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Food for thought: Data analytics in the food and beverage industry

November 21, 2017 Podcast 15 min read
Chris Moshier
Using data analytics in the food and beverage industry can help you optimize your marketing and operational strategies. Our podcast discusses best practices and tactics to leverage data and reach more customers.

In this episode of Food for thought: Data analytics in the food and beverage industry, Chris Moshier of Plante Moran and Steve Gaither, president of JB Chicago, discuss using in-store data to create timely, actionable insights and increase profitability.

Topics covered include:

  • The hurdles of maximizing value in operational data
  • How to apply your wealth of data to optimize your marketing strategies

Continue reading below for the complete podcast transcription

Data analytics in the food and beverage industry 

Chris Moshier: Today, our topic is data analytics, but maybe a bit contrarian of a viewpoint is that data analytics is not the solution. It's the enabler when you combine it as a capability along with industry experience and deep subject matter expertise, whether that's in business strategy or in marketing. One approach to leverage data analytics is to focus on in-store data. With both operational and marketing opportunities, you can show how broad data analytics can be applied, and what's also interesting is you don't need big data. Many businesses hear the big data buzz today, and they recognize that they don't have big data, but they overlook the wealth of incredible small data they do have. Even very successful and highly profitable companies that we work with often miss these hidden opportunities to tap into their data. Recently, we worked with a private equityowned food and beverage service company who served customers in three distinct markets, food service, convenient stores, and offices. Using a very simple spreadsheet, we identified opportunities to help them increase profitability through understanding fixed and variable costs for each segment.

Steve Gaither: You bring up a good point, Chris. It's really not about big data. It's about smart data. There's the old 80/20 principle, which is the rule for internal operations andproduct segmentation. A good example of that is a gluten-free bakery that we worked with. They had your brick and mortar store with your case with 200 skews of muffins and chocolates and all these different things, and over the years, they started to get into catering, which was more like 25 skews, and then also started dabbling in retail, which was more like two or three skews along the way. What they found was all their effort was going into making the products to fill up the case on a daily basis, but all their money was really coming from the catering that they were doing in between or after hours. That was really where the margins and the profits were coming from.

It took them awhile, but they ended up closing the doors and focusing on the catering and then focusing on the retail business as well, which really drove the needle. Another example was an organic farm we went up to in North Freedom, Wisconsin, which they had 12 employees, and they were the larger employer in North Freedom, so a tiny little place, but they had this beautiful, beautiful farm with these great herbs, and started to bring these products to life, and they probably had 120 skews and spending all day bottling these tinctures and these lotions and creams and they have teas as well, so just a million things in this shop, and when I sat down and asked them, "Hey, really, what's your biggest drivers and biggest sales?" 75% of their revenue came from one skew, which was this eczema moisturizing cream, and the additional 15% that equaled 90% came from the two other creams.

So here's 90% of their revenue coming from three products out of 120, and they're spending all their time and effort on these low margin, low sell-through products. The other 10% really were coming from these teas that they were actually copacking. They were getting sources from other folks and their margins were basically nothing on it, and they're in the slowest velocity aisle, which is the tea aisle. By just recognizing that if they get out of the trenches and look at what they're good at, it's really three skews, so we ended up signing a deal with them, a wholesale agreement with a larger retail CPG that was starting to extend into the lotion space, so it was an easy deal to really increase profitability and stop wasting their time.

Chris Moshier: It's crazy, Steve, as you share that. Many businesses, really, they're challenged to have a holistic viewpoint of their data because their data are stored in multipledisparate systems. We worked with one client who was also private equity owned. They operated on numerous financial systems for each of their plants, and they were faced with a challenge in that they expected to grow EBITA very aggressively. They focused much of their efforts on a few products, but they really didn't know which products were most profitable. We helped them bring together their data from these disparate systems into a four by four matrix of products. Think of it as a scatter plot, where further to the right, you have the most high revenue products, and furthest to the top, you have the greatest margin products.

So using this approach, you could focus on those products in the upper right hand corner or confirm that these are the products that we need to spend our energy on. They're high revenue and they have great margins, and then it quickly shows products in the lower left hand corner that are low revenue, low margin, and you might be wasting your time on those products. Where you have some decision points is products that are high revenue, but they're low margin. What do you do to improve those? And so using data analytics we were able to identify some areas operationally where they could improve costs on these products to improve their margins, or if you look at low revenue products that are high margin, that's a seed waiting to be nourished. It's an opportunity for you as a business owner to focus a little bit more energy on that product and increase revenues because it's such a great, high margin product.

