Episode 87: Defining Outcomes Over Output with Josh Seiden
Josh Seiden is Melissa Perri’s guest on this episode of the Product Thinking Podcast. Josh is a consultant and bestselling author of Lean UX, Sense and Respond, and his latest book, Outcomes Over Output: Why Customer Behavior Is the Key Metric for Business Success. In this week’s show, he and Melissa explore why saying “outcomes over outputs” is a lot easier than actually committing to it in practice, measurable outcomes, correlation versus causation, the problem with getting fixated on process, and how to keep your team focused on outcomes as a leader.
Subscribe via your favorite platform:
Here are some key points you’ll hear Melissa and Josh talk about:
One of the challenges companies face that prevents them from becoming outcome-centric is the legacy of how they manage their work, Josh says.
“Change in human behavior creates value, which helps us to take a huge step forward.”
Josh advises that you build a logic model with impact and outcome. Identify the leading and lagging indicators that help you determine if your business model could be successful.
Teams get so fixated on processes or methods that they don’t look at the big picture in what they’re trying to achieve and the whole ecosystem of their market. What data is out there already so that you don’t have to reinvent the wheel?
The surprising power of the words, “just tell me a story…” to help shift focus to data and figuring out what outcomes to go after.
Josh talks about the success of the book and what he might add to a second edition.
Josh says that most companies need to develop a risk-tolerant, psychologically safe environment, where employees are allowed to experiment freely to find what works best for the company.
Resources:
Josh Seiden on LinkedIn
Transcript:
Melissa:
Hello, and welcome to another episode of the product thinking podcast today. We're gonna be talking all about outcomes over outputs with Josh Seiden. Welcome Josh.
Josh:
Hey Melissa. Thanks for having me.
Melissa:
I'm very excited for Josh to be on the podcast today. Um, I've known him for many years. He works at the intersection of design product and strategy, and he's been a independent consultant for many years, a prolific author writing lean UX, uh, as one of the, I guess I would even call like one of the premier product and UX books that helped us start thinking in more of an agile way in the roles that we actually do besides just development, which I think was really, really powerful, um, for the time and continues to be like one of the premier books out there. So thank you so much for doing that. And he's also written, uh, more recently outcomes over outputs. So Josh, can you give people a little bit of a background of how did you get into this world, uh, and what you're working on now?
Josh:
Sure. Thanks, Melissa. Um, like I said, it's great to be here. Um, I, um, so I've been working in technology, uh, since the nineties, uh, I got my start, uh, sort of, um, early nineties kind of pre-internet, uh, working in a computer accessories company. I worked in product roles and eventually found my way into design roles. And I spent most of the most of my career as a designer. And then sort of over the last 10 years have kind of gradually moved, uh, to sort of working at the intersection of design and product and strategy. And so I work as an independent consultant and I work with a lot of teams to help them make new digital products and services more effectively. So help them be more agile, help them be more user centered, help them be more aligned to strategy. And, uh, and then a lot of my work involves kind of this outcome oriented thinking that I ended up writing my third book about. So, um, uh, so that's me
Melissa:
Great. And what types of companies are you working with these days?
Josh:
So these days, uh, I'm, I'm working with companies that are, um, so I'm working with some tech native companies. Uh, I tend to work with larger companies these days. Um, uh, I, I did spend a, a, a significant period of my life working with startups, but these days it tends to be the larger, more established companies. Uh, some of them are sort of digital native companies, uh, that are looking to adopt new methods. Um, and some of them are, um, legacy businesses that are trying to, you know, build new ways of working. Um, so based in New York, I spent a lot of time in financial services. Um, but, uh, these days with the sort of global economy, I'm, I'm working with companies from all over, in all kinds of domains,
Melissa:
So nice to be able to work from home too and not be on a plane every single week to
Josh:
Go see, oh my goodness. Yeah, yeah. For real <laugh>.
Melissa:
So with the companies that you're working with, um, you know, your most recent book and a huge topic, uh, that I know you're very passionate about too outcomes, uh, how do we move from outputs? Just measuring things like velocity number of features that we're putting in there to real measurable business metrics in, you know, customer success metrics, uh, when you're getting started with a company who wants to make the shift, they, they call you, they say, Hey, we wanna be more outcome centric. What are usually the trends of, you know, the challenges that they're experiencing that don't make them outcome centric right now?
Josh:
Yeah. So, so there, I think there are a couple of things that, you know, the first is obviously the sort of legacy of, of how we manage our work. That it's, it's just easier to think in concrete terms about what we're going to build. And so I think, I think that's just a natural tendency and I mean, I'm, I've been doing this work for a long time. Um, I'm a designer who's like deeply steeped in this, in the idea that, you know, you start with the problem, but I start with the feature all the time. Right. That's just how our brains work. Right. We think about the concrete thing and then we work backwards. So there's that force of habit. It's easier to think that way it's easier to manage that way, but our systems reinforce that. So we have JIRA tickets that say, build this thing.
