Danielle van Hout - Sensory as a Bridge
Welcome to "AigoraCast", conversations with industry experts on how new technologies are transforming sensory and consumer science!
Dr. Danielle van Hout is a sensory consultant and Founder of DEE-PRIME, an international research and consultancy agency for sensory and consumer research, located in The Netherlands.
Danielle is an applied sensory and consumer scientist who enjoys translating academic insights into better practices that benefit FMCG businesses. She collaborates with leading scientists to investigate and develop ecologically valid consumer research methods and effective sensory test methods, optimally suitable for knowledge building in the digital age.
Transcript (Semi-automated, forgive typos!)
John: So, Danielle, welcome to the show.
Danielle: Hello, John. Thank you.
John: Great. So I'm really excited to have you on the show today. I think you're definitely a kindred spirit when it comes to the use of data, new technologies and sensory consumer science. So I'll start with a question that is kind of the theme of the show. How do you see new technologies transforming sensory and consumer science?
Danielle: Yeah, I was thinking about that. And for my years in Unilever, with fast moving consumer goods, I think it's good to have a look at how Sangiovese used there because there are so many new technologies and to see what kind of technology gives the best impact. I think it's good to take one step back and see what is happening in the markets. And what we see around us is that a lot of changes in the food, for example, that people use. So companies need to adapt, they are put it portfolio and plant based there. They switch in protein from meats to plant based. We're seeing as a trend, but now we're seeing that it's really a revolution. Changes like sustainability, packaging, lowering sugar, salt that's a lots of requirements for companies to change their product. And also a small companies, many small companies starting up. Finding the right product to put in the market sometimes faster than the bigger companies. So I think there's a lot of demand for sensory and consumer science in helping companies do in one way transform that product. So making them more healthy or different. And on the other hand, to find really good to innovations. And I think in both places sensory should play a central role.
John: So what are some of the, sensory is really interesting to me because, yeah, like you said, so many things are changing and startups have advantages in terms of the nimbleness, even, you know, for example, my wife and I use food delivery service where, first off, we don't go the grocery store anymore. The groceries are delivered to our house. And we also actually have a meal delivery service that we use for the cooked meals arrive to, you know, at our house. And that's again, due to advances in logistics and shipping and this kind of thing. What are the advantages that you see that a large company like Unilever House? There's advantages to being small. There's some advantages to being big as well. And I think you mentioned innovation, technology, this kind of thing. The big companies, what do they have? That's kind of special for them, would you say?
Danielle: Yeah, I think they have the advantage that they have a lot of knowledge, often all from the different parts of two worlds out there and then from the different markets, and that they can easily, if they have captured that knowledge well, that they can easily learn from all of that and act fast. They often also have like, you know, if I had a really big R&D and a lot of studies done. So I think that is fairly small scale that you have and also in terms of knowledge. I think one of the and that is for Unilever, maybe it's now that I talk with my company, with many companies, I think Unilever when it's as a big company, a bit more ahead is that often in big companies, R&D marketing are very far apart. And that means that if you do innovation, that you sometimes have, yeah, R&D responsible all for the product parts, marketing responsible for the markets and for the consumers. And sensory is a little bit of each. And the most successful projects that we had, I think is why there were those two functions were really close together. And they have almost one voice so that you can connect all the dots all the way from the product to the market. And I think that's where small companies have to work like that because they don't have so much of those functions separately.
John: Interesting. So the size starts to work against some larger companies if things had siloed then R&D marketing go to separate ways and they lose that advantage of kind of integrated approach. So how do you see technology facilitating bringing those two groups together again?
Danielle: Well, I think, there are many ways to look at it. I think it has to come together. If we want to start really building knowledge on our products and our consumers so we can't as marketing function, we can't ignore all the parts that came beforehand. So the development, analytics and as an R&D, we should not be stopping once the plotting gets to offset markets. So I think the idea, the need for using all the knowledge that we have and building that into big data systems or anything will force people to work together I think if they want to be successful in that, because I had many conversations over the last weeks, and what you sometimes see is if you talk about building knowledge, is that, yeah, as soon as R&D people, you start talking about the consumer and going to consumers. It's almost like, yeah, that's marketing. That's not what I want to ask, you know. So how can you not make plans and how can you learn if you missed some dots and some parts of that pipeline, let's say put it to market.
