Lisa Beck - A Second Perspective

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Lisa Beck, Founder at Insight Factory, believes that genuine curiosity and empathetic interactions lead to an in-depth understanding of consumers. To uncover and explore actionable insights, she strives to interact with consumers in their time and space, researching life as it happens, both in the off-line and on-line world.

Drawing on a unique background in product development, marketing, sensory and consumer insights, Lisa observes consumer behavior, discovers insights and converts those insights into new and improved products and ultimately more profits. Lisa has over 20 years experience at Colgate-Palmolive and over 10 years experience consulting with sensory and consumer insight professionals around the world.

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Transcript (Semi-automated, forgive typos!)

John: So Lisa, welcome to the show.

Lisa: Thanks. Thanks for having me, John.

John: Yeah, it's great. It's awesome. One of the best things about this job is doing these interviews especially with my friends. So it's really nice.

Lisa: And this is super fun for me to be the interviewee instead of the interviewer, so to speak.

John: Yeah, that's great. Actually, that's pretty good to start. So why don't we talk, for people who aren't familiar with your work, can you talk a little bit about what you do kind of day to day and then we get into some of our shared interests?

Lisa: Sure. So what I do day to day is often very different. And certainly now in kind of the life we're living. My existence has altered quite a bit but pre-pandemic, I was doing focus groups. Shop along with consumers whom I'm going to call them ethnography. I know that that's not literally ethnography. But, you know, we go into consumers homes and observe and watch and ask questions and interact with them at home.

John: So really, I mean, pure qualitative research?Lisa: Pure qualitative research.

John: Yeah. And that kind of leads us to what I think it's really fascinating that's going on right now is this gap between qualitative and quantitative research. Of course, I love quantitative research. And so I gave a talk at SSP, bridging the gap between quantitative and qualitative data research and I know you're really excited about that. And so you reached out afterwards. What were the things that, okay, let me just take a step back, during this pandemic, you've been kind of forced to embrace more of this new technology, it's been necessary, right? So a lot of things that you were able to do in the past, you haven't been able to do. And I know you've really embraced this change. What are the things that you're kind of excited about in terms of qualitative research right now? How it's bringing in some tools from maybe more quantitative space and how you see those tools being useful into the future?

Lisa: I think it's a very interesting time because now a lot of the qualitative that we do because we can't really be in person is now online. And that just brings you right into these tools like Zoom meetings. I know everyone's probably like tired of those kind of things, but really, it's a fantastic way to interact with consumers because you might get to see them like in a traditional focus group. They're going to come to you and sit in a very kind of controlled setting. But now I get to see, like a little extra slice of life, perhaps because they're at home. Maybe their pets or their kids are interacting with them while they're chatting with me. So oddly, there's a there's an extra richness that comes to the data. And then the other thing is that when you finish Zoom or you finish a focus group, your recording is immediately available transcript. And not that they weren't fast before, but now it seems even faster that I can get a machine generated transcript in almost no time at all. And so now having that data, so to speak, so quickly, because life before was fly to where the consumer is. Hang out with them, chat with them, fly back to my office, and then start digging into the recordings, the transcripts and things like that. But I could potentially like we're chatting right now, 20 minutes after we're done chatting, I have the video, I have a transcript, I have all of those things and while everything is still so fresh, I can start mining that data.

John: Yeah. Now, it's really totally amazing. I mean, it's so much more efficient, right? I mean, all that travel time is I mean, once upon a time, actually, for you to be on this podcast, we probably would find you out here. I can imagine my budget would have had to have been like but instead we just have this conversation. So, okay, what are the benefits? Well, maybe just like do you have a preferred tool when it comes to transcription? Some things you might recommend?

Lisa: So I've started playing with author.ai and that's kind of a fun tool or it has been a fun tool because I've been able to guide it to what is my voice and I can identify myself in those transcripts. So as you and I have been talking because I'm moderating the discussion, the words that I use, I might want excluded because I'm going to bring up a topic over and over and over and over again. That doesn't necessarily mean that that's what was so important to the consumer or it doesn't necessarily mean those are the words that they're going to use.

John: Right. That's right. So if you just were to take a kind of naive transcript versus the words that were said in the session, it's not nearly as good as knowing, okay these words were said by Lisa and these words were said by respondents, right?

Lisa: Right.

John: Yeah, that's right. And what about, I believe there's a capability in there, isn't there to teach it words that are kind of technical words or vocabulary?

