Podcast

Not every store is created equal (why and what to do about it)

Callahan Agency | June 27, 2018

Every store location is unique.

As a CMO, you may not argue that there’s variability across all store locations. But are you doing anything actionable with that knowledge? You may not be sure exactly how the stores or geographies differ and you know it’s difficult to get to the data and find the answers you need to customize your marketing plan. Having analysis-ready data and the right technology to manage that data are both essential to capitalizing on areas with the most opportunity for growth.

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Welcome to Callahan’s Uncovering Aha! podcast. We talk about a range of topics for marketing decision-makers, with a special focus on how to uncover insights in data to drive brand strategy and inspire creativity. Featuring Jan-Eric Anderson and Zack Pike.

Jan-Eric:
Hi, I’m Jan-Eric Anderson, Head of Strategy at Callahan.

Zack:
And I’m Zack Pike, Vice President, Data Strategy and Marketing Analytics at Callahan.

Jan-Eric:
So today we’re going to talk about a fun topic. This is one that comes up with a lot of clients that we work with. It’s one that’s probably particularly relevant for anyone in marketing that works for a multi-unit retailer or a restaurant chain or a packaged good brand that has distribution points across many regions, states, DMAs, however it’s defined. And it’s the around this topic that not every store or geography is created equal. That is an idea that when we talk to CMOs and marketers from those types of brands, those types of companies, very, very few would ever argue that point. Would you agree?

Zack:
Yeah. If you’ve got 500 locations or 1,000 locations spread across the country or even in one half of the country, it’s reasonable to assume that there’s probably at least two types of locations in that ecosystem.

Jan-Eric:
And so Budweiser sells differently in St. Louis than it does in Boulder, Colorado.

Zack:
Right.

Jan-Eric:
And that shouldn’t be a surprise to anyone, and it’s not a leap of faith to understand that. What becomes very interesting is that when you then look at marketing plans, media plans, promotional plans that are done for these same brands, in most cases they treat the entire country the same.

Zack:
Right.

Jan-Eric:
Marketing plans are uniform, media support is uniform, and treat the country as if it’s all the same when there’s all this variability of the business happening in these different geographies. That seems like a major disconnect.

Zack:
Yeah, and I think there’s lots of reasons that could be the case. You could be running a franchise business, and you need to support all of your franchisees equally so everyone has the same opportunity. Or even if it’s not a franchise, every store manager needs to have the same opportunity to hit their targets at the end of the day. So they all need the same amount of support, but you have to ask if that’s actually the best thing for the business as a whole. We’re all here to make money, we want to be good people, but if you’re trying to maximize the value of every store or every restaurant or every retailer that distributes your product, maybe they should be treated differently.

Jan-Eric:
Yeah, maybe this isn’t a democracy.

Zack:
Right.

Jan-Eric:
Maybe democracy doesn’t play well when the opportunity for growth is different in different places.

Zack:
Right.

Jan-Eric:
So while most CMOs would not disagree that there’s variability in their system, whatever their system looks like, and that can be different based on what the businesses that they’re in. But while most CMOs would not argue that there’s variability, very little are doing something about it. It seems like CMOs that we’ve interacted with, there are some common reasons why they’re not doing it. And, and the first one seems to be a common quote back from CMOs is, “Yeah, I agree with you. I know there’s variability in my system, but I don’t know how my stores are different. I don’t know how my geographies are different.”

Zack:
Right.

Jan-Eric:
And so because there’s kind of a veil over it and people don’t know, CMOs might not know how their stores are different. They might not know what to do about it.

Zack:
Right. And this is something that is all over the marketing space, and it comes back to what we always talk about, which is the information on the business, the data, right?

Jan-Eric:
Right.

Zack:
The marketing information is really valuable, and it’s good to improving a marketing campaign and making it efficient. But when I’m trying to grow the business in the areas where I have the most opportunity and the most potential, I have to use business data to get there. And that’s not something in marketing that we’re typically used to using at that level. Think about it, every store, every day, every product, that’s the level of detail you need to kind of break apart this equation.

Jan-Eric:
Yeah. So data becomes the currency in figuring out the ledger. It’s the code for figuring out how are these different geographies different?

Zack:
Right.

Jan-Eric:
Which comes to the next main type of barrier quote that we hear back from a CMO is, “Okay, yeah, I get it. I need to get that information. But even the idea of getting that information, getting access to that information is a daunting task for me. I’m in marketing. That information sits somewhere else.”

Zack:
Right.

Jan-Eric:
It’s too scary to me. Maybe not scary, scary is not the right word, but it’s very difficult for me to envision how I’m going to have to go about this. It’s not like I’m looking for more things to do to add my to do list.

