Todd Renn
Founder of Todd Renn and Associates | Sensory Science & Consumer Insights Leader
Todd Renn is a distinguished leader in consumer insights and sensory science with over 25 years of experience driving innovation at industry giants like PepsiCo, Pfizer, Clorox, and Land O'Lakes. Holding a PhD in Food Science and advanced credentials in business analytics from UT Austin and Wharton, Todd excels at bridging the gap between R&D and marketing, turning complex data into clear, actionable business stories. Now the founder of Todd Renn and Associates, he is dedicated to helping organizations sharpen their commercial impact and training sensory scientists to evolve into strategic business partners.
Connect with Todd:
Dr. John Ennis
President of Aigora
Remember to subscribe and leave a positive review if you like what you hear!
Guest: Todd Renn | Host: Dr. John Ennis
Dr. John Ennis
Welcome to Aigoracast. Conversations with industry experts on how new technologies are impacting sensory and consumer science.
Dr. John Ennis
Hi, I'm Dr. John Ennis, President at Aigora. In this episode, I have the pleasure of speaking with Todd Renn, a true veteran in the world of consumer insights and sensory science. Todd's CPG leadership experience offers a unique view on why sensory scientists must learn AI. These tools cut through the noise to find the "so what" moments that drive business impact. We hope you enjoy this conversation as much as I did. And remember to subscribe to Aigoracast to hear more conversations like this one in the future.
(Music fades)
Dr. John Ennis
Okay. Welcome back, everyone, to another episode of Aigoracast. Our guest today is Todd Renn, a distinguished leader in consumer insights and sensory science with over 25 years of experience driving innovation at industry giants like PepsiCo, Pfizer, Clorox, and Land O'Lakes. Holding a PhD in food science and advanced credentials in business analytics from UT Austin and Wharton, Todd excels at bridging the gap between R&D and marketing, turning complex data into clear, actionable business stories. Now the founder of Todd Renn and Associates, he is dedicated to helping organizations sharpen their commercial impact and training sensory scientists to evolve into strategic business partners. So, Todd, welcome to the show.
Todd Renn
Thanks, John. Thanks for having me.
Dr. John Ennis
Yeah, it's great. It's great to see you again. We've been friends for a long time, so it's always a pleasure to talk to you. We were talking at Pangborn—that's what kind of led to this, to you being on the show—about a lot of the changes that are happening to the field. And you've had obviously a kind of long and diverse career. And so maybe it'd be good for our listeners who maybe don't know you to just kind of hear your story of how you got into sensory, how you got to be where you are now, and then we can talk about your experience as you've been, like everybody, trying to navigate the world of AI.
Todd Renn
Yeah, absolutely. So I got into sensory back when I was doing my food science program. I was actually at Florida State University working on a food nutrition master's degree, and I started hearing about this field of sensory science. And actually, I read Michael O'Mahony's book, which was interesting, and that was one of the things that kind of got me interested in sensory science. And my professor at the time at Florida State knew—or had met—Edgar Chambers out at Kansas State. And he had gone out there for a short course in sensory because he wanted to learn more about it. And he came back and he told me all about it. He's like, "You know, if you want to do your PhD, you really need to think about going into a different field, and this might be something interesting for you." So I went out and visited them and, you know, loved it and ended up going there and getting my PhD in food science. I really loved statistics, so I took a lot of advanced statistics classes there. Not sure why I love it, but I do.
Dr. John Ennis
I love it too, so it's all right. You're among good friends here with that.
Todd Renn
Yeah. And it's been great because there's such strong application of statistics to the sensory field, and I definitely wouldn't be as successful as I am today had I not taken all of those statistics classes.
