The Killer Advantage: Expanding Dave's Killer Bread Through DIY Research
Editor’s note: This article is an automated speech-to-text transcription, edited lightly for clarity.
On September 25, 2024, quantilope presented on the DIY consumer insights that played a role in Dave’s Killer Bread’s successful expansion of their product line.
The two teams implemented a DIY, tech-driven approach to insights. They ran over 60 advanced method projects on the Dave’s Killer Bread product line since 2022.
Learn more about launching new products with DIY research, how small teams can leverage advanced insights to fuel growth and innovation and more by watching the session or reading the transcript.
Session transcript
Joe Rydholm:
Hi everybody! Welcome to our session, “The Killer Advantage: Expanding Dave's Killer Bread Through DIY Research." I'm Quirk’s Editor Joe Rydholm.
Before we get started, let's quickly go over the ways you can participate in today's discussion. You can use the chat tab to interact with other attendees during the session and you can use the Q&A tab to submit questions during the session. We'll answer as many as we have time for in the Q&A portion at the end.
Our session today is presented by quantilope. Tom, take it away.
Thomas Fandrich:
Thank you very much for the intro. Good evening. Good morning, everyone, wherever you are around the globe. Very excited to welcome you all to today's Quirk’s webinar about The Killer Advantage and How Flowers Foods expanded Dave's Killer Bread through DIY research.
We are Kishan from Flowers Food, senior consumer insights analyst, and Tom co-founder of quantilope. We are very much looking forward to our half an hour, 45 minute-ish session today.
With that I'm handing it over to you, Kishan to take it away.
Kishan Rana:
Alrighty, just a quick background. I work with Flowers Foods and have been with the company for a while now.
And quick background about Flowers Foods for you guys before we dive into the topic of the day.
We're a $5.1 billion company now and a little bit of background there. We've been around for over a hundred years now and we're based out of Thomasville, Georgia.
A few interesting pieces about the company amongst many others are our brands. We essentially make baking goods, bread and sweet baked goods. One of the top brands that we have that make a part of our portfolio is Nature Zone, which is one of the nation's leading brands of bread and fresh packaged bread.
We also have Dave's Killer Bread (DKB), which is the number one organic bread brand, and we'll talk more about DKB, as we like to call it, in a few more minutes as we dive into it deeper, not only just about the brand but also about what we did with the research piece.
Another brand that forms a great part of our portfolio is the Canyon Bakehouse, which is our gluten-free bread brand, getting to specific gluten-free consumers. All the products that we make are either frozen or in the stay fresh gluten-free space.
Then obviously we also have Wonder Bread, which is America's classic iconic bread brand that most of you are aware of, which from a research standpoint we know has a very high awareness in general.
With that, on the next slide I'll talk a little bit more about DKB, right?
Dave’s Killer Bread, as I mentioned earlier, is our number one organic bread brand. Ever since Flowers Foods’ acquisition in 2015, the brand has performed really well and we're now obviously always growing from a bread perspective, but we are always looking forward to what we can do next for the brand.
And so, one of those steps was getting into the bars category and now today, we have organic snack bars.
Three great flavors for you guys to try if you haven't tried them already. If you have tried them and continue to buy them, you're like me because I like the Oat-Rageous Honey, just a shameless plug there.
Other than that, we now also have protein bars in some markets, and they will start to go national by the end of this year.
All of this was possible through intense research that we did and identifying what next we should go for. We obviously the different departments of the organization, work extremely hard to make this possible.
In addition, one context before we move on to the next one is DKB is now also in the protein bar space, and we'll talk more about some of that piece as part of today's topic. But we now have the protein bars which will start to go national by end of this year, but then we've got the DKB snack bites which are in test market now. So, keep a look out for that and we'll talk more as we look into how we got to where we are with the bars.
Onto the next slide.
There is an insights innovation process in place that we have. In full transparency, we never had a process before this because seven, eight years ago, Flowers Foods did insights but didn't have a dedicated insights team. A lot of it was outsourced.
