It’s no surprise that we are TARTLE are big on data and using it to improve lives all over the world. By collecting and analyzing quality first party data processes can be refined and better decisions made.
One famous example of this is Domino’s Pizza. Years ago, the Michigan-based pizza giant was on the ropes. Once the leading name in pizza delivery, they were losing market share to others and getting bad reviews on their pizza and their delivery times. They went from the king of the mountain to sliding fast towards the valley.
What did they do? They realized they had to take a hard look at all of their processes and possibly make some very hard choices. To do this, they developed their pizza tracker program, tracking the whole process for each pizza from order to delivery. Looking at the data gained from that software, Dominos identified a number of inefficiencies, inefficiencies that they had to fix to get back on top. Altering their mentality to treat the pizza making process like a manufacturing process, the company completely revamped its business. The process was more efficient and they were able to deliver better, fresher pizzas faster than ever before. As a result, Domino’s is once again climbing the mountain and rebuilding its reputation.
That success story unfortunately isn’t every story. The other side of the coin is represented by an incident featuring America’s biggest big box store, Wal-Mart. Sam Walton’s company has been big on using data to optimize its profits for years. Because of this, they noticed that Vlassic pickles were selling remarkably well. What’s more, they figured that they would sell even better if they could get them down to a lower price point.
They contacted Vlassic to ask if it was possible and after the supplier crunched all their numbers based on their down data, they got close enough to make the bean counters at Wal-Mart happy and the order was placed. Everyone wins and we have another success story about the power of data analytics, right? Wrong. Instead of a raging success, this seemingly smart move was a disaster. Pickles indeed flew off the shelves, until they weren’t on the shelves anymore. What happened? The farmers that grow the cucumbers in the first place simply couldn’t keep up with the demand that was getting placed on them. With no pickles to put on the shelves the orders were cancelled or significantly reduced. That compounded problems for the farmers, especially those who would have bought new equipment and altered their own processes in order to meet the sudden increase in demand. Then all of a sudden that demand fell through the floor again, meaning that the farmers wouldn’t be able to recoup all those costs.
So what went wrong? Why did a reliance on data and the in depth analysis of it lead to success for one company and a major loss for another? It was a difference in approach. Domino’s didn’t just go over their procedures or have a couple teams demonstrate their process for the board. They went into many of their stores, working with the employees and analyzing how their pizzas were made. They also paid attention to their customers so they knew what it was they had to focus on improving. In short, they listened to everyone that mattered most in the process and worked with them to find the best possible solutions to everyone’s concerns.
Wal-Mart seemingly did the same thing. They knew what their customers wanted, knew they would be even happier with a lower price and worked with Vlassic to order more pickles at a lower price. But they forgot to talk to the real suppliers, the farmers that are pulling the product out of the ground in the first place. Granted, this need not always be a concern. A single store isn’t going to strain the system. However, given the number of stores Wal-Mart has and the volume of product they move, talking to the farmers is a necessary step.
What these two examples demonstrate is the necessity of remembering that even the most scientific data analysis has to be about people first and foremost. Keeping that in mind means you not only achieve financial success for yourself, you will improve the lives of others in the process. Forget that and you will do harm you never intended.
What’s your data worth? Sign up and join the TARTLE Marketplace with this link here.
It’s no surprise that we are TARTLE are big on data and using it to improve lives all over the world. By collecting and analyzing quality first party data processes can be refined and better decisions made.
Speaker 1 (00:06):
Welcome to Tartle Cast with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.
Alexander McCaig (00:26):
Hello everyone. Welcome back to Tartle Cast. We're doing some early morning filming, watching the sunrise with a nice cup of mushroom coffee.
Jason Rigby (00:34):
Yes, must be nice.
Alexander McCaig (00:35):
I was just about to say that.
Jason Rigby (00:38):
Alex likes my shirt.
Alexander McCaig (00:39):
Yeah, it was all that, it's just, there's so much, as I was saying, baked into your t-shirt, I was dying. I was crying, I was laughing so hard.
Jason Rigby (00:47):
I think it was culturally relevant.
Alexander McCaig (00:48):
It was extremely relevant.
Jason Rigby (00:48):
Because you can use it in a positive or negative, that depends on, it's just a mirror of the reflection of who you are when you look at the statement.
Alexander McCaig (00:55):
And or, yeah, you saying it or the person receiving it.
Jason Rigby (00:58):
Right, yes.
Alexander McCaig (00:59):
It must be nice.
