be incapable of considering the complex and often nonlinear relationships between the different data sets in terms of predicting performance of locations.
In addition to this changing consumer behavior, retailers are also dealing with a rapidly shifting and changing competitive landscape. As many companies have closed locations or gone out of business, and others are varying from location to location and trade area to trade area. Finally, the modeling calculus has changed as retailers must now take into account, not only in store shopping, but also pickup and delivery and those older techniques and older modeling approaches are just not designed for an omnichannel world. To adjust to this new reality, I believe that retail restaurant concepts must turn to other technological innovations that have been in place in the last few years, such as artificial intelligence and machine learning because those tools are likely the only approaches available today to understand this new normal and enable better decision making.
And why do I say that? Obviously the biggest advantage that AI and ML, artificial intelligence, machine learning based models have is that their core strengths in the way these models and these techniques predict, map very nicely to the dynamic and shifting environments that retailers are facing. The top of that list is the ability to rapidly sift through massive data sets, both the traditional old data sets and the newer more innovative data sets. And then not only sift through those data sets, but then to apply dozen, several dozens of algorithms to find the best combination of models to interpret that existing environment and then therefore more accurately predict sales. One of the challenges with the older techniques was you took a subset of data off a small amount of data and through a subset of that data and then you applied a singular model to them. And the AI, ML techniques allow you to take on a variety of different data sets may not be properly formatted and aligned, but you can still consume it and apply many models to it.
Also, what these techniques allow you to do is it allows you to process large volumes of information and discern patterns in that information or that data that are difficult for humans to comprehend just from casual observation or from basic statistical techniques. And this is especially the case as the underlying data changes frequency or time seen as data. Patterns change, customer behavior changes, so it’s not static data, it changes day to day, week to week, month to month. The next big advantage that AI and ML models offer is speed. Both in building the initial model, as well as in the ability to rapidly recalibrate these models as the business environment changes.
It’s not just about the innovative technology, it’s about what it allows you to do and how often it allows you to do it. Market research teams, we believe will not have the time to collect, monitor, compile, aggregate and analyze all of these large data sets to determine relevance, frequently. And therefore data science teams, which don’t exist inside these market research teams, is part of the challenge here. And we have to have the properly staffed teams to build solutions in house, which is a thing that retailers typically lack.
If I kind of rewind what I think I just heard, you’re talking about kind of two major shifts here. First, it’s not just about in store, it’s about delivery and pickup so you need a holistic view when you determine location strategy and you model where to have those different locations. And second, you need to, because of the reality of how much has changed in the way people shop, history is not a good predictor and you need new data sets and new techniques and technologies like AI or machine learning to really crack the code of a predictive model or a more predictive model, I should say, in a post-COVID environment. Is that fair?
That’s fair. I think the other aspect, just to be clear here is that not only do you need a specialized skillset, when you have siloed organizations with marketing, merchandising and real estate operating differently, it’s an absolute impossibility that each of these teams are going to have the right skillsets in all three teams to support their models. I think this disciplining, the couple of points we made here today, they come together, because we need consolidation in the analytics arena within these organizations and we need better and newer tools to process the large data sets rapidly.
Okay. That’s reorient. That reorient and recalibrate, now let’s talk about reposition. It gets confusing with all these R’s. Reposition is kind of the logical conclusion of that strategy work that understanding the customer better and modeling. We know that location strategy unfortunately, is a long game. Most retailers lease their stores, they don’t own them. And every time they sign a lease, it’s five to 10 years. And what adds complexity to that is you’re signing leases at different times. Maybe you open 20 new stores in one year and 30 new stores in another year and every five years or 10 years, those renewals, lease renewals are coming up for a decision to answer the key question that may change over time, but is this still a good location? Should we still have a store here?
In a vacuum, that sounds pretty straightforward. But if you take into consideration retailers and restaurants, companies are also opening new stores and the competition within the market is changing. Some people are coming in, some people are exiting, there’s bankruptcies. There’s the real estate competitor, which is different than the peer competitor. What I mean by that is it may be someone you don’t compete with, but you compete for the type of real estate that they have and you have and you need to compete against them. All those dynamics are flowing. And when you look at all that, what seems simple is actually very complex. And the fact that they’re placing five to 10 year bets each time they sign a lease, I know Vegas wouldn’t like those odds at all.