Right. Um, and so all of our systems are built around what, what are the features that are on the roadmap that we're gonna deliver and when are we gonna deliver them? So there's that, there's our natural tendency, there's our systems. But then I think the other thing that trips folks up is that it's actually like hard to be outcome centric. And, um, you know, it's, it's a, it's a great slogan outcomes over output, and we're gonna be more outcome centric and, you know, um, it's difficult notion to even disagree with. Right. But the, the part of it that's hard is actually doing it. And, and I think one of the reasons that it's hard is that we use that word outcome sort of in a sloppy way, right. We, we use it to mean anything that is a result, um, as opposed to having a specific definition. So, um, I think one of the things that got me started in this work was this, this, this notion that having a specific definition and that definition is a change in human behavior that creates value, right. Just, just using that definition take helps us take a huge step forward in terms of making this way of thinking almost as concrete as thinking in features. And that that's a, that's a big, um, I think, uh, an important first step.
Melissa:
I think one of the things you were touching on there too, like what is an outcome? Right. Kind of confuses people too. What are we actually measuring? So when you're looking at any company, you can give an example of one or whatnot, what would you define as an outcome that we can actually, you know, change in product management or everybody delivering products? What would be our reasons that we're kinda looking at to give people an example?
Josh:
Yeah. So, so there's, um, the, the example I always give, cuz it's, I think it's an, an example that people can relate to. It is it it's a fictional example, but, um, if you think about a mattress store, right? So if, if I'm the manager of that mattress store, right? What, what's the, what's the behavior that I want to encourage? Well, I want people to buy mattresses. It's really simple. So the behavior is a ma is a customer purchases, a mattress. The problem is so, so that's, that's, that's important, right? Like having that focus and being able to measure mattress sales, but most businesses do that. They measure sales. So where's the insight here. Well, uh, there are smaller outcomes, too behaviors that people do before they buy a mattress. And those are actually the ones where we can get some leverage. Right. So what are the things we can ask the question?
What are the things that people do before they buy a mattress? And again, do so these are human behaviors. What are the things people do before they buy a mattress? So, first thing, if you manage this mattress store, the behavior that's important to you, foot traffic, right? So how do I get people to come into this store? I can try all these different experiments to see if I can get people to come into the store. Right. What do they do next? Well, they might sit down or lie down on the mattress, right? Maybe that's an important what we'll call leading indicator of sales. Right? And so if I think that's an important leading indicator, I might try all of these experiments to get people to lie down on the mattress. And then see if I increase my lie down rate, do I increase my rate of sales?
Right. And so what I'm starting to do is I'm starting to tell a, a, a story about the customer journey, right? They come into the store, they lie down on the mattress, they buy a mattress. Obviously there's more steps than that. How well can I understand that story? And how do I understand those? The like the key inflection points on that story, each one of those things that people do in that story, each one of those things to answer your question are outcomes, but some of them are gonna be higher leverage for me to focus on as the store manager, right. In order to influence the ultimate outcome.
Melissa:
That's a great example. So I'm gonna ask you the question that every single person keeps asking me, um, <laugh>
Josh:
About this,
Melissa:
What happens when it takes forever to measure an outcome? Right? Like mattress sales are, you know, e-commerce, I worked in e-commerce too. Like, it's super easy to just feel like let's tweak some stuff and see if so many purchases, because it's gonna be closer, you know, not as long of a timeframe for people to make decisions. Um, but if you, you know, some e-commerce things are taking a really long time to make decisions. Like you could look at, you know, how often do people purchase cars, like once, once in every 10 years. Right? Like that would be right. Harder to measure harder with the retention of people as well. Um, but then I started getting questions too, like agile, 2022 about government stuff. So what happens when you are trying to measure the outcome of like population health? Did we help people, you know, fight obesity or something like that? How do you help teams kind of break things down? Um, when thinking about their outcomes, if it's much longer term than just, you know, a matter of weeks or months,
Josh:
Right. It, you know, it turns out that there's a whole group of people who, who think about exactly this question and they tend to work in the nonprofit world and the social goods, social impact sectors. And, um, so sort of a lot of my thinking about outcomes is, is taken from models that, uh, have been developed in that world. So this basic notion of, of outcomes, um, is, is something that I've refined from a framework developed by the Kellogg foundation, uh, called the logic model. And so they differentiate these like long term outcomes, like population health, they would call that an impact. And so an impact is, is super important. Um, but an impact is also really hard to influence directly, right? There's lots and lots of factors that go into population health, right. And factors that might be out of our control either as a, you know, a, a team that has a grant to improve the health of.
I mean, can you imagine, like how do you improve the health of people in New York city, right. Like, like for any single team that's impossible, all you can do is contribute to that. And so how do you, to your question, how do you break that down into smaller pieces? Well, you build a model, a logic model. And so, um, at the highest level you've got impact and the next level down is outcome and the outcome is what you're targeting and you can further break down the outcome in the way we were just talking about by having sort of leading and lagging indicators, right? So the mattress sale would be a lagging indicator and lying down on the mattress might be a leading indicator. And so what you're trying to do is you're trying to say, you're trying to you, you're starting with kind of, what do I understand about my business model?
