[00:00:19] Sebastian: Thanks for joining us on a super special episode of Insightful Connections. Our guest today is Ray Fisher. Ray is the CEO and founding partner at Aha! Insights Technology. Founded in 2013, Aha! insights technology is a global activity-based qualitative research platform designed to give insights teams and consultants the tools to deploy and analyze live and asynchronous consumer and B2B research studies using the sophisticated AI-enhanced digital technologies. In addition to their leading qualitative platform, they offer services ranging from DIY to full-service project engagements. The platform is very ad hoc and friendly and also offers subscription plans for organizations of all sizes. Prior to founding AHA, Ray was a brand manager at PepsiCo, was an EVP at Influence, an early internet consultancy, and was a strategic consultant at RealityCheck. Ray, thanks for being on the episode today.  

[00:01:05] Ray: Sebastian, thanks for having me. I'm looking forward to this and excited to be here. Awesome.  

[00:01:10] Sebastian: Okay, so the first question I like to ask is just sort of a context-setting question, but maybe actually before I get into that, I want to take a step backwards and just ask you about the space that you're joining from today.

[00:01:23] Ray: Actually, I'm in my office here in Northville, Michigan. It is an old Ford plant. So Henry built this in 1936, National Historic Registry building architected by a guy named Albert Kahn, who was the preeminent Detroit architect, world-famous in his era of like 1900 to 1950, maybe. He was very prolific. All of the University of Michigan campus, the whole skyline pretty much of Detroit. And he was very tight with Henry Ford and he built all these little village plants that are around the Detroit area that are all off of this river called the Rouge River. So what would happen is he would put a plant in and then build the town around it. And literally from inside my office right now, I can look out the window and there are two pretty much mansions across the street. One was the plant manager's house and the other was the assistant plant manager's house. And I wouldn't be surprised if the house I live in a half mile from here might've been, you know, Ford employees back in the day. So this was a valve plant and the barge just would go from the main Rouge River plant where they did all their steelmaking and everything else. And they would pick up the valves here and drop materials off and just barge it up and down the river, which is truly fascinating. And the town built up around that. So we've got a main street and it's very quaint and super historic.

[00:02:37] Sebastian: And Ray, you mentioned this office space you're working in right now is a space that Henry Ford himself worked out of.

[00:02:42] Ray: Is that right? Yep, absolutely, Sebastian. So he used to hide here, the conundrum of Dearborn, Detroit and the money and the finance and people wanting stuff and wanting to sell them things. You can imagine the hubbub of the, you know, the tens, twenties and thirties, you know, he was just like, Hey, I need a place to escape. And he used to come down here. We've got a water wheel and this is known as the water wheel building. And there's actually a water wheel at the end of the building that powered this entire plant. And in fact, one of the compressors that that powered is actually still downstairs. It doesn't work, but it's there for historic purposes. And anyway, he would sit in that window and watch that water wheel go around and he would think of ideas. So this was kind of his little escape place. So he could get clarity of thought and do strategic planning and, and, you know, he's an inventor, of course. So he had all kinds of ideas and inspiration for that from this particular building.

[00:03:30] Sebastian: Does it do the same thing for you, Ray?

[00:03:34] Ray: It does.

[00:03:35] Sebastian: Beautiful. I'm wondering if you can tell me a bit about how you found yourself in the insights profession and how that origin story kind of accounts for where you've come in the years since and where you ultimately are now.

[00:03:44] Ray: That's a great question. So I went to the Michigan state university and my, my focus was advertising. So I thought, you know, mad men look like a cool thing to be. I thought, Hey, I'll be a cool ad guy. I'm going to do that. I went through the advertising program, landed a job in Chicago at Wells Rich Green, which was a very prominent, very creative shop back in the sixties, seventies, eighties. And I think it eventually got merged into other things later on, but it was a great start for me. And I was there for a year and a half, but Pepsi came knocking and needed somebody to kind of be a liaison between their packaging department, which was loaded with Michigan state people, which is kind of funny. It's in based in New York, obviously Pepsi headquarters. So I moved out from Chicago to New York to run this like liaison job between the packaging people and the marketing people. Well, eventually, you know, I caught on with some other people in the marketing group and I got pulled into the retail marketing area and eventually ended up being brand manager on brand Pepsi. But the really funny thing about all this is that when I was there, all of the research people wanted to be in marketing. And while I was in marketing, I ended up being the one who diverted over to research. So everybody was trying to get out of research and I was actually angling towards research just out of great respect for the amazing stuff. You can imagine the kinds of consultants we had and the people internal to the company, the math people, the strategic, you know, firepower we had and the external firepower that helped us do our research. It just was fascinating to me. And I didn't do anything without doing research first and it just became part of my approach to business. It's like anytime I'm doing strategy and I consider myself a strategist, anytime you're trying to do any strategy, you have to have something to pull on and what better than, you know, your customers or prospects and the feedback they give you relative to what you're trying to do.

