For product teams, the ability to measure user behaviors and make those MEASURES (not the deliverable itself) the focus of your team is the crux of becoming Outcomes-focused.
I sat down with Büşra Coşkuner, one of our favorite rising product management coaches/speakers, to discuss why product analytics is critical for continuous discovery and dicuss practical strategies for implementing tracking, setting meaningful metrics, and using tools like impact mapping to align product work with business goals.
If your team is looking to level up its approach to product analytics and work more effectively toward outcomes rather than just outputs, this webinar is packed with actionable insights. Watch the full recording below and learn how to transform your product decision-making process with the right metrics mindset.
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Main Ideas and Takeaways
1. Establish a Solid Metrics Foundation
Having a solid metrics foundation is crucial for effective product discovery. This means identifying the key user behaviors that are the leading indicators of the business outcomes you care about and tracking them consistently.
In the early days, most teams just track the big-picture business metrics like retention rates or annual recurring revenue (ARR). But to make these actionable, you need to break them down into smaller, more leading indicators that your team can influence.
To do this, you'll use a mix of:
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Quantitative analysis—like running correlation studies to see which user actions are linked to desired outcomes
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Qualitative research—such as interviewing users to understand their motivations and challenges.
The goal is to ultimately create a clear link between what users do in your product that 👉🏼 creates moments of value for those users which 👉🏼 leads to more growth for your business.
"We always start with basic concepts, right? That might mean using pirate metrics and figuring out which part of the product contributes to which of the 'R' steps. See it as a flow: What does the flow look like? What feeds into what you could call a flywheel? Then, look at which parts of the product contribute to each step. From there, figure out the success metric for that part of the product."
2. Implement Analytics Incrementally
Building a full analytics system can be tough when you don't have a lot of resources or leadership isn't keen on dedicating time to it. Instead of proposing your team stops everything to focus on analytics, you can start adding tracking little by little as you go.
Here's are some ideas for adding analytics incrementally:
- Focus on Just the Highest-Impact Areas: Start with the parts of your product that are the core to your customers getting value out of your product. You don't need to instrument every single click, page view, and usage of minor features when you're just starting.
- Add Tracking to New Features: Whenever you build something new, include the tracking code right then. There's a fixed cost to making any change in your product--so it's much easier to add tracking to something you're already building.
The big idea is to "clean up as you go." Over time, you'll build a solid analytics foundation that helps you make informed decisions.
"Maybe you already have some data, right? Show the analysis you’ve done and the decisions you’ve made based on the information and the quantity of data you’ve gathered. Then, make the case: 'If I had more data, I could make even better decisions, or I could fix that part of the product, but we don’t have enough data there.'"
3. Mapping The User Journey To Find The Most Important Things To Measure
Understanding user behavior is fundamental to identifying the most impactful metrics that can drive business impact.
User journey mapping is a way to visualize the steps users must take to realize the value that your product provides. Doing this with your team will help you create alignment on what the critical touchpoints and moments of value for users in your product.
By mapping the user journey, teams can observe where users:
- Engage Deeply: Identifying features or content that capture user interest and encourage prolonged interaction.
- Drop Off: Pinpointing stages where users abandon the process, indicating potential issues or areas for improvement.
- Achieve Success Moments: Recognizing actions that lead to user satisfaction or the realization of the product's value proposition.
Behavioral analysis complements this by providing quantitative data on user actions. Through tracking and analyzing user events, teams can perform correlation analyses to determine which behaviors are most closely associated with successful outcomes like activation, retention, or conversion.
For example, in a productivity app, the team might discover that users who complete a tutorial are 50% more likely to become long-term active users. This insight allows the team to focus on optimizing the tutorial experience to boost activation rates.
By focusing on the behaviors that predict business impact (e.g. the user converts to a paid account, renews again and again, etc), teams are more likely to prioritize efforts that actually impact the bottom line.
