How do you know if you have validated the problem space enough to start dedicating engineering resources to building out the product?

Before investing in engineering resources you want to build conviction around the following: 1. Is there a market need? Are you fulfilling a true gap in the market? 2. Do you have a differentiated vision to deliver on this need? 3. Is there willingness to pay?  4. Does the business model make sense such that you see a path to ROI for the business? 5. Is there a clear route to market (you know how to sell / acquire customers)? 6. Does your business have (or plan to have) the capabilities to deliv ...Read More

This is hard! For me, it's a mix of having a good understanding and confidence that you have  (1) a clear hypothesis that you can test with a minimally viable product that is shaped by data and customer/market research, (2) confidence that you have a potential solution that can prove the hypothesis correct, and (3) an understanding of the risk and opportunity for building that solution, including the time it'll take to build, the availability of users willing to try your solution. When in doubt, ...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

For a new product in a new market, I don’t think you can ever validate the problem space enough. Once you have reasonable confidence that the users (and buyers) exist for the problem space your product tackles, and there are viable ways to create a business out of that solution, the best next step is to involve engineering to build a prototype of the solution. Then you can test that prototype with a representative sample of the expected user base to get early feedback and iterate on building out ...Read More

In my experience, you’re ready to invest in building once you have a strong conviction that a real, significant problem exists AND that your proposed solution concept resonates with target users. I always try to look for concrete signals before green-lighting more engineering work: Deep Problem Understanding: Ensure you clearly define the problem and its root causes. This usually comes from extensive user research: talking to customers, observing their pain points, and verifying this problem is ...Read More

There are many potential signals – just one of these is enough! You're starting to hear the same problems repeated in a lot of your research You don't have a clear view of feasibility of the solutions you're thinking of (you need to start prototyping) You have a potential customer willing to pay for the solution you've shared with them Bear in mind that bringing eng resources to the project isn't a binary, and you should be partnering with an eng lead from the start of your discovery so they can ...Read More

You can't know if you've validated the problem space enough because it's not a binary evaluation- it's a judgement and investment call based on data, gut feel, and stakeholder trust. At the end of the day, you should seek to have the strength of your hypotheses outweigh the relative value of engineering time. Sure, sounds great, but what can you actually do to gain confidence in your hypotheses? Talk to real users and test something. Talk to real users about their pains – Don't rely on second-ha ...Read More

I would start with problem, root cause, solution (aka RCA) and really understand the problem you are trying to solve with your product. Once you understand the problem and proposed solution, next step would be to validate it with customers. Customers' feedback is a good indication on whether you have a good product to address problem in the market. Additionally, you need to understand the target market and your competition in the space (how does your solution stack against competitors, what succ ...Read More

Validating the problem is of course a critical step in building a great product. A couple of signals to look for Do you have a clearly articulated problem statement you are trying to solve Did you conduct robust user research to narrow down your problem statement to know what you would be solving? Users' desire to buy and use a product to solve the problem is the best signal you are looking for to keep marching forward Business viability of the problem statement - This reflects the market you ar ...Read More

To ensure that you are investing your resources wisely, it is critical to have a high degree of confidence in the problem you are going after. You do not want to spend engineering resources on a problem that may not be large enough or is technically not possible to solve. Here are some key indicators that can signal you are ready to allocate engineering resources to building out the product. However, it's important to continue validating, testing, and iterating on the product as you move forward ...Read More

This is heavily dependent on the type of product you are building, but you can get very far with high fidelity designs and manually "hacking" things together. I worked at a company where we "generated" (hand created) an analytics report for a customer (who was actually paying us) once a month. It took an insane amount of time to create (20+ hours) but the feedback on both how to sell, and how to build our product from that experience were invaluable. Start light: with high-fidelity designs and c ...Read More

Validating the problem space sufficiently before dedicating engineering resources is crucial. Indicators that suggest you are ready to proceed include: a clear understanding of customer needs, consistent feedback validating the problem, comprehensive market research and competitive analysis, alignment with business objectives, a potential market size and opportunity, confidence in achieving product-market fit, and risk mitigation through iterative learning. By thoroughly researching the market, ...Read More