Now me and my boss are a two people team taking care of the more than $5 billion that the company is today. And what the size of the company brings with it is an immense amount of research demands and it's only possible if we can answer all of those questions quickly. But also, to make sure that we give the teams enough confidence to be able to make those decisions that they're making, right? Whatever that may be, whether it's concept testing, packaging testing, flavors testing, you name it, we do it. And so that rigorous insights process.
You'll see in the next point, Tom, it all begins with some kind of foundational research. In all of this journey or the process journey, there are different touchpoints, and a lot of those touchpoints are covered up by using quantilope as a tool.
Obviously, Tom will talk more about that in detail. But I as a user, will give you a couple examples of how I use it.
As a researcher, once we've got some foundational research done, we know what's going to be forming from a product standpoint, just from an idea standpoint, really. That moves into the next step, which I like to call the ‘pre-concept conjoin.’
What the pre-concept conjoint is, is a conjoint analysis for sure, but there are elements that people can use to build their concept. Then follow that up as you might've guessed easily as a concept test to understand how strong the idea is, where the potential lies and then follow that up with additional pieces of research as needed.
There is a recommended part, but more often than not there's deviations. So, one of those paths leads to understanding flavor work and trying to understand what flavors we should come out with.
Obviously the first set of flavors are the ones that we identify by seeing and looking at charts that rank highest in terms of reach and frequency. Tom will talk in more detail about the technicality of that. But from a user standpoint, I'll be honest, I don't really need to know how a TURF works. I really need to know how to use the results from the TURF.
This is really easy to do, touting quantilope here, using the platform and you'll see in a second as we move forward.
But, before we do that, it doesn't end there, right? Once we've done some of those testing, whether it's implicit testing, the SIAT, whether we do some kind of product testing, we can have our R&D team or supply chain team, procurement, you name it. They send out the product and I can then send them a quick quantilope link to test or capture their feedback on whatever product they're testing. So multiple ways to use it, even though quantilope might not have that standard way of using it.
There's always flexibility in identifying how you can extend the reaches of the tool. And so again, happy to talk more about that one, but it doesn't end there. Like I said, there's a follow-up after that.
Then there's other processes where we do central location testing. Probably put it in a test market to launch and see how it performs.
As we move on to the next slide, you'll see we've run 300 plus projects since 2021, and it just goes on and on. There are just nonstop project requests that we're doing. And this scale is possible because of a lot of different reasons, but it boils down to two major ones.
One is the fact that I can quickly set up my questionnaire, put it into the field. Even if I have an additional step in the middle. I can have my brand teams and the agile innovation team, analyze or review that really quickly. Then I put it into the field, have the quantilope team work in conjunction to have it fielded to make sure that the target audience is right. Then comes back to me for analysis.
That's where I spend a good chunk of my time analyzing and reporting that results out. Which is also pretty quick because if you're there in that researcher space or have you been in the insights team you know people want answers yesterday and really you can't do that. But you can give it to them tomorrow or the day after. That's really quickly, within a week you can turn it around.
We can move on to the next slide. Thank you, Tom.
This is another example of the spectrum of research that we've done. This is just for Dave’s Killer Bread, this is just for DKB, ever since 2021.
Tom, you probably don't know this, but accounting might be aware of this. We've done way more than 60 plus by now, probably like 70 plus at this point in time. But this is just a recent development, so no harm.
60 plus projects is a lot! And that's possible again, with the fact that we can run these tests really quickly.
Now, you might be able to say, ‘okay, you are running a survey, you're getting some questions answered, some open ends. What's great about it?’
The greatness that comes with these automated advanced methods. It's mainly because we're able to run concept tests using the AB test methodologies, you can run some of those flavored TURFs that help you understand what your next best flavor is.
We've done this not only just for the example that we're going to see today, but we do it across our product categories, whether it's breads, bagels, buns and obviously we'll talk about bars as an example coming up. It really helps answer and touch upon various different solutions as we go ahead.
So, today we'll be talking a little bit in depth about the protein bars innovation and looking at how the flavored TURF test came into being.
It all starts with the need that we identified during our foundational research. Once we did snack bars, we knew that there was something else that we needed to do and there was a white space opportunity in the organic protein bar space.