Full Transcript
Steve Klein: Hi, welcome to this special event, all about product analytics and working more on outcomes with Büşra. I've been advised not to even try pronouncing your last name, so I'll let you do that when you get a chance to speak.
Büşra is a product management coach. She has clients from some great tech companies and has spoken at product management events like Craft Product Management Festival and more. Just a bit about me—I'm Steve, one of the co-founders of Vistaly.
Vistaly is a purpose-built tool for helping product teams run the entire continuous discovery process. We have tools for visualizing how your business grows, setting outcomes, creating opportunity-solution trees, and integrating what you're learning on discovery calls directly into your opportunity-solution trees.
The team can discuss and prioritize which opportunities will best help you achieve those outcomes. We also do assumption testing—kind of the whole process. Bushra, can you start off by giving us the quick one-minute highlight reel of your career so far? Just the high-level beats, the places you've worked, and the things you've been doing.
Büşra Coşkuner: Sure. I have a very boring career path. I've always been in product management, never did anything else. Maybe it wasn’t necessarily called product management, but it was product management in the end. I started in telecommunications and then moved over to the digital space. That was more than 13 years ago—I stopped counting because I began working on things during university.
I’ve seen big companies, really large enterprises. I’ve seen startups, and I’ve worked with cash cows, kicking off another growth phase. I’ve experienced many different things. And for the past four years—three years officially—I’ve been a product management coach and trainer for lead product managers, product management teams, and product trios, helping them develop product discovery habits.
I also focus on instilling a metrics mindset and working with outcomes, with the ultimate goal of really moving the business needle.
Steve Klein: Yes. Well, I think you're kind of living the dream that a lot of PMs have—being able to work with a variety of different companies.
There's this big movement toward more coaching and fractional product leadership, and you've had a ton of success in that space.
I want to start by discussing the importance of having a solid metrics foundation. You're often brought in to help teams with product discovery, and one of the first challenges you mentioned encountering is the need for more data about how users are interacting with the product. Can you talk a little bit about that problem and how it affects the ability to do effective discovery?
Büşra Coşkuner: I wouldn't necessarily say that's the first thing I bump into, but it's one of the first things I notice immediately—whether there is a data mindset in place or not, or if there's any data at all to inform their decision-making.
Is it random? Is it based on any kind of evidence? Even if it's not pure data—even if it's not quantitative—do you have anything qualitative that has informed the decision? If not, it's random.
I notice early on that, even when we talk about outcomes, a metric is an outcome depending on how you define it or what the metric is trying to reflect. When we say we want to work with outcomes and we do product discovery based on a desired outcome that we want to help our target group achieve, we know that by doing so, we’ll create business impact.
Teams often struggle with quantifying that outcome. Interestingly, I had a conversation today—via LinkedIn DM—about product managers and their metrics mindset. We talked about who really needs more understanding in setting the right metrics and in which cases.
The product leader I was talking to had the hypothesis that it’s more junior and mid-level PMs who need this. I told him, 'I'm working with a lot of very senior product managers.' Everything I create, every piece of content, and the people I coach—they’re all senior-plus PMs, and they struggle with this as well.
And I can tell you, they struggle with setting metrics as well. I believe one of the reasons for that is they don't get the space to actually use those skills. Companies don't have the mindset to think about it deeply—and of course, that's a generalization. It's not all companies.
But the companies that bring me in need my help because something isn't working. What I often see is a very specific KPI mindset at the business level, but not a metrics mindset on the outcome level for the teams.
That’s one of the biggest reasons why even senior product managers struggle with setting metrics. They’re simply not given the environment to practice this.
Steve Klein: Yes. Yes, a hundred percent. I feel like we see that as well. One of the underlying reasons is that sometimes people have this idea in their head of how users might be interacting with the product.
They have their own mental model about the things that lead to growth. But when you start tracking these things a bit more, and you're a little more explicit about how people are actually using the product and which actions lead to business impact, it gives the team a better sense of alignment. It helps clarify what’s really important to work on.