Jason Rigby (00:59):
Yeah, because it can be like, must be nice to... [crosstalk 00:01:04] and then jealous or envious, or you can be, must be nice in a positive way, like you go.
Alexander McCaig (01:11):
It's one of those statements that sits right in the center.
Jason Rigby (01:15):
Yes. Yeah. Exactly.
Alexander McCaig (01:16):
I guess it'd be funny from the context of someone like English second language, where you say like, must be nice. Or like if anyone was learning something contextual from a group or a society, from a cultural standpoint-
Jason Rigby (01:29):
They wouldn't understand it-
Alexander McCaig (01:30):
They wouldn't understand that at all.
Jason Rigby (01:31):
They would think you're being, it wouldn't... because I would imagine the English language sarcasm would be hard to understand.
Alexander McCaig (01:37):
So hard to understand. And so that's why I find it so comical, there's so much baked into, must be nice.
Jason Rigby (01:43):
Yeah, exactly. Speaking of baked in-
Alexander McCaig (01:46):
And speaking of more baked in-
Jason Rigby (01:48):
We have mushrooms in our coffee, the legal kind.
Alexander McCaig (01:49):
And we're going to talk about food.
Jason Rigby (01:52):
So there was a one-up Vox article on why food resources, economic specialists, are recommending big data to boost the food industry.
Alexander McCaig (02:01):
Yeah. Food's interesting. Right? So there's so, they touched on a lot of things. There's so many restaurants out there and restaurants don't have to be like Gordon Ramsey or Michelin three star. You can run a very profitable restaurant as long as you have your process down packed. And I think the most pertinent example, and I don't know why this is just becoming obvious, obvious to anybody that's in the food industry, but look at what Domino's did.
Jason Rigby (02:33):
Oh yeah. They turned themselves around.
Alexander McCaig (02:35):
So Domino's with the pizza tracker and literally analyzing every part of the process, more so, I think than McDonald's, I think they looked at what McDonald's did and then improved upon it with technology and data, but being able to track the process, the time, who's touching it, I'm talking, start to finish, understanding that whole supply chain and the manufacturing of the pizza.
Alexander McCaig (03:00):
They moved it to a manufacturing process. So rather than looking at it as like food from the artistic standpoint, let's look at it from manufacturing. How can we create the least amount of mistakes with the least amount of waste and get it out as quick as possible, because they understand that that is baked into the revenue models.
Jason Rigby (03:16):
No pun intended.
Alexander McCaig (03:17):
Yeah no pun intended. So if we can increase our slice of the pie for how much we're gathering on revenue, then let's do that by using the analytics through these big data models that we have all this processing power in the cloud to come back and be like, this is actually how we should be making the pizzas. Right? And just the benefit to us because it is in the cloud, you can track it on your cell phone.
Jason Rigby (03:42):
Well, and not only, I mean, it was brilliant because not only is it helping user experience and it's giving excellent customer service to something that you wouldn't expect it in kind of a fast food, generic. But I think the number one and most important thing that people don't realize when they're looking at data, whatever industry it in, is how they can hold the employees accountable.
Alexander McCaig (04:04):
And that's a good part because accountability, and we're not trying to make people robots, right. But holding someone accountable to a process so they don't like go outside of it, it saves them the individual on liability. And it saves the company on costs and liability at the same time.
Alexander McCaig (04:20):
And even for Walmart. Walmart, you don't think of them as food, but they have those huge marketplaces. Their number one selling product is bananas, bananas is the number one selling thing at Walmart. But what you have to measure is the turnover of that inventory, food goes bad. You can't serve stuff that's just beat to hell, nasty, covered in mold, stagnant, dying, rotting on the shelf. Right? So you have to look at your logistics and supply chain, the data from them, how quickly can you get that food to you? What the shelf life is. Right? And then after that, can we sell it in an inappropriate amount of time before we have to actually can this thing and we made absolutely no gain on it.
Jason Rigby (05:05):
Yeah. And I think that's where IOT devices will play. I mean, once they, some places are experimenting with having them on the shelf and then it knows exactly, for these robot in the hour and at September 17 we always sell 12 of them, you know? So we need to, and then it automatically through blockchain and order that product and have it, out of five or seven years of data, we know, I mean it can get that granular.
Alexander McCaig (05:34):
It does get that granular but there's also some issues with that, with the big data and the food and stocking shelves. Sometime ago, I worked at Bed Bath and Beyond. It was one of my earlier retail jobs.
Jason Rigby (05:47):
Did you have a nice comforter?
Alexander McCaig (05:49):
I always had nice comforters, very plush.