How well understood is my business model. You mentioned car sales, we've been selling cars for, you know, a hundred years or whatever. We kind of understand what that sales cycle looks like. So at this point we've got a big model and we're just kind of tweaking the smaller pieces, uh, or, or, or exploring or exploring the smaller dimensions of that model. Um, or the smaller components I would say by, by saying, well, let's, um, let's try to articulate either an important leading indicator and, and move that one, like getting more people into the showroom or getting more people to sit in the car and, you know, put their feet on the pedals or what, whatever the, their car equivalent of that is. Um, or, uh, or to create new experiences that might change the journey a little bit. Does that, does that make sense?
Melissa:
Yeah, that totally makes sense to me. I think one of the pushback I get from people when I try to break this down and explain it to them, cuz I completely agree with everything you're saying is, um, they always come back and they're like, yeah, but what about like correlation, verse causation? What if we, um, you know, what, if we have a bunch of these leading indicators, um, and it takes so long to measure if the outcome actually does happen from them or how do we, how do we do that when it's like really far out in the line and is it, how can we attribute that what we did and what we thought was a leading indicator actually did cause these other things, especially when it takes really long cycle times to do this stuff, um, what's, what's your response to that? Like how do you help people work through, you know, correlation, versus causation type issues?
Josh:
So, um, it's a real problem, right? Like that's not something you can kind of hand wave away with a framework. I think what you can do is, so first of all, it really helps to work with a good data team. Right. And it's like sort of foundational to be able to get the data and then work with a team that really understands how to, to tease out those relationships. But all of this is hypothesis testing. Right. So I think if I can get people to lie down on the mattress, they'll buy more mattresses. So I've actually got a couple of tests here. One is, can I get people to lie down on the mattress? Right. That's an experiment with a really fast cycle time. Right. Um, and so I can measure that and see if I'm being effective here. Right. And then once I'm can reliably move that number, then I've got the longer, uh, cycle time question of does that meaningfully impact sales. Right. And if I've got a long cycle time problem, you know, like, uh, like getting people to file their taxes on time on tax day, right. Like tax day is once a year. Right. So that's just the nature of the problem. So what I'm trying to do is come up with credible theories about what might move that number, but then I have to be patient to see if it actually does move that number.
Melissa:
Yeah. And I think with some of these things too, like you just kinda have to wait, <laugh> like you have to wait and see, and I don't know, like you can't make it any faster. You can do everything you can to like research it and reduce the risk and, and, you know, have as much of an informed decision as possible. But I feel like a lot of people, when they, when they ask me this question, it's like, they're looking for some kind of silver bullet. That's like, well, here's the secret to how to improve. See if somebody's 80 year lifespan is gonna be great or not, or if they're gonna live longer, you know, in two years. And it's just like, you can't, you have to, it takes like longer cycle times for like certain businesses and certain, certain things that you're doing.
Josh:
And yeah. And I think the, I think the only, the only way to approach that, um, with, with rigor is to say like, what I'm doing here is I'm, I'm creating a model of my business. Right. And there are some parts of that model that are well understood. And there are some parts of that model that we just don't really understand. Right. And so like, we can, uh, we can certainly, I I'll give you an example. Um, I, I used to work on wall street and, um, uh, my team designed, uh, trading tools for institutional stock traders. Right? So these are people who are trading on behalf of the mutual funds that hold our retirement savings, for example. Right. So trading giant volumes of, of stock every day. So we're designing these tools and we want them to place their giant trades with us and not with our competitor.
Right. And so why would they place their trades with us and not our competitor? Well, there, there are some differentiating things and we can work on those differentiators, but there are also some fundamental enablers, like in order to trade with us. And I know this sounds stupid, but in order to trade with us, you had to be logged into our system. Right. And so traders, they're busy in the morning. They have lots of systems, maybe they don't log into our system. So like one of the pieces of our model was that every morning before the market opened, we had to make sure that our customers were logged into our system. So keeping login rates high, that's a big leading indicator for us. And we, we had a lot of, of, of, uh, systems, both automated and manual systems. Like we called people and said, Hey, Melissa, time to log in <laugh> you know? Right. So, so having an understanding of, of, of the, of the key levers in your business, sometimes they're really obvious. Like sometimes we make it really hard and the, the levers are really easy. And so, um, like building that model, understanding the levers, like we should have a fundamental understanding of what those levers are. That's our job as product people, as designers, as a product team.
Melissa:
I like the, I like the whole modeling concept. I'm a really big fan of those. yeah. I think one of the, the issues that I see with people too is that they don't, I don't know why, but maybe, maybe this is more in teams that are in new to product development, but you could tell me if you've seen it too. I think they're afraid to look outside their company and look at things like research or studies that have been done that show that certain things lead to outcome behaviors and work off of that research too. So it's like not understanding I've seen it, like where people don't understand the whole gamut of the business model. Like you're talking about, we're like, how do we work? How do we actually dive into this? What's not just like in front of us from a feature set perspective, but also like when I was working in e-commerce, um, and we were trying to like improve conversion rates.