It was really about first identifying what flavors to come up with in the beginning, but then also it doesn't end there. You want to look at what's next, what is the next best flavor, what are people looking forward to? And it's really interesting and easy on my end, I say it way lightly, but easier for me to look into this but wouldn’t have been if I had to run a TURF from scratch.
So, looking at the next slide there, you'll be able to see exactly how quantilope does it. I won't do a better job at this, obviously. Tom, he's the co-founder, he'll be able to explain more in detail.
So, over to you Tom. Talk more about quantilope.
Thomas Fandrich:
Thank you very much, Kishan.
Yeah, I mean you probably also remember that Kishan, when we and the Flowers food team met the very first time back in the day, and you talked about the vision that you had of a much more DIY driven innovation research process. A process where Flowers Foods own the research, drives the progress and controls the success.
It became pretty clear to me that you folks needed a new approach to research. An approach that was able to deliver fast, you talked about that, because we all know R&D teams, marketing teams always want to have insights yesterday. So, deliver fast and at ease, and that is important, but without sacrificing the quality, the trust, the actionability of the insights.
Now the answer to such needs in the modern research is technology. quantilope is such a research technology and the form of a platform, a consumer intelligence platform made from researchers for researchers. A platform that covers the entire end-to-end process starting with capturing your business question in Kishan’s example, here it is product line optimization extension, then translating this business question into a meaningful research design, connecting it with your target audience and then finally distributing the final insights across your key stakeholders.
And the entire process either works fully DIY or more in a kind of handholding, DIT – do it together – way or with the help of professional services that we also offer to customers who do not have the bandwidth to leverage the platform themselves or just starting their transformational journey into more technology driven research. We support you in a way that makes you successful.
Now being 10, 15 years into this new era of automation, speed and ease are simply table stakes, from my point of view. Everyone expects this. What really makes the difference, and I mentioned it in the beginning, is quality, from my point of view, the depth, the actionability of your insights. Only then can you really trust what you've got and base your multimillion-dollar decisions that the product and marketing teams have to take on that data, on these insights.
Here's where we at quantilope follow a pretty unique path. A path that is deeply rooted in science and quality. Since we are offering the largest set of advanced automated methods on a single cohesive platform, things like segmentation, key drive analysis, conjoin, implicit, MaxDiff TURF, you name it, all to ensure that you are not only getting insights fast and at ease, but getting insights that you can rely on, insights that follow scientific standards and insights that deliver actionable and clear recommendations to your business.
Again, all DIY ready, but if you need a helping hand, we have dozens of research consultants around the globe to support you at any time.
Now coming back to Kishan’s original challenge around DKB and the line extension with protein bars.
The business questions we had to address together were the following.
In the first place the question was should we simply use the same flavors for protein bars that you already had in the market for snack bars?
Then after that, which combination of three flavors provides the greatest market coverage? So which three SKUs give us the best coverage in the market?
Then in the follow-up step, how would we optimize the reach of additional flavors later if we already have an existing portfolio of flavors?
And then something that was also important for your management was, is there a way to simulate or predict a couple of alternative strategies for the management in a very agile way?
Last but not least, how can we make all this work with very short timelines? We talked about that everything should be available yesterday in best case. And then in an easy-to-use manner for the research team.
After discussing this a couple of times and going back and forth, the answer to all this became pretty clear and that was automated MaxDiff TURF in that specific case. MaxDiff, in this case, to capture the consumer preferences for flavors in your example, Kishan.
Then, on top of the MaxDiff, a TURF for finding the combination of flavors, in that case, that optimizes our reach in the market. TURF is all about which combination of a specific range of items will reach the largest numbers of consumers possible. These items can be products, product features, claims, advertising media, wherever you have a kind of prioritization/reach optimization challenge, MaxDiff TURF is your go-to tool as a researcher.
And same here. It was a great way to tackle all the needs that we saw on the previous slides that Kishan and his team had needs at once.
At this point, I would love to give you a quick glimpse at how all this comes to life on the end-to-end platform that we talked about earlier. It's not meant to be a lengthy demo. It's a starting point to inspire further discussions that we can have after the presentation.
