Podcast: Convenience retail today & why small scale stores need to think big when it comes to data

Convenience stores may be smaller than standard grocery, but their impact is larger than ever and they need to turn themselves into destination locations. That means ‘thinking big’ about understanding customers and having access to the data that enables that understanding. They also need to be able to understand behavior outside of their single location or cluster to effectively plan assortments.

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    Paul Hoffman:                   Hi, and welcome to today’s conversation. My name is Paul Hoffman. I’m the director of communications and content for Symphony RetailAI. And I’m thrilled to be talking to two of my favorite subject matter experts today, Trusha and Julian. We’ll get right to it.

    Trusha Pandya:                 Thank you so much, Paul. I will pay you later for that wonderful accolade. I’ll take it. My name is Trusha Pandya. I’m one of the account directors, basically support unit for our clients within the industry. I’ve come to work with convenience as it’s near and dear to my heart, as I’ve had empirical evidence with my father and family members. So it’s a perfect blend of empirical evidence coming through the historical aspects of my career to get to this point. And I’m really happy to help everybody here.

    Julian Miller:                      And hi everyone it’s Julian Miller here. So I’m head of category planning at Symphony Retail. I’m based out of the UK. I’ve been with the company for about 15 years now, and I’ve always kind of worked very closely with customers, translating their business problems into software problems and how do we solve those business problems.

    Paul Hoffman:                   Thank you both for those introductions. Convenience today is really a movement. How can convenience stores adopt a mindset like larger retailers where these larger retailers are striving to constantly differentiate themselves by new offerings of convenience and new experiences?

    Julian Miller:                      I guess the first thing to say is, where there’s a franchise model, what you’ve got in that model is you’ve got access to the data and not just the data for your own store, but for all the other stores that are in the franchise.

    Julian Miller:                      And we’re working with customers today who are taking that data at a franchise level and they’re using it to make recommendations to their franchise owners. And it’s an interesting dynamic because remember, these franchise stores, they’re all owned businesses in their own right and their owner rightly wants to make the decisions for the business.

    Trusha Pandya:                 You know, how do you set that standard of understanding what makes sense at a very fundamental business level from a technology and a process point of view of maybe I know my store, but maybe I can leverage the franchise collective franchise groups, right? And the head office in a sense to help me make better decisions?

    And I guess this kind of goes back to sort of Trusha’s experience with her father running a convenience store. You know, he probably had to make all those decisions autonomously, right Trusha, without any data.

    Trusha Pandya:                 Absolutely. I would best pose a question or a statement of how convenient stores, how can they maintain a steady growth in this new world and uncovering the success factors of this disruption? It’s a dynamic shift or a paradigm shift for these C stores to think, okay, it’s not just my store and I need to put a sale on and compete with the person next to me. You have to be more proactive than reactive. And that’s where that technology comes in.

    Julian Miller:                      Yeah, absolutely. And I guess we shouldn’t forget that the size of the convenience market is pretty big, right. And that means we’re actually sitting on a ton of data. There’s a huge amount of data here to help us, which most of the time in the convenience trade doesn’t even get looked at.

    Trusha Pandya:                 Yeah. I agree with you. I wholeheartedly agree with you and my dad started that trend by pen and paper. He wrote down his customer base and what they liked, and he created a historical analysis, right. A straw man’s analysis of his product mix. I wish he had that solution back then, but he was on the right track.

    Julian Miller:                      But I guess a lot of that was on gut feel right?

    Trusha Pandya:                 It was.

    Julian Miller:                      But you know, what happens when the data challenges backup feel? Thinking back to your dad, what would have happened if someone had kind of challenged his thinking? Would he have been receptive to it? Or would he have said, “No, I’ve been doing this for years. I know what I’m doing.”

    Trusha Pandya:                 I think that’s where thought leadership comes in and getting that buy in from subject matter experts, hopefully like us to come in the door and help them understand the overall picture. I think that if you have that common goal of increasing your business, increasing your margin of profit, then I think everybody will be on board, but you can’t just bring the technology there and say, “Hey, here you have it.” I think there has to be a buy in process and a learning process. So it’s the people, the technology, it’s the overall understanding and that true partnership to make everything successful.