[00:05:27] Sebastian: So how did you make the move from brand management role at PepsiCo, obviously one of the big corporate, you know, research environments to kind of an early internet consultancy and how did you ultimately find your way back to Insights from there?

[00:05:40] Ray: Yeah, the interesting part there was, you know, the Pepsi experience was amazing. I was there for eight years, phenomenal run, but there was a point where I was just hoping to be in that rotation to get shipped out somewhere other than New York. I had two little kids and I was kind of like, ah, I'm from the Midwest. And I was kind of pining to get back, not necessarily to Detroit, but just somewhere in the Midwest. And I ended up, you know, moving to an agency in St. Louis. So that was what came up. I was looking for a new job. They were reorging all the time. And I just got a nice promotion just before I left, but I was given a great offer. I couldn't refuse to move to St. Louis and work on Anheuser-Busch business and get myself back to, you know, kind of my Midwestern roots. My boys at the time, I have three kids, you know, two boys and a girl. My boys at the time were like two and five, one and four, whatever it was. And I just wanted to get involved with them. You know, I was a sports guy myself and I wanted to coach and teach them. You know, I wanted to play hoops with them, rebound for them, take them out to the ball fields. It just wasn't as accessible in New York to do that. So I got there, the pace was a little bit different and I was able to put more focus on my family. So that was really what took me there. And then I had the very serendipitous opportunity to work on that Anheuser-Busch business. But then I also met one of my business partners that has been with me from the beginning. I've worked together with him for 25 years at Reality Check and he's still a partner here at AHA. His name is Jim Chastain, prolific moderator. So when we met in St. Louis, we were actually coaching our kids' teams. He had just jumped out to be a focus group moderator and it started to take off for him. And he was like, hey, I really would love you to join me to do this. You know, he knew I did some ad hoc moderating on strategic work for Anheuser-Busch. It was like, hey, can you, you know, why don't you jump out and join me? And I told him, I'd love to, I'd love to, I'd love to. But there was an opportunity at this influence company you mentioned where I went in as EVP and I ran a ton of things for that business, including business development and strategic consulting stuff. And that company was basically taking corporations, brick and mortar companies to the internet with e-commerce. So I spent like three years there. Phenomenal experience there. And that got me immersed into the technology world. I had guys on my team, I had architects, I had all those, you know, technical development people. And I got to understand their language and how to take vision of what I'm thinking, talk to them about it, and then help them build it. And that's led to me not only launching tons of companies for our clients during that three-year run, but then also in my career, launching my own products. You know, our first platform when I was at RealityCheck was called Living Diary. We just used it for ourselves. And that led to our spinning out in 2012, 2013 to form AHA. So that was two launches. So Living Diary, then AHA. And then we just did a relaunch in the last two months where we just rebuilt the entire platform. It was 12 years old. There were many things that were modernized, but the things that could be better. You know, UX has improved, different ways of doing things. And then over time, just using AI on our own platform to understand what are people using, what's important, what's most used. And that kind of drove the strategy around how we did our new UX to make it more DIY-friendly and more intuitive. The navigation was more modernized. And just, again, as we have AI, not only for us to help develop our own product, but also to help our clients to develop their studies and then also to analyze their studies. So we've got AI kind of working all the way around. But again, that really helped me. All that experience, you know, with the technology side kicked off at the influence era for me led to these three different product launches that I was completely equipped to be able to do. So we did maximize on taking on the internet, which again, if you think of it this way, I kind of look at it in three different buckets. Okay. The internet came on, now data collection became a thing. We dabbled in it in 05-06, launched Living Diary in 2007. Phenomenal, exciting time. You know, it was like data collection. There were a few people out there doing a few different things. Our first attempt at it was just using a CRM tool to collect data, basically asking open-ended questions. Then, you know, adding like video, you know, picture uploads. And then it was video uploads. So things got more exciting. Then we built Living Diary. Then five years later, it was aha. We had so much more knowledge, so much more understanding of where the technology was going that we were able to really, you know, capture all kinds of ways of collecting data, both asynchronously and live. So that was a big, big deal. And then the COVID situation came. We had just done a Zoom integration in 2019, the very first Zoom integration into another platform in the world. So we are Zoom's very first numero uno ISV, which is a huge thing for them now. And we're still just a small piece of their entire business, of course, but it's awesome that they helped us engineer it. They thought it was a great idea when we went to them with the idea of like, hey, could we integrate your tool into our platform? And they were all ears, helped us do it. And that relationship has been super strong for what, six, seven years now. And we do tons of live work with Zoom as our engine. And, you know, again, as I mentioned, we're the first ISV in the world. So that was another like inflection point, Sebastian, at that juncture.

