How Organic Valley uses AI and video insights to deepen understanding and elevate customer storytelling
Editor's note: This article is an automated speech-to-text transcription, edited lightly for clarity. To view the full session recording click here.
Most researchers know or have felt the push from stakeholders to do more with less. Organic Valley’s research team is no different. Except for their partnership with Knit.
According to Tripp Hughes, senior director of consumer strategy at Organic Valley, spoke with Knit’s co-founder and CEO, Aneesh Dhawan, about how the Knit platform allowed for more and better research to be done with less cost and more speed.
The platform incorporates AI to help analyze the data from studies using quant, qual or a combination of the two. Then a show reel can be created and easily edited in order to illustrate the insights to stakeholders.
Read on to learn how Organic Valley used the Knit platform as a layer in a project around wellness in this session from the 2025 Quirk’s Event – Virtual Global.
Session transcript
Joe Rydholm
Hi everybody and welcome to our session, “How Organic Valley uses AI and video insights to deepen understanding and elevate customer storytelling.”
I'm Quirk’s Editor, Joe Rydholm. Thanks for joining us.
Just a quick reminder that you can use the chat tab if you'd like to interact with other attendees during the discussion, and you can use a Q&A tab to submit questions to the presenters. We'll get to as many as we have time for at the end.
Our session is presented by Knit. Aneesh, take it away!
Aneesh Dhawan
Awesome. Thank you so much Joe, and good morning, good evening, good afternoon, depending on where you're calling in from. My name is Aneesh. I'm one of the co-founders and the CEO of Knit.
As Joe mentioned, I am thrilled to be presenting with Tripp Hughes over at Organic Valley on how Organic Valley uses AI and video insights to deepen understanding and elevate customer storytelling.
If this is your first time hearing about Knit, we are a researcher driven AI platform helping some amazing brands like Organic Value, Amazon, T-Mobile and others go from survey to story in days, not weeks. We do this through our philosophy of researcher driven AI. We combine the power of the expertise of human researchers with the efficiency of AI to help them go from a survey to building out an analysis plan, fielding their data, analyzing the data and ultimately getting to a report that looks and feels like something an expert human researcher would've put together because it has all the context of an expert human researcher.
We also combined quant and qual in one single study so that you get the robust “what’ embedded with consumer videos to get to the “why” behind many of your questions.
With that, I'm excited to formally introduce Tripp, the senior director of consumer strategy over at Organic Valley. Tripp, thank you so much for being here today. Really excited to talk through some of the work we've been conducting these last couple of months.
To kick things off, I'd love to pass it over to you for a quick intro on yourself and your role over at Organic Valley.
Tripp Hughes
Yeah, thanks, Aneesh.
Hi, I am Tripp Hughes. I am the senior director for consumer strategy and brand strategy over at Organic Valley. We're a farmer-owned cooperative based in rural Wisconsin, but we sell product throughout the U.S. and a few international markets. We've got farmers in approximately 28 states that own and supply the raw materials for the cooperative.
Our job on the Organic Valley side is to turn it into value added products and market it. I spend a tremendous amount of time thinking about, talking to and getting in the heads of our consumers to help really direct and drive our strategy.
In that role, I'm constantly looking for partners and systems that can really help us get to things we haven't been able to get to before.
We're going to have a good time getting into that today because Knit really helped us solve something that we were struggling with and continued to step up the game year after year as we've worked with them.
Turn it back to you, Aneesh.
Aneesh Dhawan
Awesome. Well, thanks for that, Tripp. And before we dive in, Tripp, you touched on this a little bit, but Organic Valley is a very unique and mission driven business, right?
You guys are a cooperative and I think that shows up in how you think about your marketing and research functions and the role of those functions within the business.
Can you share a little bit more about how this corporate structure within a highly competitive industry drives how you think about the role of your team and the marketing and research team largely or more in general within the business?
Tripp Hughes
Yeah, for sure. So, the cooperative business model is really a different type of business model. I wish they taught it at more U.S. business schools. I know they do in Europe a good bit, but being a part of a cooperative is really fundamentally about cooperating.
It's about coming together with your partners to try to achieve something that you can't do on your own. And in the sense from a marketing cooperative, we've got these family farmers, roughly 1,500 of them across the U.S. living independently day-to-day, working seven days a week on their family farms and to help them get to market, they pull together through this cooperative business structure. In that structure, we're on the business side.
Our role is to figure out how to take this raw material from 1,500 raw material suppliers. It happens to be highly perishable. We've got about 24 hours to figure out what to do with the raw product once it comes in and convert it into a finished good, which is often a gallon of milk, a block of cheese or a pound of butter.