Another problem we see a lot from teams that don't have a metrics foundation in place is they struggle to get product leadership, the CEO, or other decision-makers to slow down feature development. They need space to instrument the product better and build more analytics and tracking into it.
Do you have any advice for how companies or product managers can talk to leadership about strategies to get time and resources for building this into the product?
Büşra Coşkuner: So it's a bit of a trick question, because it's similar to the conversation around tech debt, right? How do we get space to actually clean up our mess? If you don't have any tracking in place, it is some sort of mess that you need to clean up.
When I started working at Doodle, my last employer before I became self-employed, we had zero data. You would not believe that a 10-year-old company, a cash cow for its investors, had no tracking in place. Well, it's not entirely true.
We had data in the backend, like anything in the database, and we had some very specific tracking in place, such as tracking specific events. But if you want to understand how people are using your product, that's not enough.
You need to understand behavior. People who go through this journey or that funnel create a loop or an effect. People drop off more often if they're not able to get the value of a feature earlier than they should. If you want to understand these connections, you need to track better. That doesn't necessarily mean tracking everything, but tracking better.
The problem with asking for time to fix this is—you won't get it. So, don’t ask. If you can't make the case, there is no business case that will help your manager understand. "Look, if we spend time to add tracking, we will understand our users better, build better products, and make more business impact."
Either you have a manager who knows it's important and will give you the time, or you don’t make the case. Then, you do it in a sneaky way. What could that look like? It’s like breaking a monolith—cleaning up that mess.
Stopping feature work doesn't help. Whenever you touch a part, you refactor that part. Whenever you release something new, you add tracking to it. Adding tracking is not a big deal. It's just a little bit of code, relatively speaking.
Or, you annoy your engineers by writing a million tickets with one event per ticket, saying, "This one next, place this one next, and then this one." Eventually, one of the engineers—and I apologize to any engineer who has worked with me—will get so annoyed they’ll say, "Come on, let's just put this together in one single ticket. Stop annoying me, and you know what? We'll get it done in an hour."
Otherwise, it's going to be constant context switching, and they'll lose time on other things. So, sometimes, you need to do that.
Steve Klein: No, I love that. I almost think about it like the way I clean the house. Sometimes it’s kind of like a "cleanup as you go" approach. You do a little bit here, a little bit there, and then after some time passes, you realize, "Oh, okay, the house is clean."
We actually have a decent amount of the product instrumented now. And then there's the tried-and-true method of just annoying someone on the dev team until you get it, which can be shockingly effective.
Büşra Coşkuner: If you really want to make a case—if that’s the culture you’re working in—then, do one or a couple of sticky tickets and show it. Or, use what you have. Maybe you already have some data, right? Show the analysis you’ve done and the decisions you’ve made based on the information and the quantity of data you’ve gathered. Then, make the case: "If I had more data, I could make even better decisions, or I could fix that part of the product, but we don’t have enough data there."
Bring that to the table and see how the manager reacts.
Steve Klein: To use kind of a cringe analogy, you're almost building an MVP by adding a little bit of metrics. You can prove the value of having that, and then make a bigger case around the importance of what the team is able to show—that you're able to make better or faster decisions.
There are lots of different things you could track, like adoption metrics, pirate metrics, daily active users, monthly active users. There are so many different classes of things we could track.
What kinds of things do you advise product people to focus on? What kinds of metrics do you advise product teams to start with when they're just getting started?
Büşra Coşkuner: Yeah. So I started our conversation by saying that we need to make sure we're creating business impact, right? Whatever we do needs to move the business. In the end, no business success means no product will sell—and no job. We've seen this in the last two years.
When you start conceptualizing what you should measure, it depends on your goal. I always say, start with the very basics. We always start with basic concepts, right?
That might mean using pirate metrics and figuring out which part of the product contributes to which of the "R" steps. See it as a flow: What does the flow look like? What feeds into what you could call a flywheel?
