• John Ennis

Richard Popper - What Job Do You Want Your Data to Do?

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Richard Popper is CEO of P&K Research, a consumer research agency dedicated to helping client companies improve and grow their product portfolio.

Richard began his career as a member of the Behavioral Sciences Group at the U.S. Army Natick RD&E Center, and, prior to joining P&K Research, Richard held management positions at Ocean Spray Cranberries in R&D and Marketing.

Richard regularly contributes to professional societies on topics of consumer and sensory science, has published over 25 journal articles and book chapters, and serves on the editorial boards of a number of technical journals.


P&K Research

Transcript (Semi-automated, forgive typos!)

John: Richard, thank you very much for joining us.

Richard: It's a pleasure to be here. Thank you.

John: Great. So, Richard, something you and I we're talking about right before this call that I think is really interesting is the importance of considering the context of experiments. I want to talk to you about like the different types of data also that are being collected these days. But I know you have some some thoughts around how the context itself is a type of data and like to hear your thoughts on how you see context impacting the sort of information that you get out of an experiment and you know how you would recommend that people keep track of the context of the experiments that they run.

Richard: Sure. I think context has the potential to be a critical variable in our efforts to understand the product experience. There are lots of challenges and I think in some cases where recognizing that the very controlled and somewhat sterile environment of a typical central location taste test may pose some challenges in terms of being able to predict what the product experiences like when you're in a more realistic and representative environment. So I think the question is, when does context matter and how could we introduce context into a central location testing environment, or do we recognize the intrinsic limitations of those environments and really try to get out into the field, whether it's in a more typical home use test or in a more sort of on the go situation. So I think all of that is kind of up for discussion and consideration. We don't know how much it matters. I would say that many products are now so situationally positioned that to ignore the situation in which the product is intended to be consumed could very well be detrimental.

John: I see. And something kind of related to that, that I know you are doing is collecting diary data or trying to get data that maybe is little more in the moment. Do you see that is related to helping to solve this problem, trying to collect data that maybe is outside of the lab, but still in a scientific framework?

Richard: Exactly. I think there is just intrinsic limitations to how much we can bring the context into the lab. And there's a lot of interest in virtual reality. And we're developing some tools for immersing a person in the laboratory, in a particular situation and introducing a context. But ultimately, you are trying to really understand what goes on in the real thing, in the real context. And I think mobile data collection just offers a lot of opportunities these days through various, you know, either apps or web-based apps to get in the moment responses. And we've done this to encourage respondents to provide some reactions that you solicit through the app or have them initiate their responses based on when they're using your product and why they're using the product. I think mobile technology has really transformed diary data collection. You used to be the case that not so long ago you'd have to have someone fill out a paper diary and the reliability of those data was always in question because they wouldn't necessarily compliance wasn't necessarily what you'd hoped for. At least now you know what the compliance is. There's a timestamp associated to the response. And it's a lot more convenient for the participant, too. People interacting with their phone all the time. So it makes it easy.

John: Right. And related to that, of course, something that's happening on its way is the whole, you know, speech to text the fact that people can evaluate things kind of in the moment out loud, right?

Richard: And I think that, you know, it's a topic of common interest to your company and ours in terms of how you can get technology like Alexa to provide a facility to collect in the moment responses that don't require a pause and move to a tablet or a desktop computer. So I think there's a lot of potential there to eliminate the distraction from the response itself. And keep the focus on the experience, which is what we want.

John: Right. And related to that, of course, are wearables. And I have some thoughts also on wearable on the idea that we might collect. This is something I think is still a little bit in the near future. I know there are some work to be done on wearables right now. But what are your thoughts on the potential of wearables to provide, you know kind of in the moment behavioral type data that is collected, what's the right way to say this? It's sort of a passive form of someone providing data. They don't have to actively provide it. What are your thoughts on the potentials there?