Once we turn it into those products, we again have a very short period of time to turn it around, get it out to our retail partners and have them get it into the hands of our end consumers.
So, cooperatively, we've always found that when we go to business with a partner, we really do try to treat it like a partnership. We try to treat it as if we're both trying to come out ahead from where we are. The idea of a zero-sum game is not really a part of the cooperative mentality.
Early on I saw Aneesh was doing something really interesting, and I said, “Hey, I don't know where you're going with this. I like what you're doing. I think you can help us right away and I think you'll continue to help us.”
In that partnership, we struck up the ability to do a trial. We struck up the ability to get some tests running.
Once those tests finished, we worked together to give feedback on what was working and what wasn't working. The team consistently has been so responsive to working with us.
I think in the end when we help them, it's making their product better too. So, it's self interest in our part, but it also is for the greater good.
Aneesh Dhawan
Absolutely. Very much a true partnership mentality working with you all. That's been one of my favorite parts about our work together, as well as some of the really cool insights that we'll touch on today.
Now Tripp, one of the things that you mentioned early on in our partnership was that at Organic Valley, because of how it is structured and because you're trying to make your marketing and research dollars stretch as much as they can, you're always looking to experiment and think about research differently. I think you were one of the first to introduce me to this idea of a “research sandwich” framework.
We'll kind of go through each step-by-step or I guess slice-by-slice here over the next couple minutes, but before we dive in, could you just set the context for what the “research sandwich” framework means to you? How have you guys thought about this, and when do you think about applying it within the business? Before we dive into this, what are the specific ingredients within it?
Tripp Hughes
Well, I mean, of course, the ingredients have to have cheese, but we're covered there.
We're constantly trying to figure out how to layer in components. It is rare that we have a partner that solves everything we need to do. Especially as we're getting asked to do more and more with less and less budget.
So, being able to go in as I'm working, whether it's through a new concept for a new product and innovation, working on positioning or working on insights, what we do is use Knit at several stages of the game.
We may start out with a complete open space exploratory. Once we've refined what that exploratory space is, we can quickly cycle back and start to hone in on what the concepts could look like. We pull back again and come back and really make sure we understand what the key motivations and components are.
So, it's very much a layered process that we go through. And I think that's really what we're talking about building out here.
Aneesh Dhawan
Awesome. Let's dive into each one of those layers.
Tripp, to your point, often you're starting off with more of that exploratory layer, and we've been supporting you guys there in your research and hope to continue doing that as well.
Could you speak a little bit more about how you think about the structure of that tactically? Is this quant, qual or some hybrid of the two? And what are you hoping to get out of that first phase of research before you dive into the second and third?
Tripp Hughes
I mean, I'm going to state the obvious. The obvious piece is that you guys were early adopters of being able to do qual at scale. So, where we traditionally might start with a quant or where we traditionally might start with a qual, we're able to do both with you guys.
We're able to get some of those quantitative questions framed up and understood. We're immediately able to also start to get some key exploratory in, whether it's open-ends or what we often do are the video questions. Those video questions layered on to the quant part of it really gives us a much better framework to start with.
Aneesh Dhawan
Yeah, absolutely. We'll see some examples of those video show reels throughout the presentation today.
But from there Tripp, you mentioned you'll use that to explore and frame some of those questions and then dive into the meat of it, no pun intended, with some of the more deep dives, the in-person explorations.
Can you walk through that a little bit more? What is the value you're hoping to get out of that meaty part of the research? The more deep dives, maybe even in-person stuff that you're doing?
Tripp Hughes
As a researcher, as a strategist, I love being able to have conversations with people and quant doesn't let us do that. Even open-ends don't really let us do that. So, the ability to get in and ask very specific questions is fundamentally key.
I'll give an example of something that I think we're going to share.
We were diving into the topic of wellness with our consumers. There's a lot of different aspects around wellness. It's taken care of yourself, self-care, it's nourishing deeply, all these different components.
In the first run through with Knit, we took apart and were able to deep dive in and get a base of what those core components were. In the second run through with Knit, we were able to go in and start to take each of the components apart in depth.
This set me up then to where I actually took that information and went out doing some live ethnographies where we went shop along, we went in store. This wasn't with Knit, this was with myself, and my strategic partner. We found a lot more insights there that we were able to again come back and double down and confirm the structure of where we thought we were.
So yeah, I guess I'm jumping ahead of myself just a little bit.
Aneesh Dhawan
You're good, you're good.