Then, look at which parts of the product contribute to each step. From there, figure out the success metric for that part of the product. For example, my goal might be to measure activation—how do I know that someone is activated? If you don’t have data right from the beginning, make an assumption.
Go talk to people, get qualitative input, and make an assumption about what you believe might be the activation moment. Create a metric from that and figure out where in your product you can make this happen.
Steve Klein: Can you go a little bit deeper there? It sounds like you're describing this process of identifying activation. Let’s talk to a bunch of users about their experience using the product. We’ll get some users who activated and some who didn’t.
It sounds like you’re describing putting together some kind of journey map so we can find trends and see, "Oh, it looks like the people who do activate are doing these kinds of activities more than the ones who don’t." Is that how you’re thinking about it?
Büşra Coşkuner: Yeah, let me actually share my screen and let's put it together on a Miro board. So there's two ways to go about it. One is, let's say this is LinkedIn's R flow. For example, we have a free part, we have a paid part. So let's say somehow there's an acquisition thing happening, right? People come in, they get activated. They retain as free users, they pay, and then they need to be activated again. If the premium offer doesn't give them additional value, they will churn. Somehow, they will retain, and somehow they will tell others, "Hey, pay for it." Or they stay as free users and tell others, "Hey, come on the platform."
Okay. So the first way to go about it is checking the numbers. What is happening in our product? Where do we see a dip? Well, actually, the first step is defining those metrics. Like, what's the metric here? What's the metric here? How does your product work on a very high level like this, right? You know, what is the metric here, and then figure out in your analytics where the dip is and where it goes.
And if we see it's an activation problem and think if we fix that, the other numbers will improve, then go deeper into this metric. What does that mean? Then this metric becomes the lagging indicator, which you need to break down. So, activation.
I'd say activation, lagging metric, whatever that is. It's the, I don't know, seven friends on Facebook—whatever that means for LinkedIn, right? So we have an activation metric there. And then again, you can go deeper in your product and try to understand where activation happens.
Now you might have data and scientists, and so on. So now we're getting to the very complicated part. But maybe it's not about activation. Maybe it's about user retention. That might be a bit easier, right? So, retention lagging metrics. What leads to retention for LinkedIn? Everyone might shout, "Daily active users."
What does that mean? What is daily active users? Now we come to the second way you can do it, and that's what you triggered. They've redesigned everything—user journey. It's the easiest way you can come up with that: the user journey. Okay, user logs in, right?
Then, user browses. Actually, the tiny step in between is, user sees feed, user browses. And now you're like, "Oh, but maybe they see that they have a DM incoming." So, user checks DM. Then you're like, "Oh, maybe the user saw some connection requests." User checks connection requests. And maybe it’s someone like a creator who writes a post. Then, you have different things that can happen, right?
Now, you see when I say user journey, I'm not talking about this linear way of looking at a user story map, but a very precise journey of what is really happening. And now you can say, "For retention, we figured out a user can..." Quantitative data tells you what is happening. Qualitative data tells you why it's happening, right? So, maybe you browse your data and see that somebody who writes a lot of posts is very active. This feeds into daily active users.
Somebody who constantly checks their DMs is very active. Somebody who constantly connects with others. Maybe it's not only checking DMs, but what happens after checking is user chats.
You figure out that actually the user who is also chatting with others is super active—not the ones who only check the DMs. They are not active.
So, then you figure out these are your leading indicators for daily active users.
Now you can define what daily active means. Now it’s not vanity anymore. And you can say, "Daily active users of X," or "Daily active users defined as users who do X within Y time frame."
Steve Klein: This makes sense.
Büşra Coşkuner: Right. And this way, you have basically defined your retention lagging metric and know exactly what’s leading to that. Now, we could go one step deeper and say, "This is still very difficult to track." In LinkedIn's case, it’s probably not difficult to track. But in your case, maybe it’s something that’s difficult to track.
