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Finding Value Propositions That Resonate - Simulated Shopping

Alan Klement
VP of Products at Gtmhub
Ep 08

Finding Value Propositions That Resonate - Simulated Shopping

Hosted By
Ryan Hatch
Robert Kaminski
Guest
Alan Klement
Heading

Episode Details

Learn how top companies are using Simulated Shopping to quickly test, iterate, and find value propositions that resonate with the market. In this episode, Ryan and Robert chat with Alan Klement, VP of Products at Gmthub, to unpack how top companies are using Simulated Shopping to quickly test, iterate, and find value propositions that resonate with the market.

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Show Notes

Alan's Website

GTMHub

Key Discussions in this Episode

  • Why New Products Fail
  • Data from the Past Doesn't Work in the Future
  • Creating Predictions in the Market
  • Information Gathering Through Shopping
  • Gathering Pricing Feedback
  • How to Apply Simulated Shopping to your Process
  • Arlo Example

Transcript

Ryan Hatch: Welcome to exploring product. I'm Ryan hatch and joined by co-host Rob Kaminski. We are super excited today to host Alan Klement. Alan, welcome to the show. 

Alan Klement: Hello. Thanks for having me 

Ryan Hatch: wonderful. For those of you um, who who don't know, Alan, um, Alan's a thought leader in Jobs-to-be-Done theory and consumer and market behavior.

Uh, he's also VP of products with an S at the Gtmhub, And today what we're gonna talk about is simulated shopping and finding value propositions that resonate with the market. Um, so Alan, super excited to to have you on today and super excited to unpack, uh, simulated shopping together. 

Alan Klement: All right, let's do it.

Ryan Hatch: Wonderful. So before we get into simulated shopping it, it, its- itself and, and, and what it is, I'm sure people are gonna wanna know. Um, I thought I might just start off. with, You know, what is simulated shopping and what, what problem or what questions does simulated shopping, um, help us answer. And I know that, you know, one of the biggest problems in, in product is that most new products fail.

[laughs] Right. So Alan, you wanna take that a little bit and just talk about why, why new products fail and, and what you think some of the root causes are. 

Alan Klement: Yeah. I think it comes from two, two main reasons. I would say first off is, and we were talking about this before. Uh, the problem I would say is, is prediction, right?

So, I mean, just very simply a, you know, if, so we all use theories in our daily lives. I mean, when I am anticipating just whether or not what happens if I throw something out of the window what happens, and I anticipate that. Oh, I got the loud kids here in the background.

Ryan Hatch: [laughs] That's okay man.

Alan Klement: Yeah? All right. Very good. Um, so I, even when I'm thinking about what I should do Is everyone 

Ryan Hatch: are we losing him? You still there? Alan?

Rob Kaminski: I think he might be good now. Still there,

Ryan Hatch: there we go. Alan, we, we, we, we lost you for a second. there. 

Alan Klement: Yeah. Yeah. I think I had a power outage over here. Huh? that's uh, moving through the more of the woods has, has, has, has done that for me. All right. So it's, it's all about prediction, right? So we use theory it's in everything that we do in our lives, right? Theory is just basically using a, a belief about how the world works to anticipate what will happen in the future.

So, and you know, whether we're aware of it or not, when we are developing new products, we are making a prediction, right? We're making a prediction. I think people are gonna love this thing, or they're gonna love this." So on and so forth, that is technically a prediction. And whether you are aware of it, you are using some theory to construct that prediction.

Um, so I guess the first thing there is a lot of people are just not aware of that, that they are actually using theory, Um, and, and even if they didn't know they're using theory, they probably don't have a very good theory to, to, to work with. The second thing though, is that, which kind of is related to the first is that if you, uh, you know, don't know that you should be using theory or that you are using theory, you have no, no way of asking why, like, why thing, things went wrong.

And this is for example, one of the big criticisms I have with the lean startup approach, which is, let's be honest, it's trial and error. Oh, we'll do this. And then we should learn something and then magic will happen. Then we'll know what to do next, but there's actually no theory behind it. Meaning there's no mechanisms to help you ask, well, how do we find out which parts of it broke down or not?

So I think, you know, a big challenge in increasing the success of. Products that we uh, release to the market and minimizing the risk of them being a failure. I think starts off with recognizing those two things, right. That we're making predictions and that these predictions are based upon a theory. Um, but then also that we should have a theory that enables us to ask questions when things don't work.

Ryan Hatch: Mm-hmm [affirmative]. Yeah. One of the things we talked about, um, was it's impossible to guarantee success. Right. And that we don't, we don't live in a de- deterministic world. Right. Prediction cannot be 100%. 

Alan Klement: Yeah. Yeah. It's, it's uh, it's right. You know, it's, it's possible to do everything right. And still fail that's tha- that's the way it is.

So, um, which again, you know, th- having the right theory about understanding why maybe something did or didn't work will help you kind of zero in on that, which is, you know, a, uh, a good thing. because it could be again, if you, if you have a good theory and we can talk more. about, You know, examples of that going, going forward, but you could say, look, it looks like that this product should have been a success, but it wasn't, maybe we just released it the wrong time, for example, or maybe we should wait a couple years and try again.

Or maybe we should, you know, take some other approach to do this and not just completely give up on the idea 

Ryan Hatch: Or, or, or maybe lots of things, because there are a ton of variables that play into whether product's successful or not. 

Alan Klement: Right. 

Ryan Hatch: I think that's part that's. That's why it's so hard. Um, you also talked about like with creating new products data from the past, won't invent the products of the future. You wanna talk a little bit more about that? 

Alan Klement: Yeah. So it, it's, important to understand, and and this goes back to the theories part. So I'll be using the word theory a lot. I'll kind of give a little context of that. Um, when I talk about theory, I'm I'm talking about Jobs-to-be-Done theory, which is this study of it began with the study of why consumers.

We call it hire-and-fire products, or we could just say adopt-or-abandon products. So we're just trying to come up with some theory to help us predict that behavior. But while we were doing that investigation, we started recognizing that uh, this characteristic of markets and understanding them as evolutionary systems and, and then that kind of comes back to what what you were just uh, alluding to because, um, because they're evolutionary systems, there's three important criteria.

The first one is that they uh, have irreversible time. So things change over time. And also this means that markets and consumers have a memory, which is important. And the second thing is around they, they produce variety, which is of course, making new products or consumers adopt uh, adapt products. And also third thing is that there's some selection process going on.