[00:10:55] Sebastian: So you mentioned a couple of, to borrow your term, inflection points through the course of your experience in the insights industry, and I think outside of the insights industry, importantly as well, developing some of that expertise you brought in later to aha and reality check. I guess my next question for you is, you mentioned sort of the evolution of the internet, COVID, and now I think a lot of people would argue we're at a similar inflection point with AI in the market research industry. What are you seeing in this moment that are the potential opportunities and threats for the industry?

[00:11:24] Ray: There is so much going on all the way around. A couple of key things, you know, I try to say this to everybody and I hit a lot of conferences and I see you at conferences as well. So we get a really good pulse of things just by being out there with clients, friends, new people we meet, other industry experts, seeing great speakers talk about their ideas and thoughts. And you can see how these conferences have gone from like lots of different methodological presentations into almost like 80% of all presentations are, have something to do with AI. As we mentioned AI, so I'd say one of the bigger opportunities, the first one was analysis. Okay. And I do want to say it is still a human-centered business, and I don't know where your point of view is on it, but mine is that a moderator, they need to still read the data and clients still need to expect and deploy people to analyze that data as a human and use the AI to support all of the efforts that that moderator has, you know, in terms of like, it's going to make their digging through the data faster. Once they've got their feelings and thoughts around what things are, it can produce reports and it helps to organize data in ways that I, it's just mind boggling how good it is, you know, just to organize data around, you know, different thematic areas. And we also use like objectives, so I could put the objectives into the AI so it knows exactly what you're trying to accomplish in a study. So beautiful stuff there. So the opportunity there on the analysis front is tremendous. One of the concerns I have though, is that I have a lot of moderators as clients who have come to me and basically said like, Hey man, things have really, you know, while the industry does have its slope points, we've seen things in 23, might've been a little bit slow. We were okay. 24 was still a little bit sloggy for various reasons outside of our, all of our controls. This year has been pretty good for us. And I still hear that it's a little sleepy for some people out there, but some are concerned that people are just relying on AI. It's just like, let's just use AI moderators and stripping out the human piece. And I'm hearing it from the moderating community, you know, the, you know, qualitative researchers out there are saying, man, my business is slow. What's going on? And I heard it at the conference too, that there was a lot of concern about AI moderation. Okay. And they were hearing it and coming back to our exhibit, our booth, and basically just venting and saying, Oh my God, this is all about, you know, AI moderators. What is that going to do to my business? And it obviously could hurt, but it shouldn't, you know, my whole point is like that data still needs to be human digested and AI needs to be a way for the moderator to work faster and for the moderator to work smarter so that they're able to go ahead and organize data better, find all the supporting artifacts that, you know, quotes and clips and things that support whatever things are coming out, but then also having the ability to go in and take a look at things and say, okay, I've got a supposition here or a new theme that I, it's just coming to me just by, because I've read all this data, I've got some things the AI hasn't pointed out. I want to go test out those theories and see what the AI thinks about those. So it gives you an open query capability too. But again, I still see that as human directed, but I do have some concerns because there's so much corporate pressure on use AI to make yourself more efficient, use AI to save the company money that people might be bypassing that human element and going straight to AI driven reports. I think that's a little bit scary. Honestly, I don't think we're ready for that. We all know about AI hallucination. I think everyone's working hard to eliminate those kinds of things. But at the same time, a human can't be beat yet. You can't be beat yet. If you're using AI, it's going to make you a better moderator. But if the AI bot is just doing its thing, I would be very wary about that. But I think people are taking that risk just because of the pressures to, to use AI and to, you know, create better economic efficiencies, you know, in research departments.