That’s right, Bart. Existing store strategy and lease renewals are a big part of the reposition puzzle, but you also have to obviously consider the new store side of the equation. Taken realistically, market planning for retailers needs to look at both existing and new stores together to maximize revenue and profitability for that portfolio of locations, both existing and new. As you mentioned before, the calculus is different because of the pandemic, which has accelerated the omnichannel shopping behaviors of customers and also dramatically changed the competitive landscape as retailers go out of business or change how they do business or close stores.
Collectively, we believe this is going to drive the most dramatic shift in location strategy, arguably ever. The store trade area is a pretty simple concept, where 60, 80% of the customers came from, also needs to change with the changing times. It used to define in store purchases, but now it also needs to consider pickup, delivery as well as other tangential businesses. We think of Kohl’s accepting Amazon returns. What’s a trade area now? Is that the Kohl’s customer or the Amazon customer? The concept of trade area itself needs to be dramatically rethought as part of this exercise.
It’s a completely different view of things. I totally agree. And I don’t think it actually stops there too. Part of repositioning is not only about where you put the stores and the strategy and all that around locations, but it’s the store itself. It’s the restaurant itself, the box, the configuration. What’s in the four walls? Whatever you want to call it. And similar to lease renewals, restaurant and retail companies are in this kind of perpetual remodeling mode where they’re developing new prototypes of how they want to operate the store or the restaurant. And then they’re retrofitting older locations with some of those new prototype elements. And obviously net new stores are the new prototype as well. But when you look at what needs to change, as it relates to the box, we’re not just doing, hey, let’s change signage and let’s add a new department to the store or let’s do this.
We’re doing fundamental changes to the box in order to be able to handle pickup and be able to handle delivery. The scope of change is massive, both in what the overall prototype needs to change to and then how much you really need to change those legacy locations. Because a lot of them are good locations. You’re not just going to throw away where you’ve been and just pick up and move to all new locations. How do you retrofit all those existing stores and restaurants to these new capabilities that they need? And many companies are already in full swing on this, take an example of a lot of the QSRs. That’s a space that seems to be moving very quickly on this, Burger King, Taco Bell, McDonald’s, Chipotle, KFC. Some of the same names we talked about before that are innovators, are rethinking these prototypes, they’re removing seats, adding drive thru lanes, toying with ideas like conveyor belts and digital cubbies and things like that.
Let’s flash up a graphic. I saw this in the Wall Street Journal recently and I thought it was really interesting. This is kind of a view of a conglomerate view of what the quick service restaurant in the future might look like. When you see this screen, it’s got a what? Six, seven, eight, nine different elements that are highlighted. A couple of them, look, there’s three drive thru lanes, not one. We’ve seen two, but I’ve never seen three. Digital menus. They talk about QR code enabled pickups so you kind of go up, flash your phone, it reads a QR on your order. Boom. Here comes your food. There’s mobile order. Obviously walk up windows, which as you know, we’ve all seen enough YouTube videos where people try to walk up after having too much to drink at a drive thru and try to do a walk up, it’s not possible, but obviously, that’s something that’s a reality now and we need to be able to walk up and pick stuff up. It’s changing dramatically and that’s just in the QSR side. And I know, Pranav, it’s happening in traditional big box retailers as an example.
It is. It’s happening quite a bit in big box retailers. I think you talked about a couple examples earlier of a Target as an example. And who have companies that have embraced digital commerce and omnichannel prior to the pandemic have really done well in the pandemic. Walmart is another great example. Let’s pull up a couple of illustrations of what a company like Walmart has done. We talked about the store being the linchpin in an omnichannel world. And this is a perfect example where Walmart is attaching your small automated warehouse to the side of its store. What it allows them to do is to hold inventory for thousands of SKUs that are purchased frequently in store or through delivery and pickup so they don’t have to stock all of that on shelves and they have excess capacity available inside the warehouse attached to the store.
They’re also installing technology that allows store associates to fulfill orders faster. And finally, Walmart is developing pickup and delivery drive thru lanes, almost like a QSR restaurant where customers can quickly grab orders and deliveries, orders and deliveries can actually be better managed. Like we said, you’ve never seen this much change to a store location strategy or store formats before, but a company like Walmart has been working on these innovations for years. I can remember driving through Bentonville and seeing a multiple different Walmart store formats and recently that’s one of a kind. I’ve never seen this before and now we are seeing those concepts that they were testing out two, three, four, five years ago, actually become a reality in a Walmart near us.