Uh, those are the three characteristics we can go into that if, if you like, but going back to your initial thing about. Why data from the past does not work in the future. Well, it's because of this irreversible time element. So if you're sampling the market, for example, you know wh- what consumers are interested in last year, and then trying to use that to you know, anticipate what they will want.

Six months later, it's not gonna work, right. Or you have to to take into consideration that, you know, th- that that there's a half-life to that data because the information markets are always producing new information. Consumers are changing what they want, you know, variety new products being introduced.

So if you're just relying only on data from the past, I mean, sure. You might have a perfect, perfect understanding of what the market was a year ago, but [laughs] that may not be relevant today. It's, It's kind of like using. Uh, the kind of analogy that I like to use, that when, when people look to, you know, data about the past market data from the past, and trying to use that for the future, that's a bit like using a weather report from six months ago to determine what you're gonna bring on your trip next month.

And It's just, it's just ridiculous. So that's why we have to always kind of r- respect this um, irreversible time element of markets and not, you know, I I think directionally, you know, data from the past can be helpful, but only directionally, we should always think about you know, exploring the market in ways by like putting new things into the market and seeing it, how how it reacts.

I think that's the best way to, um, to, to think about it. And I think, and no I'm going to do it, but it's it's cliche, but that guy, he, he knew his stuff, uh, Steve jobs, right. It's totally cliché, but God, he really nailed a lot of things, right. This is what he meant when he said that. Sometimes consumers don't know what they want until you show it to them.

That's what he was getting at. Right? Because again, if you ask consumers what they want, well, they're just gonna tell you what they want today and they're gonna react to what's what they do or don't like about how they live and work today. They're not gonna be reacting to some new possible future that you are thinking about creating, 

Ryan Hatch: right. The best way to predict the future is to invent the future. 

Alan Klement: Exactly. Yep. 

Ryan Hatch: Right. And and the weather example's interesting because if you just like average the, the weather in the last three months and just, you know, project, well, that must be, you know, what's gonna happen the next 

Alan Klement: Right.

Ryan Hatch: ...three months just 'cause it's law of averages, law of large numbers.Right. 

Alan Klement: Yeah. 

Ryan Hatch: And all of a sudden, uh, well, we're hitting, we're hitting fall now. Like the past three months is zero predictor of, of the the next three months.

Rob Kaminski: Right. 

Ryan Hatch: and you know it's, it's, it's interesting. Cause you talk about theory and I think about, well, what does that, people, what does that mean to people? And I mean, one of the things that I think w- with the weather analogy, it's like data.

If we just take the data and just average it for the next three months, it doesn't work. But but you know, the only reason weather predictions are even possible is because they they put that, that, that data into a theory, into a model and and the model is used for prediction, right? 

Alan Klement: Yes, exactly. Yep. That's exactly it.

That's our, that's why it's, that's why, whe- when I'm you know, encouraging people to think about, um, you know, how to create new products or, you know, explore what customers want or will, will want right, in the future. Uh, it's best to work with with now data as it were as, as much as possible and even better, if you're able to generate no- now, data, Um, and you know, th- using some sort of research method that could kind of help you, you know, it's like, you know, you, you won't really know like how the bear will react until like, you poke the bear.

So it's like, you, you have to poke the bear and see what happens. And then you'll know you can't like, Because maybe he'll just continue sleeping or maybe he'll get up angry or whatever it is, but you won't really know until you do it. So I, I I kind of use that same thinking. Um, when when thinking about products, you, won't, you won't really know what customers want until you put the thing in front of them and, and see and and see how they react to it.

Ryan Hatch: Yeah. 

Rob Kaminski: It's interesting. It almost makes me think like the, and we're gonna get into simulated shopping obviously is, is the core of this. You're you, you talked about past data, ideally you have current data or something really close to present to make decisions. Uh, but I almost view and and we'll get into it, but simulated shopping is a way of almost creating future data or 

Alan Klement: Exactly, yep.

Rob Kaminski: ... backcasting it into the present, so to speak 

Alan Klement: Yep.

Rob Kaminski: ...so you can make decisions. So I think that's super fascinating. 

Ryan Hatch: Yeah. th- Uh, Alan , when you and I started talking about this stuff several months ago, um, I came to you because I had just launched, I had just done all of this research for a new product, done all my, um, customer discovery. Very well, super proud of it. Um, came up with, came up with the offer, the value prop, the whole thing, put all the messaging, everything that seemed to be like, man, this was gonna be it.

And I put the landing page out there and set it live. And and what I got back was not a lot of conversion rates, like very low conversion rates. And I had a lot of clicks on my ads had like almost no signups, if you will, or conversions. And [laughs] I called you, cause I was like, Alan, what do I do? Right? Like. Just just, just try to bounce this off somebody, because what lever do I pull on?

Right? What, which lever do... is it? The pricing that's wrong? Is it the value prop? That's wrong? Is it the, the target audience that's wrong? Is it the messaging? The imagery, like there's so many things like a value proposition is so it's so many things tied together. And what you told me on that call was, was Ryan, they're all interrelated, right?

They're like, so inter interdependent that the experiment I was running, it it was just really hard to see, um, to know what to do just from a landing page test. And so what we're gonna talk about simulated shopping is how you can actually get that, get people's responsiveness to, um, to the, the, the problem and a solution all at the same time.

But I, I'm I'm excited for where we're going. But I think that was one of the things that I realized is that the the problem and the solution they're in a value prop, they were all so so interwoven. And I think that's, what simulated shopping. I hope, um, is gonna help us with is teasing those individual parts of the offer, um, uh, to be separate things.

Alan Klement: Yeah. Uh, If if I were to suggest someone how they could visualize it, your, your offering and when I say offering, it's the whole thing. It's the, the, the product, the price, the positioning, the company making it. I mean, it's, it's everything together. It's, uh, you know, it's, it's kind of, it's really difficult, you know, we can, and we do do it, but it's through iterative experiment, but you know, you can't really so cleanly isolate variables, right.

Because they're all tightly coupled. So like, think about. it, You know, you, when when, when I think about an offering, think about it like as a spiderweb. So like when you pull on price, when you pull on that one, when you start pushing in on price, well, then every, it's gonna, you have to figure out how to affect everything else at the same time.