Dr. John Ennis
Right. No, that's good. Yeah, you know, it's funny, but I never was even aware sensory was a field. I just grew up in it. It's like the fish in water. Like, when we would go out to dinner with my dad, he would always have us rate all the food and stuff and make us rate—he'd make maps and stuff. So it is interesting to me to hear about people discovering sensory as a discipline, because I just thought this is what people did. In fact, when I'm at dinner, I'm always, you know, with non-sensory people, it's always weird for me. So. Okay, well, let's go on then from your... So you got into statistics and did your PhD in food science. So then let's take you kind of through your career. Can you take us through the kind of steps after Kansas State? Where did you go next after that?
Todd Renn
Yeah, sure. So I started early in my career at Ocean Spray up near Boston. And I first started doing training, retraining their panels up there and bringing more statistical and training rigor to their panels, and also did some consumer research. So that's where I learned—actually, you know, it's interesting, my first boss was Carla Keston.
Dr. John Ennis
Oh, okay. Carla. Yeah, I've known Carla since, I don't know, 1993 or something like that.
Todd Renn
Yeah. So, you know where I met Carla was at your dad's training on Thurstonian Scaling way back in the day.
Dr. John Ennis
Yeah.
Todd Renn
So when I heard that Carla was going to be my boss, I was actually really excited because I had already known her and we were on pretty much on the same page in terms of how we think and the methods that we use and the whole idea of Thurstonian Scaling and design thinking, things like that. So it was really good to work with her as my first boss.
And then ended up going down to Minute Maid, went back home to Florida where I had grown up and spent a few years there. And then a friend of mine was working out at Conagra out on the West Coast in the old grocery foods division. So I joined him out there. And that was really interesting because that was the first time I had gone outside of beverages. Conagra has such an enormous number of categories, you know, across different types like frozen foods, refrigerated foods, shelf-stable.
And early in my career, I was pretty much the resident statistician in those first three jobs basically until I got to further up in Conagra, and then we did have a statistician in Omaha, Jeff Garza.
Dr. John Ennis
Oh, I know Jeff, yeah.
Todd Renn
Yeah. So he and I are good friends and we met there and I continued to learn a lot from him. But certainly early on in my career, I was the resident statistician. And that was back when SAS came out, so it was all the SAS programming. And that was one of the interesting things back then. It was, if you knew SAS, you had a much better opportunity to get a job. So many of the job descriptions said, you know, "Need SAS programming." Right? And so I started with that. And then later on a lot of the point-and-click things started coming out which made SAS a little bit obsolete for a lot of us practitioners. But so I went on with that. I learned a lot of my stats, especially Design of Experiments from Tom Carr. I'm sure you know Tom, he's a legend in the field. So I was always really into statistics as I went up the ranks.
After Conagra, I ended up going out to Clorox. And it was interesting there because what attracted me there was, one, I was able to work on things that were outside of food. Right? I worked on things like cat litter and fragrance and odor control, trash bags odor control. I worked on charcoal, auto products, you know, a few food products here and there, but a lot of non-food, some cleaning. And what was really nice about that job was I was able to prove to them that the principles of sensory transcend from food and beverage to these non-food categories. And the whole idea of this experiential nature of sensory and how it impacts the consumer not just from a tactical perspective and a functional perspective, but from an emotional perspective.
And I did like a big training program for them. They weren't really strong in sensory at the time. And so I put together basically this 10-module training program certification program for everybody in the team. And so we built up our sensory capabilities over time.
Then I ended up at PepsiCo. I was the Global Head of Sensory and Consumer Product Insights. So I had like a hundred people on my team, 14 different countries. It was a great job, learned a ton, had great people working under me. And after that, I went over to the agency side. So I worked for MMR Research Worldwide. I was Managing Director there for about four and a half years or so. And that was really interesting because then I got to see what goes on on the other side of the fence, right? As a client, you're pushing all this stuff out, and then you get on the other side. And it was good because it really was the same thing because it was still all about managing clients and doing work for clients.