I think just teasing that out a little bit. To your point, when you do those deep dives, in this case you went in-person started talking to customers, you do often have those questions.
Could you speak a little bit more about the ability to then be able to double tap into some of those questions through that quant qual? Where does that unlock the most value for you all?
Tripp Hughes
Well, I think two parts.
One is Knit is pretty fast. So, we do a lot of iterative questioning. And so we'll do a round, we'll get the responses back, we'll find stuff right away that we want to build and incorporate into another round. And with you guys, we often will shotgun.
So, my main first round, may have been on a somewhat singular topic, let's say it's wellness. But let's say in that wellness round we came up with areas around nourishment, self-care and living your best life. We're able to immediately then double tap into each of those in their own round. Again, I may end up getting even more insights out in that round where we may even go one more round further.
I'll give you an example. In this go round with you guys, we heard some insights about a key retail partner we were working with. So, we circled back, and we did another session just focused on the insights around how folks were viewing their shopping and their relationship to health and wellness with this key retailer.
It's a beautiful kind of chain that comes together.
Aneesh Dhawan
Yeah, absolutely. I love the layering of the research there, which is why I think this sandwich metaphor is a great one.
To ground that into some tangible research here, Tripp you mentioned, you just wrapped up a pretty deep dive into this exploring this topic of wellness and how consumers define and live well.
There were a couple of themes that we wanted to explore with the audience today, starting with this theme of nourishment and the emotion behind it. I really love this line that you shared with us, which is that at the end of the day, what food makes us feel like is just as important as what is actually in our food.
Why don't we just take a minute here, Tripp, to expand on some of the insights that you got through this work that was supported by the Knit platform, but also some of those deeper dive ethnographies and in-person stuff that you did. Could you speak a little bit to some of those key insights, what you learned and how this is being applied across the business today?
Tripp Hughes
Sure. So, what we're seeing on the right side of the slide is what nourishment feels like to consumers. Our quotes out of Knit.
As we're having these conversations, consumers really get to express it in their own words. We then are able to use Knit’s AI to go in and organize and structure it and then find key themes.
To the left side of the page, we're laying out the key theme areas that were starting to emerge as we were specifically talking about nourishment.
Again, we started up with a higher-level study on wellness. We realized that nourishment was one of the key areas. Then we came back in and spoke specifically about nourishment. It's really powerful when I'm able to go back to the team and not just give them the framework structure but to give it to them in the language of the consumers as well.
Aneesh Dhawan
Absolutely. To your point there Tripp, I just want to take a second here to walk everyone through how you were able to do that on the platform.
As I mentioned before, the output of Knit is a report that looks and feels like something that an expert researcher would've put together. It has all the key takeaways, the insights, recommendations and next steps.
What you'll see here is how we kind of gathered and bubbled up to the top some of those key themes and quotes that Tripp talked about.
Our AI actually identified all of the key insights around this topic of nourishment, generated a sizzle reel, a showreel, a one- to three-minute-long video, a compilation of people talking about that theme, which we won't look at right now, but we'll dive into later. It went through and actually explained the rationale behind the analysis, gave a quick summary of what the insights were saying, but then also made it really easy, as Tripp mentioned, to find those themes and sub-themes down to the respondent level.
If you want to either show the video or pull out those specific quotes, really bringing a theme to life, it's really easy to find that within the dataset. And because Knit combines both quant and qual data, you can then cut and slice and dice that data through different lenses.
In this case, if you wanted to cut all those qualitative themes by a quantitative attribute like generation or gender, our AI tool makes that really easy to do.
Tripp Hughes
To think about four years ago when I did qual at any type of scale and we were hand coding everything and we were going back and trying to analyze it. It was brutal.
This used to be a $20,000 project. And we were able to do rounds of it for, I don't know, anywhere from $2,500 to $5,000 or something like that, depending upon how many audience people I'm trying to pull in. Don't use my numbers, because you have no idea how many audience members I'm talking to.
But my point is, I'm able to reduce the cost significantly and get much better results.
Aneesh Dhawan
Absolutely. I think that is ultimately what leads to that ability to iterate as you were talking about.
Another area where we saw the voice of the consumer really come to life, Tripp, is around some of the insights that you shared around the context of nourishment. Where is it happening, when is it happening? How did this shape how you think about where Organic Valley or how Organic Valley shows up for its customers?
Tripp Hughes
Yeah, I mean one of the key pieces is again, an understanding, if it's starting on the left with what their experience was, and trying to work it back into the understanding of what these moments are.