Steve Gaither: Yeah, Chris, you're right. Sometimes these guys are just stuck in the trenches and it's really hard for them to see the business that they're working on day by day, it's hard for them to really see it from 10,000 foot above.

Chris Moshier: It really is hard to create value from data. There are probably three hurdles that we've found our clients face, and optimizing the value they gain from theiroperational data. The first is probably the biggest and it's transforming their business culture, particularly for businesses that have been historically successful, and smaller businesses that have built their success on tremendous gut instinct, it's hard as they grow to rely on data-driven decisions. It's hard to find opportunities to bring data in as another input into these decision.

A second challenge that we've found that our clients face is finding the right experienced analytics resources. People who are able to bridge the gap between asking the right business questions and understanding how to answer them through data analytics. In a world right now where data analytics is such a buzz and hot topic, skills resources can almost call their shot and decide, "I might want to go work for some of the largest companies that are leveraging analytics." It's hard for smaller businesses and even in food and beverage, to bring in analytics people who have these skills to leverage the highly advanced technical processes in data analytics, but also ask the right business questions.

A third area that we've seen our clients are facing challenges is organizing the right high quality data from these disparate systems. Oftentimes, in order to make a meaningful analysis that's timely and provides actual insights, it requires processes to be in place from a data analytics perspective, which are sometimes challenging to implement. We worked with a national food and beverage manufacturer who had multiple disparate systems that managed their trade promotion process. They had a pretty interesting challenge. They had a $5 million discrepancy between these two systems and their trade promotion, and so leverage data analytics, we were able to help this client of ours identify the root cause for that discrepancy, which helped them make better decisions and helped them better understand their data.

Steve Gaither: Well, you're right. Especially in the food and beverage business, there's great third party resources out there with IRI, SPINS, Nielsen Data to really tell what's going on in the category and on the shelf.

So one real example is a frozen pizza company we worked with. It's pretty amazing. They had 28 grams of protein and only four carbs, which is usually the other wayaround, and this was because they had ground chicken and Parmesan instead of dough, so you had this highly functional, great-performing pizza that happened to taste great, so when we actually looked at the IRI data, what we found was two stories going on. One, there was a premium set with Screaming Sicilian, Palermo, etc., that were getting six and seven turns per store per week, and then you had the gluten-free set, were Amy's and Udi's and the folks like that, and they're getting twos and threes, so the story coming out if it was you cannot be a healthy pizza and sell. People go to the pizza aisle to sin, give them taste first.

So when we went to the brand, we changed the name to Real Good Pizza, so first of all, thumbs up is real good pizza, right, so anybody, any consumer going just wanting to sin and have good pizza, they know exactly what it is. It's really good tasting pizza, but for the conscientious consumer, that 25 to 55 year-old female that cares about what she puts in her body, how animals are treated, and our carbon footprint, that's really the rise of this industry. We can really make the difference with her and say, "Hey, here's something with real ingredients that's good and good for you." Really changed the game.

So when we went in here, our goal was to, hey, no matter where they put us, we're going to lead with taste. We can either take the leftovers from the premium set, or out-velocity Amy's and Udi's because gluten-free basically means bad-tasting. We can come in with a great-tasting pizza that happens to be gluten-free.

Another totally different example is look at Katlin from Simple Mills. She was a company really focused on almond flour, which was a highly functioning, great tastinggluten-free ingredient, and what she really looked at was, "Hey, I've got this really slow velocity aisle of baking mixes," but instead of running away from it, she charged right in and said, "Hey, I can bring new people to the new category, so I can out-velocity these guys by bringing the people that, hey, didn't want ... That wanted clean ingredients, that didn't want gluten, that ran away from that aisle a long time ago. I can bring that back in with this almond flour," and she did with these great-tasting baking mixes. She out-turned everybody and totally owned the almond flour category in the baking mix space, but that's not where she stopped. She said, "Okay, now that I've got this, where can I go next?"

So let's start to move towards the perimeter where velocity is, but let's try to find white space, so looking at our IRA data, etc., you can look at the cracker category and see there's not a lot of stuff going on there, so she charged in, went in therewith her ready to eat crackers, out-velocitied the other crackers. Did the same thing with cookies and really turned this Trojan Horse strategy by looking at IRA and data, out-velocity them all and really won the game.

These early stage companies use generalizations, best practices. You can steal some of the insights from the big guys and carve your own white space when you're these small companies and be a little bit more agile.