And then I went out on my own and worked with a friend of mine that owns a consulting company, so I went out on my own for a while. Then I started getting really interested in this whole data analytics thing. And it really kind of fit in pretty naturally with my statistics background, right? All the multivariate statistics. And I'm looking at this saying, you know, all these machine learning ideas, this AI stuff, it's kind of complementary to what I'm already good at and what I already do. So I decided to get a Business Analytics certificate at Wharton, and that kind of opened my eyes to business analytics and all the techniques associated with that. And then I got even more interested in those things and I ended up doing a postgraduate program at the University of Texas in Data Science and Business Analytics. So now I added to the business aspect and the sensory aspect and the stats aspect, the whole thing of data science. Machine learning, supervised and unsupervised machine learning, and how applicable that actually was to the work that we do. Right? So we were still doing things like PCAs and Cluster Analysis and Predictive Modeling, all the things I've already done, but now it was a big plus-up versus what I had been doing previously. And so that's pretty much where I've ended up now is this whole data science thing and programming in Python and doing all my analysis in Python.
Dr. John Ennis
Yeah. Well, you gave me a tremendous amount to work with there, Todd. There's quite a lot of interesting threads to pull on. So one thing that I was thinking about actually that I never really put together, when you mentioned how the food side of sensory science, I think, is connected to the emotional reactions—the deep emotional reactions that people have to products, right? And how that transcends maybe the chemical senses. I never really thought about the fact that I think the chemical senses being kind of the oldest sense evolutionarily, they're the ones that maybe are the most... So maybe sensory scientists have had the inside track on studying emotions. And of course, emotions come out through music and sound and stuff. But I do wonder if sensory science... like what's your experience on that? Like when you were generalizing from food to non-food, what carried over? What didn't carry over? What were some of the things you learned during that time period?
Todd Renn
I think the biggest thing I learned was the emotional aspect, actually. When you're looking at things like cleaning products, that scent of cuing "clean," right? Is a very emotional thing for people. So people want their house to smell good after they mop the floor, right? They like a particular scent. But there's an emotional aspect of it. How good do they feel after they've done that? How good do they feel when their family comes home and the house smells very clean?
Something like cat litter. You wouldn't believe the emotional connection to people's cats, right? And what they have and how important litter is to their life as a cat owner. Because odor control is like, is the number one need for a cat owner as far as everything that they do. Food is second, and then...
Dr. John Ennis
And then the love.
Todd Renn
And then the love. Just like, "I gotta feed them, I have to clean up after them, and then I'll love them." You can make that a slogan now, right? So it was interesting the emotional aspect of that and the odor control and how people felt differently about their cats when they did... So there were... we did these huge segmentation studies where we looked at people and we came up with different segments. And the two most interesting ones were the—what we called the "Sensible Scoopers." And those were basically that just, they like clumping litter. They just wanted scooping and cleaning to be absolutely as easy as possible. Then we had a segment that they called the "Meow Mommies." These were the cat owners that were mostly female, they were like super attached to their cats in a lot of ways. And this whole idea of odor control and them actually believing that their cats feel better when they can go into a clean litter box, right? They also feel better when people come into their homes and they see, "Oh, you have a cat, but I don't smell anything." That's great, right? So there's definitely a functional and an emotional aspect to it.
Working on Kingsford charcoal. We did a lot of testing on functionality—like how long does it burn? How hot does it burn? But it's not until you get out into the field and start doing observational research and ethnographies with people who charcoal barbecue, when you see the emotional aspect of it. The fact that it is... it's actually ritualistic. Right? You look at it as grilling is functional, right? But gas grilling is functional, but charcoal grilling is kind of experiential, right? In some way. But there's something that was different about charcoal grilling that was very ritualistic and there was a lot of emotional cues involved in it. But you still had all the sensory senses, right? You could see how long it burned, you could smell the food, you could hear the sizzle. All the sensory cues were there, but it wasn't until we were able to connect it to the emotional aspect of the "Kettle Captain"—right? Those are the ones that were like the expert grillers and they loved to do it. But it's an experience. You want your family over, you have to have your beer, you know, the game's got to be on, there has to be music, there's a party going on around you. So it's very different than tasting salad dressing, you know what I mean? So these non-food things really were a much more emotional and experiential type of product to work on than just straight foods.