Then you could go down a level deeper and find maybe just output metrics, which are also fine, that serve as leading indicators for an outcome metric. For example, let's talk about my matcha business.
Steve Klein: Okay.
Büşra Coşkuner: Let's make the cut here with the LinkedIn example and talk about my matcha business in Excel.
Steve Klein: Actually, real quick, before we move on, I just had one question pop into my mind. I kind of want to close the loop on that. So, we’re saying the way to go through this process is detailing and tracking all the different things folks are doing in your product. Then, looking in the data to do some correlation analysis around the ones who do these actions and tend to retain at a much higher rate. Is that what you’re saying here?
Büşra Coşkuner: Exactly. Yeah, good point. So here are the two ways of figuring that out. Actually, three ways. Three ways to figure out if one thing is really leading to the other.
First, you can do a correlation analysis in your front-end analytics tool. Any good front-end analytics tool has a correlation analysis functionality, so you can easily use that if you’re tracking.
If you’re not tracking or you're in an environment where you don’t have a lot of data—either because you’re not yet tracking, or maybe it’s the industry, the product type, or whatever—then ask your users.
Steve Klein: Interviews.
Büşra Coşkuner: Right. The third option is hiring a data analyst or scientist, depending on what titles are in your company, but someone who can run a causal analysis on your data. This is the most expensive option. So, before you get to that, try the following:
If you have data, then proceed in this order. If not, ask your users first before you try anything else.
Steve Klein: Yes. Okay. It's interesting, but I think in any case, it seems like what we're doing is coming up with this theory of value.
The idea that people don't wake up every day and think, "I'm going to go to LinkedIn because I just love to visit the website." I go to LinkedIn because it's doing some kind of job for me or providing some kind of value to me.
Büşra Coşkuner: Exactly.
Steve Klein: And all products have that same... People don't use products just for fun. We use products because they are doing a job for us, especially in a B2B context. Doing this process helps us to be very clear about our value to users, and coming up with a definition or measurements of that so we can focus on those things.
Büşra Coşkuner: Yeah, absolutely. The way I do that with teams is I create an impact map where we start by ensuring everyone is aligned on strategic goals.
Basically, we talk about the business goal for the year—do you know why you're doing what you're doing? Then we add business context, because a goal without context isn't worth much, right? Often, the business goal doesn’t have numbers attached to it.
So, I ask for the numbers. Then, we talk about actors. An actor is anyone who can have a positive or negative effect on us achieving the goal. This could be colleagues, departments, or even external factors, like the government for companies like Airbnb. It could be anyone who can help us or stand in our way.
If it's B2B, we talk about the actor's goal—specifically, the impact they wish to achieve by using your product. In a B2C context, it's about outcomes. What outcomes is the actor wishing for? This is where we begin to discuss solutions, and here’s where things often clash.
The big point or clash usually happens because there’s no alignment on what’s happening. It’s critical to understand which goal the product team should aim for. Maybe you have OKRs in place, or some other goal, but the product team may have no idea why it’s the right goal.
That’s what I see in the majority of cases. The task is to align these numbers and understand what metrics are relevant so we can attach a solid metric here.
If we believe the goal makes sense, great. If not, then we might need a different goal. Sometimes, the metric itself will show us it’s a bad goal. Maybe, in our correlation analysis, we see no connection, or after interviewing many customers, there’s no clear pattern.
So again, here’s where data, whether qualitative or quantitative, becomes essential. The business goal doesn't always have to be very quantitative. I do ask for numbers, but it depends. I think this is a good place to show the matcha example.
Steve Klein: This all makes sense to me. One thing that came to mind is the idea that even when we’re looking at the data and trying to do correlation analysis, it can still be hard to identify the best behaviors or actions that people take in our app that predict if they'll retain or convert—or whatever business impact we’re looking for.