Like a lot of us, I get a lot of questions from people that are like, okay, if we're making these hypotheses about leading indicators and outcomes, how do we know what's good. Right. How do we know what the goal is? There was so much research out there about what's a good conversion rate on an e-commerce site. And like, you have to make sure it's good for you and you fit all the parameters of it obviously. But, um, we just did a bunch of Googling and found a bunch of studies that showed us what it should be. And we were like, wow, we're not anywhere near that conversion. Right? Like we're not anywhere near these goals. Like why don't we take that as a starting point and just see if we can get to that goal if that's even possible for us, um, to work that way. So I, I don't know if you've observed that with teams, but like, it's definitely something I, I think we get very like focused on the stuff that's right in front of us. And we don't consider the whole of the experience or the customer to really think through these things or look at what other companies are doing or outside of us what's happening in the ecosystems or where our customers, you know, live.
Josh:
Uh, yeah. I, I think that's, that's right. I mean, I look, I, I saw something like this just yesterday. Um, I teach a, a, a class to, uh, product managers. Um, that's very inspired by kind of the lean startup movement and, and, you know, for listeners who might not be super familiar with lean startup, like lean startup is, is, um, all about saying we are building a, uh, we're creating a thing within conditions of extreme uncertainty. And so we have to test our ideas, right? And so the, the unit that I was teaching yesterday was about how do you run an experiment to test your ideas? And we'd assigned it as homework. Um, and, uh, and, and, and this team came back and they said, look, we planned an experiment last week, but we weren't able to run it because we couldn't find the people to talk to.
But instead we discovered that there was this study out there that answered our question. The question was, uh, you know, are people willing to make a certain kind of purchase online? And we found this study that was exactly as asked and exactly answered this question. And so we brought this in, is this okay? <laugh> we were like, yes, that's okay. That's perfect. That's great. You didn't even have to run an experiment. Um, because all you did was you went out and you entrepreneurially, like with an entrepreneurial spirit, you found the answer to your question, right. And that's really what we're trying to do by hook or by crook, um, is to really embrace that kind of, uh, scrappy spirit and say, here are the things we don't know about our model. Let's go find the answers, but sometimes we get so, uh, fixated on methods or processes or the quote unquote right way to do things that we sort of lose, lose track of the, the forest for the, for the trees. Um, so this team did an amazing job and they exactly answered their question and they moved themselves forward. Like they were really able to move to the next most important question. Um, but they, they weren't sure if it was okay that they had done that.
Melissa:
<laugh> I love that. Yeah. I think sometimes we forget that it's all about, like, it's just about answering the questions. Like, no matter how you answer them, if you get the, if you get a good answer, you move on, right. That's the whole point of this entire process of product development is, you know, just keep answering them until you feel certain enough to say, Hey, I'm gonna make this bet. Let's see how it goes. And it's not gonna be, we're thinking it's not gonna be a total failure, but let's give it a shot. <laugh>
Josh:
Right. Right. Yeah.
Melissa:
It's, it's like you get two into the weeds of, we have to be running this experiment this way and we have to be doing this this way. Um, I thought it was really funny. I think I shared it with you on slack the other day, when, uh, somebody commented on LinkedIn about my book and they were like, Melissa talks about the build trap, but I wanna talk about the thinking trap and how, you know, if we're not shipping and learning, you know, we're, we're gonna just get stuck, you know, thinking all the time and doing this strategy. So I was like, that's the lean startup. You just try to like, reco the lean startup methodology. I'm like, what happened? Did we forget like about these things that exist that are actually like really good practice out there? Like, it's not, it's funny cuz I feel like we just went like waterfall agile, lean startup, anti lean startup. Now, Hey, we do need some strategy if we wanna be experimenting to bound ourselves. And then it was like, Hey, like we should be shipping things. <laugh> yeah. Jack full circle. <laugh>
Josh:
I mean, I think, you know, it's, it's the, the way we learn in organizations has, uh, it it's kind of mind blowing it's, you know, the, the way it's, it, it's, it's, it's an oral tradition without a good history, you know? And so we, you know, there's all these ideas that you see, you know, coming up over and over again with new names, you know? Um, and uh, yeah,
Melissa:
Yeah. We just gotta do a little research, but I think it gets back to like sometimes you just gotta Google some things and see what's out there and see if you can use it instead of trying to reinvent the wheel.
Josh:
And I, I think, you know, I think one of the things that, that sometimes I see, uh, when I'm teaching this stuff is that we get really hung up in. So, so lean startups sort of brought with it, this sort of I'll call it I'll say was inspired by the scientific method. Right. And some people really kind of embrace that whole well we're using the scientific method. Right. Um, and we've got hypotheses and we're gonna prove this. And I mean, even, even your question about causation versus correlation, like I'm not trying to trivialize that, but I am saying like, what we're doing here is, you know, we're, we're not trying to land a rocket on the moon. Right. We're we're not trying to, most of us, I guess, are, are not working on curing cancer with the products that we're shipping. Right. What we're, what we're trying to do is we're trying to make better business decisions, you know?