[00:15:23] Sebastian: Ray, one of the things that you've been talking about lately is, is sort of this idea of the power of qual, right? And I'm wondering, kind of on a related note to what you're just talking about, what is it that qual has to offer that maybe are some unique aspects of qualitative approaches that are being unlocked by, you know, technology, in particular, AI these days?

[00:15:45] Ray: It is deeply personal. If you look at surveys, you know, and there are definitely roles for surveys, but I can talk about some issues with surveys that we've all seen in the last couple of years that are a little bit troubling, is just the proliferation of fraud in the quant survey area. In qual, if you're using the right recruiting methods, the right recruiting standards, the right recruiting partners, you've got vetted, profiled people. They are coming to your platform, they are producing video responses for you, they're showing you product usage, they're taking you to the store on a store trip asynchronously and recording that and talking to you or doing it live. So, you've got all of this evidence that they truly are who they say they are, and they truly are interacting with the goods, products, and services that you are trying to understand better. So, you would know if they're a fraud, and it can happen, somebody can slip through, but if you're doing, you know, qual studies and you've got 50 people, 60 people, 40 people, if you have one or two bad apples, those are pretty well identified. And there are tools out there to help understand if any responses, particularly text-based responses, are driven by AI, where somebody might use chat GPT to try and answer a question. Pretty obvious, and we have some tools that kind of ferret that out. But again, when you look at quant, you know, you're just getting kind of anonymous surveys. I don't think you can adequately reach all the people cost-effectively that you might be trying to do research with. If you're doing executive-level stuff on the B2B side, yeah, you can find those people, they're very expensive, and we do that all the time, but you've got to pay for the vetting of that recruit. But if it's like, you know, just a general study with, you know, household income over $100,000 and you need 1,000 responses, are these people really vetted that they truly are making that level of income for a few points in a quant survey? Qual pays. You know this as well as I do. People get paid decent money to be motivated to commit to a qual study. And think about a traditional qual study, three days, you know, 30 minutes a day, that's 90 minutes of activity with text-based responses, projective techniques, video responses. Oftentimes a store trip is worked into the activity flow. So there's just so much richness there that's irrefutable. You know, could somebody fake you out? Possibly. But again, the partners that we work with have vetted the people that are coming to the party. And you yourself, as somebody who does recruiting, you know, you truly understand that, you know, you got to vet these people, they need a profile, and you can't be embarrassed on the other side or your reputation's at stake with your clients. So I think we're producing a better quality respondent and better quality studies. And I know we've talked a little bit about, and I appreciate you reading one of my recent blogs about large-scale quant versus, you know, large-scale qual versus, you know, big quant. And when we talk large-scale qual, you're probably talking 100 to 150 people, okay? But a 3,000-person quant study, okay, there's a lot of people in there. I mean, how accurate is all of that data? Are you going to be better off getting deeper, richer stuff that is both quant and qual, but you have a large enough sample size that you're statistically relevant and projectable? You know, that's the key thing. And that's that 100 to 150 number, which again, expensive for a study, but again, the quality of the data, you know, is worth it, in my opinion, 100%.

