Introduction
This is a follow up to Product Discovery – Part 1 – Beyond the Checklist where we looked at a story as a lesson before considering the more tactical get-it-done checklists.
Here is product discovery at the highest level, summed up in two bullet points.
- Living in the Problem Space: Asking good questions.
- Living in the Solution Space: Coming up with answers that are: 1) viable; with market demand via value propositions(s), and 2) feasible; technically and operationally producible within some defined financial criteria, as well as compliant with any regulatory issues.
That’s it! Everything that follows are the details.
Let’s Discuss the “Fail Fast” idea for a Moment
Fail fast can be a useful concept: test, iterate, and quickly address issues. However, it’s crucial to learn from others’ successes and failures to avoid unnecessary risks. So yes, you’ve heard the mantra about failing fast and iterating on product ideas. The key idea of fail fast and iterate seems to be that it gives permission for risk-taking. Thankfully, there lately seems to be a trend to back off the “Fail Fast” meme and a move to “Learn Fast” as a healthier euphemism. (See Learn Fast, Not Fail Fast, and How To Transition From A ‘Fail Fast’ Mentality To A ‘Learn Fast’ Mindset. )
The evolution may due to abuse of the original idea which likely started as a casual comment and grew to something beyond. I’m suggesting there’s a difference between taking risks and just being reckless. Defining that line is the challenge.
As a Product Manager, your role is theoretically simple: ensure your team builds the right product for the right market at the right time. This is where discovery comes in. It’s the process of understanding customer needs. We’ll look at the importance of doing that and a checklist of methods to effectively conduct discovery for technology products. You’ll still likely fail on occasion and learn. But perhaps failing fast shouldn’t be lionized to the point where it seems like a goal. Maybe it’s good to have it as a well-known outcome so as not to be so afraid of it that we don’t take risks. But it feels like we went through a few years where it was blithely invoked to make recklessness acceptable. Customers deserve more. Investors deserve more. Agile, Lean methods and similar can help adjust along the way. But they don’t always actually help us discover first principles in a problem space.
Product Types – Big Picture View
It’s usually useful to distinguish products along at least these two dimensions:
- Product Stages
- Brand New Never Seen Before Products
- Improvements to Existing Products
- Complexity or Scope
- Relatively Simple
- Large to Giant
New vs. Improved seems an obvious enough distinction. But Small to Large can be arbitrary. If you’re building something truly large, a one week design sprint with sticky tabs might not cut it. Maybe you could use a short design sprint as more of a brainstorming start point. But making it “the thing to build the big thing?” Perhaps not. The Design Sprint is just one style; a tool. Properly applied, it can be powerful. Improperly applied, it may be more motion than progress and head down wrong or weak paths. And worse, do so with false confidence.
Design Sprint vs. Discovery?
Here’s a question to consider for complex projects… When do you think people do their best work? Under frenzied pressure? Or when they do some thoughtful consideration? Sometimes it is under pressure. But for truly complicated things? Perhaps not. Speed is great. Unless you’re running into walls. Can Discovery happen in a day or even three or ten when just gathering up certain types of information or getting critical research questions answered may take days or weeks? Consider using ideas of “Design Thinking” instead of just filling out the templates a Design Sprint may call for. Both of these ideas are user-centric, but the Sprint may be too much of a narrow time-boxed short-term approach.
Don’t get sucked into the latest fad because someone had you read the book on the latest thing. A Design Sprint might be perfect for your goals. However, you need to use judgement as to what approach is best. Sometimes “slow” is smoother. And smoother can be faster. Maybe you heard this story as a child, “The Tortoise and the Hare” from 1915. Think about the nature of what you’re trying to do. If a Design Sprint makes sense, great. If a larger scope using Design Thinking makes sense? That’s fine too. Some other Discovery methodology? All up to you. The method is secondary to the goal; simple understanding of actual problems.
Why PROPER Product Discovery Matters
Product discovery is essential for several reasons. Firstly, it minimizes the risk of building things nobody wants. By investing in understanding your customers and their pain points, you can ensure that your efforts are focused on creating real value. Secondly, you can speed up overall time to market – even if the upfront discovery was costly – by helping you prioritize features and avoid unnecessary rework. Lastly, it fosters a culture of continuous learning and improvement, crucial for long-term success. Doing sensible discovery doesn’t make you “anti-agile.” You can – and should – use discovery processes to refine backlogs and mitigate risks. But only you can determine what the cadence should be.
Key Principles of Product Discovery
Before diving into the specific methods, it’s important to understand the key principles that underpin effective product discovery:
- Customer-Centric Approach: Keep the customer at the center of your discovery process. This means actively listening to feedback, observing behavior, and empathizing with needs. It does not mean sitting around brainstorming with your team alone. People like to think, “well I’m a customer.” While this may be true, people come in a variety of cohorts. You might be correct in your insights. But what if you’re not? Will fundamental mistakes here result in just re-work? Or might they be a company killer?
See Jeff Bezos for thoughts on customer centricity… focusing on customers vs. competitors (YouTube), and some quotes on this topic. - Iterative Process: Product discovery is not a one-time event but an ongoing process. Regularly revisit and refine your understanding of the market and customer needs.