And so being able to use evidence to do that, even a little bit of evidence, I, if, if we can get some confidence in that is important. And so I think, you know, uh, like in that last story, like we could develop the evidence ourselves. Great. We could run an experiment or, or we could just go see like, is there evidence out there? Like how could we get this evidence for, to make a better decision faster in a, you know, safer way. And so I think that kind of like, like to me, like it, it's important to kind of ground out a lot of noise. Like what are we really trying to do here? All we're trying to do is make a slightly better decision, you know?
Melissa:
Yeah. And that's the whole point of like, everything that we talk about is just how do we make better decisions? Uh, one thing I see a lot of teams get hung up on too, is when it comes to like outcomes is like precision and perfection in getting to what that outcome should be. Uh, and they they're like, well, I'm in a really complicated business, right? Like, we'll talk about, you know, what you're calling like the legacy businesses that we see software a lot of times is separated, not separated, but it's just a piece of the whole of what they sell. And, uh, the teams that are working on the software are like, Hey, I can't impact it. All right. How can I possibly, uh, you know, pinpoint what my outcome's gonna be? Right. What if I get it wrong? How do I calculate that? How do I get into it? Um, and I see a lot of teams use that as an excuse to not try to do outcomes right. Or to not get in there. Have you observed that? And, and what do you do when, when teams are like, so hung up on getting extremely precise about, you know, what that number should be?
Josh:
Yeah. Well, I, I, I it's, I guess I have to say like most of the teams I work with are struggling to get data. And so, um, I think it's easier to have that conversation about precision when you're swimming in a world of data, you know? And so, you know, I think teams that really have a lot of data, like good on you, you know, <laugh>, um, I've worked with some that have a lot of data and the ones that have a lot of data tend to also have data scientists who are, are working to try and tease out the important I'll call them nodes. Like, like if you have a, a model with sort of a logical flow of this, then this, then this, like, you know, the, you can try the, those teams can help finding like the, the significant nodes. But I think for kind of to go back to the kind of legacy business where you're delivering across a lot of, um, modalities, right.
You're talking to people they're coming into your store, they're entering the bank, they're coming to the loan office. They're, they're doing whatever, like being able to go back to what that example of the mattress store and just saying, like, let's not think about the technology system right now, but let's just tell the story of how the user goes through this process. How does the customer go through the process? How do you acquire a customer? Well, first we have a meeting with the customer. Okay, good. One outcome would be getting more meetings. Right. And so how many meetings with customers do you have each month? You have 10. Awesome. Could we next month have 15. Right. And I think like, like, so, so being able to tell that story of like, we have a meeting and then we have a, a second meeting where we have a pricing conversation and then we have a third meeting where we do contractor review or whatever that is like, um, being able to think across all of the dimensions of your service delivery, not just the ones that are enabled by technology or just the ones that throw off what we think of as data, right.
Is an important part of the story, cuz it helps us focus on what are the really big, meaningful interactions that we can focus on. And so one way that I, one thing that I do to get away from precision is to say like, let's actually look at the big, meaningful chunks of this story, you know, does the customer enter the store? Does the customer log in? Can we get them to show up at the website? Um, and it's a, I it's a little bit of a sort of, you gotta crawl before you can walk tactic, but I think it helps us, uh, it helps the conversation when we can kind of focus on the, the data. Right.
Melissa:
Yeah. And that's why I got really excited about product operations. Cuz I, I found that same thing where like teams just didn't have data to do these models. Right? Like they they're just like sitting there flying blind and being expected to commit to something. And they have absolutely no inputs to build a model to commit to. Um, and some of the organizations are, are not working, I think hard enough to, to fix those problems for their, their people. Some, but like some are taking it for granted. I think like how much data you need to actually be able to commit to outcomes or, or model out what's going to happen for your company. They just wanna see a roadmap and that's when they become like, you know, fiction because you have no data at, to input onto that roadmap.
Josh:
Right? Yeah.
Melissa:
Yeah. When I was, um, you know, working with the consultancy and we had analysts, uh, and we were building strategies for a lot of the companies at insight, you know, everything came down to modeling exactly like what you're talking about. And a lot of times we didn't have the data at our hands and we would have to like export CSVs from the financial data systems, from the sales systems, from Salesforce, from, from everything else. And then our analysts would sit there and start to put 'em together about what does that mean and how do we sift through this data and get what we need. Um, and then we would build exactly what you're talking about. Like these models of, you know, what's the whole ecosystem and what are our percentages and how can we see, like if we do build this thing for, you know, sales, how much could sales possibly take on to be able to create revenue on that with the current team that we have, right?