[00:19:20] Sebastian: Yeah, just, you know, circling back a little bit to one of the things that you said about, how can we be sure that these people would be participating for such a small incentive? Don't get me wrong here. I would never argue for lower incentives. You know, it's one of the things I believe is that we've got to keep motivating people to participate in research or we're going to have quality issues, right? But one of the things that I think is unique about qual, and you see this on the front lines recruiting people, is there is something inherent in being listened to, right? In a context like this, in a context like the conversation that you and I are having right now, that is, you know, rewarding for most people. And you think about most people's lives, right? You know, most people's lives don't involve a great deal of being listened to, right? Like, you go, you go to your house, you go to work, you know, you got a whole bunch of competing interests always, right? And, and, you know, it's rare that you'll have a 90 minute block where somebody is going to hang on every single word you say in in most people's lives, right? But that's what qual has to offer. And I think it's a powerful motivator. And we can see that it's a bit of a different x factor to just the incentive, because in many qualitative approaches, there is a requirement to tell some people, hey, you know, we're not taking you into this focus group, but we're paying you anyways, right? And logically speaking, if it's just about the money, I mean, that should be great news, right? You know, you get your hour and a half back and you're getting your hundred bucks or 125 bucks or whatever, right? But people get disappointed when you break that news to them, right? They're bummed that they don't get to be in the focus group, right? Now, I would never say that we can rely on that alone to turn people out, right? But there is something in addition to the money about the format itself that I think is motivating. And that motivation is part of what ladders up to sample quality, right? That we see in the in the qualitative space, not necessarily to fire any shots at our quantitative colleagues. But I think that that's one of the reasons that qual as an area is potentially less impacted by some of the sample quality concerns that are emerging in the industry right now.

[00:21:20] Ray: And Sebastian, you know, I hear things like the quant side of things, again, friends, of course, friendly competition world, but we're all battling for the same pot of money. You hear these things where they're selling qual at scale, okay? And that means they're firing open-ended questions into these quant surveys. And I've tested this out. I've tested that out with video. I've tested that with open-ended questions. We do it with APIs with quant surveys from time to time. Those open ends from quant sample, because they're not really paying them too much, even when you do offer them more money, they're just used to pressing buttons. So when you ask a question like, well, tell me what it is that you did or did not like about this particular idea. And they say, well, I just didn't like it. That's not a qual response. You do that in an asynchronous study and you say, okay, you looked at this idea. Tell me what it is you liked about this idea. And you're going to get a 300 word response about what they did and didn't like. Okay. And so the depth of that response is so important and so much richer on the qual side that I just don't even believe in looking at these little dinky responses from people. We don't even really know who they are, just ruling today when we've got this ability to have all of this validation of responses from great passionate respondents who are contributing to this study because they care about that particular product category. And let's face it, they're getting paid decently. You and I talked about this a while back where the inflation finally kicked in. COVID, we started to see the numbers start to pick up, regular inflation was happening, but then incentive inflation eventually happened. I remember the three-day study was always a hundred bucks. Now a three-day study for really the respectful incentive for 30 minutes a day is 150 bucks, as you know. That's lucrative. That's good. That makes it feel good. You're getting 50 bucks a day to spend 30 minutes a day sharing your thoughts and being respected for those thoughts and being in a conversation with a moderator who's probing you all along the way. So different experience, different output, higher quality data.

[00:23:30] Sebastian: You've made the argument for large-scale qual. I'll separate that from qual at scale that you just mentioned, right? What changes have you witnessed that maybe signal that now is the moment for this as a methodology where it may not have been in the past?

[00:23:45] Ray: I think just those fraud concerns, you know, just on survey fraud. I mean, we've seen it. It's not everybody, but it was out there and it was epidemic and it was going on for, I think, a long time. It's just a point in time where, you know, hey, if you really have to rely on data, you know, you don't need 3,000 people in a quant survey that you don't know who they are. You'd be a lot better off with 125, 150, 100 completes on the qual area where you, again, can mix in all of the qualitative techniques that we have and other people have out there on their platforms, but also mix in quant questions. So you're getting that beautiful blend of quant and qual. So you're getting the select whatever option applies to you, but then with depth. So it's a little bit different. You know, it just is totally, I think, just a higher quality approach. I, if I was back in corporate like I was earlier in my career, I would be way more comfortable to do that. And we're seeing clients buying into that concept. We've always had those times where people come to us and say, hey, we need to have 125 completes on a qual, a multi-day qual study. And we love those, you know, but again, we're seeing more of that now and we're marketing to that. And there is a lot of interest in that particular area. So we're seeing it. It's a story that resonates. And I first tested out the whole concept at Quirk Chicago a couple of months ago. I think it maybe was in May or whatever, March, late March, early April. That was where I just, everybody that came up to the booth that we had a chance to talk to all the corporate people. I was just like, hey, have you thought about this? You know, as an approach and there was definite take. And then we just decided, hey, we need to button things down on our end, push a little bit harder into that zone, lean into it more, which we're doing. And I think that the future, if that message gets out there, I think it will resonate. And I think you'll get more corporate people who are responsible for producing, you know, incisive reports with strategic significance are going to say, okay, I want to eliminate the risk of error, fraud, you know, manipulation, whatever, and have it be validated stuff. Again, here's the numbers that came out. They are projectable. They're not 3,000, but they're still bonafide quant numbers supported by all the depth of qual with the why's. Why did they think that way? Why did they feel that way? What would they do differently? All those kinds of things.