So that's kind of how, how how we think about when we're kind of designing uh, these experiments. It's not, it's not the statistical way of, you know, from Fisher and you know, all those guys 150 years ago when they were studying, you know, wheat crop and so and so forth, or even kind of, uh, an industry where a, a lot of statistical work fi- first, first came on.

You know, Yeah, you can isolate, you know, when you have a machine arm moving around putting it in a screw, okay. You can isolate parts of that thing and and break it down. But you know, when you're talking with human behavior and, and and how people think, you know, they, they think about it all together. You know, like, again, I, I I think you talked about this before.

I, uh, people get blown away when I show them the the Prada phone. Right. Or or show like a screw of the Prada phone. And they're like, actually I showed to someone last week and he said, oh, what is that? some kind of cheap iPhone knock off?"

Or not cheap, but like some kind of, I iPhone knock off? and I said, no, this this came out six months before the iPhone.

And he just like "What?" [laughing] Um, and you know, I was like, well, it was just that it wasn't Steve jobs standing up there and in this black t-shirt waving it around. it was released at consumer Electronic show by LG and no one cared So anyway, it's, it's, it's it's I guess also going back to that topic of it's possible to do everything right. And still fail. Uh, you know, there are you know, certain variables that maybe you can't control or too difficult to, to isolate on their own. 

Ryan Hatch: Yeah, so go, everyone, go check out Prada phone.

Alan Klement: Yeah.

Ryan Hatch: ... Jobs showed me and I'm like, whoa, this is, this is crazy. Um, but let's get into it. So let's, let's start talking about simulated shopping. So let's do some great background Alan. So I'm gonna do is, um, is pull up your dream one. And just have you start talking and walking through the deck slowly and kind of the intent behind each of these uh, screens. And this is, this is meant to be by the way for context, this is meant to be, um, you sitting down, one-on-one kind of a customer interview type scenario.

Alan Klement: Yep. Yep. So what we are, the questions we're trying to answer here, right. Is, um, well actually let me back up here. The, the purpose here is we're trying to create predictions right? going back to the very beginning of this call, right? So we are trying to help us, You know, create really good predictions of if we do X, if we put X into the market, you know, a product at this price that looks like this made by this person, and so on and so forth, how will consumers react to it?

That's what we're trying to, do. and we can kind of come back into, actually, if you wanna have an interesting discussion we maybe um, look back this afterwards. It's actually okay to do this one-on-one and make an inference about the whole. We can kind of come back to that. Why, why that works, but I'll, I'll, I'll continue on, on, on this thread.

So for simulated selection here we are, uh, trying to again, again, you know, make that prediction of will the market select this tool, Then we're going back to the idea of markets as evolutionary systems, right? Irreversible time produce va- variety and selection. So we're trying to probe it like, you know, how will the markets select for this, for this offering, um, and will it select from this offering?

So the idea that, that we do and then th- there's different ways. so Honing down now to what's going on here. we, We had a customer dream. Now this is, this happened about a year ago. So I I'm, I, I feel, and they've pivoted dramatically since then, which I can talk about why, but, so this is why I, I feel it's fine to talk about this.

It was about a year and a half ago. They were trying to figure out, okay, we, we have this actually maybe I'll, I'll jump right to it. Um, they have this, or this kind of like headband product that you wear? So they had developed this technology. That was really great. It's kind of a, it's, it's a, we'll call a sleep lab in your home, which I think was even one of the, the, messaging that, that we tried, which since I'm all on it, this freaks people out, by the way.

So that was kind of an interesting thing, people were like, what does that mean? But, uh, you know, again, the idea is that they had developed this technology that measures your brainwaves as you're sleeping, and that this can be used to help, help you figure out what you should change in your lifestyle. So you get better sleep.

And So like, they have some amazing technology and like, okay, how can we, how can we uh, monetize this if you will, and they tried different models. And it had never really broken out of, I mean, they, they'd sold thousands of units, but you know, of course they wanna sell millions. Uh, and, but it never kind of got out of that, like early doctor kind of, uh, you know, people who really like to kind of, you know, those um... how do you describe it, Ryan, you know what I mean?

Those like, I I'm, I'm trying not to say geeky people, but I think you know, what I mean.

Ryan Hatch: Yeah. Tinkers, you know, yeah.

Alan Klement: Yeah, tinkers, like those people. Right. So it, it, it is, so they have some uses like that, but they're trying to go more through that diffusion curve. How do we diffuse this to that early majority as it were, Right. And so we're trying to figure that, and they're trying different offerings and things and they couldn't get it.

Right. So I'm like, okay, well let's actually do this semi-selection study and see what happens. So we've gone through different iterations of this, but the idea is this is basically we start with first off kind of. you know, How would you kind of describe yourself right now? So this is how we start off. So this is our, the, the purpose of this particular slide is to find out, okay, the person I'm talking to, you know, if we're to kind of come up with our ICPs, which one do they fit into?

Or Are they both of these scenarios or are they just one or the other? right? Are they someone who maybe has chronic sleep problems or are they someone who maybe doesn't but is, we call these life lifeline, lifestyle optimizers? Or are they somehow some combination of both and that can help us-

Ryan Hatch: And you kind of ask them, hey, are you more, this, or are you more of that?

Alan Klement: Oh, yeah. well I actually, it's very simple. We say, oh, how are you doing? Um, I'm gonna show you this screen, read it, and then tell me which one that you identify with most. And they'll just read it to themself and they'll be like, oh, and you know, it, it might sound weird, but almost 100% of the time is like, oh yeah, I'm definitely the one on the left, not the one on the right." Or they'll say I'm not really either of those. Uh, I mean, it's kind of interesting how people will give you when you make it this concrete, they will give you a nice concrete answer, which I think is why we use visuals too. It's like, I'm not really that person on the left or they'll be like, yeah.

That's me like that alarm clock is right next to me and my eyes are open all night long and it drives me bananas. 

Rob Kaminski: Yeah. 

Alan Klement: Anyway, so [crosstalk 00:21:30]. 

Rob Kaminski: Alan, I have a question for you 

Alan Klement: Yes.

Rob Kaminski: ...and I don't, we don't have to go down the rabbit hole with this, but how do you decide who you put this in front of? So like, what were you using 

Alan Klement: Yeah.

Rob Kaminski: ...for your recruiting to jumpstart this?

Alan Klement: Yeah absolutely, So these are people who should be our customers again. So we think of like, who should be the customers again? Not trying not to think of going backwards. Like that's why we didn't like, oh, people who are, you know, currently, I mean, you can do that. People are currently buying X, but you know, again, if, if it's a new thing and you're trying to create a new market niche, then technically that niche shouldn't exist.