Dr. John Ennis
Interesting. Right. Because you don't have to... yeah, you have to eat. Everybody has to eat, right? So you've got some needs there. That's very... that's really interesting. You know, I'm definitely the Sensible Scooper in our family and my wife is the Meow Mommy. So, for sure. We have a robot actually, we have one of these awesome cat litter robots. But we can talk about that a different time.
All right. Well, now that we're getting into technology, let's talk a little bit about what we were talking about at Pangborn, which is... you know, you kind of alluded to this a little bit already, that there was SAS and then... It's funny how there's always this interplay between technical, almost text-based interface, right? Where you're writing code. And then at some point somebody comes up with a GUI. And then there's a level of abstraction where then you have to go back to code. And I feel like that was happening in 2015, is when I got really into data science, because it was clear to me so much of what we were doing was repeatable and you had to have scripts or you weren't going to make any progress. You can't really... Yes, I know you can have scripts that come out of GUI-based programs where it tracks what you do and gives you a script, but it's just not the same as having your hands on it.
And it's been interesting to me to see how the AI world is going through that same path. But before I share my thoughts, maybe you could talk about when did you first start to realize that AI was going to be a big deal? What motivated you to get into it? Like a question that I think is a good one is: Is it more about the opportunity of what you can do with it? Is it more about the fear of, like, what's going to happen if I don't do this? So maybe talk about how you got into AI and what your motivations were, what your experiences have been, what lessons you've learned.
Todd Renn
Yeah. So the first... my first really foray into AI was obviously Generative AI, was in the area of focus group transcripts, right? Translations. You do focus groups, you do six groups over two days, two hours each. That is a lot of talking. Right? You get the transcripts, then you have to go through them, you have to read through them, you have to take your highlighter out, right? You spend hours and hours and hours combing through these transcripts trying to figure out what the common themes are. And also you used to have to wait sometimes days to get the transcripts after the recordings. So now I have the app Rev on my phone and I just put my phone down in the room and it records the entire session. And then it has an AI feature in it where then it'll do an AI transcription. So it'll transcribe it, download it, just pop that thing into ChatGPT and say, "Pull out the top five themes," right? "From this transcript." And you can do that like literally on the spot in like a few minutes coming out of the focus groups.
And at the end of the day, you know, you have these long debriefs back in the client room, so to speak, and you're going through all this. And then you would go out to dinner with the focus group moderator and you sit there—and yeah, you're having a glass of wine, you know, you're eating food and things like that—but you're spending a lot of time thinking and going through, you know, what you heard and what you think. Now suddenly I have these downloads, right? Of the transcripts, throw it into ChatGPT, it pulls out the top five themes and then I can say, "And now pull out verbatims that support these themes." And it just made it so much faster. It didn't solve the problem; it solved the problem of filtering through all of it, right? It did the heavy lifting so that we could spend our time at dinner talking about what are the implications of what we heard, not what did we hear, right? So the AI took care of that—what did we hear? Now we're like, okay, what does that mean? And so what? And what does that mean for tomorrow? What does that mean for the client? And so at the end of day two, when we've done all six groups, you know, by the next morning, the client has a very full summary of all the focus groups where that used to take days. Right? They would have verbatims and those types of things. So that was really where I first started.
Then I went to... I did this data science program. And with machine learning, we had to use Python. And I'd never used Python before. So I think the first month of this program was actually just learning how to code in Python.
Dr. John Ennis
Were you using R before that? Or what was your language of choice prior to that?
Todd Renn
Actually wasn't using anything before that.
Dr. John Ennis
So Python was your first main coding language then?