Like everything was like a piece of the pie. We knew that as product people, we might be able to build something, but because of the entire system around it and how we actually sold it and brought it out to customers, you know, we weren't responsible for the full outcome. We were only responsible for a piece of it. And we had to model back what is our impact on the piece of that entire outcome for the company. Um, and I think teams take for granted, like how much work actually goes into that modeling. Like <laugh>, I think we, we sit there and, you know, think, oh, we'll just get around a table and decide on what outcome we'll have in this meeting for our, you know, for our stuff, for what we're looking at. But for us, like when we were building some of that stuff, like I had people working on it too, like analysts working full time on it for like months, for two to three months to be able to like draw a picture of, should we make, and this was higher level, like strategic thinking, like really big pushes, but it took us a really long time to be able to get to a place where we felt confident in the data and felt like, yes, we should be going this way because we've looked at everything we could possibly look.
We did the, the testing with our users. We brought that data back into it. We did the whole market analysis. You know, we brought all of those things together to actually be able to decide and commit to, Hey, we, we feel pretty good about these numbers and these outcomes going forward.
Josh:
Yeah. Yeah. You know, a lot of the, a lot of the work that I do with clients is I sort of lead them through that initial modeling. And so the initial modeling is, uh, let's just tell a story, just tell me a story. What's the thing we're trying to achieve. Well, we're trying to grow sales in the Northeast. Okay, cool. That's the thing, that's the, you know, that's our happy ending is we've grown sales in the Northeast now let's, let's tell a story, like start to finish of a sale in the Northeast. Let's just tell that story. And once we've sketched that story and we say, first, someone does this, then someone does this. Then someone does this. Um, then we start to say, okay, everybody in this room, do we have data that says one of these things is important? Yes, no. Right. If we, and a lot of times we don't have that data, just the situation you're describing.
Right. We have to go get it, like, okay, let, now at least we have a framework for what's the data we need to go get. Right. We think this is the story. So now let's go fill in the numbers and then confirm it when we've backed our story with the numbers. Right. And then we can start to make a decision about where we think there might be opportunity or, you know, what part of the story that if we improve this part of the story, the, you know, we'll actually see that salesing that lift. So, um, I think it, it, it, um, for me, a lot of times it starts with a, uh, with the, a surprisingly basic set of fundamentals, which is just, let's tell the story, uh, kind of in a, uh, cross channel way of, of how we interact with our customers and users, you know, and it's surprising to me every time, uh, that, that, that knowledge is, is latent in team's heads. Like people know it, but, but it's not like it doesn't exist as a, like a story on a wall somewhere, you know?
Melissa:
Yeah. And I feel, I, I think it's just really good advice. The modeling that you're, you're pointing out to me, it doesn't sound like you're just even helping product teams. It sounds like you're doing business model analysis. Right. Like you were laying out the whole picture and then going back and like pinpointing, you know, where can we improve? Which I think is just good practice for any company.
Josh:
Yeah. Yeah.
Melissa:
So you've had outcomes versus outputs outcomes over outputs in the book, um, out for a little bit, uh, what's been the feedback and, you know, what are you thinking about for next, with this book?
Josh:
So, uh, I have to say that this book has really surprised me. So I, I published this book in April, 2019. Um, so it's, uh, it's a little over three years old now. Um, and it it's done really well. It's, it's a very short book. It's, uh, it's compared to a normal, normal business book. It's, it's about a chapter and a half. Um, and I had to, I had to really cut ruthlessly what, what I was gonna cover. So it really covers just like the, the kernel of the idea. Um, and, uh, you, you you've, you know, you've written a wonderful book, so, you know, like it's, it's you write something it's painful to cut it, you know? Um, but I, I think one of the things that people value about the book or the feedback that I've gotten is that it, it describes the topic well, and that it's really short, you know, that it was designed. I designed it to be read on a, a flight from New York to Boston and yeah. Easily accomplished, you know,
Melissa:
That can do that. I have
Josh:
<laugh> <laugh>, but at, at this, by the same token, like I keep looking at the book and, and people keep asking me questions about it. And I keep thinking, yeah, like that would be an interesting chapter to add to the book. And so I'm, I'm torn between, uh, I'm thinking about a second edition of the book to update, you know, some of the things in the book that I think could be clearer. I've, it's been three years now that I've been teaching this material since I written the book. And I've, I've learned more about it. I've learned, I think, clearer explanations. So I'd like to improve that and, and I'm really interested in what readers who've read the book, um, would like to know more about. Right. So that's, that's something that, uh, you know, if, if, if, uh, folks are listening to this podcast and if you have read the book, um, I'd love, I'd love to hear your thoughts, like, you know, reach out to me on Twitter or on my website or whatever.
So I'd love to hear from, from your listeners on, on what they would like, but to me, so the topics that, that are on my mind about the book, I think some of what you've heard me talk about, the storytelling piece of it, people ask, how do I, how do I figure out what the outcomes are? And so I think like I'd like to expand that in the book. And then the other thing people talk a lot about these days is, um, OKRs and outcomes. And they're really, they're very, very tightly related. So, um, so I'm, I'm thinking a lot about that topic too.