John: Right. And it sounds like, and correct me if I'm wrong, but I think that this is what I'm hearing is, you see sensory consumer scientists are sitting in the middle between these two groups. Is that correct? So it's quite interesting to me to think of it that way. So when things are really functioning well, how can a sensory consumer scientist help R&D and marketing like speak to each other better or work better together? What have you seen that has been a really successful function for sensory consumer scientists in terms of helping to provide this kind of integrated approach to product development? Can you speak to that a little bit?
Danielle: Yeah, I think I mean, in many companies that there are R&D in sensory and especially consumer sciences, both in R&D and in marketing. And I think the most successful projects are when they work together in one shift project and one longer term plan and commit to that plan. And then I mean, sensory is just one of the functions in a company, so it should not, how do you say that overplay its role and say it's the center of everything or something? By working really closely together with your counterparts in the marketing sides of sensory and consumer, you can also force or stay close to discuss so the marketing and R&D and the part of developers or the project leaders that they need to have an aligned plan and that they need to think about, yeah, I want to have this product on the market, but therefore I need this data and this information.
John: And so I suppose that's one of the functions of developer is to helping into the design that kind of research? What would you say you're most interested? What kind of projects you're most interested in?
Danielle: What I would like to help companies with is really to see how they can do in a smart way sensory consumer research. Of course, looking at a project objective that they have, but also thinking for the longer term how they can use that data so that every test that you do count. And I want to help companies to make such a strategic plan to look at what do we need to test, what type of products? Maybe we only need these products for the first projects, but on the longer term, we want to have a broad of sensory space. So maybe we should add some extra things. Also, what kind of questions do you ask? Because we know that some questions and some tests are efficient and valid, but maybe not useful. If you want to combine it with other data, they are only maybe a picture for that moment for that task. So how can you do it more smarter? So I would like to help companies. to make that sort of transition.
John: So does that involve looking at, I suppose I mean, it's something I'm very interested is looking in historical data to see what we already know before we plan the research. Is that kind of overlapping with what you're describing? I mean, when you come into a company, you're going to look at what's already been done and try to see, you know, what research we need to conduct?
Danielle: Yeah, I think that's the most useful way I think because when I talk about things like models and connecting data, then sometimes you see people looking a bit puzzled and they say I need to do enormous amounts of testing. And then you can ask, well, what if you did this 10 years ago and you had all the data that you collected until now that you can use now. So it's not a matter of doing hundreds of thousands of tests, but just making sure that the tests you do count. And by looking back at the data that you have and the tests that you have done, you can also look at, well, maybe here I can still get some useful information out, which helps me to make my new test a more effective because maybe I know from a previous test already that these kind of differences are not noticeable by consumer. So I know how my product stats should be.
John: Right. Yeah, there's so much of what you're saying here, I mean, the like for me, okay, so many things I'd like to say in response. When the design of new research, we do already know a lot, right? And I think a lot of times it's we've got subject matter expertise or research matter experts whose opinion we rely on one design to do research. But actually, there's a lot of data that we have access to. We could just go back and get access to it. So what's going to happen here is I'm going to go on a long talk about graph databases. So I have to be careful. So we just move on. Maybe that leads to another question. So what are some of the new technologies that you do see that are transformative for sensory consumer science? I know you're really into ecologically valid testing. What do you think about some of these data collection methods that involve augmented reality, you know that are trying to set a context, using technology to help with kind of...