Lisa: Sure. You can teach it things that are maybe very project specific for e-write so you can train it to those words so that they pop up. The one kind of interesting thing that I've noticed, a machine generated transcript as opposed to a human doing a transcript for you. That human automatically cleans the transcript to some extent, right? The machine the machine generated transcript gives you every literal word.

John: Right.

Lisa: And so it's been fascinating for me in terms of human conversation. I don't recall the conversation with that much repetition. Like, naturally we filter out some of this extra stuff and again, when when a human is going to do the transcription, they filter out whatever seems like extra stuff. So it's been kind of fascinating to see literally what was said.

John: Right. In some sense, bias is a filter that that it's like, you know, I mean, everyone carries around expectations for what they're going to see in the world. And then you get all this information and some get screened out. Right? And it may actually be a negative thing. I mean, bias is oftentimes negative. Right? But just as a term bias just means that you're bringing some structure that's called leading to an interpretation. And that is interesting that in some sense, the human transcribed data is it's biased in a good way because they've gotten rid of the things that are extraneous, whereas the machine, the transcript is unbiased. It's just the pure. These are the words that were said. Now, that's quite interesting. So what would be some of the other, so how is that useful to you then in your day to day work? Do you use it to like, how do you supplement what you are already doing with this new source of information?

Lisa: Well, so, you know, because I have the transcript that comes from a machine generated will automatically have keywords at the top so it's easier to search. It's quicker to search is one of the things that I found and even just literally having those keywords, now I can look very quickly from group to group to group to see is that something consistent or is it not consistent because you leave a focus group, and as a client or observer, you probably feel well that group is very positive to my product. That second group was kind of negative to my product and that third group, I don't really know what they thought. But I can immediately see that kind of headline of keywords and that helps me determine, was that really the way it went down?

John: I see.

Lisa: What's that first group so much more positive? Without having to read through the whole transcript, you can see those keywords and get a feel for what the conversation was about.

John: That's really interesting. And so when you finish a focus group, then you usually take some notes before you go and look at the machine generated transcript?

Lisa: Absolutely. I mean, my standard practices is either depending on how long the project is. There's either multiple or at least one debrief with the client. And that's where I kind of like to hear, what did the client hear that they expected to hear? What did they hear that was a surprise, were there any aha moments in what was talked about? And then are there any things that they would like a mind for? So if there's something that maybe we just kind of went very quickly or something that after maybe we talked to two or three groups of consumers, if we wanted to probe on more and maybe in those first three we didn't probe so much. So please go back and see what the conversations were like there so that there's always that I always have some level of debrief with the client. And honestly, I have my own kind of thoughts on how or what information came out of each group at least very top of mind, and so taking that in combination with the transcripts and being able to like I said, do it so quickly. So almost immediately after is very helpful. Because as time goes on, our recollection of conversations changes.

John: Yes, definitely.

Lisa: Right? And so a conversation that you had with somebody last week. Your recollection may not be as accurate as it is an hour after you had that conversation.

John: Yeah,. Well, sometimes I can't remember when I get to the end of the sentence what I said at the beginning of a sentence. So I definitely see the value to what you're talking about. Okay, so on the one hand, you know, the Human You is an expert who can read all sorts of cues that a machine is not going to be able to read all sort of some cues, right? And you have knowledge of the product category. I mean, you have all sorts of information that's informing your filter in a positive way. Right? On the other hand, you also are bringing various biases that we all have, not necessarily biases in the negative sense, but just like you said, shortcuts for making sense of the world that the machine doesn't have. So it's like you have this other person that is there who's helping you. They have a great memory and they have no experience of the world. Maybe I suppose if you're getting into topic modeling or sentiment analysis. They have some experience. But, yeah, it's like a second opinion that has a kind of different perspective than you have.

Lisa: Absolutely.

John: Yeah, that's really fascinating. So it's kind of like a diversity of opinion. Okay, that's, what is your dog's name?

Lisa: So sorry. That's Bradford and that's been another kind of he comes to focus groups now and that wasn't, you know a reality in the past. There was you know, I had to kennel him and leave him behind. You know, it's fun because sometimes consumers have pets and they react to Bradford or vice versa and it actually like it opens the rapport so that was initially kind of a struggle going online as opposed to being in person because, you know, depending on the topic, you want consumers to really open up and tell you things. I mean, they're going to be very giving of their time and their opinion. And, yes, I'm a stranger, but I don't want to be a total stranger to them when they're talking about things. And so I've been surprised how kind of being in my home environment makes it more approachable in some way because now we really are all in the same boat together.