Zack:
Yeah, because it’s usually sitting with either the finance group or the information technology group and a database that they maintain and they control. And it’s stored there for financial purposes. The accounting group probably uses it for stuff. It’s probably used in distribution and ordering product and all that type of stuff, but when I haven’t dealt with that group on a transfer of data before, I may not even know where to start.

Jan-Eric:
Right.

Zack:
Hey, I need store sales information. That might be my request, but what I really need is, hey, I need every day, every product, every store, and I’m going to use that against my marketing information to try and figure out what’s going on. So making that type of request, I think there’s a couple of schools of thought. There’s, hey, let me be really structured about my request. Let me request everything on the front end that I think I’m going to need. Work with the right people, and try and get it all at once. Or what I think we’ve seen work better is let’s do a little to learn a lot in that equation.

Maybe I start with a segment of that data. Maybe it’s one portion of the country, or maybe it’s weekly sales just for every store. I don’t need products, yet. I just need store sales. And use that to start getting some early wins in the process, some early insights to demonstrate why we need more because it’s sometimes hard to pull this data. For an IT person, they have to go into the database, they have to typically write some form of query. Sometimes the data isn’t that clean. So there’s a lot of cleansing and organization that has to happen through that process to get it in a way that a marketing person can use it. So it is a fair amount of work in many cases. And if that person can understand, oh geez, they’re actually using this for X, Y, and Z, I can see how that could impact the business. It may allow them to assign some higher priority to this type of stuff.

Jan-Eric:
And so the thought is that the idea is to that to try to make the idea of getting that information a little less daunting. Start with a little. You experiment and learn, build credibility and trust along the way, and then you can kind of go on from there. But it brings up another issue then that we’ll hear from CMOs is that I don’t really have the bandwidth to deal with this project. I understand there’s value in it. I understand there is something there, and I know that there’s value in it. I don’t have bandwidth to deal with it. Even if the data is available, can my department even take on the idea of analyzing store by store what the differences are?

Zack:
Yeah. It sounds like a lot, right? If I have 1,500 locations, that’s a lot of data. You’re not doing that in Excel, right? Unless you’ve got a really nice clean dataset that someone has curated for you on the front end. But if you’re going to take this type of project on, which is very important, there’s gotta be some layer of technology to make this possible, and it’s going to be different in at least 90% of the cases that I see. It’s going to be different than the technology than the team who houses that data is actually using because they’re using it for different purposes. And many times they’re not using it in as dynamic of a fashion as you will need it when you’re actually trying to make decisions off of it and asking new questions of it. And so the way that we solve that is with a collection of technologies.

It’s a collection of data-focused tools that are designed one, to house all the data, organize it, and visualize it. But then another big piece of that is the automated processing of that data, and this is where it can really ease the burden on the people pulling this data is if we can automate the process of transferring the data from wherever it is sitting to where it needs to go to be in a dynamic format that makes everyone happier, right, because if I’m looking at this stuff weekly or even monthly, I don’t have to go in with a new request. I’ve set up the process and the piping and everything to get it to where I need it to make it ready and usable.

Jan-Eric:
So I think that you’ve identified another podcast topic that will need to address around how to select the right technology for a tech stack. So we’ll revisit that on another day. But as we talk about the benefit of the technology and a tech stack as it relates to CMOs and this whole idea of trying to understand how stores are different, technology becomes your friend, and tech stack can become your friend in building in efficiency and, and basically housing an organizing lots and lots of data that minimizes the work requirements from humans and your team, your human bandwidth, that can start to give you some insight that can inspire different types of thinking about how things are different within your system essentially.

Zack:
Yeah. We have this term called analysis ready data use.

Jan-Eric:
Yes, I love that.

Zack:
And all it means is I’ve got the data in a nice clean dynamic format that I can ask a lot of questions of it very quickly. And why a lot of people have a bad taste in their mouth, especially when you’re talking about data this large, is I send a request over to my finance group or IT group, I’ve got three questions in that request. They come back with a very clear dataset that answers those questions and only those questions, right?

Jan-Eric:
The problem becomes when you look at that information, and it sparks three more questions.

Zack:
Exactly.

Jan-Eric:
Now, you’ve got a new request that goes to the bottom of the queue, back to that planning group.

Zack:
Exactly.

Jan-Eric:
And hopefully you’re going to get it back in a week depending on what their bandwidth is.