So there is no kind of prior To base it upon you. You have to kind of like, say like, look, we think that these types of people should be buying this product. So th- that's literally kind of how we start off. 

Rob Kaminski: Got you, So you use basically criteria that's available and you make a judgment of like 

Alan Klement: Yeah.

Rob Kaminski: ...people who do that should do this in, in our theory approach. 

Alan Klement: Yes, absolutely. 

Rob Kaminski: Great.

Alan Klement: Uh, so I could continue on to the next parts. Ryan walk through it. 

Ryan Hatch: Yeah, totally. I think one of the coolest things is like, um, actu- I'm actually neither, right? Like, 

Alan Klement: Yeah.

Ryan Hatch: ...oh, then who are you? And then, oh, you, you're realizing that I'm missing a segment in here. right? 

Alan Klement: Yes, exactly. That's the other thing too. Like, and then that also kind of helps us figure out that, like, if they say that, then we'll continue the conversation for a little bit, but we ignore everything they say, for example, because you know, that, that's also an important thing to realize is you wanna make sure you find out who you should not be talking to.

Because yeah, there, there have literally been calls where that, that happens. They'll say, oh, actually, you know what, I'm I'm neither of those, you know, I, I sleep fine and, uh, you know, I don't do any yoga or I don't buy anything like that, or I'm not really a big exerciser. and I'm like, oh, okay. Well, all right, well, uh, how are you doing? [laughs] 

Ryan Hatch: yeah, you could actually detail people pretty fast.and feel like- 

Rob Kaminski: Do you kick them out of the call or do you, do you actually have that conversation of like, what else is going on in their world? 

Alan Klement: Yeah. Well, I mean, we, because we have them on the call and we kind of go to the next one, like, oh, so then you've, you know, have you ever done these types of patterns? And so, I guess that means that you've never talked to your doctor about sleep problems or you don't read sleep journals, or you've never kind of explored how to optimize your sleep your fitness.

And they're like, yeah, well, that's, that's not me. Okay, great. You know, it's kind of further confirming that that-

Rob Kaminski: Yeah.

Alan Klement: ... uh, disqualifying them. So then what what we'll do is then we'll kind of slowly roll out, um, some text. Now this is kind of what the, also the other thing too, is that we wanna feed. Okay. So going back to the theory part jobs theory, right?

So, um, we see consumers as, as you know, adaptive learning agents. So like for example, I, I call shopping is a information-foraging process, which is basically I am gathering information to construct my preferences, right. To to figure out what I, you know, what's interesting to me, what's not, what should I buy? So on and so forth.

So to help us kind of understand that process a bit more, we slowly feed them information and see how each new bit of information changes their opinion, or if it does at all. And so we'll slowly tease out things. Okay. If you see, you know, so, okay. Sleep checkup. What do you think that means? Right. And it's also the other thing too, which is, I, I, I think and, and all, this is actually probably a good point to mention it.

Ryan, you know, you had talked about, you know, your issue, i- issue with your thing before, and that is, you know, uh, a lot of times people think, oh, okay, well for developing a new product, we'll start with some needs analysis. And then we'll, you know, figure that out and then build some product and we'll study things in isolation.

And then we'll put it all together in the end and then, and then we will uh, release it. But what's, you know, you know, again, what we're trying to do here is to kind of, you know, find out from them if they're able to even comprehend or extend what you put in front of them, like, for example, a great way to disqualify what someone says Is, if you show them a new product you know, I don't know, here, here's this new thing, you know, isn't it cool. uh, wireless headphones, or whatever, and if they, like, and if they say, oh yeah, that's totally cool. That's totally cool. That's so neat. I like that. Okay. So great. You know, I like the color. I like the box Like, they're talking they're reacting to the thing, But th- but what you wanna get them is say like, oh, well, what changes for you?

Like, how is your life different because of this? And if they're just kind of like big question mark on their head, they're like, okay, they're just, they're, feeding me BS. Or they're just, they're just reacting to this because maybe th- that's what they want to hear. They want me to tell them that their baby's pretty, but if they can't connect those dots in their, in their mind, then you know, that, that you're talking to a dud here.

So again, so if I put like sleep checkup in here and they can't really, you know, as I unfold information, so I, you know, go through it, like, what do you think about this? If they can't really like, explain what that means to them or how it does or does not resonate with them, then, you know, you basically throw out what, wh- what this person.

is saying. So that's been kind of the process. So we kind of are just slowly unveiling to them, like, okay, well, here's kind of, you know, some, some of the descriptions here, here's some of the things of how it might work. And again, we're just kind of teasing the information out, okay, here, here's the headband. How do you think, you know, what do you think about this again?

Like, like, like this is a good example. People who give you good information are gonna say, oh, okay. That headband looks cool. That looks really neat. Um, I'm sorry. That's bad information, right? Because okay, great. They're just telling me my baby's cute, but they, but, but when they say things like, "Uh okay, that looks uncomfortable.

Yeah. I'm not, or like, I don't know. Is that gonna give my wife cancer as we're like laying in bed? That's good stuff. That means that they're actually are mentally simulating consumption of the product, which is what you. want. So again, it's, we're just kind of feeding them so I say, we're giving them, we're kind of get feeding them more information.

Are they able to connect the dots more? Are they kind of getting it like, how they're reacting to little bits of it at time? Um, you know, and then this is kind of just, how we like reveal the product to them. And, but also as we're doing this, we also try to to tie it back. So we said, okay, you know, we're showing you this, we're showing you some of the qualities of this new thing.

Like you said, that this, you know, so this, oh, that's pretty cool. I get to see, um, this, uh, my sleep stages. Right. Okay. That's interesting. Well, do you remember, you remember way back when we talked about this, how does that help you with this? And they were like, oh, uh, I'm not really sure. Then again, you're kind of maybe like, okay, maybe there's something wrong here, but if it's like, well, if I knew my sleep stages, then I would know the optimum time to go to sleep or, you know, I would, uh, I would, I could get up earlier in the morning, which is, I've always been wanting that, So on and so forth. It's like, Trying to get them to again, mentally simulate, engage and project themselves in the future with this product, which is what people do when they're shopping. I mean, that's exactly what you're doing when you're, when you're looking at Amazon, like, what you are doing? You're thinking about using the thing.