Sometimes, we have to experiment a little and use our best guesses around what we think the drivers are. We can then work on improving those areas and see if retention actually improves. If we improve these upstream factors, does retention improve? Any thoughts or additional insights on that?
Büşra Coşkuner: Yeah. So basically, here we talk about bets, right? You need to talk to the actor that's important in this case. Talk, talk, talk—talk to that group of people and listen to what they will say first. This will give you some assumptions and ideas. If we can't get a good answer from our quantitative analysis, it's time to rely on qualitative input.
It sounds easy, but it’s really difficult because there needs to be alignment on the strategic level before you can make any bets, right? Then it’s bet time. You’ll say, “Okay, we believe,” and you see, I already started with an assumption—a bet is an assumption in the end, right? So we believe, based on this evidence or input, that the best way to move forward is to focus on this.
Steve Klein: Love that. Before moving on, any tips for the teams we talk to? They often say the key result they’ve been assigned is something like “improve retention” or “increase ARR.” These are big, lofty lagging goals. Any tips on how they can talk to their leadership team about breaking these down and having their goals more focused on leading indicators? Any advice there?
Büşra Coşkuner: So I love impact mapping for that kind of exercise. First of all, you need to speak business language. What does that mean? When they say something like, "We want to get X U.S. dollars," or in the eurozone, "X euros" (I’m in Switzerland, so we’re not using euros, but anyways, let’s say euros). They want to get X revenue or additional ARR by, I don’t know, 2050—which is way too long. Let’s say 2030. That’s already a business context. This is not the goal; this is the context. So they will not assign you that piece specifically, but that’s what they know. They will assign you something like “increase ARR by X percent this year.”
Somewhere in their spreadsheets, they’ve calculated that if they improve ARR by X percent over the next five years, then by 2030, they will reach this number. So the first thing you have to do is ask for insight into that calculation. You need to understand what is going on in those spreadsheets before you can do anything else. It’s very important to gain that understanding because, after that, once you know where the ARR comes from and how they’re calculating it, you can start thinking it through with your team.
So if we have this goal now, who’s the target group that can help us achieve it? And who’s a risk? Who can prevent us from achieving it? We have to manage risk. In B2B versus B2C, what do we have to offer them as a solution? And their goal—what is their goal in using our product or any kind of substitute or competing product? What are they trying to achieve? That’s where we start. Once we have that understanding, then we can start breaking it all down.
And how does that work? Let’s say the goal is to increase ARR, and LinkedIn creators are the next bet, right? So we bet on creators, and they will help us increase ARR by X percent. Alright, so what is the creator’s goal? It could be multiple things. It could be gaining more followers or fame. And then it could be selling stuff. Exactly—now you’re like, wait, why do they want to gain more followers? It’s either because they want to sell stuff or because it proves they’re famous.
Then, you ask, why do they want to be famous at all? Maybe it’s a life goal, and that’s fine. But in the end, creators don’t become creators just because they have a lot of free time or they’re masochists, right? They’re trying to do something bigger, which is selling stuff—that’s maybe their real business goal. Then you can say, okay, if the creator’s main goal is to sell things to their audience, we can start to segment them further.
You’ve got B2C creators, but there are also B2B creators—those who offer a service versus those who try to sell templates or monetize newsletters, which is a big thing right now. So suddenly, we have different segments. You make this thought exercise, and then you can break it down even further into outcomes. Suddenly, being famous or gaining more followers is just an outcome that we help them achieve in order to sell stuff.
Steve Klein: Yes, that's great. And the thing that this feels like to me is being clear about what your near-term strategy is. It's, "Hey, we want to increase ARR by X percent by the end of the year. What do we think is our strategy for actually doing that?"
We need to help the users that are coming in get value out of our product in these specific ways. I think it makes it clear for the entire product org, from CEO all the way down to IC PMs, around the strategy. What do we think is important? What are the things that we actually need to achieve?