Danielle: Yeah, I think that's great improvements that we can do over the last 10-15 years and especially last couple of years. I was visiting in the software company and they had all those immersive wombs and virtual reality glasses. And then you see how easy it is in a way to just set up an experiment. And it's almost like since we have the sort of holodeck in future that we can make. It's almost a step towards that. So creating in different context for consumers in their studies. That is becomes really easy to do. But I think there is also a downside to that. I think we should still even with virtual reality, or augmented reality rooms, you should still keep in mind that the consumer knows that they are being tested. So we also need to spend time on the way that we do the tests and the way that we introduce the products. So we need to make sure that the context that they see around them, whether that's a beach or cafe, that also their mindset is connected to that.
John: Connected to the context.
Danielle: Yeah. So giving them also a context on the product that you are testing. I'm not saying that you should exactly say, well, this is Coca-Cola, the new formulation with less sugar, no, but you can give them an impression of the brand or anything to help or ask them questions about how they would normally use it. Things like that. Anything to help them to put the reference, frame of reference in their mind. Because I think if you don't do that and in the past, we often didn't do that and we just did blind liking testing. They will create their own reference and that will vary doing the test. So you want to avoid that. So I think, yeah, we have all those technologies and I think it's really exciting to see what we can do and how we can compare effects of context. But we should also keep in mind the way that we ask the questions. And then the way that we puts people into that context and warn them up to the task.
John: Right. I know that's a topic that you're very passionate about is right. The way the questions are asked and, I mean I know you've done a lot of work on, I mean, your body of research is very impressive. And I know you've done a quite a bit of work. There's this whole spectrum, right? You've got different testing on one end and then you have all of the way to like the augmented reality or the testing, you know, in real environments, like something I've gotten involved in lately is using Alexa for collecting data. That's really exciting, I actually do, I think I was the first person history the world to do a Alexa based survey in the shower. I was shampooing my hair answering a questionnaire like I set up a shelf in my shower and Alexa was asked me questions. We did a shampoo survey demo and I did the survey and I was shampooing my hair, answering questions. It was crazy.
Danielle: Yeah, that's great. You can suddenly meddle things that you would never do otherwise.
John: So that's really exciting. So you've got that stuff on one side, but then you have the kind of you know, also the way you ask questions in a different test matter or the structure of the different sets. So how do you see this kind of spectrum playing out? I mean, do you think that there are just many tools that we should just understand and pick based on the context? Or do you think that we're moving in a new direction as far as sensory research, that there come a time when different testing won't be as important and it'll be more focused on, you know, these headed up more technologically supported methods?
Danielle: Yeah, I think it will be in one way or the other. It will still be necessary to see to find out if I make changes to my products or to my packaging whether people will be able to tell the difference. But I think also for different stashing, we always thought that this fact clinical test that you can just do into boots and everything and we do it in that rate, just like a machine almost. But I think also we saw that with panelist that if you give them a context or if you make up a context and helped them a little bit also, then they are performing better. So I think there is, I'm not saying that we should do all different stashing in virtually settings, but, yeah, it's not so black and white.
John: That's very interesting. And that's a theme I notice with the new technologies is that so many things that used to be discrete are actually continuous and that there's, I mean online-offline is another example, right? Or you know, you've got quantitative and qualitative research. I think those are going to come together as more like focus group data gets transcribed automatically and then you start to apply quantitative analysis to you know the data or even like we're talking about with Alexa, that you're going to have text, you know, people talking, but it's going to be captured by machine and it's going to get processed in a quantitative way. So, yeah, it's really exciting. So, okay, let's talk a little bit more about, I want to come back, you know, because one of the things I think is kind of special about Unilever, I don't know what extent you're at liberty to talk about this, but I think Unilever, while the big companies, is the one that I think has done one of the best jobs of acting like a startup, you know, in terms of like taking some of the kind of nimble mindsets that like a startup might have, but applying it at scale. So, you know, what advice would you have for somebody who's in a large company to be able to act more quickly or be more nimble or to be more flexible? Do you have any kind of general advice based on what you've seen in terms of mindsets that are like kind of more adaptive or, you know, things to watch out for, maybe even just as simple as a common problem that my clients have? They are very forward thinking. But the management maybe is not as forward thinking. They have to communicate new ideas. So like, what advice do you have for people who are in a large company? They want to use some of these new technologies. But, you know, there maybe is a certain amount of institutional momentum that they're working against.