Richard: Well, I'm excited about the potential. I certainly anybody that's worn a fitbit or another kind of wearable device, watches that, you know, track heart rate and so on. There's a lot of potential, I think, to tap into those technologies and begin to correlate the kind of data that we can collect with those devices with other behavior or even attitudinal responses. I think the technology is probably needs a few more. You know, a couple more years or maybe, you know, maybe it'll be available sooner. I think the other issue is going to be privacy and how to deal with privacy concerns. When you know, when it comes time to asking consumers in a piece of research to share to share this kind of data. So I think we'll have to see where that goes but I think the wearable bases for biomarkers, you know, it's a lot more exciting to me than what we might record in the lab.

John: Interesting. So that we get back to that theme of ecological validity that you're out in the in the wild, so to speak. Like in the data, rather than it being a more artificial form? I see. Now, it kind of hints of something where I think when you talk about, like there's data privacy in another topic that is really important, data ownership, right? That when you think about how Google has gotten so incredibly wealthy. A lot of it is they just laid claim to publicly available data. The pictures of the streets, for example, they just drove the cars around and took pictures of everything, then ask permission. They just claimed ownership of all that publicly available information. And I think that's where a company like P&K that has an interesting technology that has a large panel. You're in a position where you can, everything can be on the level. There's no sort of spying that happens. Instead, you say to your panelists, hey, we'd like to collect these data. And here's your benefit that you get in return and they can agree to participate or not. So I think that in a lot of ways that you're well positioned to have someone has their eyes open when they're aware of the sort of data that they're surrendering to you, right? Very different situation then, you know, like are worth it. I'm sure that data has been sold all over the world for whatever purpose it might have found. And I don't get any money for it. I don't get anything.

Richard: Yeah. Well, your data and mine's, you know. So we've misled the whole world.

John: Yes. Little did I know, I just put it on my cut and whatever. But in your case, you have a panel, how large is being case panel at this point?

Richard: Oh. Probably somewhere around 350,000 across our markets. And of course, those are centered on for test centers. And we'll go and work with Internet panel providers for truly national representation. But I mean, I think it's all based on opt in. So I think you're right. You know, we don't have anybody that hasn't consented to having their data collected. So I think it's just that some data, I think, might be more concerning than others. If you ask me to, you know, to rate a product on a nine point scale, it's one thing if you want to collect my heart rate, maybe that's something else. But I think people are generally very permissive and that's been to the advantage of all those companies out there that have just being able to lay claim to these data.

John: Right. They're surveillance capitalist. That's the term surveillance capitalism. If you haven't read Shoshana Zuboff's new book, I would really recommend, like to all listeners, read or listen to surveillance capitalism. It's incredibly cutting critique of the way the tech companies have really driven wealth inequality by claiming that all the data for themselves and then the benefit for themselves. So that's its own set of problems we're going to solve today. But in your case, I think you have a chance for people to give you permission. You can say, look, we would like to collect these data from you. And here's what you get in return. And then they can make it, you know, an assessment of whether or not they want to participate. So we were talking a little bit also before the call about kind of inputs and outputs, the things that you think are important. Like, I think we're all realizing liking is not sufficient for trying to understand purchase behavior or some of the other variables we care about. What are some of the key inputs and outputs that you're looking to try to collect over the next few years?

Richard: Well, I think purchase behavior and sort of response over a longer period of time to a product experience is going to be important. I think it depends on what it is you're asking the data, what kind of job you're expecting the data to do. If you're trying to make some distinctions between a few variants that a fairly similar to one another, maybe we don't need to go much further than we are going today. But I mean, even back in the days when I was at the Natick RD&E Center, the idea that liking was predictive of consumption, we disconfirm that pretty easily. You give somebody like a combat unit, a military ration, and you record what they eat over an extended period of time. And liking hardly changes, but consumption decreases due to monotony. If that's the only thing that they have access to. So, you know, we've known for a long time that there's obviously a dissociation between, you know, liking and consumption. And most of the time when there's a new product on the market, it's got to display something else. It's not incremental consumption. It's consumption of one thing in place of another. And so that introduces choice as an important measure. And again, that goes beyond liking. You know, I can like two things the same and choose one over the other variety of reasons. So again, that's where behavioral data will become more and more important if we can get you know, if we can get choice and behavior, consumption behavior into the set of metrics that we provide to our clients.