Büşra Coşkuner: And now let's break that down because we’ll turn it into a metric. So gaining more followers is an actor's outcome. The actor, or the creator, will measure that by the number of followers, right? And then they might aim to increase that to 10K within six months, for example. That’s a metric—it’s something they will measure you on. It's not a metric we measure directly, but they measure if we are successful by looking at this number.
Yes, so it's totally customer-centric behavior and measurement from their point of view.
And now we can flip it around or take it even further and say, what does that mean for us? This could also mean something like the percentage of follower growth, right? But as a creator, you’re not looking at percentages. You’re saying, "By then, I want to have achieved this number of followers." That’s how they will see if we were able to make them successful.
Büşra Coşkuner: Now, for us, this could mean something like a user journey, right? What is the user journey? How do we know that someone has gained followers? For us, it could mean the percentage of users who identify themselves as creators and who achieved follower growth by 500, for example, within a given period.
That’s the linear thing. So let’s say—I don’t know, I’d need to calculate—but let’s say 20% growth month over month. That, or something like it, would be an indicator for us that a creator gained, say, 500 followers within six months.
Steve Klein: Yes.
And this is where it gets interesting. This is where you have to experiment and figure out the right cadences and the right amounts. Ultimately, you’re trying to get to some representation you can track that serves as a proxy for achieving the outcome. For LinkedIn users, that could mean growing their audience.
Yes, the ultimate goal is to sell or to be able to sell things. But having that line in the sand that you can run experiments against, and then seeing if moving the needle there has upstream effects, creates clarity for the whole team working on it. It's super valuable.
Büşra Coşkuner: And also, to give you another level of that, this is the actor’s view on what success means, right? It’s not dogmatic; it’s about using your brain power to understand what we need and how we understand if the actor is making progress in the right direction.
Now, we could say, at a third level—and there are more, but let’s keep it simple—what can we improve to help them achieve that? For instance, it might be improving the number of clicks or improving the visibility of the follow button, which was a thing.
Steve Klein: Yes, it could be any of those things. But I think the key takeaway here is that this whole process we’ve been talking about sets the stage for effective discovery, especially with metrics at the core of it.
It can be deceptively hard to grasp as a concept, but I’m glad we were able to tie that whole thread together.
Büşra Coşkuner: Yeah, exactly. And it would look something like this. You need discovery all the way down. You need to understand where things come from. And discovery isn't just qualitative stuff, it’s also quantitative—anything you can get as input.
And the output of discovery? It’s not about finding the truth. The output is about making decisions. Learning or making a decision—that’s the goal.
Steve Klein: Yes, exactly.
Steve Klein: Okay. Uh, there's, you know, a hundred more things I want to ask and talk about.
We'll have to save them for another, um, another round of this. Um, I want to get to some Q&A that some folks have been submitting and had submitted a little before the event. Matt is going to throw some questions up on the stage. Matt's our MC in the background. Uh, cool. I'm going to read it out and then you can give it an answer.
Um, Büşra, do these techniques and metrics you've reviewed today primarily apply to B2C or can they be used in B2B?
Büşra Coşkuner: It doesn't matter. Like, I'm working with a team right now that is B2B2C. It really doesn’t matter. So, um, in the past, I used to think kind of similarly. I was thinking, well, data and quantitative stuff only work in B2C, but in B2B you can't.
But it’s really about how you cut your personas—whether it's based on demographics or other contexts. In B2B versus B2C, it may be demographics, but the context is: Can you collect a meaningful amount of data? Yes or no. If your business model allows you to collect that data, then you can apply anything I’ve mentioned.
It might be a bit more difficult in one case than the other, like getting access to certain things, compliance, or even regulations. But the thinking, especially like building the impact map and breaking down metrics in a tree, the method and first principles behind them still apply.
Steve Klein: Yes, I agree. Largely, I do think there is a bit of "magic" to B2C products in a way. People’s choice to use and continue using B2B products is almost a bit more logical or value-driven, whereas in B2C, people decide based on personal identity and how a product makes them feel, which can be harder to measure.
