Tips for Evolving Product Discovery Methods

Some Highlights: 1. Flip the traditional design-thinking script: Focus first on deeply understanding the core capabilities of the technology, not a specific problem to solve. Let natural cognition make connections later, using flash narrative to explore potential futures. 2. Use the AI expansively at first without directing it, to unlock its full potential. Avoid tight guardrails/constraints initially. 3. Define the current value proposition in terms of customer goals, context, and target users. Then assess how AI can expand each element. 4. For new value propositions, use analogies and metaphors to establish a coherent vision and avoid scope creep. 5. Assemble collaborative, multidisciplinary teams to navigate uncertainty and integrate diverse perspectives through rapid prototyping and feedback loops with actual or potential customer segments..... "Two of us (Johnathan and Jennifer) recently conducted research showing that the main thought process for this style of innovation is to start by understanding the core functions of a technology, then explore how it can be used to solve problems across different domains. Other hallmarks of emergent thinking include evaluating ideas without understanding the criteria for success, improvising ideas with little preparation or planning, and changing a project’s target outcomes. These activities tend to run counter to good business practices promoting efficiency and reliability, and they may even violate some of the core tenets of design thinking — namely the need to identify a clear user problem to address before generating ideas for a solution. Yet, they’re also critical when trying to leverage ChatGPT (or any other emerging technology, for that matter) for innovation." Discovering Where ChatGPT Can Create Value for Your Company https://buff.ly/46X2aqT #ProductStrategy #Innovation

The pressure is on. Answers no longer pop out of surveys. We make decisions through uncertainty. How can you ensure that you’re learning at the speed of change? Clients and colleagues in my network are adopting new methods to gain insight into the complexity and nuances of people’s needs and buying behavior that traditional surveys and segmentations don’t enable. Five recommendations to update how and what you learn: 1. Build your critical thinking muscle. Take accountability to integrate human knowledge, creativity, insight, and hard data to get to the “so what.” 2. Seek the leading indicators of the future. Lagging indicators point to the past. 3. Prioritize a focus on behavioral data and signals of emotional influences on decisions vs. surveys with self-reported attitudes informing stable segmentation models, which may mask opportunity. 4. Flatten the silos in your organization. Build thought partnerships between marketing, product, market insights, data analytics, and tech/manufacturing. 5. Embrace iterative experimentation across a portfolio of user “jobs to be done” as a robust method to identify innovation opportunities and modernize your existing go-to-market strategies. #innovation #learning #strategy

Is the process of innovation in need of innovation? Most innovation processes are linear. First, you do A. Then, you do B. Each stage-gated step earns you permission to move to the next step. If you’re lucky, you make it to the MVP step, where your prototype arrives in the hands of users. The steps from start through MVP are usually product-focused: What’s the idea? Who needs it? What are their pain points? Which features address the pain points? Answering those questions is a terrific way to build a product. But it’s a terrible way to assess the most important questions: Is somebody going to buy this thing? How many somebodys? It’s not that innovation teams ignore the question of demand. Pre-MVP surveys often assess new product interest. Surveys, however, don’t tell you if people want to buy your product; they just tell you whether people *think* they want to buy your product. Even worse, in many cases survey respondents are paid for their opinions. Are you really going to get a good read on how they will behave when they encounter your product for sale in the real world? 💡 Here’s an idea: Don’t put marketing at the end of the process. Put it at the beginning. Answer the hardest question—does anyone want this product?—as soon as you can. You may be thinking: how do I know which product to market? It’s early days. Good news: you can test-market multiple product concepts or multiple ways to position a product. Use ads. Be honest (“in development” should be prominent). See who clicks. See how many click. If it doesn’t meet your hurdle, try again or pull the plug. Learning early is better than learning late. Lean Startup and its MVP approach were arguably the last big innovation in innovation. But that was over 15 years ago. Isn’t it time for a new look at the process of innovation? #innovation #marketing #demandvalidation #concepttesting #heattesting

