Episode 02: Data & Things

10,000 Feet the OST Podcast Episode 2 - Data & Things with OST CIO Jim VanderMey and Alex Jantz

In this episode:

As companies build deeper connections to their customers through IoT, a whole new world of customer data opens up to them. And while data is valuable, we can’t forget that the only way to get data is through people consistently using your product. And the only way to get people to consistently use your product is through making something that people want to use.

On this episode, Lizzie Williams talks with Jim VanderMey, Vervint’s co-founder and Chief Innovation Officer and Alex Jantz, Solution Architect with plenty of experience in IoT.

Together they discuss the value of data and the fine lines of user experience you walk to obtain it. They also share some interesting connected products on this episode we wanted to link for you. Here are a few mentioned:


This podcast content was created prior to our rebrand and may contain references to our previous name (OST) and brand elements. Although our brand has changed, the information shared continues to be relevant and valuable.

Episode Transcript

Lizzie Williams: Hey, everybody. On this episode of Ten Thousand Feet, the OST podcast, we have two of our connected products experts joining us. We have Jim VanderMey, who’s actually our co-founder and Chief Innovation Officer here, and Alex Jantz, who’s a Solution Architect, who’s been on our team for over seven years. It was a pleasure to get to chat with them about data and things. I don’t want to tease it too much, but toothpaste and Match.com did come up, so we hope you enjoy the episode.

Lizzie Williams: Today, we are talking about a nice, just loosely broad topic of data, but we have the data experts in the room. So I’m looking forward to where this conversation may or may not go. Do either of you guys have anything on your mind right now that you’re sort of like, “I definitely want to talk about this”?

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Jim VanderMey: We’re going to talk about things and data.

Lizzie: Oh, yeah, right. Things and data. When we say things, do we mean smart things?

Jim: Well, it could be a smart thing, it could be a dumb thing—

Lizzie: Or a connected thing?

Jim: Or it could be a connected thing, and there’s a difference between those.

Lizzie: What is that difference?

Jim: Alex?

Alex Jantz: So, kind of prepping for this. I was thinking this morning, brush my teeth and I saw on the on my toothpaste thing, it was like whitening and plaque fighting, right? Looking at that, I was like, “Why is there so many different kinds of toothpaste?” That ended up kind of being where my head went with this whole data and things and what is connected or not connected. So, do you know why there’s so many different kinds of toothpaste?

Jim: I would expect it’s through market differentiation and product differentiation.

Alex: It’s all the same recipe at the end of it. Now there’s some whitening edition, but really, it’s all about customer segmentation. So they want to find out if you’re interested in gingivitis or plaque or whitening, they’re trying to pick that up through product sales.

Jim: In other words, if you have four different kinds of toothpaste from the same manufacturer, it’s essentially the same thing.

Alex: Yes.

Jim: But they’re going to be able to identify what you’re interested in based upon what toothpaste you buy.

Alex: Exactly.

Jim: So they’re going to generate data to sell Procter & Gamble gets data about their consumers, right?

Alex: So they have to create 10,000 different kinds of toothpaste for them to figure out who you are and what you like to buy and differentiate you as a customer. Kind of get market trends that way, right?

Jim: One of the interesting things with smart connected things is that you’re actually getting data directly from the use of the thing and not just what you’re implied by the purchase of the fact.

Alex: So what if you didn’t have to manufacture 10,000 different kinds of toothpaste?

Lizzie: Are there any smart connected toothpastes right now?

Jim: Not toothpaste, but there is—I use a smart connected toothbrush. Based upon what I’m trying to achieve in my dental health, it prescribes different behaviors in tooth brushing. And then it monitors my behavior to see if I’m doing them and it gives me nudges for.

Lizzie: Like keep brushing or like, what’s an example of it?

Jim: So making sure that I brush in each zone correctly with the right amount of pressure for the right amount of time and then it reminds me about did I do, did I floss, did I rinse? And then it gives me a timeline, a trending analysis to see how I’m performing against my goals.

Lizzie: Oh my gosh, that is intense. Do you like it? Do you use it?

Jim: I do use it.

Lizzie: Okay. Alright.

Alex: So your feedback loop is your toothbrush instead of your hygienist asking if you floss and—.

Jim: Right.

Alex —feeling guilty every six months?

Jim: Yes, exactly.

Lizzie: Yes. I swear, I do. Then why are your gums bleeding?