Todd Renn
Yeah. Since SAS, right? Since SAS, because I didn't have to do that. I always had statisticians. But then when I went out on my own in the consulting world and I'm doing work for other people and they need data analysis run, so now Python is my go-to. But it's my go-to because I learned it in this data science program and I was really interested in the data science aspect of machine learning. So that was the benefit of that program actually was learning Python. And I saw I can use Python in every data analysis problem that Sensory and Consumer Science can throw at me and do it incredibly powerful.
Dr. John Ennis
Right. Except for maybe three-way mixed effects ANOVA. That one's a little tricky in Python.
Todd Renn
Good point. Good point. That one's a little tricky. Luckily I don't go too much into that kind of level, it's more superficial. But...
Dr. John Ennis
That's the one thing that's keeping R in there, is three-way mixed effects ANOVA. That's its one thing that it's still got.
Todd Renn
Absolutely. It's... R is a very powerful tool but I just... I never had to learn it, which was good because, you know, as I got later in my career, I was more in management, right? And less in the actual application. And so now that I'm back to the consulting world, now I'm back to the actual doing the work. Which by the way, it was interesting, I found out that I really missed doing that work. I'm like, I didn't realize how much of a stats nerd I really was until I started doing the stuff again and remembered how much I loved doing it.
So to your point, the Python and, you know, running scripts and it's very powerful, it's very customizable. Some other things like XLSTAT, I'm sure they're very good, but a lot of it's black-boxy and built-in and there's not a lot of flexibility versus with Python, there's a ton of flexibility. Now, I am not a Python coder and the program was not intended to make us Python coders. So I lean a lot on AI—Claude, GPT, now Gemini 3.
Dr. John Ennis
Yeah, Gemini's... awesome. Actually you kind of turned me onto... now I'm trying to learn the whole Cursor thing so I'm not going back and forth, but...
Todd Renn
We do some pretty complex custom analyses in Sensory and Consumer Science and it's not always the same thing. Right? It's different every time. And Marketing wants their tables run a certain way and R&D wants their tables run a certain way, right? As far as statistical analysis and the statistical annotations and things like that. Well, that code can get complicated very, very quickly when you're trying to add letters into cells of numbers. So I use things like Claude to help me program it. The thing is, I know enough about the Python to know what it's doing when I see the code. I know exactly what it's doing. But I would not be... I wouldn't spend hours and hours and hours trying to come up with that on my own.
Dr. John Ennis
Right.
Todd Renn
So what this does is it enables me to do the work that I would need to hire an intern or a statistician to do. I can run it very fast and then I use my experienced brain to then do the interpretation and the "so what, now what" part of the analysis. So...
Dr. John Ennis
It's really the same experience you're having with the focus groups. It's very analogous actually that with focus groups you had this, you know, labor that had to happen to get to the point where you could have the "so what" conversation and then that got automated. And now it's the same thing with writing Python code. I mean there are people that derive a lot of satisfaction from writing Python code, but I don't think either of us are those people. We want to get to the point of like, what... you know, why are we running the code in the first place? And the code is a means to an end.
Todd Renn
And it's an absolutely necessary tool, right? I look at it as a functional tool, a means to an end to do what I need to do. And it's one of many tools that we could be using. But it's the tool that I use. It's open source, right? So it's free. So I don't need to spend thousands of dollars on a license every year. It's open source, it's free, it's easy to use. There are so many code databases, there are so many libraries. A lot of great things about using Python but, you know, not being an expert coder really comes into play.
The other aspect of it too is my creative mind and I have ideas for an analysis. And I'll plop that into Gemini or Claude and say, "Give me some ideas of how I might look at this data differently."
Dr. John Ennis
Yes. A thinking partner.