    Paul Hoffman:                   Julian, you mentioned data a moment or two back. When you consider franchise convenience stores and company operated convenience stores, what are the differences in how they consider and use data?

    Julian Miller:                      Yeah, I think inherently in a company operated store, there’s a lot more sort of top down in terms of there will be functions that head office to determine exactly what the best assortments are and to create the planograms and to get those out to the store. That actually brings with it its own challenges, because if you don’t do that well as a head office, the planogram gets to the store and we see this when we go and talk to store owners and they go, “I don’t use that planogram.” And you say, “Well, why is that?” And they say, “Well, it doesn’t fit.” They say, “My space here is completely different to the planogram that head office have given to me.” And they say, “Oh, and by the way, if I followed the advice of this planogram, I’d be out of stock on that bottle of wine in two days because it’s my best seller.”

    It’s not just about pushing planograms into the stores and asking them to implement them. It’s about then actually taking feedback from the stores and say “Well, okay, that planogram doesn’t fit. What does the space actually look like and what items are we recommending aren’t right for your store?” And even better, if you can feed that data from the owner back into the system as a data point and you can on it next time. So the next time you give that store owner a planogram, he goes, “Oh, this has got all my best selling products on it and actually it does fit my space.” That kind of helps with that whole idea of trust and it helps perhaps some of the other recommendations that are coming from the franchise head office to be more accepted and more likely to be implemented in store.

    Trusha Pandya:                 You made a very good point out of that. You made a lot of good points, Julian, these private store owners or these franchise store owners need to establish a support system in a way. And the head office could be that established support system versus a big brother approach, right? So it’s that collaboration and understanding to get to that common goal.

    Trusha Pandya:                 In conversations I’ve had before, I’ve actually been part of C-store coalitions where they’ve voiced some of their concerns. And part of it is that consistency, that awareness of, how to again, make those better decisions. They understand the operational values of what it takes to run and operate a store, but the changing that mindset and opening their eyes, if you will, of how to create those incremental growth, profit growth in areas that they would never have understood before. Focus on the growth and expansion in a very strategic way. Don’t have a growth engine that leads to operations that are fragmented with inconsistency. Focus on that product assortment, then add in loyalty afterwards because once you have that product assortment, you know what’s working and what’s not. Then you tie it into that loyalty and then again, drive that data analytics. Deliver best offers to your customers.

    Paul Hoffman:                   So obviously convenience stores have a limited physical footprint. They have limited floor space, shelf space, storage space, and even limited human resources. When you consider these restrictions and you consider clustering and assortment optimization, how does this all play out for convenience stores versus their larger competitors?

    Julian Miller:                      Yeah, I think it just reinforces how important getting the right assortment is because if you’re a Walmart or Kroger, you’ve got a lot of shelf space to fill, right? And you can have the whole assortment and you can have the full amount of choice. But when you’re a convenience store, you’re obviously inherently limited by space. When you look at all the potential combinations, now in a convenience store, there might not be that many combinations to choose from because you can’t fit that many products in, but actually you’ve got a huge market of products to choose from. But let’s take alcohol, beer, wine spirits, often convenience stores will give over a huge amount of space to those products, but that’s just proportional to firstly, the demand of those products in the store.

    But secondly, the size of those categories in the marketplace. I mean how many different beer skews are there in the US market, 10,000, 20,000? It’s a lot of products to choose from and to get that sort of optimal choice in your store to meet specifically what your customers want, if you simulated every possible choice out of those 20,000, it would be a really huge number of simulations you’d have to run through. And even with technology, it’s really difficult to get to the absolute optimum answer.

    Trusha Pandya:                 And can you imagine adding the layer of the visibility to your CPGs?

    Julian Miller:                      Yeah, absolutely. It becomes very complex problem at that point. So how did your dad decide what percentage of the store to give over to beer or to wine because those tend to be the categories that play a big role in convenience stores, right?