Let's quickly switch screens here and jump right into the platform to see how all this would work in practice on the platform before handing it back to Kishan.
Alright, we are on the quantilope platform now. You can see a couple of cards on the screen. This is your home screen that shows you all the projects that the team is working on.
We want to quickly jump into a TURF demo project, into the plant-based meat demo that we have.
Let's imagine we are a plant-based meat company. We may already have a couple of products on the market and are now thinking about the product line extensions with new plant-based meat products.
The big question is, which one should we introduce and how many people do we reach?
With that, let's jump right into the project here.
What you see on top of the screen is the end-to-end fashion of the quantilope platform. It starts with setting up your team, setting up the projects, etc. keeping track of everything. That's the managed section.
Then putting together your survey either coming from a template or from scratch, whatever you like, connecting it with your target group.
Then as soon as the first answers come in, the analyze section analyzes everything for you in real time. Then you can put together the final report where you story tell the insights and share it with your key stakeholders.
Let's jump into the survey part of the platform.
I'm not going to show you how to drag and drop questions. Nobody wants to see that. Everyone knows how that works.
Let's quickly scroll down into our questionnaire here where we see the MaxDiff TURF and how easy it is to set something like that up.
You see the number of items here that we all want to prioritize, the different plant-based meat products. Then you name your buttons where you prioritize those and the choice tasks that we see in a seconds, and that is actually already it.
To let you get a quick feel of how easy it is to really set something like this up in a DIY fashion. Let's do that together here.
Jump into our method browser where you find the secret source of our platform or the DIY ready advanced methods. Let's quickly drag and drop a MaxDiff TURF into our questionnaire here, as the new page 15.
What we see then is a kind of prebuilt skeleton of the MaxDiff TURF that now just wants you to put in the items via product claims, flavors that you want to prioritize.
Let's kind quickly jump into Excel, copy our first 10 plant-based meat products here, put them into the platform. Let's add the right labels for the buttons then here ‘most likely to buy,’ ‘at least likely to buy.’ After that we quickly add a little header text, title for our question here.
Then we are actually already ready to go. That is all we need.
Now the last step is to choose a design.
Design means how many items does every respondent see? How many tasks, choice tasks are they going through? How many items per task do they see?
You do not have to think about that as Kishan told you earlier. You can simply apply the design that we as a platform recommend based on your number of items, complexity of items that you uploaded earlier.
If you know what you're doing and want to tweak it, you can always tweak it, right? You can change the number of choices. You can change the items per task and then apply it.
Then the system will also give you feedback if what you have chosen is stable, reasonable and can be done.
In my case, I simply applied the recommended design. Design is now applied. We can go back and we are actually ready to launch this thing and go live.
This is how quickly you can set up a MaxDiff TURF, in less than 90 seconds. As you can see, this is the task, you simply choose from the five alternatives that you see here, the most preferred one and the least preferred one. Then you go further and you want to see the second of seven choice tasks.
In this case, everything is device agnostic, it works on mobile, it works on desktop. Making sure that we also reaching Gen Z and Gen Alpha with simple and playful surveys.
Now we would launch it and go live.
I'm not going to dive into our fielding part here. I'm not going to answer our NPS score. I'm not going to bias the results here for our product team. I'm going to jump over that if you have questions about that, we can talk about that later.
Now let's jump into analyze where we get insights, of course about all the other simple questions that you put in your questionnaire. What we want to talk about today is the TURF part that we see here in the method section.
The first chart that you're going to get from a MaxDiff Turf is really the single reach of the alternative items that you tested. In our case here, plant-based meat products. We see that burger patties would lead the field in terms of the percentage of the people we could reach with plant-based burger patties followed by ground beef, breakfast sausage and so on.
Now the challenge is which of those products should we combine to reach the maximum number of people out there if we had, for example, five SKUs. This is where the turf helps you with just one click of a button to calculate the incremental reach of the different SKUs.
It starts with studio. Let's put in five and quickly simulate how many people we would reach with a portfolio of five plant-based meat alternatives here in the market.
Burger patties would reach 65. Then we would reach another incremental 12% with chicken tenders and another 7% with breakfast sausage, barbecue ribs and chorizo ending up with a total reach of 92% in the market.