Todd Renn
A thinking partner, exactly. So an example of that is I was working with a client and they were basically building a beverage and we did this "building blocks" exercise where, you know, you go and you pick your base and then you pick your flavors and then you pick your sweetener and then you pick how sweet you think you want it to be. And so the consumers are building their ideal product, right? From their own internal mind. And we had them build their top three. So build your first one, build your second one, build your third one. And this is all qualitative stuff, right? So this is focus groups. And so I was able to take that data and—instead of just saying, you know, X percent of people said that they wanted black tea as the base and X percent said they wanted green tea... I mean I still did all that, but there's no insight to that. Like what are the combinations of ingredients that then popped to the top? And then what's the degree of interest in those things? So just saying the percentage of combinations that came out on the top actually was just very superficial.
So, you know, I was using Claude and GPT and Gemini and just, you know, used them as a thought partner and came up and I basically ended up doing this kind of pseudo-TURF. Right? Where I was able to look at not just the combinations and the frequency but then rate it based on given an importance weighting, if that makes sense. And so basically I came up with a solution from qualitative data, basically for the client to say, "Here's your top... here's your line of your top five flavors. Here's one, two, three, four, and five. You know, these are your top three no matter how you look at it. And in the next two flavors, you know, you have four or five different options." So depending on how varied you want your line to be, you know, you can go this route. If you want it to be, you know, very much lemon-based, there's lots of lemon flavors that you could use. I don't know how I would have done that, you know, any differently with even just a regular like XLSTAT or something like that.
So it allowed me to give something to the client that was far above and beyond what they would have done. And you know how long it takes to run a TURF and how many tens of thousands of dollars it takes to run a TURF. This client doesn't have a lot of money. Right? So they can look at this and say, "Wow, this building blocks exercise and all the qualitative data that went along with it gives me an idea of what my line should be, where I should focus my resources, and, you know, pretty much how sweet do consumers expect these things to be." So that's one way that I used AI in both a qualitative to make it more like a quantitative recommendation and representation of the information.
Dr. John Ennis
Yeah. No, that whole Quant/Qual connection is really interesting to me right now that... You should look into vector embeddings. I don't know how much you've been playing with those. We can talk later about embeddings, but there's some really, really interesting stuff I think with... yeah, Quant and Qual, that's all about to happen. And let's have another call. You and I can have... we can nerd out on vector embeddings. You'd probably appreciate that.
We're actually almost out of time amazingly. So why don't we talk about... So you've talked about your experience. Why don't you talk about your advice? Your... you know, people who are now just getting into AI. You know, I think you're ahead of a lot of people who are, you know, they've got stats, they hear that they're using Copilot, but everybody—you know, everybody's using Copilot. So what do we need to do to kind of go beyond Copilot? That's something I'm thinking about a lot these days. Like what are the ways that you're finding yourself getting a kind of an edge using AI that you just wouldn't have available to you otherwise?
Todd Renn
Mm-hmm. Well, again, it's the speed aspect, right? You can get things done much faster. You can eliminate some of the routine work and the arduous work that frees you up for thinking. I will say the one thing about AI, especially Generative AI, that one has to be careful of is the whole hallucination thing, as one, right? It does come up with some weird things that just... you look at that and you say, "Okay, that's just like physically not possible." Right? It makes sense to AI because it's basically just predicting what should come next essentially in some of these LLMs. So you have to be able to look at that and know if something makes sense.
So it makes me nervous a little bit when I start hearing people say we don't need as experienced people if we have AI because they can do the heavy lifting. That's actually not the heavy lifting; that's the routine, that's the mundane. The heavy lifting actually comes into the thinking. So you have to really have a very strong understanding of your field and your practice and be able to know when one of these LLMs is just throwing BS at you, right? And being able to synthesize that and know what makes sense.
And also understanding how to prompt it. That's one of the things that I learned as I went is how to prompt these things. And the output is only as good as your prompt and the context you put into it. So...
Dr. John Ennis
Context is very important. Yeah, you have to really think about that.
Todd Renn
Absolutely. Just like in sensory science, right? The context of the evaluation, the context in which you put the information into these Generative AI programs is very important. But it can't be your voice, right? It can't sit in a room with a VP and talk about the data, right? It can't extrapolate to emotional aspects. It can't tell you what the tradeoff is between a point-three drop in overall liking and saving ten million dollars. Right? What's that risk factor, right? How do you balance the risk and reward of using sensory science to actually make business decisions?