Jason Rigby (05:52):
That's very important. People don't realize how-
Alexander McCaig (05:54):
Sleep is so important.
Jason Rigby (05:55):
We're learning that with our whoop, but people don't... one thing I've learned is making your, and I've shared this before on my other podcast, is making sure your room is nice and not, like some people put their computer in their room-
Alexander McCaig (06:07):
No I've removed all that-
Jason Rigby (06:08):
All screens, and that you should make your room be really nice. I'm redoing my room right now.
Alexander McCaig (06:13):
I go into my room solely to sleep.
Jason Rigby (06:15):
Yes. Yeah.
Alexander McCaig (06:16):
Nothing else happens in there.
Jason Rigby (06:17):
Yes, well...
Alexander McCaig (06:21):
Damn. Put myself in a corner in that one.
Jason Rigby (06:23):
You are getting married so once you start the marriage life-
Alexander McCaig (06:28):
Shame on me.
Jason Rigby (06:28):
Nothing happens but sleep.
Alexander McCaig (06:30):
Yeah, nothing but sleep will happen, for sure.
Alexander McCaig (06:33):
But the point is that's solely what you do, now but when we're looking at, getting back to Bed Bath and Beyond is that, we would track those things. How much will people buy in a specific day? And then that would immediately go into the ordering, the fulfillment system and it would be ordering more products. And then what you realize is that you'd have 500 comforters because they sold last year at this time, but you only sold 20. So now you've got an issue with stocking on your shelves. And it's not that the trucks don't show up in time, shipping and handling is actually quite quick. Freight can show up next day. It's amazing all these distribution centers, but the problem is that the system was only observing it, oh my gosh. But it was never focusing on the customer actually itself, it never talked to the customer.
Alexander McCaig (07:21):
So now the food industry has realized that, okay, we can observe what's going on from our stance with our power, but we also need to understand the behaviors and preferences of the customer itself. The person who's actually doing the purchasing, because if we don't know that the food's still going to sit there. If it isn't the right price and we're not buying the right type of cabbage, I don't know how many people he cabbage, I eat it every day. Then we shouldn't have it on the shelf. If there's one buyer out of 10,000, let's not order that.
Jason Rigby (07:48):
Well, I think it's looking at short term data compared to looking at long-term data and then looking at seasons.
Alexander McCaig (07:54):
Yeah. And the real time data too, I don't want to order in very heavy meals, warming comfort food during the hotter months or maybe it's something you always purchase in colder climates, but it wouldn't make sense here. You're like in New Mexico, you just need to find the balance. You have to strike that balance with your data analytics of finding real time what's going on with preferential choices of a person because that's very variable. But the things that aren't so variable is the food you order. You can say, I want to buy this. I don't want to buy it or I can buy more.
Jason Rigby (08:28):
It's like yesterday I went to Costco and because of this lockdown, and then you can only have 25% capacity in Costco. So there's a long line. And then I went in there and automatically my mind shifted to, okay, I had to wait in line for like 20 minutes to be here-
Alexander McCaig (08:43):
Got to buy more?
Jason Rigby (08:44):
Yes, I need, and I got in this and I'm like, no, I'm single. It's me by myself. Hey ladies.
Jason Rigby (08:52):
Sorry. I had to do that. It must be nice.
Alexander McCaig (08:56):
Yeah, must be nice.
Jason Rigby (08:57):
And so, yeah. And so I automatically wanted to buy more, but then I had to stop myself and say, one person can't eat 86 apples in two weeks.
Alexander McCaig (09:06):
I cook with onions as a base in most of my meals. And I was going to Costco and I'm like, look at the size of that sack of onions. And I'm like, I got to get my hands on that. Right? And then I got a big bag of onions sitting around, I got to double down on how many onions I'm eating because I bought way to much-
Jason Rigby (09:20):
It makes you do that. Yeah. You should see my fridge. It's like super packed because I did that. I was like, well, let me go ahead and get this. Let me go ahead and get that, I need, let me get some extra hummus. Hummus lasts me a long time. And I got two Costco hummus.
Alexander McCaig (09:35):
Those are huge. That's the oasis hummus [inaudible 00:09:41] I know exactly what they are.
Jason Rigby (09:42):
It's delicious.
Alexander McCaig (09:43):
But it's funny how, from the perspective of a person individual, if you make them wait, it's almost like the idea of scarcity becomes worse and worse. We're so used to instant gratification. And so we're worried, well, what if I have to wait again? I don't want to have to come back and do that because I'm lazy and it annoys me, this process. So I'm going to double down right now.