Danielle: Yeah. I think it's also a matter of trying to take one step back and think how from your stakeholder point of view, what they would need. So not pushing the science, not saying yeah, but this is really important that you ask a question in this way. Time to get that what do they need in their way? What do they need to get the projects faster. See, that's one big team or big play count and try to find people, especially in a big company, who are willing to to look at new alternatives, who want to look at opportunities because I think that many people in many companies like that. And I think the best thing that works is if you have a demo, if you have a way to already demonstrate what this gonna do. So what now that PhD on linking sensory and consumer with deep primes had their sensitivities, as soon as you have one study done and you show the value that it can bring. Then people want it also for other products. And then you can expand on it. So I think demonstrating the value is really important. But just telling them that they should do it differently. I think that's a really hot way to get it.
John: Yeah. That really resonates with me. I've definitely got it even to my consulting work that, you know, when I started Aigora, I was talking about dashboards and whatnot, but as soon as I actually had dashboards show people totally different. Right? Like, oh no, now I understand. The interactive documents that's another one now that I like. Now that actually have demos, maybe I'll show you some later that are pretty sweet, but like it's a big difference between describing something and showing something. So, yeah, that is a really good point.
Danielle: And it helps your own view also if you have such a demo and you have such an example. That's how you can make it better. So without such a thing you also stop.
John: Right. So even just getting something small going will be valuable. Rather than waiting for management to approve some big thing, try to get something small going. And that can demonstrate value and help you to learn at the same time. Yeah, that's really good advice, it's excellent advice. Okay, so now let's kind of think about thinking looking forward now, the next couple of years, what do you see as the areas that are most important to sensory consumer science over the near term? Far future is hard predict but like in the next couple of years, what do you think are the areas that we should be thinking about focusing on in sensory science?
Danielle: Yeah. We talked about, of course, the different realities. Virtual reality. Things like that. So that is creating a different context. You can also you have all those technologies that can help you measure people's brain and people's body, activities like skin contraction and eye movements.
John: Yeah. Facial recognition, the muscles stuff.
Danielle: All those things. Yeah. And I think by combining that also with explicit measures, we will learn much more on what we can say from all those measures when there is really noise and real signals and what you can, how you can interpret it. But I think the biggest one will also this whole digital, so all the learning from the data that we have. I think that's a really big hit.
John: Now, I completely agree with that. I think actually we're talking earlier about the advantages that large companies have. And you said, okay, they've got the knowledge from all around the world. Well, where that is actually fit instantiated is in all the data that they have. They've collected all this data from all around the world. And if the data are just lying fallow and they're not being used, right? That's a huge loss, right? I definitely, this is something I'm really passionate about. We can talk more about a graph database is I think the vision there is that before you do any research, you should be able to do what are essentially a virtual pilots that you go to your data and you say, okay, what would be the studies we would run if we're gonna run pilotsis? Maybe you already have data that's actually a lot like the data you would collect in a pilot and you can go to your database and harvest the data and analyze it and get, you know, maybe do a bunch of studies in a week like virtual studies. So, yeah, I think that's really exciting.
Danielle: And that's also needed because if you need to change your products like what we establish it, you need to change your products really fast to changes in the market. Yeah, you need to have that because you can't overtime wait for a new tests to be done.
John: Right. Yes. That's right. Yeah. And I think, you know, at some point you will need to conduct new research because there will be holes. But you should only be conducting research where you have holes, not repeating work you've done. Do you have an opinion on Bayesian statistics? That's something that I have started to really advocate more for, is because that's a way that historical data can get implemented, right? Even just in different steps. You're in a product category and Gemma Hodgson, I'm sure, you know, she and I have a poster on this since sensory science has coming up. You're in a product category, you really should see what are your historical Dee-Prime in this product category to set your prior, you know. So do you personally have a preference on this? I'm not really sure if this is like..