Jim: Right. And so you create a different feedback and that’s part of the data that’s generated by the thing is it creates opportunities for feedback that’s different than what we had before, through a stickers on your refrigerator kind of thing.

Alex: So you as an individual, you get the feedback you’re looking for, sort of like proactive behavior sort of things.

Jim: Right.

Alex: And then back to the market segment, was there an onboarding survey or is there sort of a followup activity that they’re sort of asking this information?

Jim: Yes. I actually tried multiple brands of the smart connected toothbrush. I found that one brand, actually tried to monetize the space of the mobile app. They were pushing ads to me while I was brushing my teeth and I didn’t care for that experience. But they were collecting data about me and they were trying to monetize that, while the other application that I’m using now just provides useful advice and feedback while I’m using the toothbrush.

Alex: One feels like it’s too in your face where they’re pushing too much toward you, and the other one feels like it’s a little bit more symbiotic.

Jim: Yeah. And I think that symbiotic is a good one where they are giving me value. It’s not just about what they’re trying to accomplish as a manufacturer of a connected toothbrush.

Alex: Right. Yeah, it’s one of those like connected toothbrush. It could be one of those like, eyeroll shrug sort of things, right? But like, once you start digging into it, from a company or product standpoint, there’s a lot of data behind the scenes that is useful for their company; or for them to be able to sell back to like a P&G in effort to figure out who’s buying what? What are they interested in? Are they dealing with cavities? Are they more interested in whitening?

Lizzie: And if it actually adds benefit to the user, right? I think that’s where it all goes back where if Jim actually has better dental hygiene because of it, then it makes it worthwhile. Right? Whereas I’m sure we’ve all seen there are just a plethora of badly used connected devices out there, you shared a website a while ago that I won’t list the name because it’s got a swear word in it. But just like people connecting products just to connect the product, just as a market differentiator without the thought behind “Why are we doing this? How do we make this a positive experience for user?” What are some of those? I remember, I think you were the one that shared that.

Alex: It’s a hash—It’s the Twitter account, Internet of Shit.

Lizzie: Oh, yeah. Oh, okay. Alex gets to say it, not me.

Alex: Since I’m a guest, we get a pass?

Lizzie: Yeah. Right. You do get a pass.

Alex: So there’s is all about taking IoT to the extreme. There’s a lot of products out there that really provide no real benefit to a user other than having a product which is connected. It’s kind of that the hot trend or has been for a while now.

Lizzie: Novelty.

Alex: And the unintended consequence of that is sort of what they try to explore, right? So they’ve got security exploits, they’ve got data breaches, they’ve got all this sort of thing that they’re constantly following. Early on, it was more around like, “Why would you need a connected mirror?” And that was actually something Amazon put out there, right? They would give you fashion advice mirror it take a picture of you, then they use ML and AI to give you fashion suggestions.

Jim: ML, machine learning. AI, artificial intelligence.

Alex: Yes. Thank you.

Lizzie: Good catch, Jim.

Alex: So at the at the end of it, that’s one of those like, “Do you really want IoT in your bedroom? Do you want it to look like a mirror and constantly record you?” That’s one of those things like — it’s good to have those cautionary, not like Orwellian, necessarily, but like people, Twitter accounts, maybe sort of regulating– self-checking the situation.

Jim: So I think, we can feel, we have intuition, when we’ve gone too far and you’re explaining some of that, and then it gets put on someone’s Twitter feed. The other part though, is when and how do we design products to make good use of data? I mean, do you have some positive examples, do you think?

Alex: Yeah, absolutely. Actually, one comes from you, Jim. So you’re all about the — What do we call it, the measured self?

Jim: The quantified self.

Alex: Quantified self. And so you’ve got this posture thing that you wear in your back, right?

Jim: Yes, upright.

Lizzie: Was that like the little thing on your spine?

Jim: Yes, you put it between your shoulder blades.

Lizzie: Yeah, I’ve seen ads for that.

Alex: So that’s like an individual seeking this sort of information, right? And you’ve already determined that you want to have better posture and that’s something that you’re grappling with, maybe it’s a health thing.

Jim: And side comment, whenever you mentioned that product, immediately everybody in the room gets good posture.

Lizzie: I just did the same thing. I’m like, “Oh, boy.”