John: Right. Now, actually it's a theme I've grabbed this podcast series that I've heard is the increase in diversity that like when it comes to testing. That once upon a time, if you did the CLT (Communicative language teaching), you pretty much were getting people who could afford who had a lifestyle that for whatever reason supported coming to a location in the middle of the day, which isn't just the hour or wherever it is the location. It's a half an hour there and a half an hour on the way back and there's a lot of lifestyle factors that enable or make it impossible for somebody to do that. Right? So that was always a kind of bias in our research in terms of sample bias. Now, someone as long as they can have the call, I have been in call with my one year old daughter on my arm.

Lisa: And hopefully they're not listening out loud because now they're doggie at home is going crazy. You know, trying to communicate with Bradford and if machine learning could tell me what he's saying that would open up a whole new set of product categories to research.

John: You know, there are people who claim to have solved that problem. I'm a little skeptical of that. So, Lisa, what do you, so it's definitely great that I think that right now we have some already some advantages with kind of further diversity or inclusion in terms of new people that can participate in the tests. You have a kind of window and tools homes. What are some of the benefit like when the pandemic is over? Because actually I'm starting to get optimistic with the vaccines that vulnerable populations will get vaccines. We'll be able to go back to some sort of life like we knew before the pandemic. What do you see as things are going to keep? Things that you're going to continue to use? And then I'd like to also talk to you about technologies that you would like to use in the future that maybe are not part of your regular work right now, but either are going to come online or things you're looking forward to things that you see that are interesting.

Lisa: I think the things I would like to keep is I want to keep the idea of doing qualitative online. I think that for certain product categories and certain projects. It's going to lend itself well. Like doing online qualitative has always been, not always been, but it's been around. It just wasn't as utilized as it was until we were forced to this is how we're going to have to do it. So there are some things that, you know, it'll be really great to get back to, hey, we're doing a central location with you know, I don't know how many different food products. We're going to bring the consumers in and chat with them after. And, we want them to taste things as part of the focus group. That's almost impossible online. Not that it's not doable, but the level of control is, your microwave and my microwave are different. So even if I say, hey, we're going to go microwave these samples and come back, everybody's having kind of a different experience because of their home environment. But then there are some categories. Some things that are very personal topic and the online is a great way. So maybe it's some kind of health care product or a sexual health product or something that you just don't want other humans to know that you're necessarily consuming. And so it makes it easier if I just have to show up online and talk to Lisa. I don't have to go to a facility and check in and sit in the waiting room like I'm in a doctor's waiting room wondering who else thinks that I have this particular medical condition. Hey, are they here for this group, too? I think that there's still going to be a usage for this. The other thing is, is you can't deny the speed that comes with online focus group. So I feel like and I too, like you, I'm very hopeful that we're going to return to doing all of the things. But I think that online qualitative is going to stick around. And going to be part of my practice moving forward. I think it's just another awesome tool. The other things that I want to keep is I love the idea of the super fast transcriptions, the machine transcriptions. What I would love to see is, you know, the online platform lets me have a great look at everyone's faces. So all eight people in the focus group, I'm scanning them and looking at them and I can do this in a focus group room. But depending on the physical setup, you can't necessarily be keeping eyes on everyone. I would love to see some kind of machine facial expression reading, you know, and I as a human do that. I look to see our people engaged to one another consumer is talking. Are they not engaged? Are they nodding in agreement or are they skeptical about what this other person is saying? I'm trying to read that room, and I would love to have you know, like you said, I've got an unbiased person giving me now a transcript. I would love to have an unbiased so-called person giving me that facial read.

John: Right. And timestamp and you can match it up with the transcript, right? So, yeah, that is fascinating. And so you think when you go back to CLT's that if you had a camera on every person's face during the CLT, that would be?

Lisa: And some facilities have that. They can give you that in a recording and you can look at that. I think that that can be a helpful you know, now that I have the luxury of being able to see all of that together. You know, I used to kind of leave it to my expertise if you want to. To kind of read the room and see how things are going, but to know that I could have kind of a postmortem on that and have a machine go back and say, you know, yeah, when I looked at such and such consumer where they really skeptical?

John: Right. Yeah or you could even name emotion or you find anomalies. You know, show me all the times anybody looked disgusted and then you go in and have the timestamp, then you go the transcripts and see what was being discussed at the time. Yeah, it's fascinating. And of course, you know, we have an interest also in some of the natural language processing tools, like the topic modeling, what is being discussed, sentiment analysis, and of course, all of the part of speech tagging, that kind of thing. I mean, it's really interesting, so when you kind of look to the future, what are the things that you're most excited about?