Zack:
Right, where they assign priority because a finance group especially probably has priorities that you don’t even know about, right? They’re dealing with the next acquisition that’s coming up, or the next new product that’s getting ready to launch and where it should be distributed, or they’re dealing with the auditors that are in that week. There’s all kinds of other things in their world that this person trying to figure out how best to spend their media is not my highest priority. So-

Jan-Eric:
Yeah. So having analysis ready data, it really appeals to the purchasing person who is in marketing, who is naturally inquisitive, naturally curious, and tends to follow with more questions. Having analysis ready data is perfect for that kind of personality.

Zack:
Right, and having a good piece of technology to be able to handle that data, keep it clean, and keep it flowing, right, that’s the one of the most important parts of this equation is the piping to automate this process from them to you. So that when you get those first three questions answered, you start asking your next three questions, you’ve always got fresh data to keep that stuff rolling, right?

Jan-Eric:
Right.

Zack:
The whole point of this is to make decisions to change some type of thing we’re doing in the marketing space or the way we’re executing against a product and then measure the results, right?

Jan-Eric:
Right.

Zack:
Planning is very important, but measurement of the plans I put in place is really the meat of the equation, so then I can make better decisions in the future. So having the piping in place to keep that stuff flowing, once I set all those plans, figure out what I want to do, the measurement function of that becomes very easy because I’m using the same data used to plan on. It’s just freshened up, and I know when my stimulus was changed.

Jan-Eric:
Yeah. So you and I worked together using the tech stack like this on behalf of our clients. So we can talk a little bit about what are some of the things we’ve done with it. When we’ve done an analysis, we understand the variability in the system and what it leads to in terms of so what action can we do with it? It’s not information for the sake of information, it’s looking at this information with a bias for action.

Zack:
Right.

Jan-Eric:
What can we do? So we talk a lot about putting stimulus into the marketplace and monitoring business results in what’s the impact on business, using that analogy, having a plumbing established to understand business performance in real time allows us to look at when I put this in, how did it change things? But even taking a step back from that, understanding the variability can lead to segmenting different store locations, different geographies, different states or cities or sales regions and identifying growth opportunities, and then building plans specifically for those regions that starts to look different. We have executed in the past a variable media flighting strategies-

Zack:
Right.

Jan-Eric:
… for a client that has a clear seasonal pattern, but a seasonal pattern that varies even within an individual state.

Zack:
Yeah, right.

Jan-Eric:
So we have been able to execute a digital media strategy that has variable flighting, that mirrors peak sales opportunities that are driven by factors that have nothing to do with media.

Zack:
Right. Think about … California is a good example. Southern California and Northern California are different climates.

Jan-Eric:
Very different climates-

Zack:
… and different time-

Jan-Eric:
… beyond even the weather.

Zack:
Right, exactly.

Jan-Eric:
They are very different places.

Zack:
Exactly, not only just weather, everything’s different. So why would we ever treat those two the same? I mean, even think about something simple that’s actually relevant right now is when school starts and stops. If I’ve got any type of restaurant business, any type of business where I see elevated levels of activity when kids are not in school all day because their parents are out doing stuff with them. There’s like all kinds of stuff happening. Schools come in and out different times in different parts of the year. That’s stuff you can actually build a plan around.

Jan-Eric:
Think about suntan lotion and a spike in spring, around spring break for warm weather destination, spring break travelers and planning around spring break. The precision that can be done by understanding the opportunity for different points of distribution-

Zack:
Right.

Jan-Eric:
… without having to look up all the different school districts to find out when is spring break.

Zack:
Right. Why am I advertising when spring break is over in a certain area, right? Why would I ever do that?

Jan-Eric:
Yeah. And I’m no spring chicken. I’ve been doing this for a while, and I think back to even roles in planning media for clients less than 10 years ago. This still was true about the variability, but I was limited in that time with what I could do from a media standpoint to be able to accommodate it. The advancements in digital media allow us to act on a lot of these insights. So there’s never been a better time than right now to really understand how not every store is created equal in a system. Utilizing technology to make that much easier to get over these barriers of I don’t know how they’re different, getting the information is scary, and even if I did, I don’t have bandwidth to deal with it. Tech can make that much easier to do, and there’s never been a better time to do it now because the media landscape, that promotional landscape, the way that we can create action from this insight, we’ve never had more possibility than would you right now. Now’s the time to do it.

Zack:
And your competitors are doing it, too, Right? So every client that we work on together at Callahan is doing this at some level, right?

Jan-Eric:
Yeah.

Zack:
We’re not the only ones.

Jan-Eric:
Zack, this has been great. It’s great conversation. Hopefully you found it useful and thanks for joining us.

Zack:
Yeah.

You’ve been listening to the Uncovering Aha! podcast. Callahan provides data savvy strategy and inspired creativity for national consumer brands. Visit us at Callahan.agency to learn more.