So we're trying to recreate that experience. Like, are they mentally simulating using this thing? So if they say things like, oh, that, that looks hard. Or, oh no, yeah yeah, that's, that fits right in on top of my desk real easily. Then, then, you know, that you got a winner. so I'll, you know, this is, this is this is pretty straightforward, like, and sometimes you get a little interesting things as you get more and more granular, like a simple example here is when we showed them this screen, they said, oh, Perrick, Um, I mean, God." They'd say like, look I, It was kind of funny. This was almost every respondent said this like, oh, Perrick, Yeah. But that's, that's not a real person. Like, I mean, we know that's like some kind of bot, right?" Literally Perrick was on the call with us, [laughs] listening to this. So he actually was on he's like, no, actually you're talking with me and I thought it was kind of funny too. And so [laughs] this deck is much longer than, than what we would do. Uh, I'm actually, This is kind of a, a summary. We would never show people this many slides. It was just this, for, for this purpose. This is just kind of, um, you know, the summary of everything. because we were iterating upon things. So wh- when people would say, oh, that that makes no sense to me.

I don't understand that they would try to update it for for the next experiment and see, okay. They couldn't tie that particular chart back to how how it helps them overcome their insomnia. Let's change it in a way where maybe we we think it will. So then, you know, this is basically going through the product and then, you know, we try different variations.

Like we try Okay, you've got the product. Now what about this sleep club. thing, Right. And, and here's some of the qualities of it and yeah, and yeah I mean, this was a big dud. People are like, look, I, I don't trust that because, uh, you know, who are these people on these forums That's probably gonna be more more confusing. There's gonna be misinformation?

I don't want that, you know, who, who are these people on there just making things up ra-ra-ra,you know, just like raising all these anxieties and, and trust issues. 

Rob Kaminski: Yeah. 

Alan Klement: And then finally, you know, we, we get to the end part, which is we do like that game of, uh, Price Is right. As it works. So, you know, here are some different, different pricing things or packagings.

How much do you think these cost now? The exact number is not something that we trust because, you know, people just make you know, make things up, you know, like, oh yeah. thought $100 is a fair price. Okay. Well, all right. Well, okay. Like a lot of people just kind of like, will go and write that down, but I'm not gonna trust people because hell, like I paid this person to talk with me.

I'm not gonna trust what they say, but. What we can learn and is reliable is what choice set. They put it in. So when they say $100, well, why $100? Oh, well, because I've seen products like this before A, B, C, D, which are out there on the market right now. And that's how much those costs or, oh yeah.

This would probably be $300 because you know, this that's how much I pay to go see a sleep specialist and this would would replace seeing a sleep specialist. That's what you wanna know It's not the $300 is that it replaces a sleep specialist as it were. And then we show them the price, kind of see how how they react to it.

Um, and then, you know, which is, you know, it it, it became a fun game with them, you know, they perceive it. Um, but again, it's kind of like, you see what's your reaction. Um, and, and in this particular case, it was that makes sense because products like that cost like. that, But they couldn't really rationalize it with what it really replaces.

I mean, some people said, well, I guess it would replace all of the effort in going to see, see a specialist, you know, taking a day off work, um, seeing a, you know, a doctor's office for, for half an hour. And then, and then, you know, paying some copay or whatever it is that was mostly that like, that was like at the like high end of it, of like the closest to accepting the onus to pay.

But at the very, like other end, we have people say like, that's ridiculous. I had this app on my phone, that's $5 a month. Um, and it has the sonar thing or what, I don't know what it..." they'll say this. I don't know what it does, uh, but it just kind of like measures me while I sleep and tells me if I'm sleeping good or not.

And that's good enough. So it's, you're not gonna get like, oh, should it be 279 or 289?" Those are things you have to do, like actually for the market, but from broad strokes directionally, Which is what we're looking for. Right. We're looking for multiply-by-zero factors here. Like, what are the, what are the the critical points that will cause this to be a failure?

Which again, going back to the prediction thing is also how we work. We can't, and we were talking about Ryan. We can't predict if a product will be successful again, no one can do that. Right. That's why, you know, weathermen don't, don't predict more than 10 days out. And even then it's kind of like dicey, but, but what, what we can do is say why it won't work.

And so actually when we're working with our, with our clients to construct, um, a prediction matrix of this, we highlight the multiply-by-zero factors and say why it would not work. So we classify things, not as it will be a winner. And here's how much we say, like it's either low risk or almost no risk or extremely high.

risk." And then some, some of them give me some plans of, okay, well, if you want to reduce these risks, you know, here's what you want to do. So like, in this example for dream, we said, we basically, the, our recommendation, our prediction was this is a high-risk offering for the biggest reasons. First off is trust. People perceive the way you know, they, they never heard of this before.

The way the styling of it, the colors, the packaging, so on and so forth, it looked like a startup. Everyone kept describing it as a startup and people did not want to trust medical advice from a startup or invest $300 in a product from a startup. So that's like the first thing it was like, look, high risk is people just don't don't trust the brand for one at this price.

point And also on the subject matter. And then, um, yes, the, of course the, the other big thing was that when people are putting into a choice set, it was very often things like, for example, going back to what I said before sure. It's inconvenient to go to the doctor's office for an afternoon, but my company's insurance pays for that, I paid $20. So, you know, it's inconvenient, but you know how people, people are crazy, like they, they will burn half a day to save 50 bucks. So it's like, so when they say that, like, okay, high, high, high, you know, high risk there for that. And a few other reasons there too, but you know, it is mostly the two biggest things were, it was the trust, huge, huge tank on the trust issue.

And the other really big multiply-by-zero factor was, was the choice that they were putting into, like, Uh, you know, I'll use some app on the phone or, you know, uh, the, that, or, or the other thing too, if they did mention the Ōura ring, which came up sometimes they were like, yeah, but the Ōura ring seems more comfortable to use as I sleep.

That looks really uncomfortable. So again, the progress was there. So, you know, we, we, we would validate, yeah. You know, this part is totally accurate. The ICP is right. The progress, the needs and so on and so forth. What didn't work was the offering. We couldn't get fitness in between the offering and the type of progress that consumers want. 

Ryan Hatch: I mean, I think for me, one of the things I love about this, I think it's actually the, you talked about mental simu- uh, mental simulation. right, And it's actually like a tweet. I have favorited it since, I don't know when I favorited it, but like 2016 or something. It was when we were talking about this stuff.