Chris Moshier: That's terrific, Steve, and I think it's awesome looking for white space as well, and when we talk about looking for white space, you're looking for white space in the grocery store, but you can enrich your data, your operational and your customer data with third party data to find white space in the geographic markets, and so we've helped clients in the past, food and beverage industry, to enrich their customer data and see where they're doing strong and where they're doing poor to identify opportunities where there are geographic markets that they can grow into, markets where there's less fragmentation and they can go in and have a greater impact.

Steve Gaither: That's a really good point. I'm a partner in a food investment fund and we're working with these early stage guys, 200 to 750K in revenue, and what I love about these companies is that I've found out that distribution really isn't the key. It's really all about velocity, so nothing is worse than being in 2,000 stores with a couple hundred K in revenue.

I like these folks that are in maybe six to 20 stores, and you can say, "Okay. We're in six to 20 stores. Let's use this as an opportunity to really figure out what gets this product moving off the shelf or velocity, so let's test between placement, where we placed in the store, pricing, us against the competition, promo, whether that's sampling or promotional pricing, display, can I get on the floor instead of on the shelf?"

What you'll find is there's a certain combination of those that work really, really well and a certain combination that doesn't, so if you figure out what works, then you can go to other retailers and say, "Look, I figured out how to get a product that normally turns at threes and fours, I figured out a way to get sevens and eights," whether it's what Tiesta Tea did right by going bulk tea and actually putting, much like when you go to a yogurt shop, and you walk in with a little bucket, and you expect to walk out paying $3, but you're filling your own, and you walk away with $30 worth of yogurt. Same concept with bulk tea. They found a way to get the velocity up 12X by looking at that.

Once you figure out that velocity game, then distribution comes. Every retailers wants you, but we do a little bit different. If I'm in the Midwest, I want to own home because trade spend is really expensive. If I have sprouts in the southwest say, "Hey, come on down here, we love your product," I'll be like, "Hey, thank you, but not right now." I want to own home. I want to get the velocity. I want to make sure I can grow this company to where if I can get three to five hundred stores in the Midwest and get this company to 1.5 to 2.5 million, then I can do a series A defining my own rules. I can say, "Hey, here's my series A raise, but instead of giving me a 2X, I can grow to 10 more regions because I did this smartly. While I know you're not going to give me a 10X but you're going to give me better than 2X so let's go," so it really changes the game.

Chris Moshier: And I love how you've talked about using placement and pricing and promo and display as inputs into understanding what's the best combination, and it's an area that we leverage data analytics to identify what are the variables that are most highly correlated with successful markets? And using machine learning algorithms to identify that it's these two combinations that really is what tips the needle to being a successful market. We've learned, Steve, that data analytics, it's an enabler. It's not the complete answer, and as you've talked through this, it's just amazing to me how complex and how many variables there are. You have to have the right business knowledge to look at the skews and the analytics capabilities to analyze the data and the industry experience to ask the right questions.

When we look at some of the biggest disruptors in industries ranging from home entertainment, where Netflix entered the door, it's completely changing the industry for Comcast, or we look at public transportation, where Uber has completely changed the industry for taxis, or even hospitality where Airbnb, again, it's completely changed the industry. There's one common denominator. Those three examples I've shared, they have done a better job than anyone else, any of their competitors at recognizing the value of their data, enabling their success through tapping into the vast amount of data which they create.

Steve Gaither: Yeah. We actually have a joke, which is sometimes you have to be dumb enough to figure it out, so sometimes when you're stuck in the trenches, looking at your data, same thing day in and day out, you don't find those opportunities. For example, with retail, you're looking at your COGS, your margins, your trade spend, etc., and that's just the way it is. After you subtract everything, you have to be at 35 points, and you've got yourself a business, but there's other white space opportunities in there. If I go with Amazon and go online, a lot of that trade spend, the distribution, the brokerage fees, those things go away. If I go with food service, not your traditional food service, but the ever-expanding cafes, office delivery, hospitality, captive audience plays, I could do those same things without the trade spend or without the support or a promo needed. That really changes the game, so I can go national while I'm keeping regional on grocery channels. Now I can go national with my food service and Amazon, and once again, change the game just because I'm looking at other channels that the other guys aren't.

Chris Moshier: It's amazing how data analytics really can help you make a strong data-driven strategy. I think if there's one take-away from today's podcast, it's to focus on making big data small, and more approachable. We want to thank you for listening, and remember to check out additional food and beverage resources. Look for our next podcast with more trends and hot topics geared to the food and beverage industry.

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