Melissa:
Yeah, that's great. I think those are two really good ones. Um, I like the concept of, you know, thinking through the models too, and just like, how do you help people put together, you know, a basic model of the whole view of these things. It's a good skill too. It's like, sounds a little more generic modeling skills, but like, I think they need it. And after hearing you describe that, you know, on the podcast, I feel like that's a huge key for being able to define these things.
Josh:
Yeah. I, I, I maybe I'll, I'll tell you one more story about this. I, I, I worked at this, this company I described on wall street. We had, we had two products, uh, that we offered in, in the financial services sense of product to, to traders. And one of them was a huge hit and it, the company was built around this, this wonderful way of trading. And then we had a second product that just never really took off. Like we just couldn't make it succeed. And we had these, these folks come in, uh, a wonderful consulting company, uh, called Humantific and they describe what they do as sense making and, um, which of course, you know, the, the traders just thought that was a ridiculous thing to, you know, uh, but, but, but so they came in, they, and they, they came, we held this sort of executive meeting and they, um, they said, okay, help us understand what your offerings to the market are.
So let's just describe the first one. And so we had, you know, the, the, all the senior leaders of the company there, and they described the first product offering the one that was very successful and the consultants just, they had a, you know, big, uh, whiteboard and they, they drew diagrams and everybody agreed and it was easy and quick and, and everything was great. Then they said, so let's, let's do the same thing for your second product. And we must have had 15 people in the room and no one could agree on what the product was. They couldn't make a diagram of this product because nobody in the room agreed. We'd been selling this product for 3, 4, 5 years. Everybody thought they knew what it was, and everybody disagreed with what the fundamental product was. So just being able to, to tell that story of like what the product is and, and, and make that fundamental model, it actually in that, in that one hour provided a enormous value to the company, just to make clear what was not clear.
Melissa:
Yeah. I love the sense taking stuff. I think it, like, it helped me dig through some of the stuff and, uh, you know, that I was working through with product and just helped make it clearer. But, you know, I think everybody's reactions kind of the same as your traders when they walk in, like, what is this like mumbo jumbo stuff that's
Josh:
Coming over sense making yeah.
Melissa:
This wishy washy, like, you know, humanities driven thing. And it's so funny because for me too, like I, I see most of the issues that companies have, um, come down with <laugh> we don't have a shared understanding of what we're actually doing, where we all agree on it, which is like problem number one. Um, and we can't, we don't all collaborate together as a team to commit to something and move forward. Uh, instead we're all like little fiefdoms out there, you know, just doing our own little things. Uh, and I feel like 90% of the issues that a lot of places can have, you know, have come together of like, did you, did you talk to that person? Are you speaking the same language? Like, are we, are we all on the same page, right?
Josh:
Yeah. Is there, can, can you get aligned around a single clear thing right. Or, you know, are you fighting or you all can't see it. And so that's why you're fighting, you know?
Melissa:
Yeah. Do you think there for like a company that really wants to focus on outcomes too, what types of do you see like culture being a big part of that? Um, and what kind of shifts do you find from like a human side need to be made to be successful at this?
Josh:
So I, I think that the, the it's a, it's a great, uh, a great question because it's, um, maybe something that we don't always pay attention to. I is, I, I started by saying, you know, shifting to outcomes is hard. It's hard because it's abstract is one of the reasons why it's hard. But the other reason that it's hard is because we are, we're, we're trying to figure out this model we're trying, and, and to do that, we have to test to see if we're right or wrong. And so instead of being able to sort of say, well, I'm gonna build this feature that I know is gonna, you know, gonna win for whatever reason. And telling that kind of confident and concrete story we have to, we, we're telling a different story, which is, we're saying, um, I don't know if this is right or wrong, but I'm gonna test this thing and see, and it might work, or it might not.
Right. And in order to work that way, uh, we have to have a, an, um, a risk tolerant and a kind of a psychologically safe environment, right. Where it's okay to put your hand up. It's actually encouraged to put your hand up and say, I actually don't know the answer to this, but I am gonna take responsibility for finding out. And in order to find out, I'm gonna run an experiment that might work and it might not work, but then it'll at least have yielded knowledge. And I think that's a big shift for organizations. It's a big shift at the leadership level. It's a big shift at the management level. It's a big shift at the staff level to be able to embrace that, um, kind of, uh, learning oriented, uh, behaviors that thinking about outcomes require. Um, and, uh, and there a lot of kind of cultural reasons why we don't do that safety and, and
Melissa:
Yeah. You know, actually on a, the episode right before you, uh, well, before this is a dear Melissa, but the one before that, uh, was a guest on named Tara Scott who, uh, teaches psychological safety and evaluates organizations on it. Um, and she talks a lot in the episode about how can you tell if your organization is psychologically safe and as a leader, how can you tell? Um, and it's pretty fascinating. She only answered, she asks like four questions in the thing, and it will tell immediately whether or not people are, you know, willing to have these debates or take the risks that you're talking about. So I think that's a really nice tie in to, to all the stuff that you were just saying,
Josh:
It's it, you know, it's, it's something I think that we, um, those of us who've had the opportunity to work on teams where, uh, this works right, uh, that psychological safety feels for me anyway. Maybe I'll just report on my experience. I've had the opportunity to work on those kinds of teams and that feels normal. And I take that for granted, and I know I shouldn't, but I do. And then I get into environments where it's not normal, it's not present and it feels deeply broken, you know, but I know that, that, you know, uh, many of us spend our working lives in places that are not safe. Um, and, um, and, and, and, and where we don't have the agency to try to necessarily make things safe for ourselves. Right. And so that's really, really hard. And, and I think that like embracing this way of working, uh, requires finding, uh, uh, a team and environment to group of allies where you can move forward on this, uh, safely with, without putting your career, honestly, putting your career at risk. Right?