It was this realization that before people buy products, they first have to imagine in their mind how it will help them, how it will change the way they live and the way they work. And what I love about this about simulated shopping here is a couple things. I love the, um, incremental revelation. I'll call it, um, right where it's starting out with just the sleep checkup.

So what, what is that now I have to imagine in my head what that is, and you're getting a, a real sense of. Um, positioning. And can you, are you communicating clearly just, uh, there's just the concept and you can do this with, with nothing, right? Like you can do these first slides, um, in in just text form. I think that's, that's pretty fascinating.

And you said the sleep lab, like the next slide, caused people just to kind of like, uh, cower away from a lab, what am I a rat? What am I gonna have a whole bunch of electrodes in my head? Right. Like, um, it it was, it was off-putting. I mean, what, what powerful things to be able to realize this stuff really, really early on, um, before your deepened development cycles. Um- 

Rob Kaminski: So ryan, you triggered me. That's um, like going there with figuring it out early on. And so, Alan, I'm super curious, And is this you, something you said about, we can't predict a product will be successful, but we can predict why it won't work. Like that really stuck with me. And so I I almost look at this tool as is it an evaluative tool, simulated shopping, or is there a space for it where you can use it as a, a launchpad to innovate, right? As you're kind of conducting the early steps to this, or is it a binary, will this work, will it not? And we'll show you why, how do you look at this? And where our, even our listeners might apply this? A lot of folks are working in startups with half-baked ideas. Is this for them?

Or do they have to have that? You talked about future kind of in their mind, that vision, and then work back to see whether it's it's gonna be real or not, whether it could, might actually work.

Alan Klement: Yeah. I, I, you know, I think, you know, and I think, I don't know if it was on this call or time during our uh, warmup time, um, before this, but you, you, you kind of talked about like, you know, which is what people uh, it's, it's like, sometimes you see in Twitter, people are saying like, oh, how do I find an idea for a startup?

Or like, how do I find an idea for a business? Right. I, you know, a lot of people do that. And then they think well, I'll start by looking at people's problems, like, oh, okay. Um, and we kind of talked through that and we can digress. And maybe why I don't like that, but I I, I would use this. And and kind of going back to, to what Ryan said, said a bit, you could even use some some of this stuff by kind of having conversations about how people react to to this kind of stuff.

Use this as, as like inspiration, right? Like for example, suppose all that we had was literally this right. We don't, we don't even, this is like where our ideas stop. Right. And then, but you know, let's just, just kind of put some words, literally word because words aren't words and pictures and experiences and things, those are all information.

So this is just a different type of information a consumer is getting to arrive at some judgment. So, so for this, you know, and and kind of where I'm I'm, I'm bringing this to, we found that, for example, the combination of words, personalized recommendations, got everyone excited. Right 'Cause it wasn't just personalized.

And it wasn't like, even down here, like by only because like, for example, it'd be like, oh, we can make sure that you get the right recommendations even, or, you know, uh, a a brain. because you know, we went through different versions of this, right. So people are, oh, you know, measure your, your, your, your brain activity and get sleep recommendations.

People are like, ah, you know, whatever. But for whatever reason, the combination of personalized recommendations that was like, oh, that's what I need. And then like, well, why is that important? Well, because I read all, I read all this stuff from, from, uh, you know, sleep, whatever, sleep magazines and you know, the top 10 ways to improve your sleep.

And it's all general, it's not for me individual." And then when I go to a doctor, you know, the doctor's supposed to give me a personal recommendation, but you know, she's just trying to get me outta there. because she's got 10 other patients to see. And she just basically asks a few questions and always gives me the same medication or something," Which is a generic response. So people are like, I need personalized recommendations. So even, I mean, hopefully I'm answering your question, but that can even be a way of, of like kind of probing that kind of, you know, fuzzy end of, of like, I guess, opportunity fishing. If you wanna call it of 

Rob Kaminski: Yeah.

Alan Klement: ...figuring out kind of like, you know, like, like where do we begin with?

Rob Kaminski: So if I were to play that back a little, what I'm hearing is you should have some thought into what the idea is. It ends up being the landing ground to trigger that where they go mentally, which might take you to a new idea. But without that initial thought, you can't just say, like, ask them about their problems.

Otherwise to use your example, they might not be shopping for personalized recommendations. 

Alan Klement: Yes. Yes.

Rob Kaminski: ...It might just be something that is there that they're not aware of. And only by you putting it out there in words or a picture, can you trigger? Oh yeah. And then you dig deeper is it real by asking why and how to actually validate that? 

Alan Klement: Yeah. So again, you know, and I I, I, I love kind of challenging or, or poking people about like, oh, you talk to people about their problems or things of that nature.

And you know, again, people like, you know, we as product people, that's what we do. We sit around thinking about people's problems or innovation, that kind of stuff. But like, look, we, we got to get out of our own, like our own biases here. Like most of people are like, "God, I got to pick up, pick the kids from work.

Uh, I got to make dinner. Like they're not thinking about granular detail, [laughs] whatever problems around like, you know, ra- ra- uh, cord taping up cords are just like, ah, these fucking cords, like, ah. they're not like thinking in minutiae detail on a scale of one to five. like How does it feel? Like they're just, they're not there.

They're like, ah, I got the kids are you remember before my kids crying, like what's going on. there?" like, it's, they're, they're just not there. Um, but the other thing too is also is that like. we, Yeah, I think this is actually part of the the, the more interesting thing for it. We like when it comes to our problems or our experiences, we develop habits.

What's the faster way? Con-, Your consumers are fish and water, not, and they're not asking about like, why are we swimming in water? So when, when people are using a Blackberry with a keyboard, they're like, well, yeah, that's, they're swimming in water. Well, yeah. Well, that's how you use a smartphone. of course. Like What do you mean uh, keyboard or what are you talking about? that's how it's, That's what a smart smartphone equals screen plus uh, keypad like that's it, they're swimming in the water. So if you ask them problems, they're just gonna be like, Well yeah, sometimes my thumbs get tired or sometimes it doesn't, doesn't feel quite right my fingers. are..." They're just reacting to like the water, they're swimming in, you have to like pull them out of that water and be like, well, Hey look, there's land. Oh, what. what?

Ryan Hatch: Yeah.

Alan Klement: "What is it what is this air you're talking about, you have to show them new things and see how they react to it. 