- Collaboration: Involve cross-functional teams in the discovery process, including designers, developers, marketers, and sales.
- Data-Driven Decision Making: Use both qualitative and quantitative data to inform your decisions. This helps balance intuition with empirical evidence.
Methods for Product Discovery
For a technology product, there are several methods you can use to effectively conduct product discovery. Here’s checklist of items to consider:
1. Market Research
- Competitive Analysis: Study competitors to understand their strengths, weaknesses, and positioning. Identify gaps and opportunities in the market. Even if you’re generally customer centric, there’s obvious need to look at competition. There may be some table stakes items you simply need to have to compete in the “Battle of the Checkboxes.” (However, ideally not much, otherwise you may have other problems given if this is an issue you likely don’t have much strategic differentiation in the first place.)
- Industry Trends: Keep up with trends and technological advancements to help anticipate market shifts and emerging needs.
2. Customer Research
- User Interviews: Conduct in-depth interviews to gain insights into problems, behaviors, and needs. Use open-ended questions to encourage detailed responses.
- Surveys and Questionnaires: Use surveys to gather quantitative data where possible. Use these to validate earlier interviews on a larger scale.
3. Empathy and Design Thinking
- Personas: Develop detailed personas representing your customers. Real ones. With actual research. There are AI generators available to help create these. These might be useful given AI large language models are trained on massive amounts of data. However, is that data truly legitimate for you? Maybe it is. But it’s also possible it suffers from a variety of bias issues; either based on the data source, the AI company’s “cleaning up” data, or parameters set in the foundational AI model itself. (Here’s my article on User Personas, with a link to a template.)
- Customer Journey Mapping: Create a visual representation of the customer’s experience with your product or others’ products. Find decision making and pain points that may be opportunities. You’re looking for inflection points. What Google Consumer Insights research calls Zero Moments of Truth. Also, here’s my article on Journey Mapping, also with a link to a template.)
4. Prototyping and Testing
- Wireframes and Mockups: Create low-fidelity wireframes and high-fidelity mockups to visualize ideas. This helps get early feedback before significant resources are invested.
- Usability Testing: Test prototypes with customers to identify usability issues.
5. Analytics and Metrics
- Web Analytics: Track behavior on websites / apps. Look for patterns.
- A/B Testing: Compare different versions/features.
6. Beginnings of Product Roadmapping
- Prioritization Frameworks: Use frameworks like MoSCoW (Must have, Should have, Could have, Won’t have) or RICE (Reach, Impact, Confidence, Effort) to prioritize features based on their potential impact and feasibility. There’s obviously tons more frameworks. You likely already have a favorite. That’s fine. Just try to make sure the framework maps well to your target market. Our goal isn’t to exercise our favorite tool(s). It’s to apply the right tools to the job at hand.
7. Tools & Frameworks
- Determine Appropriate Tools to Use: Design Sprints can be useful for a wide variety of projects, especially of a simpler variety. And Design Thinking is another methodology that can be effective and may offer wider scope than Design Sprints. Other frameworks might include “Jobs to Be Done” and “Opportunity Solution Tree.” Seek out and study all of these so you can choose if any of them work for your job at hand. Also understand there are additional frameworks from Kano to Double Diamond and so on. However, some of these are actually more about prioritization or built more for when you get deeper into solutions spaces. That’s not necessarily wrong, but arguably takes you out of pure discovery. These might be considered more end-to-end development efforts. Again, you have to look at them and just have them available in your toolbox. It’s your judgement that’s needed to decide what’s most appropriate to use.
8. Data Discovery
- Data Quality and Availability: What’s Data Got to Do With It? Data isn’t people? Perhaps not. Nevertheless, while you’re going down a disocvery path, look for what data sources might be useful for your product and begin investigating the five V’s. Volume, Velocity, Variety, Veracity, Value. If your product might at all possibly be using any form of Machine Learning (ML) and Artificial Intelligence (AI), then the quality of data is massively important. There is non-trivial risk in various ML models, especially those that are genreative. Discovery is not a bad place to start considering this issue, specifically in relation to your customers. Specifics on data assessments and such for ML Opeations (MLOps) pipelines is outside the scope of this article. But if your general concept involves ML/AI components, discovery might be a good place to start considering these issues.
Implementing Product Discovery in Your Organization
To successfully implement product discovery, consider the following steps:
- Build a Discovery Team: Assemble a cross-functional discovery team. Ensure members have necessary skills and resources to conduct research and analysis.
- Establish a Discovery Process: Define a clear process for discovery.
- Grow a Culture of Experimentation: Encourage a mindset of experimentation and learning. Allow your team to take calculated risks and learn from failures.
- Invest in Tools and Training: Provide your team with the tools and training they need to conduct effective product discovery. This might include research tools, prototyping software, and workshops on design thinking.
Conclusion
Product discovery is a critical component of successful product management. By understanding your customers and validating your product ideas, you can ensure that your development efforts are focused on creating real value. Use the methods outlined in this article to guide your discovery process, and remember that product discovery is an ongoing journey. Keep learning, iterating, and refining. Your competition will be doing likewise. And maybe, just maybe we can learn fast and not fail at all, or at least less.