Jason Rigby (10:02):
Yep. And that's what's happening. It is nice that the 25% occupancy though. No one's in the aisles.
Alexander McCaig (10:07):
Yeah. It must be nice.
Jason Rigby (10:08):
What's crazy, so I looked at their capacity chart when I walked in because I wanted to see, so it's 400.
Alexander McCaig (10:14):
That's it in that huge building?
Jason Rigby (10:15):
Yeah, in that huge building. And so they could only have 25%. So they could only, and they were keeping track of it with the little trackers, how many people walking in when they showed their card. So they can only have a hundred people in that huge building. So it's like a ghost town-
Alexander McCaig (10:27):
You know like 30 of those already are employees.
Jason Rigby (10:29):
Yeah, exactly.
Alexander McCaig (10:30):
And what's interesting about Costco, is Costco does a lot of food too, and they store a lot of stuff on the shelves. So as a business for your analytics, you also are going to have to analyze, well, how much of my stuff are preserved, very shelf, long shelf life items. And you've got to strike that balance also.
Jason Rigby (10:45):
Yeah because they have a ton of produce.
Alexander McCaig (10:46):
Yeah. And if you were really going to use big data and you want to-
Jason Rigby (10:52):
Produced. Did you notice how I said it?
Alexander McCaig (10:53):
Yeah, produce.
Jason Rigby (10:56):
I separated with a hyphen, pro-duce. That's the way I said it. I paused between.
Alexander McCaig (11:03):
Well then what's a non-duce. Or an anti-duce?
Jason Rigby (11:06):
Yeah, exactly.
Alexander McCaig (11:09):
Like why is the pro in front of it? Now that I think about it.
Jason Rigby (11:11):
I don't, yeah exactly.
Alexander McCaig (11:12):
It's like a forward looking duce-
Jason Rigby (11:14):
Professionally duced.
Alexander McCaig (11:20):
Must be nice.
Jason Rigby (11:21):
Oh yeah, must be nice. That's funny. Okay. Now that I'm crying.
Jason Rigby (11:26):
So you've heard about the classic Vlasic pickle story from Walmart, right?
Alexander McCaig (11:30):
No man. I don't buy those because they put food coloring in them.
Jason Rigby (11:34):
Well I know that but this is food distribution and data. So the buyers in Walmart, this happened a few years ago, the buyers in Walmart decided, the big jars of Vlasic, the big dill pickles?
Alexander McCaig (11:46):
Yeah. It's like who in their right mind is buying that many pickles. Can't even store them in the fridge-
Jason Rigby (11:49):
But they started selling them like crazy. So they were like, so they went to Vlasic and said, we'll double down on our order of these big jars, but we want to get them to $3 and 28 cents. Can you get them... so they're running math, running math, running math, trying to figure it out. I mean, this is like margins, pennies. Everybody's going back and forth, back and forth. They spent like a month trying to come up. They finally got it to like 3.29 or something for that. I mean, that's a ton of cucumbers. What they didn't realize was that, could the farmers keep up with the amount and they couldn't. So everybody had cut deals. So Walmart in their legal clauses had, if you can't give us the supply then we're out. And so that's what happened.
Alexander McCaig (12:34):
Now the farmers are screwed. So they run a nice, steady pace and because Walmart wanted to pinch pennies through the data analytics. You have to be careful when you're analyzing data. It's not just about you. There are systemic effects to the choices you make.
Jason Rigby (12:47):
That's what I wanted you to hit. Yeah.
Alexander McCaig (12:48):
Yeah. And this happens a lot with Walmart. So even, not to get too far off track, but if you look at Walmart and how they look at their labor, I know this actually quite intimately, I understand this process. They, management only does the analysis. That's all they do. So if the higher management comes down, they're like looking at all their individual stores, like they're out in the Midwest and they're just pumping emails over to the store managers all day long, saying, you're not hitting these numbers. You have way too much turnover of your people. People are quitting more than you can actually hire them. You need to fix this. And the guys were like, how am I supposed to fix, they're human beings. Right? And you're asking me to push these numbers that systemically are not physically possible. So the pickle problem is currently happening with their human labor and Walmart's the largest employer in the United States.
Jason Rigby (13:40):
People don't realize that.
Alexander McCaig (13:41):
They don't get that. Right. It gives up, even though you might not like Walmart, it's giving people a lot of opportunities.
Jason Rigby (13:46):
Well, I think in the lower income brackets, it's huge. You know, so I mean, that's what, and I love it just because of the simple fact is, a lot of people don't realize this. Everybody craps on Walmart all the time, but one night-
Alexander McCaig (13:58):
They take produces.
