Through continuous discovery, let's talk about using opportunity trees and rapid prototyping to continuously find ways to improve the metrics we believe will grow your product.

Jon Kinney
Partner & CTO
Ryan Hatch
Head of Product Strategy & Innovation
Billy Sweetman
Head of Design
Andrew Verboncouer
Partner & CEO

Now, we're going to take a look at continuous discovery. Here we've got a customer journey and we've mapped out all the different events and moments across that journey. Our customer's going to experience, we really want to dive into one that's not tracking here, and it's not doing what we think and how can we make some impact there?

How can you get people beyond maybe this point of friction and get them to that next magic moment? This is behavior driven. It's not necessarily driven by a bug in our software or a bug in our product. It's something that's just not doing what we expected our users to - our customers to do. How do we get here? How do we find these opportunities?

We're really looking not at the math side of this anymore. It's about human emotions. What are some of their fears and assumptions going into the product? For this example, we're showing maybe a customer views, their first data visualization. It's only hitting 20% and we need to get it up to 80. And that's the thing that we're going to tackle as we move forward.

So diving into that, you know, we're going to assume a bunch of opportunities as we go through, we might look at some initial stuff and say, Hey, this isn't tracking the way we thought. So here's a bunch of opportunities we think we can go after, but how do we validate those opportunities are actual opportunities?

So we have these opportunities and we really want to focus on this metric of increasing it to 80%. Now we're going to come into our first discovery loop and that's our exploration loop. In our exploration loop, the first thing we're going to do is we're going to get some feedback and some data we might be talking to our support team.

We might be looking at customer surveys. We might actually take some time and go to the app store. Look at app reviews, maybe some social posts of what people are posting on Instagram or Twitter about our product. We might be doing observation sessions. We might be looking at users, actual workflow through our product and really finding out what is the issue.

We're not going to find the why yet, but we're definitely gonna identify the what and that'll inform us. When we go into our customer interviews. Customer interviews is really the most valuable thing we can do. We should be talking to customers at least once a week.

We're going to find out areas of friction, pain points they have, things that they're struggling with, and we're going to pull out these different patterns from their stories or observations from their stories. And we're really going to understand our product better than we ever could just looking at that raw data. These customer interviews are going to help us identify those different opportunities, right?

Like we said before, the stories and the patterns are really going to help us identify what we could go after and what we probably can't go after. These are the things that we're going to tackle when we get into this opportunity tree.

Now we can take those opportunities that we assumed existed. And now we've got some actual information to choose and to put together real opportunities. Here, you can see we've killed one-off. Doesn't make sense. Didn't work, not something that we heard in our interviews. Maybe something that didn't show up in our research at all.

We've got two that maybe aren't big enough. We don't feel they're big enough here at this moment. We might come back to those later, but we've got one that we really want to go after and tackle. Within that opportunity, there might be multiple solutions we're showing here is maybe four potential solutions that we're going to go test.

And now we're really going to take these solutions and take them into our second loop on the discovery side, which is prototyping.

In the prototyping loop. The first thing we're going to do is really spend some time designing out our solution. Here, we might create a small team of stakeholders. This could be designers, folks from product, maybe some developers involved.

We might be running design sprints, wireframes really taking multiple solutions through this all at the same time. We might be using exercises like crazy aids, and really understanding how does this fold into our current experience. And how does that data and information architecture work with what we currently have?

We might be throwing out a ton of ideas here, but we're might going to bring a couple of forward into the prototyping phase. We moved to prototyping. We really are working from that lo-fi to that hi-fi, right. We take these crude wire frames and those crude solutions, and we're starting to make them look higher fidelity than before.

We're focused on what does it feel like to click through this experience? What does it feel like to go through end to end? We want to be leveraging our design system because we don't want to be creating a bunch of new stuff that doesn't already exist. We want to be spending time talking to maybe some of our development partners to see what might work. What might take a long time? Might be something that's a lot easier to focus on right off the bat. When we get a couple of prototypes that we like, we want to move it right into user testing. We're going to recruit users either from our existing user base that we've interviewed, or we might get some new customers or customers from a competing product, depending on what we're going after.

We're going to be doing interviews and observations. We were listening to those moments of joy or moments of friction as we're going through those different prototypes that we. Here, we're really observing a lot of different metrics, like time to task. If it's something about completing a certain task, what's the usability of the product.

I want to make sure that it's easy to get through whatever we're trying to measure here. I'm going to be AB testing two solutions against each other, or AB testing against the current product and the current experience, these data and observations and all these competing solutions is going to decide when we get back into our opportunity tree on what solutions move forward.

We're going to impact those different solutions that we kind of started in that opportunity area that were very abstract. And we got a little bit more concrete. It was moving into the solution phase and we want to kill those bad ideas fast. So you see here, we killed two more solutions. As we kind of went through, we have one that we felt is really tracking well, and we can probably move this back or move this into our delivery.

And then we have another one that maybe is something we had to come back later. Maybe we got to run another exploration on it. Maybe we gotta run another prototyping pass on it. The next thing that we're thinking about now is designing out our in- product experiments. You know, who are we targeting with this? What is the segment we want to go after? What does this success criteria or the failure criteria for this specific experiment?

We want to decide those now, before we get financially, emotionally invested into our. Into our product or into our experiment. So we can have a clear mind on what we decide to move forward and not we're going to move into the delivery phase, but we're still about learning.

It's not just about shipping features.


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