It’s staggering when you really think about how much we’ve advanced. It’s almost like anything you look at, whatever trend was going on, the pandemic just accelerated it. Some people were well positioned for that and others are not. Let’s kind of wrap this up and talk about, what does this all mean? The so what? At some level, just what we talked through alone, the amount of change is really hard to wrap your head around. There’s so much that needs to be done. And it’s not just some of the strategy stuff we’re talking about. It really spans what we call the entire store lifecycle.
We created this graphic to kind of articulate what we mean and what level of change we see happening throughout the location lifecycle itself. It starts, as we said, with location strategy upfront, predictive analytics, that’s the element that drives the sales forecasting market optimization models that helps you set that overall strategy. And that needs to be reoriented around an omnichannel customer and it needs to be recalibrated from a model perspective to include all these new data sets and all this technology that we’ve talked about before.
If you look at the transaction side of things, you’re going to need to negotiate more with landlords, whether that’s an existing location where you’re going to have to change the location to kind of create the store of the future or you’re renewing your leases and renegotiating or you’re doing new deals. There’s just a ton of transactions going, a lot of negotiations. Dozens or hundreds of design and construction projects. You’re going to have to develop those new prototypes. You’re going to have to roll them out for new stores. You’re going to have to retrofit existing stores, that’s hundreds and hundreds of different projects. Leases, you’re going to have to pay, account for, administer new leases and maintenance.
Maintenance has fundamentally changed. We haven’t really talked about that, but because of COVID, we’ve seen a lot of deep cleaning and a different regimen of how you maintain the actual stores, keep them clean. And then we’re also bringing in a ton more technology. A lot more technology means a lot more maintenance and upkeep to make sure that the store experience or the restaurant experience can keep going. It doesn’t stop there. All retailers also have office space. They have a corporate campus or a number of office locations where work has changed and they’re going to need to re-plan that space and you need space planning and stuff like that. I think this kind of highlights for me, just a point of reflection and rethinking about how do you manage this whole thing?
Yeah. What are highlights to me, although it’s a selfish self promotion about us, is that the table stakes have changed dramatically. Customers, companies, retailers, organizations, require cohesive technology strategies that go across the full lifecycle of real estate. And that need is now stronger than ever before. I think attempting to retool location strategy and then executing on it cannot be done with a patchwork of point solutions or legacy technology that’s not adapted to the changing consumer behavior over the last decade or more. It requires a single, we feel, a single purpose built solution. That’s in the cloud, that leverages innovative technology such as artificial intelligence and machine learning. And the reason for that is pretty obvious, it’s a fast changing environment. You need adaptive technology that is scalable and easy to change to your changing businesses. Okay, I’ll get off my soap box there.
Well, I say I can’t disagree with you and I’d probably lose my job if I did, but yeah, absolutely. Doing this with manual processes, Excel, a couple point solutions there, or some of these old technologies that have been around for decades, is pretty much a non-starter. We’ve we’ve seen that.
Okay, let’s wrap up. We’ve covered a ton of ground today, starting with kind of the changing consumer, how that has accelerated, omnichannel development, how the role of the brick and mortar store as evolved and arguably is the linchpin to success of an omni-channel retail strategy. We went through the three R framework, so reorient, recalibrate and reposition. We think that’s a great roadmap to help retail and restaurant concepts navigate and emerge out of the pandemic in a much better position and honestly be prepared for whatever may come next, because we don’t know exactly what that is.
But we will continue this dialogue over the next weeks and months in the Location is Everything podcast. And each of these episodes, we’re going to, it won’t be us. It’s going to be deeper dives with experts and other industry leaders to help guide the way for us and for you as we go through this major transition. As you said before, I can’t ever remember this level of change in our industry. And because the reality is in today’s environment, location is everything. It’s a good name for a podcast series. Well Pranav, I’d like to really thank you for helping me lay the foundation here. I’ll invite you back on a future session when we need to dive deep in some areas that I know you’re an expert at, but until then everybody, we’ll see you next time on Location is everything.