Ryan Hatch: And I think that's also why you, you know, uh, you're, your consulting agency that, that was, um, um, was called actually, you know, idealized innovation, right? 

Alan Klement: Yeah. 

Ryan Hatch: It's the idea, it's the idea of taking them out of the water, the idea of bringing them into the future. Right. But we have a- as innovators, it's our job to innovate and create a new tomorrow and, and and project that and cast that for them. And that's what we're kind of doing here. I love how, like, if, if anyone listening has got an idea and they're trying to go validate it, there's lots of different ways to do it.

Um, but you can see here how, how effective this could be, rather than going and running, you know, several thousands of dollars in keyword test testing and trying to, you know, launch all these, all these ads. and All those things. It, It could be as simple as a slide like this, right. And 10 conversations to figure out directionally what resonates, what doesn't, what freaks people out, what what new, what new anxieties am I creating with a sleep lab?

Um, and and I think what's powerful about this to to do this kind of work. You don't have to have a finished product to do exercises like this. Right. You can just get with a designer and dream up the future and run people through this incremental revelation and slowly feed them information. See, you know, what, ch- what, what lights them up?

What, what shuts them down and causes them to, to kind of back away from the offer. Um, like this is very powerful stuff, Alan. 

Alan Klement: Yeah. It's, it, it, and I, I swear, I I didn't get the idea, um, from that movie inception, but this, this is like, you're basically doing inception here. Right? You're you're creating the world. For them and then like wanting them to pour their secrets into it.

Like that's, that's kind of what they do with that movie. I love that poetic language. Right. It's like well, we're trying to extract information from people. We construct this world, like, he's like, "We, we'll make a room with a safe and then, like, by definition, they will put their like secrets in the safe. So that's kind of what we're, we're we're doing something like that here.

We're trying to you know, construct this world for them and like, can they put themself in that world? Um, because if they can't then, then you'll know that, that, that this limited or the high-risk opportunity here. 

Ryan Hatch: Yeah. 

Rob Kaminski: Yeah. 

Ryan Hatch: Alan, do you wanna share the, um, the Arlo example? I know we have a couple more minutes. I think that'd be a super helpful example to kind of close, close in, on, close out on.

Alan Klement: Yeah, absolutely. So I'll actually, we'll begin. I think wh- what might be fun here is to show, um, the end result. Why not, let's just, let's- 

Ryan Hatch: yeah, let's do it. 

Alan Klement: Let's let's let's let's so this was, uh, make sure I'm showing the right thing here. All right. So if you see, okay, that's it. Um, where is it? So this was a product that, um, was released, I don't know, I guess maybe two years ago or a year COVID threw everything off really.

You know, it's like a big time where I think 

Rob Kaminski: calendars don't work anymore. I'm with you. [laughs] 

Alan Klement: Yeah, exactly. Yeah. Like I, I think it was beginning of 2020. right? All right. And, uh, look at this, you know, uh, lot, lots of good reviews here. Amazon's choice. Um, you know, this return would be a tremendously uh, successful product for Arlo. Um, and we, um, I know, I I don't wanna say that we predicted the, the success, but we predicted that there was low risk of introducing this product, but we did this study, um, two years before this product was released.

So we were able to effectively, you know, anticipate, uh, how things would unfold two years before it came out, which is particularly important in the CPG world. That, That's actually, um, a lot of our work. I mean, it, it applies for software too, but this simulated selection work is particularly nece-, I wanna say necessary for the CPG world. because it takes think about it, you know, you got a line of vendors, you got a line of factories, you got to get the machines. Like it's, it's, it's huge. risk. 

Ryan Hatch: The long lead time. 

Alan Klement: Yeah. Yeah. And even here, um, I'll just turn off this I don't know if the sound's gonna go through, but like watch this little part, this little scene right here.

This guy's, like creeping around he looked at this comes up on the person's phone is looking in, oh, what, what's going on there. Okay. I, I Got to do that. So this is an ad for that device. And so now what what I'll do for fun is now I will show you the study that we did. Uh, let's see here. 

Ryan Hatch: I think everyone's gonna freak when they see How similar it is. 

Alan Klement: [laughs] Yes, yes, there it is. So, um, there it is. So you can see here, you know, here's the guy creeping around the yard, he looked at the thing, you get the notification on the phone, it comes up, they see it, you know, and it comes right up. So like they even literally took the storyboard we use in front of consumers as validation for the product validation, is a strong word.

But you know, test the product and actually looks like they kind of just hand over to the creative team, like make make the ad out of this, which is also kind of comes back to what we're trying to do here. Again, ads are market information. It's, it's it's consumers are taking this in and coming to some interpretation of it. So you kind of are showing them ads. Right. And seeing how, 

Ryan Hatch: Mm-hmm

Alan Klement: ...you know, um, or how they react to it. It's, It's the same thing -so here 

Ryan Hatch: Your own little infomercial right here. [laughs] 

Alan Klement: Yes. Oh no. Well actually. 

Ryan Hatch: Walk us through it, Alan.

Alan Klement: Excuse me. Yeah so. Excuse me. Yeah. So, so this was more of, so this was before dream was a scenario where they had a product and were trying to fit into a situation or like what kind of futures can we, you know, might we create with this product?

So they they were like a technology and search. I hate to say that it's sort of a problem. but I don't want to say that in in search of an opportunity, right. This was the other way around, which is we were like, okay, we know directionally the kind, kinds of experiences consumers want, but we're not really sure even what kind of products to make.

So this was totally fuzzy things, right? And, and this was helpful. because, like Arlo at the time was already a, a camera company or a security camera company so, y- you know, the- they had some idea what people were into, but they were trying to find more opportunities for variations of their products. And so we just kind took what was some basically built upon.

You know, because this is looking for like an adjacent possible product not a completely you know, new thing. We were able to kind of build upon, you know, some previous research, previous products that, that people were buying experiences, that that they knew were like. So we knew that people were using security care cameras to watch things at night, but how can we improve that product or experience and create adjacent possible products.

So that, that's what this is. Then we just put these different experiences in, in front of people. Um, and again, here, there's like, no, or like barely any product here. There's no screens in here. Like I think the most version of the product that you can maybe see is like this little corner here what's this, but like, like, that's all you get.

It's not the floodlight camera that you saw before. it's totally different. So like we knew what type of experience, the product was supposed to make. We just didn't know what the product would look like. So this, this is an example of what we're doing. there. Uh, let's see I think I got to do the, uh, this side here where's the navigator.