Danielle: Well, I have been working on the proposal in that area with Michael Hueltes, also from New Zealand, Oakland, because I think indeed that's a very exciting area. And I still find this quite complicated area. But I think there's a lot of value from thinking beforehand of what information you already have and use that in your models .
John: Yeah. I totally agree with that. Yeah. Bayesian statistics gives you a way to quantify what you already believe. Instead of having opinions, you come in with quantified belief. You say, okay, we believe Dee-prime is probably in this region, is probably not here or maybe it's over here. But like, you know, you have that's how you start based on what you've already done. Yeah. So that's really neat.
Danielle: And then every new test you do gives you a new information to optimize.
John: Yes. Right. So then you get credit for the fact you've done 100 tests in the area. You actually know something. So you should be allowed to use it. That's really good. Okay, I'll show you my little, we have an interactive document, we actually have a cool little, yeah, you can put numbers at the top and the whole document changes before your eyes. It's pretty cool. Okay. So we are actually amazingly almost out of time here, so maybe we can just wrap up by talking about, you know, an advice that you would have for young sensory scientist? Someone who just graduated, you know, from bargaining or wherever, you know, they have a degree in sensory science and now they're gonna go out into the world. What advice would you give to that young sensory scientist to make sure that their career is starting off on the right foot?
Danielle: Yeah, talking with a lot of people and depends, of course, where you're starting to work here. But if I look at back at my years in the company, I think it's really valuable to go outside your comfort zone and change jobs and take opportunities so that you also see it if you get the opportunity to not do only the research, but also do, for example, the people management part, leading a team because you learn so much different things. And also running a project which might be not really your thing in the end but you learned a lot of from it. So try to be versatile in the things that you that you can do and, yeah, how do you say that? If you have a conversation and you want to sell something, a new idea or something and it doesn't work, try again and look back at why did this work and try to look at it from the other point of view from the other person. So because I can be convinced about something but ever done the staff to talk very technically be out about my science. I lose them because they think, yeah, that's probably very well, but I don't have time for this.
John: Yeah, interesting. So basically to me what I'm hearing is act like a consultant because those are the skills a consultant has. So, yeah. I mean, it's true though, in today's world, you know, you have to be able to integrate new information quickly. You have real interact with a lot of people. You know, you have to be constantly improving, you know.
Danielle: Yeah. And also, sometimes you might hear people a lot young people also say that it's not something for me. For example, I don't aspire to do this, but sometimes you can be surprised that if you do it, then suddenly you see you like it, that all those challenges in that. So don't say too fast or I'm not a project leader or I don't want to do this. That's not my type of thing. Because some things that I did and they thought I would be really good in them like I didn't like at all because it was just trying to push people to work harder and to do the things in time and all those things like I think I never would have believed when I was at high school that I would do PhD because I found writing boring but by having good supervisors who make you enthusiastic about writing then suddenly you you really get excited that you have a Friday off to write a paper. So you can surprise yourself by doing things that you might doubt in the beginning.
John: That's great advice. Excellent. Okay, so then just kind of wrap up here. How could people get in touch with you? What are the best ways to contact you?
Danielle: Well, I'm on LinkedIn, so I'm Danielle van Hout. But I think if you just spell my name, you find it. Dee-Prime, the company you can get email by firstname.lastname@example.org.
John: And are you on Twitter?
Danielle: I'm on Twitter, but not yet so active. So I'm starting to learn.
John: We're on LinkedIn. That's what we do. Alright, Danielle, this has been great. I think it's been really helpful conversation. Lot of good insights. Any last comments? Anything else you want to impart?
Danielle: Well, yeah. Thank you for inviting me. I think these podcast are really useful and it's great to talk about this.
John: Okay, wonderful. Great. Thanks a lot. Alright, take care.
John: Okay. That's it. Hope you enjoyed this conversation. If you did, please help us grow our audience by telling a friend about AigoraCast and leaving us a positive review on iTunes. Thanks.
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