They talk about what are the settings. They give us a lot of insights that, again, you would never get on a quant, but to get them at scale on a qual was pretty rare. You certainly weren't getting them in open-ends.
So, my team is always thinking about, “now what, so what?” We always have to put it in a framework that helps us understand the full landscape of how consumers are thinking, and what they're talking about. This is this idea of laying out a framework and getting into it makes it so much easier then for my team to pick it up and work with it.
And you've talked about show reels too. The ability to then come back and put together show reels that can also bring this to life is the final punch on the pudding.
Aneesh Dhawan
Yeah. Awesome. And just bringing that ability to life for everyone on the call today. To Tripp's point, being able to really visualize that is super important. So, just walk through how we were able to do that on the platform.
Once you identify the theme in this case, social context, where does nourishment pop up around that social connection? You can see the AI identified that theme and the different sub themes there. Then automatically generates this video show reel for you.
In this case, it's about a three-minute video compilation of people talking about those different themes.
Tripp Hughes
And it's super editable too.
Aneesh Dhawan
Exactly. We make it really easy to edit.
To Tripp’s point there, if you wanted to edit one of the videos, you simply highlight or deselect what's been highlighted, and it will cut the data there.
Then you can also add your own different elements, such as your own branding, images, videos or soundtrack as well. We have all the latest hits in the land of elevator music here, so it really helps. Got to dress up your video a little bit more so that you can share it across the organization.
Tripp, as we wrap things up here and head into Q&A, I just want to drive home some of the key themes that you spoke about in this world of doing more iterative research, running research faster, stretching your dollar as much as you can. I know there were a couple things that stood out to you when it came to working with our team and the Knit platform.
Can you just speak a little bit more about some of those things around the ability to scale affordably, the ability to get this research done faster through our AI technology and again, touch on some of the ways you've been using the different outputs?
Tripp Hughes
Sure. I mean, what I like about Knit is the flexibility. I may do a project where I'm talking to 10 people. I may do a project where I'm talking to 200 people but the framework and structure is the same for either one.
This idea of scale is super key. This idea that I've talked about is we may start with a big group and then we may shotgun out a bunch of smaller groups to really refine some of the ideas. We can do that super easily in terms of identifying the audiences that we're working with, we know how to move it through.
I think one of the things we've talked about is Knit’s, super great at helping us set up the surveys. So, understanding what we're trying to get to and how to structure it.
In terms of the affordability, I am going to let you speak to that, but I can say I do this at a fraction of what we used to do it for. I'm able to do, therefore so much more of it.
Speed and technology wise, the turnarounds, depending upon how tight of an audience and Aneesh knows, I work with a lot of tight audiences, the speed's still pretty darn good, and the speed to report is instantaneous.
Getting to those final outputs and exports, the customization of the reports, we still go back into old data that we've run and pull pieces out that have come up in new research that we've done. Somebody may bring up a topic and we're like, “Oh hey, let's go back and look at that study we did before and see if this was popping up at all.” And it often is. So, the ability to understand and move between the different projects is pretty key for us.
Aneesh Dhawan
Awesome. Well, Tripp, I appreciate you laying out the different ways you've been using Knit and what stood out to you.
It's just really interesting to hear how you guys have leaned into that iterative nature of research. I really think one of the things that you said that really stuck out to me is with a lot of these new technologies, especially a lot of the AI technology out there, Knit included, you can just run new types of research faster at different scale and at a different price that allows you to maybe run it even more than ever before. That opens up a lot of new ways of thinking about research and a lot of new cases for research, which has just been awesome working with you guys this last year or so and seeing that come into play.
Tripp Hughes
One piece I would say is somebody asked me, “Are you talking to AI bots?” And I'm like, “No, we're talking to a real audience. AI is just the analytics.”
Aneesh Dhawan
Yeah, absolutely. We're believers in talking to your customers directly.
We have a couple minutes here, I'll pass it over to the audience for any questions. I know we have a lot of folks in the CPG and food manufacturing space either listening to this webinar after or attending it live.
I just want to plug, we did just release a report a couple of weeks ago called ‘What's in My Food?!” that dives into consumer sentiments around artificial dyes and ingredients. Super relevant and timely with some of the policy changes happening here in the U.S. So, check it out.
We dive deep into a lot of the topics that are helping our partners navigate some of these changes and would love to share some of those insights with the broader community.
It's available on our website for free and we'd love for you guys to check it out.
With that, I'll pass it back to the Quirk’s team for Q&A. I know we have a couple of questions here in the chat that hopefully we can get to.