Now, many times we already have an existing portfolio of products and the question is, can we also optimize around that? Yes, that is possible.
If we go into that little menu here, we could say ‘yes, we have certain chicken products,’ for example, that we want to log in and optimize around that.
Now the algorithm puts chicken tenders, grilled chicken and chicken wings in as kind of given, and that has to be there. And then we optimize around it and we see the burger patties at another 40%, Italian sausage is another 7% of people.
Something that's also possible is to remove certain items of flavors completely from the analysis.
Let's assume there's a burger patty scandal out there. We do not want to have that product anymore in our calculations and in our simulations. Then we simply go and hide that from the analysis. Then what we see is that ground beef comes into place and takes over the first 42% of the market before chicken tenders and breakfast sausage follow.
I think you have a good idea how quick and simple and playful the whole platform works.
You as a researcher do not have to worry about the scientific grounding, the accuracy of everything. This is all taken care of. What you can focus on is the relevant business insights, the strategies that you've arrived on and the management recommendations that you come up with.
As soon as you have something that's interesting, you simply add this to your report, then those charts kind of flow into your report repository that you see here. From here you start building your story. And that is what happens in the insights dashboard.
Let's quickly take a look at one of those dashboards for 20 seconds. That is what you see here. It's like a one pager website. You can quickly design it yourself or fill with content by dragging and dropping either text boxes that you see here or by uploading pictures, by putting in your charts that we have seen before in the analyze section.
By the way, all those charts are not static images. This is all living and breathing content. That means you could start putting together an executive summary here, while your study is still in the field. We all know those examples where we try to reach the last 10 people somewhere out there, but the results are really stable so why should we wait for the last 10 people out of 500 or something like that before we craft our story. So, you can do this while the study is still in the field and whenever new data comes in, those charts populate in real time.
Then you see a nice example here of an interactive living and breathing dashboard. Yet you can either export or share it with people, your important stakeholders, password protected, whatever you need.
With that, let's go back to our story about Dave's Killer Bread and the line extension that Kishan was working on with the team in the past, and learn more about the results that you got from the platform and the management implications you derive from it.
Over to you. Back to you, Kishan.
Kishan Rana:
Absolutely. Thank you for handing the mic back to me, Tom. That was a great demo. As you guys saw, really powerful, really easy, don't need to know much about what goes on in the background, but really need to know how to implement. So, bring your business acumen in and combine that with ‘what's the story.’ So really helps you do that pretty quickly and easily.
When you look at the next slide, one of the results that pops out of the TURF analysis is the single reach chart. This is an example of the protein bars and obviously all the flavors in the TURF are masked out.
But what you can see is we do have some current flavors in the market and we kind of plot them in and we say, okay, alright, let's run a TURF with our current flavors in, we have our current flavors identified. What does the next flavor look like?
First of all, we'll plot it in, and we'll identify where on that spectrum of single reach each of our current flavors fall. As you can see, it falls across the spectrum because every now and then there are some unique flavors that only a few people, but then there are some popular flavors that most people like. And then there's some in the middle that, yeah, I eat this on an occasional basis, but this is not something that I spare my taste bud with, but again, would still like to have that in the mix.
That's really one of the reasons why we had our current flavors in the mix kind of spread out.
And so, when you look on the next slide, it gives you the incremental reach chart, which is, as you can see, the tool will spread out this chart and say, okay, you should have flavor number one 42% reach as your number one option.
If you want to add another one that comes in another incremental 21% reach, just like Tom showed in the other example of the plant-based meat options. So, what third flavor should I come out with? Four, five, and even I can go up to 10 different flavors if I wanted to and just to see what it looks like.
But as you saw from the previous slide, you already have current flavors. So if you go onto the next one using that locking capability that you saw on the platform, I would lock the current flavors in and then the platform would say, ‘oh, okay, you locked your current flavors in, the next best flavor that has the highest incremental reach, in this example, is coming in at 15%. Then we can go into that piece and select that particular flavor.