Alex: So that’s a device you have to wear. I think other companies are thinking about how to promote wellness in different ways, right? So it’s all the way back to companies that produce furniture. Can they give you proactive feedback and how you’re sitting because it’s already built into the product? So we deal with furniture companies that are sort of using their ability to connect products and they’ve already got the furniture market. So they say, “Why don’t we just build the sensors right into the chair itself rather than be something that you have to wear around?”

Jim: And I think that was one of the most interesting projects that we’ve worked on. Herman Miller taking the iconic Aeron chair that’s been around for decades and then changing the fabric in the seat — was that called the platen?

Alex: Yeah.

Jim: Okay. The fabric and the seat to make that into a sensing platform. As a consumer, I think about that and say, “Just a second, if they’re able to sense, they’re probably able to tell how much I weigh. If they’re able to identify good posture, bad posture, sitting versus standing. So what data are they collecting about me and how is that being used?

Alex: Right. I think that is going to be one of the constant lines that companies are going to have to walk, is what’s too much, or what’s the right amount of data. And so there’s the whole — Can we make it anonymous? If it is anonymous, is it worthwhile to collect or is it so important that it’s something that we just want to leave behind? So for example, Herman Miller kind of talked about the weight thing and they determined like, although we could collect it, it’s not something that we would ever really want to have something happen with that data. So they decide not to actually save it.

Jim: Right. So I think that’s a really interesting point because what has value to me as an individual’s using the chair means that I can’t have it anonymized. You have to know that I am sitting at this sit stand desk, and that I’m the one who’s in present and for me to get the value out of that application. But the facility manager, I have to specifically anonymize the data to create a different value proposition. And Herman Miller has yet a third value proposition based upon knowing how all of their tables and or all their desks and chairs are being used across all of their customers. So you actually have three different ways of consuming the data, depending on who you are in relation to the product.

Alex: So there’s something that you as an individual are getting out of it still, right? Posture, feedback, healthy office situation.

Jim: What healthy office situation like? What would that be?

Alex: Like a nudge in the case that you’ve been sitting too long, maybe some proactive feedback to say it’s time to get up and stretch and walk around. Better setup of the chair, so if you’ve ever looked at like a more complex, high-end office chair, they’ve got knobs and levers. Right. So you’ve been getting this setup right, is important. What is lumbar? And why is it important to me? I think that’s one of those like helping the customer think through. Getting them situated in a way that’s healthy it’s sort of that holistic view that they’re looking at too.

Jim: So by taking a complex product interaction, and using data and making it smart, you actually can reduce the simplicity of a otherwise fundamentally complex product?

Alex: Yeah, I think you’re getting into a lot of that right there. That’s a good point.

Jim: Okay, so simplification of the product is one way to leverage data. And then other ways is then to create a different — How does the relationship with the product change then because I have data? What do you think?

Alex: As an individual?

Jim: Yeah.

Alex: It depends, right? So if you’ve got the toothbrush kind of chirping back at you saying you’re not brushing enough, that could be a negative interaction. Or if you try to spin it around and be like gamify it and try and make it more positive. And depending on the user type, two people could interpret the same interaction very differently, right? So I think getting that line balanced is kind of difficult for companies as well. How do we make this informational but not overbearing? How do we give feedback in the case, “Oh, you’ve been sitting too long” but not critique you? And so one of the things that we’ve been thinking through trying to help think through is how do we degrade the amount of interactions that we’re pushing towards user in the case that we’re not getting positive feedback back from them. If they end up getting like — if we push them information and they ignore it, we track that, we say, “Okay, we’re not going to update you again for a little while. We’re going to wait.” And then when it’s really important, we’re going to try to update you again, if you ignore it again, then we’re going to realize that you really don’t like this sort of thing and we’re going to try to turn it off for a while.

Jim: So I think that’s a really interesting point is that the interactions that are now adaptive, which are based on data. There’s a learning that’s occurring and I think notification fatigue is a good example of that you’re alluding to, that through the data, I am now creating an adaptive and changing experience with the product. My Withings scale, for example, if I don’t weigh in on it, as with the frequency that I have reminded my scale that I should be weighing in, it will send me a notification saying, “Hey, Jim. Haven’t seen you for a while”, in a conversational tone, and just gently remind me that I should have weighed in. But if I ignore it for a period of time, it gets a little more insistent.

Alex: So it amps it up.

Jim: It amps it up.