And, but I'll jump to something else here. So when we're looking for multiply-by-zero factors, right? This is, this is a a good example of this. So they has the other idea called party mode because, okay. We make these cameras. So, all right. Well, what if instead of for security, we use it for some sort of like uh, you know, they call it party mode, but yeah.

you know, kind of like sharing events or like, like broadcasting from, from your home. And so like, okay, well let's But like, that, that's all kind of how they were And there was, it was like lots of internal debate. Like some people loved it at the company. Some people hated it, but they couldn't like, they were totally you know, logjam.

Ryan Hatch: Yeah so the question is, Hey, um, we're not sure what features to put into this thing. This is a feature that we're debating 

Alan Klement: Yeah.

Ryan Hatch: ...help us decide.

Alan Klement: Yep. So we're like well, let's put the, you know, not the feature in front of people, but the experience in front of them and see how they react to it, you know? And, uh, you know, the short of it is [laughs] basically, um, uh, basically I'll tell you, every so out of say about 15 participants, I'd say 12 of them outright rejected. like hugely they're like, that is creepy. That is weird. Like, they are just like no way. Because, like, and and this is again, how do we know that's reliable and valid? because they were very imaginative about it. They would say like, "Look, Like when I'm having a party, like, I want people to like, let loose and like have fun.

That's their opportunity to actually be off camera. Like one guy even said like, like, he's like, you know, what, like what if, what if my neighbor's wife is like fooling around with some guy and I catch it on camera? Like, what do I do? Do I tell him about it? Do I not? Like they're like, but that's what you do when you're shopping for a product.

Ryan Hatch: Well well, it can be part of the highlight reel. [laughing] 

Alan Klement: So like, they were just like, like, uh, you know, no not for my parties, you know, we're supposed to let go. We're supposed to be, you know, off, off camera as it were. So that's what it is. But, and again, maybe, uh, Rob, this goes back to your earlier question of like, um, less about studying problems of putting new experiences in front of people and and seeing how they react.

Another part of jobs theory is this understanding. that a, Uh, behavior that consumers do is called reinvention, which is where they will take an existing technology and adapt it in some way. So it fits, so it it, it affords them something new. Like for example, you know, um, a trophy, you know, affords me like, you know, I can show off how cool I am or a personal accomplishment, or I can put the trophy on the floor behind my door and it affords me like, stop the door from opening.

Right. So it's or I reinvent the product to to a change it's, it's affordances. So they, so, so three people did that where they feel like, look, I would not do this for party mode, but you know what, um, I would use this and they'd say like, which is not here at all. they would say, I would use this, like, you know what, I uh, I forgot what it was, but the they're like a reseller of like products or something like that.

And they're like, you know, I do these at home. Like, you know, how like those Tupperware parties kind of thing like, oh, uh, fashion, she was a fashion person. I do these trunk shows of like my jewelry. And like right now I just kind of go around to like things and, you know, events or something like that. And kind of, you know, on the sidewalk and try to sell my stuff.

But if I had this, I could broadcast to Facebook. I could stream my trunk shows and promote my business. Oh, So, so, so now is, yeah, so it is for this. The other scenario where someone, um, uh, reinvented the product, she's like, you know, I will not use this for, for party, like informal parties, but you know what?

Um, I host lots of charity events at my house. And right now I pay $3,500 for some video guy to come around and record it for me and edit it and so on and so forth. But if I had this, I could just set a few cameras around the event. Broadcast it stream it live to Facebook for my donors to watch, or maybe watch at a later time so that I could, you know, kind of promote my charity as it were. and, and, And my, you know, get more money basically for, for, for my charity 

Ryan Hatch: Sure. 

Alan Klement: and and it'd be easier and cheaper and faster, and I could stream it live. and I and I don't have to deal with this video person, which might make people feel awkward, like guy walking around with the camera and putting in people's faces anyway. So like, that's like, again, like going to like tapping into that mental simulation that consumers do when looking at some product and it's affordances and see how they react to it.

Ryan Hatch: Love it, alan, this has been so great. Uh, Thank you so much for sharing, you know, these, these assets and and real life, you know, case study examples. Um, I think that's super helpful. One of the things that we've been trying to do more of is, um, you know, to talk about how new, how new products. Is is one thing, but just to kind of stay in the, the verbal and kind of headspace is, Isn't good enough. I think that we really wanna, uh, share tactically with, with people how to do this stuff on the kind of boots on the ground. And you've done that here today and showed, you know, this really great stuff for um, simulated shopping. I think it's very powerful stuff. I think one thing that I would note, like, you know, if people have heard about, well, solu- problem interviews and, and solution interviews.

Um, when I, when I first saw this, I just knew right away, this was something special because typically in like, a, if you're wondering out there, how is this different than a you know, solution interview? Um, often what that involves is like a clickable prototype. You show them something and then you ask, well, would you want to buy it?

And I think this is uh, a lot, um, There's a lot more here with gradually, gradually revealing the messaging, the positioning, um, really, really being able to take them on, on a journey and and to having these, um, storyboards, if you will, that can help them imagine this future state where, like you said, it's almost, it's almost not about the product at all here.

Like there's very little product in, in this, in this example, right. It's more about um, them imagining themselves in this experience. And is that experience desirable? Um, so fantastic stuff, Alan. 

Alan Klement: Absolutely. Yeah. Well, I hope it, it's helpful and uh, inspirational to people. 

Ryan Hatch: So where can people find more about Alan? How do they connect with you?

Alan Klement: Oh man. Um, so [laughs] there I am on Twitter, but I have not because I've been working so much, um, have not been tweeting much. I would say, you know, um, re uh, Revealed dot market is, is, is is our uh, consulting business right now. Um, you know, if you wanna contact us through there, um, yeah, probably and following me on Twitter soon, we'll be releasing the, uh, totally revamped, um, you know, educational content, which will be for free, you know, videos and all that kind of stuff. around jobs theory, because we wanna help people be successful at it. Um, then also we'll, we'll be releasing some educational content around how to do, uh, simulated selection. So how how to design these studies, how to plan for them, how to, uh, you know, evaluate success, how to create uh, predictions out of it. So that's follow me on Twitter or sign up and follow us around on Revealed. 

Ryan Hatch: Wonderful. Alan, thanks so much for joining us today on exploring. Product. Thank you so much. Good bye everyone.


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