Jason Rigby (13:59):
Yeah. They take produces on them.
Alexander McCaig (14:01):
Producing on Walmart.
Jason Rigby (14:03):
But they also sell the most organic food and they will change. They will adapt to the market. So the more organic you buy, then the more they're going to, they just want to have-
Alexander McCaig (14:12):
Walmart's very responsive to people voting with their dollar. And so from a preference standpoint, as they're analyzing, they're going to analyze the crap out of anything and other produce.
Jason Rigby (14:21):
Right. It's the same with dog food. They bumped up their game on dog food because they know people are trying to be a little bit more healthy with the dog food. So they don't, I don't say necessarily care, I think they do, but they're just a large, super large corporation that if we purchase, they're going to go off of what we purchase.
Alexander McCaig (14:38):
Right. No, that's exactly correct. And so if we just circle back around then. If the restaurant industry is at 899 billion or whatever the projection is for the size of it, it's not going to be that hard for you to become a leader in the restaurant industry. Even if you have like junk chains, I wonder how some chains survive just driving around, I'm like, how are these people in business? It's the fact that they've nailed the processes. They've nailed down their data, their suppliers, all that stuff. And that's what makes them successful. And you wonder why, wow, that was such a good one-off restaurant, very niche, how did they fail? Well, they failed because they're good at making food, but they suck with the manufacturing process. They're bad with their data. They're bad at collecting information from their customers. They only know how to please and then create art. There's a reason why art doesn't sell, art has a slow inventory turnover too, right? And you have to look at that, but that doesn't mean you can't serve quality in the restaurant business but do it efficiently.
Jason Rigby (15:38):
So speaking of quality, if a restaurant chain wanted to buy data from Tartle, how would they do that?
Alexander McCaig (15:44):
That'd be really easy. So they would sign up as a buyer on Tartle, go to tartle.co. They get themselves started, they put in their initial profile and then they would say, okay, I want a population of people in my general area. And I want to know what their preferences are for buying, what their incomes are, things that they're allergic to, things that they want. And I also can survey those people, get their data on other restaurants they are going to. So from a competitive standpoint, I can do a little bit of espionage by understanding behaviors and the wants and how they actually interact with other businesses that are my competitors.
Jason Rigby (16:20):
Yeah. And that's the beautiful part. Whenever you start getting real specific with your data and you go directly to the customer, then when you get that data back, now you can use that in your marketing to somebody you know that's responsive to your restaurant chain.
Alexander McCaig (16:35):
Oh, most definitely. In the marketing, you can also realize, you know, I really shouldn't be stocking this stuff, or this is not something I need to be carrying.
Jason Rigby (16:41):
Right. You could ask somebody that, have you gone to this restaurant? Yes or no. Yes. Well then, would you buy this? Did you order this, this or this? These are our specialty, four specialty dishes or which would you prefer if you had a new menu in it? And then you're going to get simple survey. You're going to get it back directly from them and what you should do or not do and I'd imagine you'd see through-
Alexander McCaig (17:00):
You'd want a straightforward answer like that. Right? And it's literally as simple as going on to Tartle very quickly, finding the data you want and going to purchase it from those, the appropriate parties. This is crucial information to the longevity of your business. Oh, and it's also an R and D expense. Hello taxes? You know, nobody wants to pay them. And it's funny that restaurants have only now alike it stated in here, begun to find this information is crucial. Information is always crucial. It has always been crucial. And just because we call in a data, it doesn't mean it's any different than information. It's just a better availability of it. You were getting less of it before and now you can get more. You need to make those transitions. You got to understand that if you don't have the details, how are you supposed to operate, how can anybody command anything if they have no communication?
Alexander McCaig (17:54):
Gordon Ramsay cannot manage his restaurants if there's no communication between the front and the back. And how are you supposed to manage the health of the restaurant in general, if there's no communication between the customer and the business, right? Or, and then the customer and the farmer. Ask the farmer and ask the customer at the same time, look at the balance from both sides, be holistic with your data and how you analyze it.
Jason Rigby (18:16):
I love that. And people can sign up at tartle.co.
Alexander McCaig (18:20):
Yep. Dot co. T AR T L E.co. Thanks everybody.
Speaker 1 (18:30):
Thank you for listening to Tartle Cast with your hosts, Alexander McCaig and Jason Rigby, where humanity steps into the future and the source data defines the path.
Speaker 1 (18:48):
What's your data worth?