    Trusha Pandya:                 I guess that’s where he did lean on other owners that were friends in the area where they gave him their opinions on what has been working and he leveraged their knowledge.

    Julian Miller:                      Yeah. I guess what’s interesting is the basic principles haven’t changed. The convenience store owners of 30 years ago, 40 years ago, they were making the same decisions just with far fewer data points.

    Trusha Pandya:                 Yeah, you’re just expanding the data points.

    Julian Miller:                      Yeah.

    Paul Hoffman:                   So one thing I think we’ve all probably noticed in our travels is that convenience stores can be highly saturated in certain areas like urban environments when you consider them compared to standard grocery and drug stores and big box stores. So how does this affect their ability to effectively cluster and provide the right assortments for specific stores?

    Julian Miller:                      It’s interesting, isn’t it because what we find when we cluster convenience stores is the clustering isn’t always as obvious as you might think. So, you tend to think that there’s a correlation between demand and the demographics of an area, but actually when you look at convenience stores, you sometimes find that what customers want in one convenience store is completely different to what they want in a convenience store that’s 50 yards down the road. It sounds counterintuitive. You know, why would that be? The demographics of the area of similar.

    But I think it’s because the stores are fulfilling different purposes, different needs. Although they may be from the same area in some cases, in other cases they might be transient shoppers passing through on their way to work or whatever, which means that they’re actually from a different demographic to the people who live in that area. You really need to understand not from a demographics level or a geographical level, but from a demand level. So you need to look at the data at a very granular, very detailed level. Understand where those patterns correlate with one another and then those are the stores that you can cluster together and you can deliver similar assortments and similar planograms to.

    Trusha Pandya:                 Julian, you make another great point as you always have within this conversation. I was thinking back again to my dad and giving that perspective, he had a unique combination of both transient plus that corner market feel where there wasn’t a grocery store within a few miles of the area. So that lent itself for my dad to carry milk and eggs and bread at a higher level, right, than just having candies and snacks, correct? Plus he had the beer and wine for the transient folks who wanted to get some gas and get some beer on their way home.

    Julian Miller:                      It’s really interesting, isn’t it? And again, I think it’s an example of your dad kind of understanding his customers, which is the cornerstone of effective retailing, isn’t it? I think again, it kind of comes back to how do you do that at scale and how do you do that efficiently across a thousand store chain retailer or a 10,000 store chain retailer, for example?

    Paul Hoffman:                   So from a technology standpoint, how can convenience retailers compete with the larger retailers?

    Trusha Pandya:                 Think very strategically, act very tactically to make a very strong impact in your environment. Whether you’re a one stop shop of a retailer, of a small C store. Maybe one or two or four C stores grouped together in your organization. Or too many. To a hundred to a thousand. The same concept and the same layers of foundation apply.

    Julian Miller:                      Yeah. And I’d say too to convenience retailers, your data is your biggest asset and it’s different to the other retailers. Convenient shopping behavior is completely different to how people shop in a supermarket or how they shop on Amazon. Your data will have the answers. Use the data and use it well. And that’s where you’ll find the competitive advantage

    Trusha Pandya:                 Data doesn’t lie. You’re right.

    Julian Miller:                      Trusha kind of brought the interesting perspective and, all the things that she talked about that her father was doing in his store are actually all the same problems we’re trying to solve in the software. The only difference being we’re trying to solve them at a much greater scale in the software. So what we do in one store, how do we scale that up to a thousand stores or 20,000 stores, for example?

    Trusha Pandya:                 I am one of the luckiest girls in the world to have my dad as such a pioneer as an entrepreneur, doing his best with the tools that he had, but then empowering me with all that knowledge to help our customers to make the right decisions because they want not just to survive, but thrive, right?

    Julian Miller:                      I what you’re saying is, is for you, it’s personal, right?

    Trusha Pandya:  It’s very personal. I think it’s that gratitude of understanding from a different perspective of you’re just not presenting technology for the sake of technology. I’ve seen what worked, I’ve seen what hasn’t worked and I can honestly say, I can help. We can help.

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