So there's a lot of aspects of those—the communication, the storytelling. You know, I do use the AI in storytelling from the standpoint that I put the story together and then I'm like, "I'm not really sure that the order makes sense," right? Because I just put it all down and get it all out in the slides. And so I'll put it into back into the GPT or Claude or whatever and say, "Give me some feedback on the order and the logic of this presentation." So instead of spending hours beating my head saying, "Does this logic make sense? Does this logic make sense in this order?" and moving slides around PowerPoint constantly, that helps me get to my endpoint much faster. And I know my story arc then is followable by, you know, a senior executive or something like that.
But again, it's about, you know, understanding what it can give you and what it can't give you. Don't check your brain at the door. It's not going to give you all the answers that you need. Right? It's a tool to enable you to do something better, do something faster, but it doesn't take away the expert.
Dr. John Ennis
Definitely. No, I agree. I'm actually going to make "Don't Check Your Brain at the Door" the show title because that's really good. Because it's so true. And I know... I think about this with my kids that they're growing up in a world with AI. And I think we were actually enormously lucky that we got educated in the "before times." Because I do think that AI will really help with education, but it has to be used very strategically, you know? And the children have to be taught to use it. And unfortunately, the teachers don't really know how to use it themselves, so you can't teach what you don't know. So I do think that people like you and I who have spent a lot of time working on our skills and thinking about how to do things are well positioned now to use these tools.
And I think AI is kind of a strange tool because it depends on the skill of the user maybe more than... like if you have a power saw, right? You know if you can use a power saw or not, right? It's obvious if you don't. Whereas everybody thinks they can use AI because you're just talking to it and everybody can talk. But the reality is how you do that matters a lot. And you see stuff—I mean I see stuff on LinkedIn, people get bad results and I just think, yeah, well, skill issue here, you know? Like it's... you have to get experience, you know?
That I mean I tell my employees and my wife is a power user of AI for sure... You just use it for everything you can because it's a new thing, we're all figuring it out together. And the only way to learn it in my experience is just by doing it and just get started, you know? That's the advice I have.
Well, Todd, this has been really very enjoyable and we could talk for quite a while longer. We can do a six-hour Rogan-style podcast someday.
Todd Renn
Yeah.
Dr. John Ennis
How can people get in touch with you if they want to follow up? Maybe they want to hire you for a training or to have you come in and do some consulting work for them or they just have questions. What would be a good way to get in touch with you?
Todd Renn
Yeah, either LinkedIn or at toddrennconsulting@gmail.com. Anybody can reach out at any time. I can provide a Calendly call link if people reach out to me, they can see what my whole calendar is and set up a meeting, exploratory meeting, and discuss things. And I'm happy to talk to anybody really. And even if people just want to call and pick my brain. I enjoy conversations like we're just having right now, and I learn by talking to people. So even though I'm a consultant and, you know, I still like to just... the main goal in what I do is really try to help people make better decisions and do things. So that's how they can reach me.
Dr. John Ennis
Yeah, that's great. All right, Todd. Well, thank you so much. It was a pleasure.
Todd Renn
Thank you.
Dr. John Ennis
Yeah, thanks.
(Music fades in)
Dr. John Ennis
Okay, that's it. We 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. And if you'd like to learn more about Aigora, please visit us at www.aigora.com. Thanks.
Dr. John Ennis: Okay, that's it. We 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. And if you'd like to learn more about Aigora, please visit us at www.aigora.com. Thanks.
AigoraCast: Conversations with industry experts on how new technologies are impacting sensory and consumer science.
Visit your favorite platform and remember to subscribe:
Please leave a positive review if you like what you hear!
John Ennis is a leading expert in sensory science and consumer research, with extensive experience in statistical analysis and product development methodologies.