Lizzie: It changes its tone. It’s like, “Jim, get over here now.”

Jim: Well, no, it asked me if I needed help. So the idea that, “Oh, maybe it’s no longer paired.” Maybe you don’t you’ve forgotten how to use it. Maybe you didn’t change the battery. It assumes that I want to do the right thing and that there’s some impediment that I’m facing, which was a really interesting interactions–

Alex: Because the thing probably knows that it’s connected and it’s got a full battery.

Jim: Yes. It knows that. It’s interesting how our relationship with the physical world changes because of data.

Lizzi: Yeah. I had a really positive–but wasn’t expecting it–experience last night. I just bought a new car and I was driving out kind of in the boonies to my Aunt and Uncle’s house, and it was dark out and it’s like, it’s winter, so I’m tired, ready for bed at 5 PM basically. I’m driving and I kind of leaned on my right arm rest, sort of slouching a little bit. I had my heated seats on feeling good. And then all of a sudden on my dashboard, it was kind of dinged. I was just sort of alerted by it and it was like, “Sounds like you need some rest” or like wake up. And then I had a little picture of a coffee cup on it, because clearly it was identifying that I was in some sort of a posture that felt like I might be falling asleep, and it did wake me up. I mean, I wasn’t sleeping, but it did jar me enough to be like, “Oh, yeah, okay, I need to focus more now. I’m obviously getting a little too comfortable in this position.” But I wasn’t expecting it. But it was a really positive experience that I wasn’t quite prepared for.

Jim: That feedback looped through data?

Lizzie: I had never thought of the notification fatigue, though. That’s a really interesting concept.

Alex: So if that coffee light stays on, it becomes meaningless.

Lizzie: Yeah, absolutely.

Jim: How many of us have driven with the check engine light on for weeks?

Lizzie: Or even the beeping, you know, when you’re about to run into something, it’s like if it does it, when you’re within 20 feet of something, then all of a sudden you don’t hear it anymore. Right. So how do you continue to make those moments meaningful? So that they work?

Alex: You know, it comes with a Starbucks subscription.

Lizzie: Deliver coffee.

Alex: Then we’ve gone full circle with that.

Lizzie: Right. Yeah.

Alex: But yeah.

Lizzie: Well, I’d love to kind of shift the conversation a little bit just because I know Alex and I, and Jim were talking in the kitchen a little bit yesterday and one conversation came up about how some companies when they’re trying to find data or market access, they kind of can test. You used the example in the kitchen of was OkCupid where they were starting out in one way and totally created new market opportunities based on that data. Can you give our listeners a little example of that? I just thought it was fascinating.

Alex: Yeah. So dating websites are sort of like back to that customer segmentation. Right? It was like OkCupid had their big data play for a long time. And so it’s like, their OkCupid blog was where they published a lot of their data and data insights. And for a long time they’d be like, super in your face, right? It was like, you know, what are the preferred matches of people in the Midwest? And what are they talking about? Right? So people in the Midwest like long walks on the beach, even though there’s no beach around. But the thing that they really explored, which led to changes in the product, instead of like that product feedback loop was they everybody wants to have sort of a niche that they play into. And so, rather than these broad sweeps of large dating apps where you’re just in a big pool of people. They would have, you know, a smaller, mirrored site which would be dedicated toward farmers.

Lizzie: Yeah, we were talking about how all of a sudden it’s like you want to be dating people like yourself right instead of a huge pool. All of a sudden it’s like single farmers become a niche dating —

Alex: Or Christian Mingle —

Lizzie: Yeah, right.

Alex: So the segment that large user group into what would be an identified niche. So it goes back to that like whole Colgate thing. How do you identify yourself? What is the thing that you’re on the dating site for?

Lizzie: Right.

Alex: Or in the case of like Tinder, right? I mean, that’s a really easy one people are there for or at least when they have kicked out.

Lizzie: What are they there for, Alex?

Jim: So I have the son of some of our best friends met his wife on Tinder. Nice. So and it was purely as a joke on both of their parts.

Alex: I think that actually worked in reverse, right. So Tinder started as something and it became something else.

Jim: Yeah. So the—so data and the ability to collect data and then we then morph our services and our products based upon the feedback that we get, as people use these things. I believe that what we’re now seeing are people developing products specifically to collect the data. What are some guidelines that people should have as they develop products to collect data? What do you think?