[00:26:09] Sebastian: And what do platforms like Aha, and if you want to speak specifically to Aha, what do they have to offer, you know, in this sort of movement towards a larger scale qual?

[00:26:19] Ray: One of the bigger things that came about, it was really got hot in COVID, was what I call hybrid studies. And those are the multi-day studies. And I'm sure you're doing them all the time or involved in them. And that is, you know, let's do a three day study and then let's grab 12 to 18 people for IDIs or focus groups. You know, you might do three focus groups, you might do 10 IDIs. Okay. So you're able to identify star performers. So not only have you done this bigger study, but then you can also look at all of the different people that participated and say, okay, that person's super interesting. I would love to talk to them. And they may not be supporting or on board with wherever direction the study was going or about your product. It may be negative to your product. It may be positive towards ideas, whatever it happens to be. But, you know, they've got a point of view and you can see what that is. You've done some video responses with them during the course of those three days. So it's kind of a screen test of sorts, but you can pick people from different segments and you can pick star performers that are going to give you a point of view that you're going to really be engaged with in interviews. And it really does supercharge interviews coming out of a three day study, going into do IDIs or focus groups after that gives the moderator tons of stuff to work with. The client should be engaged because there's briefs and debriefs along the way, and they should be watching some of the action or at least getting top lines before they go into the live sessions. And it just becomes just a deeper, more connected and more strategic discussion with, again, vetted star respondents. So that has been a big evolution of, you know, the last few years. And again, that all kind of got started in COVID where it was like everything went digital, which was great. And that was that really big inflection point. But again, I think we're in another inflection point where this quality thing, you know, in the options that you have out there to do data collection, does Qual have another rising moment in the sky? And I think it does. Ray, last question for you today. What keeps you motivated? Love what I do. Okay. I've been doing this. I've been in the research specific space for now 23 years. Love doing what I do. I've been using research for a lot longer than that. I hate to date myself completely, but I just, I absolutely love what I do. I built a team here and a product and this new, this relaunch of our platform right now has been super energizing. It's nice to have done it before. I know you're a tech focused person. When you do something the first time, it's a journey, it's an adventure, and it turns out great no matter which way your work ethic gets into it, your smartness gets into it, and you put out something good. But to be able to do something like the second or third time with your own product is just amazing. This is our second go-around with AHA specifically, although Living Diary was sort of, you know, AHA before AHA. It's really our third kick at this. And it's just so fun to go through that process again. And just, it is exciting. I mean, AI changed everything, everything in the last couple of years. I mean, we knew it was coming and we got ahead of the curve and we've been using AI for almost three years now, but it's gotten better each and every year as to how we use it and where we apply it. So the excitement of that and with the platform relaunch and the way that AI is going to work on the DIY side, you know, I kind of call it, you know, supercharged DIY powered by AI, which is just phenomenal to help people through the process of things that they wouldn't normally know how to do just coming in off the street. So fascinating times all the way around. And it's just, AI is just going to make things go faster. Like the technical or technological evolution is on and it's just kind of in its infancy. So where is this all going to go? Exciting? I don't know. We're a part of it. We're in the thick of it. We have our ideas. We're doing things that are positive and forward-looking, but it is a very, very exciting time for research and qual research in particular. Ray, thanks so much for being on the show today. Sebastian, I appreciate it so much. Thank you.

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