Melissa:
Yeah. I think that's a really good point. And I've also been on the side where either working full time or consulting, I've been in different types of organizations where you feel safe, and I've been in ones where I do not feel safe, or nobody feels safe. Uh, and you know what, the ones where you do feel safe, you see the results, like you see them working towards outcomes. People are happy to show up to work. They are excited to dive into stuff. They wanna learn new things. They like wanna get into stuff. And if you're making any kind of shift, like, you know, you're you or I are like brought in to do when it comes to moving product teams in this direction towards, you know, making great business and customer results, I feel like you need that. Like, that's the groundwork that has to be there for you to be able to make some of these more tactical shifts about how we measure things, how we proceed, how we do the experiments.
Um, so I, I really don't think it's something to take for granted when I was at, um, the agile conference last week, too. I, I noticed it was like, it was weird. I, you know, I'm talking about what I observed in some companies that are broken. And, um, there was a bunch of people in large companies who came up to me afterwards and, you know, some of them recognized and were like, what can I do to fix it? And then there was other people who were just like, ha ha you described us. And they just, you know, they were just like, yep, that's us to a T we're broken. And we're like psychologically unsafe. And our company's a mess. And I'm like, cool, what do you <laugh>? And it's like, well, I'm a leader. And I'm like, great. So like, what are you doing to fix that? Like, I, I, I feel like sometimes we have such like a learned helplessness in some of these places and the organizations are huge and they're massive, and you don't have to take on the whole place to, you know, change everything to make a shift. But like, if you are working with a bunch of people or leading a team, you can start by with your team. Right. Trying to measure these things, trying to work out that way.
Josh:
I think sometimes, you know, I've, I've certainly met leaders who, uh, are leading in maybe ways that are less effective than they could be, but are effective enough. Right. And so I don't know that they're making an explicit trade off, but you can see them making a kind of, um, uh, perhaps, uh, um, unintentional trade off. Right. They're saying like, look, I come into work every day. I drive a nice car. I go home at five. Like, my job is secure. Why should I change to this crazy whatever stuff that Josh is talking about or Melissa's talking about, or whoever's talking about, like, there's nothing, there's no upside for me in that volatility, there's only risk. And so, you know, I think, I think like sometimes you meet, especially in big companies, like, okay, we could change, or we could just complain, go home at the end of the day. And I, I, I, I, I personally find that really frustrating, um, which is why I'm an independent consultant <laugh>, uh, but I think, uh, it's real.
Melissa:
I agree. And I, I think that's a really important lesson. So if you're a leader listening to this podcast and being like, why is my team not focused on outcomes? Why are we not making all these shifts? Nobody's doing anything, um, might be a good point turning to actually look and say, you know, what are we doing to incentivize people to work this way? Or what am I doing as a leader to help make this a place where people can do that and can take risks and measure outcomes. So I think we will leave you go ahead.
Josh:
I was just gonna say, and one thing you can do is to model it. And so instead of always being the person with the answer, uh, and the, the confident here's how we solve it, um, I think it's okay sometimes to say, I don't know the answer, but I'm gonna find out, or I don't know the answer let's find out together. And I, I think that's a great first step is to pick some, pick, pick a few questions where it's okay to do that, uh, and to, to model it and say, I don't know, let's figure it out together.
Melissa:
Why is advice to leave you all with, well, thank you so much, Josh, for being on the podcast, where can people learn more about your work or get in touch with you to help them start moving more towards outcomes?
Josh:
Um, so you can always, uh, find me on my website, which is, uh, Joshua side.com. Um, you can find me on Twitter at Jay sitin. Um, and, um, yeah, I'd love to hear from you and Melissa. Thank you so much for having me. It's always, always great to connect and chat.
Melissa:
It was great to have you on here too. It's always good to catch up. Yeah. And for those of you listening, uh, thank you for joining the product thinking podcast. We will be back with another episode next Wednesday of a dear. Melissa, if you have any questions for me, please go to dear melissa.com and submit them all there, and I will be answering them. And if you like this, we appreciate you subscribing so that you don't miss an episode. So make sure you hit that subscribe button wherever you're listening to this podcast. And we'll see you every Wednesday.