John: And what are some of the ways that you're going after that? I know you and I are talking a little bit about the possibility of monitoring actual purchases of the people that opt in, you know, from your panel that's an idea. Do you see other forms of data collection that might be more informative along these lines?

Richard: Well, I guess the question there is, what is it that, you know, what is the time frame over which you are collecting these kind of responses? And what are the questions that we're being asked to answer and in what time frame are we being asked to answer though? So I mean, if it's a matter of a company that is introducing a product on a limited basis in a test market. It'll be very interesting to track the responses from early adopters. And if we can have a way of collecting their actual behavioral purchases through, you know, some kind of credit card system or other ways of recording verified purchases and then collecting additional attitudinal and opinion data from those individuals, then you're beginning to really link the behavior to the expression of the product experience. And I think that will be very powerful.

John: Now, that is very exciting. So interesting. You know, something is that I kind of hear in these calls oftentimes is designed to understand the relationship of the consumer with the product over time, that it's not just this one off experience in a central location, that the fact there's this time frame. I think that's really interesting.

Richard: Yeah, I think that, you know, some of it is a question of a novel product. Sometimes companies can be somewhat overly optimistic about assuming that over time people will get to like it. Well, may or may not be the case, but certainly familiarity is going to be important. And if you can build familiarity, it can it can change acceptability. So yeah, in many cases, a one off experience is it's not going to be very, very telling. And you want to get some kind of measure of extended use under it all comes kind of comes together, right? Extended use under the right context with a typical choice alternate is available to the person.

John: Yeah, that's exciting. I mean, it is interesting how much this all facilitated by technology, right? Because you're not going to be able to have the people come in to central location all the time. You're gonna need this data collected in the real world, on apps, this kind of thing.

Richard: That's right.

John: Well, that's great. It's amazing how time flies in this call, but here's the kind of next question I have for you is, how have you seen the requests that you're getting from your clients evolve over time? When you think about the last 5, we will say 10, especially 5 years, what have you seen in terms of like what your client needs are and how they're evolving as, you know society is changing?

Richard: Well, I think that there's definitely been an evolution. Of course, I may be dating myself, but going back enough years, it used to be the case that it really that the questions of interest were about the product by itself, sort of in isolation of other elements of the, call it the marketing mix. So, you know, for food products, you know what is the taste experience like? Well, I think everybody has now understood that since in the real world, people don't experience products unbranded without package, without a price, without an intended use, as it's communicated by the manufacturer. That those elements are important. Again, it's part of the context. So for sure, those have changed a lot of the study designs. And what's come with that is a desire to answer more and more questions within the same piece of research. And partly it's because companies are trying to squeeze as much out of the study as possible. And also, you're getting multiple ownership on the client side. So you have not just R&D, but R&D in marketing or sometimes marketing or consumer insights. And as the constituencies grow, the interest in what kind of data are being collected becomes a lot more. I mean, those objectives become more diverse. So, you know, I'd say, you know, when was the last time, you know, we answered a question about pricing. Well, or value perception. Well, those are pretty common kinds of questions and at least from a price value perception. And you can only do that if you provide some kind of context. So the questions have changed. The constituencies have changed.

John: Now, your background is psychologist or are you PhD in Psychologist?

Richard: Experimental psychology.

John: Do you think that psychology has grown in importance in the field? Because it kind of seems like it has to me.

Richard: Yeah, absolutely. I think the appreciation of decision making and of the interplay between what is top of mind and conscious versus what might be unexpressed and subconscious. I think a lot of that has definitely grown. I think that a lot of talk about system one and system two is could create a lot of interest. And well, we've got to understand the automated and the sort of implicit response. And, yeah, I think that's certainly valid. I think that emotion has been a hot topic, at least for, it was particularly for a few years. I think the downfall there is that just adding an emotion questionnaire in a central location task is unlikely to reveal much that is really beyond the hedonic. So I think the challenge of how do we measure emotion in a meaningful way is still one that we need to address and figure out.