If you look at the next slide, every now and then we'd run a certain flavor-based TURF and the team would come out and say, ‘oh, we think based on all these results, we would need to add a few more flavors in the mix.’ Because, keep in mind, its context based.
All of these choices that the consumers are making on the platform are based on choosing that thumbs up, thumbs down symbols and choosing those flavors. But it's only in the context of what flavors you put in.
So, for the lack of a better term, it's garbage in and garbage out. Whatever you're going to put into the system as your flavors, those are the ones that the consumers are going to rank. And if you want some more flavors, you run another round.
So that's what we did. We run another round and said, ‘okay, these are some new flavors.’
Let's see what that fallout looks like.
Again, locking in the current flavors and looking at some of those new set of flavors. And so we said, okay, if you wanted to come out with let's say, three new flavors just as a thought exercise and see what that comparison looks like from an incremental reach perspective.
Round one set of flavors were 25% incremental, round two set of flavors, 29% incremental. We'll take it. So that was the idea here.
But then you go back, and you've shared these results with the innovation team, the brand teams and they go back with the R&D team, sit down with them, say, ‘Hey, this is what the consumers want, what can we make?’
R&D team comes back and says, ‘we can make X, Y, and Z, but we can't make the ones that you have in this mix.’
So, then you go, okay, let's go back and run another set of research. But hold on, before you do that, there's another tool in the mix that is really powerful.
If you move on to the next slide, Tom, that's my favorite chart of all. This is the substitution map.
This chart is the one that I probably spend the most time on having conversations with the brand team, innovation team and then they go back and also interact with the R&D team to land on what we should do.
Why this is great?
Because, first of all, the substitution map will fall and it'll give you this grid. And on this grid the distance is what really matters. The closer the flavors are to each other, the more substitutable they are, hence the substitution map. Every now and then you'd see certain flavors go together, and every now and then you'd see they form like a cluster.
And so again, I don't need to know how Tom and his fantastic team does this or makes it happen, but what I need to know is what I can do with it.
So, I found these cluster charts, as you can see, circle them out and tell the team, ‘Hey, you can choose from these clusters one particular flavor if you wanted to.’
So here's an example of which is perfect because as you can see, four clusters here, we have a current flavor in each of the three clusters, but one, so that was an easy job for me and I went back to the team, ‘Hey, there's a whole cluster that we're missing of flavor in. If you want to pick a flavor.’
Obviously you can see from the incremental reach chart how much individual reach it has from an initial understanding standpoint, from an incremental reach standpoint, we lock in those current flavors, pick the one that the team can make as a part of the simulation as the fourth flavor and see what those numbers for the total reach looks like.
And then go back and say, Hey, it's not too bad. Or if it's bad, you can tell it's bad. But if it's not too bad, mostly it won't be because if we're pulling from a separate cluster, you're broadening your reach speaking loosely.
So, with that fourth flavor coming in from a fourth cluster, I can run another incremental reach chart and share with the team and the team can build onto their business case and move on with that. Sharing the idea that, okay, you can make this one, or when the R&D team cannot make a particular flavor, that's when I come back to this chart and say, okay, well you can't make a flavor from the one that was best performing due to whatever feasibility analysis. That's not my job to know it's theirs. And they tell, okay, we can't make it. There's issues or whatever challenges are there in place. So I say, okay, what if can you make another one from the same cluster because they're closer to each other and hence substitutable.
Maybe I over explained that part, but really that's how the process goes, and we land to the next fourth flavor to wrap it up and move on to the next one.
This is how we make decisions.
Really we help understand speed and agility. That's how quickly we can do these insights with clear recommendations that we move forward with.
Then as Tom mentioned, and let him share more details of this, democratizing those advanced insights so that we can use them. Because as researchers, these are meant for us in easy to use, easy to implement and helps us focus on our time much more wisely in telling the story and helping to make decisions for the brand teams and the innovation teams.
On the next slide, we have a question for you. This is how we are making decisions. How are you making your decisions?
Thomas Fandrich:
Great question. Let's talk about that together with the audience. Thank you.
Thank you very much Kishan and thank you everyone for joining and listening today. We are very much looking forward to the Q&A part of our session.