Alex: Thinking through the way we’ve collected data in the past, IoT is just another facet for that data collection. And so like the fundamentals, back to the OkCupid blog are still the same, right? It was like, how do we identify either funnels or segmentation groups and build hypotheses and test those hypotheses of your product with the tools at hand. And so in the web 2.0 days, it was creating events, maybe creating AB testing within the actual framework itself. So that we could see—put all hypothetical against the actual framework and how does it validate? I think that’s one of the things that we’re moving into with IoT is IoT initially was like, can we do it? Almost like the early adopters, were the science projects right? Nest creates new user experiences by putting IoT into products. Their consequence ends up being that they have a whole ecosystem that they are able to understand either home security or how you live or what your preferences are, they can adapt their products to better fit where the market is evolving into. So what started as a thermostat is now a whole ecosystem of home security.

Jim: Well, I think that that’s an interesting point because they actually took features out of their product. A few years ago, they changed the way that they report energy consumption over time, because they found that most of their consumers weren’t looking at that particular data point.

Alex: It wasn’t important to them.

Jim: It wasn’t important to them and yet, they Nest was spending a lot of money for data retention analysis. And they were also supporting that feature set in their, in their mobile application that they found that people just weren’t using. So they were able to eliminate that.

Alex: So if you take the same concepts like you know, we’ve had a good foundation and data concepts for web for a long time. If you take those same concepts, they poured actually pretty easy over into IoT once conductivity is stabilized and established, right? So the hard part is getting it connected, and keeping it secure and maintained. But, on top of that, your internal data group has all these questions about who are my users? How are they interacting with it? And allowing them to have access to the product team either during dev or after launch so that we can say, all right, the firmware might not be collecting this now. But is it something we can collect or quickly collected by way of a combination of firmware and maybe an interaction model with a mobile app? Those hypotheses and sort of like feedback loops into the product development. I think those are where the magic on that data side is.

Jim: And I think that’s a really interesting point because historically, product engineers have not thought about data capture as a fundamental capability that they had to design around. And so as elevating those questions earlier in the development cycle is probably something that we need to encourage people to do more is to say, “How do I build this product to answer these questions?”, not just build the product to perform a function. It’s not just a feature play anymore. It’s now a customer identity and data acquisition play.

Alex: Right.

Jim: So that’s an interesting elevation that I don’t always think about.

Lizzie: Speaking of elevation, I think it’s about time to land this plane. Is that is that a good airplane?

Jim: That was a terrible segue way.

Lizzie: It didn’t land. I don’t know. Okay, well–

Jim: Yes. What happens to planes when they lose altitude quickly?

Lizzie: “Oh, we’re going down.” No, I just wanted to take a breather. I feel like you guys had so much, so many great examples throughout this conversation and I certainly learned a lot I hope our users or listeners did as well. And as we’re wrapping up, we’re trying to close every one of our podcasts with a question. And that question is, what is your favorite game?

Jim: My favorite game is trying to avoid our marketing department when they asked me to do podcasts.

Lizzie: Oh. Cruel. Cruel. Cruel. Alex, do you have a better answer?

Alex: I’ve got three boys, all four and under and so mine is hide and seek. Awful at it.

Lizzie: Oh, yeah.

Alex: Like I could stand in the room, just standing still–

Lizzie: Just put your hands over your face.

Alex: Like dinosaur. They can always see movement.

Lizzie: Oh my God.

Jim: So I do have a Hide and Seek story. I have a two-year-old granddaughter, and she is just learning how to play hide and seek. And she tells me where I need to hide. And she’ll say, “Grandpa, go hide in the closet.” And then she goes, counts in 10. And then she comes out. And she finds me in the closet.

Lizzie: Hey. She’s learning.

Jim: And if I try to move, she hears me and she’ll peek out and say, “Grandpa, don’t move.”

Lizzie: So cute. Yeah, we just like to ask that question, because obviously, you both know this, but not all of our listeners do. But our headquarters our OST headquarters are actually in an old game factory in Grand Rapids, Michigan, called the Drueke Game Factory. So we just think it’s a fun way to end each podcast. And I really appreciate you both being on and taking the time. And hopefully, we’ll have you back on again to talk more data and things.

Jim: We should talk about security sometime.

Alex: Yeah, we didn’t get into the whole “What does it mean to connect IoT devices?”

Lizzie: Sounds like a great followup podcast.