John: Yeah, it's interesting. Yeah. Obviously, you know, facial recognition or sentiment analysis because there are migas efectiva is trying to solve this problem. That's a company came out of MIT Media Lab. Trying to monitor people's facial expressions. And I suppose, again, that gets back to data ownership. You would ideally like people to understand that they're surrendering that information, right?

Richard: Right. And, you know, I think some of those kinds of measures, facial measures and other behavioral measures that you might collect while someone is watching, say, an advertisement, you know, it's more challenging when you're, you know, having somebody consumer product and as you know, facial movement that is part of the product, the consumption and product experience, but ends up being noise for a lot of the other measures. And there's some progress made in trying to overcome those things.

John: Yeah, that's interesting. Yeah, I definitely can see that. So, okay, well we've got about 5 minutes left. So there's a question that I always like to ask, you know, my guests is advice you have for young sensory scientists? That you're now, are you 25 years in the field, Richard?

Richard: No, thirty.

John: Thirty? I mean, you've been now in several different walks of life and you've seen a lot of changes. What would you recommend to young sensory scientists getting started?

Richard: Well, I think understanding the foundations of our field. I think understanding that is going to be important. I think, paid off for everybody that's taken the time to acquire the understanding, the knowledge of the fundamentals of the science. So that's largely psychology and some physiology as well. I think there's a lot of myths in the field of sensory evaluation and I think understanding what's fact and fiction making that distinction is something that is obviously important and to the extent to which you have a good foundation in the science, it makes you able to question what some of those myths are and be able to have a more realistic census to what's important and where you can possibly push the envelope or what advice you can safely ignore, because it's not really based on any substance. So my sense would be, you know, make sure you're well grounded in the science. And then I think the other thing is in my career, I've experienced it from three vantage points. I have experienced it from the from the government side, which was more it was applied research but it was really you know, it was semi-academic. I've experienced it from the corporate side and I've experienced it now for the last few decades from the research agency side. So I think that having different vantage points really contributes to a better understanding and a richer appreciation for what field can contribute.

John: Yeah. So would be willing to take on different perspectives?

Richard: Yes

John: You know, I hear a lot move around inside the company. If you can try to have different roles, say, oh, but if you're going to be in different companies or even in different sides of the industry, I could see that.

Richard: Yeah, I think that, you know, in my years at Ocean Spray, I was both on the R&D side and on the marketing side and that was very distinct to me. It was more market research, but it was an innovation team. And so it was a very different experience. You can to your point, have different experiences within the same organization. But I think that's valuable. I think it's you know, you don't want it gets stuck in just, you know, one role, one vantage point.

John: Right. Yeah. That sounds really good. Alright, Richard, this has been great. I definitely learned a lot from talking to you, so I appreciate it.

Richard: Well, thank you very much. I've had fun chatting, so it's been good.

John: Do you have any final words of wisdom you'd like to impart?

Richard: No, I don't think so. I just think that we have to be braced for changed because it's a lot of change. Technology driven economics are changing. Companies are changing how they're allocating money. They're looking to, you know, getting answers with less investment. And so a lot is, on many fronts things are changing and so we just have to be change agents and feel comfortable with change.

John: I totally agree with that. Okay, so if someone wanted to get in touch with you, Richard, what would be a good way for them to find you? Would LinkedIn be a good avenue?

Richard: LinkedIn would be perfect. They can check me out at my personal LinkedIn page. Also, they can access the company website, www.pk-research.com and connect with me that way. Look forward to questions. Happy to answer or comment on anything that sparked somebody's interest coming out of this podcast.

John: Okay, great. Alright. Well, thanks again, Richard. And we'll put all the links, you know, to your LinkedIn and to pin to your website on show notes.

Richard: Perfect

John: Alright. Great. Thanks a lot.

Richard: Thank you.

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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