But generally speaking, I think the process and way of thinking apply to both.
There’s often confusion about what KPIs and metrics are. How do you define the difference?
Büşra Coşkuner: KPIs are a subset of metrics. A metric is an umbrella term that just means any measure with a value attached to it. KPIs, or key performance indicators, are metrics specifically used on the business level.
Typically, we attach business metrics to KPIs. For example, KPIs are business metrics like ARR,
Steve Klein: New signups.
Büşra Coşkuner: Yeah, new signups, average revenue per user, average order value, GMV, that kind of stuff. These are all business metrics.
Steve Klein: Yeah, it's generally just the metrics that are more business-related and that the company deems especially important.
What cadence do you recommend for reviewing product metrics and iterating on product decisions? Should this happen every sprint, monthly, or quarterly?
Büşra Coşkuner: Well, it depends on the product's maturity. I believe we need to be more entrepreneurial when building products. With that mindset, you'll have a sense of when to look deeper and when to give yourself and the product some breathing room.
When you're in the early stages of product development, you won’t have a lot of data—because you haven’t collected it yet. So, in the early stages, it’s more about being close to customers. When you finally have data, you’ll naturally look at it more often—partly because of curiosity, but also because you need to ensure the product's success.
If the product isn’t working, you’ll need to kill it, and you need to do that fast. To understand that quickly, you have to look at qualitative and quantitative data often. I remember, when I was working at Doodle, I was responsible for building new scheduling solutions like Doodle 1:1 and booking pages. I was checking numbers daily, especially during the early stages, to see how many people who started using the product continued to use it.
Once things stabilize, I started checking metrics weekly. When you add new tracking, you also need to check daily for the first week to make sure it’s working properly. Afterward, it becomes more routine. When the product matures and grows, you may check metrics monthly or quarterly, depending on the company size.
Steve Klein: Yes, two thoughts. One, you should use metrics to inform your decision-making. Two, only look at the things you're tracking when they help you make a decision.
Continuing to look at metrics just out of curiosity can become a form of procrastination. I’ve fallen into that trap of checking too often, tracking too much, or checking specifics of how each customer uses the product. But if it doesn’t change the decisions you're making, it’s not worth doing.
Büşra Coşkuner: Exactly, and that’s a good measure. Ask yourself, "Does it help me make decisions, yes or no?" If not, wait two weeks and check again.
Steve Klein: Okay, I think we have time for one more. Is it being recorded? Yes, it is! We’ll send a recording out by the end of the week, so keep an eye out in your email for that.
Great. Last question: What would you do in life instead of product management?
Büşra Coşkuner: Whoa, that’s a coaching question I need to take with me and think about! Well, it depends. If I had endless money, I’d probably keep working on software as a service with a friend, building it, and then moving on to the next one. I’d try to make one of them big—so it still sort of involves product management. But you know, it’s that entrepreneurial thing.
Steve Klein: Yes, love that! Even without the money, for me, it’s fun. I love building things and working with people.
Büşra Coşkuner: Well, if I had endless money, I would probably hire a private teacher, travel the world, and do whatever I want.
Steve Klein: Got it, full reversal! Just travel and forget work!
Büşra Coşkuner: Exactly, haha!
Steve Klein: I’d love to do that too! But okay, that’s all we have time for today. Thanks again so much for joining us, and thanks to everyone who attended. I know you all have busy lives, so we really appreciate you taking the time to hang out and talk about these topics.
Where can people find you?
Büşra Coşkuner: LinkedIn is the easiest way. I’m pretty active there, though I do have some quiet weeks when I’m overloaded with company deliveries. You can also check my website, which is being updated, for more information soon.
Steve Klein: Cool. I know you’re in the middle of one of your metrics courses, but everyone, make sure to follow her on LinkedIn to keep up with future offerings. Thanks again, Büşra!
Büşra Coşkuner: Thanks, Steve!