Lisa: So I'm most excited about, honestly, what you talked about that idea of bridging qual and quant. I don't know, for whatever reason, you know, these two worlds are separate. Like I don't understand why they necessarily have to be separate or why quant people have to fear, maybe fear isn't the right word, but be skeptical of qual people because we don't live in data and vice versa. I think there's just so much richness when everything is combined together because no one particular piece of research is going to answer all of the questions. But the more integrated approach you can have and the more data you can collect from a respondent when you have them and have them in the moment is just going to give you so much more on the back end.

John: Yeah, it's really interesting how you and I have this shared interest in understanding the consumer experience and it's really nice to see that these tools are helping to support like giving us a way to come together. Right? Because, you know, I mean, something I'm passionate about is the smart speaker research which is kind of the opposite. Where we've got a quantitative survey which now is starting to be more and more like a survey that a person might give. Right? But with some of the advantages that you've mentioned where it's not, not maybe that's invasive, someone might feel more comfortable actually talking to a smart speaker about certain topics than another person. You wouldn't want a person in the bathroom with you under some circumstances. You know, I mean smart speaker is actually more discreet. So, yeah, this is really a very fascinating. Okay, Lisa, we have actually run more or less out of time here.

Lisa: Oh my goodness, how did that happen?

John: I know, it really is amazing, but I do want to get your advice for young research. I mean, I would say, like when I came into ASTM we met because you were the kind of I think you were the membership director. I'm not sure the title was.

Lisa: Membership secretary.

John: Yeah. Some sort of free lunch, that's what I remember. And when I saw you at ASTM and I didn't know what I was up and there's this nice person who's helping me figure out how to get oriented, offering me food. So I think you are really a great resource for people to reach out to for advice in sensory. But if you think about this big picture of your career, what have been the decisions you've made or the actions you've taken that you feel have helped you the most and that would help other people as well?

Lisa: So here's the interesting thing and probably this is true for some of your listeners. I certainly didn't plan a career in sensory. Honestly, I didn't know what sensory was until I started my working life. And so I think the advice I would give is follow your passions. You know, my educational path may or may not have led to sensory and so just because you don't have a particular degree or certification, don't think that you can't approach the field. But just be sure that you're genuinely interested and genuinely curious about something, you're going to have a lot of passion behind it. You're going to have a lot of drive for it and it's going to be an area for you. So don't just discard something because you think you don't have necessarily the formal education in that particular field. You can learn and there's so many people to learn from and, you know, you brought up ASTM. I think if you're a young sensory professional, ASTM and SSP are great places to network. I have never had a colleague turned me down on advice. And of course, we're all very conscious of not sharing any particular secrets. But if I want to know how to approach a particular problem, there are so many people to reach out to and there's such a willingness in the community to help one another.

John: Yeah, sensory is a really open community, really. I love the field of sensory honestly.

Lisa: Like I said, I kind of fell into it. You know, when I was at Colgate, I started in product development. I did some time in marketing and just through an odd set of circumstances, I ended up in sensory and consumer insights on a temporary assignment that turned out to be a much more permanent career choice. So, you know, just follow your heart, follow your passion, and you're going to find your way.

John: Yeah, I agree with that. You know, it's this quote I came across is that you can you can fail doing something you hate, so you might as well fail doing something you like. Because you know, this idea that like you can work some soul sucking job and still get fired. You know, like you might as well do a job that you like doing or you might as well be working on something you're interested in.

Lisa: I think the reality is if you like doing it and you have passion, the reality of you like getting fired is probably not going to happen. Now layoffs happen, industries change, you know, those kind of things. But you're not going to be out and out fired because you have a passion and you have a drive for things and that's always going to be useful to an organization or like you and I, you can end up doing it as a freelancer and have so much fun working on all kinds of different products and areas.

John: Yeah, that's right. Definitely. Do what you love is very good advice. I think you should do what you're good at too. If you're not good in anything you love then you probably find something else to do. But do what you're good at, do what you love, do what is useful to other people. Yeah, this is all really good. Okay, well, Lisa, this has been a pleasure having you on the show, how can people get in touch with you?

Lisa: You can find me on LinkedIn and you can find me in the company website.

John: Okay, great. How about Twitter, are you on Twitter or now?

Lisa: I'm on Twitter. You know, I'm going to say that I'm not super active like industry, you know, unless there's a conference going on, then I tend to tweet about the conference. But, you know, hey, I'll tell you that if anybody has advice on how to manage my social media presence better, I'm all ears. So come to me millennials and let me know.

John: That's funny. I think Zoomers now is another new thing.

Lisa: I don't know.

John: Okay, great. Well, thanks a lot, Lisa.

Lisa: Thank you.