Tanguy Crusson

Great question. It's really hard to prioritize small, iterative product improvements against large new features/bets. In my experience you need both, as well as a few other aspects. The way we do it in my teams is to think of it as balancing investment levels between different buckets, and to dedicate capacity to each of these buckets. Otherwise it's a constant struggle. We've tried to describe it in this section of the Atlassian product discovery handbook talking about ideas, and that one about ...Read More

There are multiple things you need to get right before you start building a product, because the most likely outcome of creating one is that it will fail. To see an example of this you can watch this talk for how we did problem and solution discovery when creating Jira Product Discovery Validating the problem space is important. For that I think the recipe is pretty straightforward: talk to customers and prospects and let them guide you. It's quite important to go in there open minded about what ...Read More

You should think of it as: it should be ready to be shipped when it's the first shittiest version, and when it's the best version of itself. The question should not be not so much about readiness of the solution, but readiness for whom? I've witnessed teams who got too excited when creating a new product and opening the floodgates to let anyone try it too early. That usually doesn't end well... You don't want many people jumping on the solution when it's not ready for them: first impressions won ...Read More

The following stand out to me for what I've witnessed: The team is acting like the product they're creating is going to be successful. I'm amazed at how many successful companies try to build new products like they do for their existing successful ones: same processes, same success metrics, etc. They don't have the urgency, move too slowly, learn too little, are complacent. The market is a graveyard of failed products and businesses: the most likely outcome, when creating a product, is that it w ...Read More

I don't really have a "scientific" answer to this - I've always done this in 2 ways: creating a bottom-up model, and trying to find data from other companies/competitors to benchmark against. Bottom-up model I've usually done this in a giant spreadsheet, trying to build a model with a lot of documented assumptions. Here's how we did it for Jira Product Discovery: First, decide on how we would package and price the product, which we did via conversations with our early lighthouse customers, a sur ...Read More