I am constantly thinking about how to foster innovation in my product organization. Building teams that are experts at execution is the easy part—when there’s a clear problem, product orgs are great at coming up with smart solutions. But it’s impossible to optimize your way into innovation. You can’t only rely on incremental improvement to keep growing. You need to come up with new problem spaces, rather than just finding better solutions to the same old problems. So, how do we come up with those new spaces? Here are a few things I’m trying at Duolingo: 1. Innovation needs a high-energy environment, and a slow process will kill a great idea. So I always ask myself: Can we remove some of the organizational barriers here? Do managers from seven different teams really need to say yes on every project? Seeking consensus across the company—rather than just keeping everyone informed—can be a major deterrent to innovation. 2. Similarly, beware of defaulting to “following up.” If product meetings are on a weekly cadence, every time you do this, you are allocating seven days to a task that might only need two. We try to avoid this and promote a sense of urgency, which is essential for innovative ideas to turn into successes. 3. Figure out the right incentive. Most product orgs reward team members whose ideas have measurable business impact, which works in most contexts. But once you’ve found product-market fit, it is often easiest to generate impact through smaller wins. So, naturally, if your org tends to only reward impact, you have effectively incentivized constant optimization of existing features instead of innovation. In the short term things will look great, but over time your product becomes stale. I try to show my teams that we value and reward bigger ideas. If someone sticks their neck out on a new concept, we should highlight that—even if it didn’t pan out. Big swings should be celebrated, even if we didn’t win, because there are valuable learnings there. 4. Look for innovative thinkers with a history of zero-to-one feature work. There are lots of amazing product managers out there, but not many focus on new problem domains. If a PM has created something new from scratch and done it well, that’s a good sign. An even better sign: if they show excitement about and gravitate toward that kind of work. If that sounds like you—if you’re a product manager who wants to think big picture and try out big ideas in a fast-paced environment with a stellar mission—we want you on our team. We’re hiring a Director of Product Management: https://lnkd.in/dQnWqmDZ #productthoughts #innovation #productmanagement #zerotoone

Tanguy Crusson has spent 10+ years at Atlassian, where he's taken several products from zero to one, including HipChat, Statuspage, and most recently, Jira Product Discovery. In this episode, we dive deep into the struggles and lessons of innovating and building new products inside a large company. Tanguy shares candid stories about what's worked, what hasn't, and everything he's learned about successfully building 0 to 1. We cover: 🔸 Why large companies with so many advantages still fail at creating new products 🔸 How to avoid common pitfalls like competitive myopia and premature scaling 🔸 Lessons learned from acquisitions 🔸 Lessons from competing with Slack 🔸 Insights from the success of Jira Product Discovery 🔸 Tactics for protecting your “ugly babies” 🔸 The power of “lighthouse users” 🔸 The importance of having a “why now” 🔸 So much more Listen now 👇 - YouTube: https://lnkd.in/gr9f4D45 - Spotify: https://lnkd.in/gmiuz944 - Apple: https://lnkd.in/gWGAc5ZX Some key takeaways: 1. “Don’t eat your own bullshit.” When launching new products within companies that have already seen some success, it’s easy to assume that your existing playbooks will work again. But what got you here won’t take you there. You need to define, test, and validate your assumptions, because they may very well be wrong—especially when targeting new customer segments. 2. Startups benefit from starving. Starving creates hunger, which drives people to solve problems with resourcefulness and urgency. When exploring new products in a big company with excessive resources, you need to create scarcity to emulate this startup starvation. This generally means operating as a small, scrappy, siloed team. 3. The most likely outcome when launching a new product is failure—even at big companies that appear to have many advantages. It’s important to ground new product launches in this reality so that you can deter the company from over-investing, which ultimately serves to reduce hunger, slow things down, and decrease the chances of success. After all, why invest heavily in something that’s most likely to fail anyway? 4. Success for new products should be measured differently from existing ones, both in terms of metrics and time horizons. In general, new products should be judged by whether the team is answering the right questions at the right pace and whether the team is still excited about the new bet’s potential. It’s a common mistake to judge new products by metrics that a big company is used to, like MAUs or revenue. However, if a team is optimizing for MAUs or revenue before they’ve worked to understand the problem, they will be working on the wrong things. 5. Atlassian uses a four-phase approach to launching new products and deciding whether to invest in them further: Wonder, Explore, Make, Impact

Which one drives higher revenue: product discovery or experimentation?  The most innovative companies excel at both. They're masters at knowing WHEN to discover and WHEN to experiment. This distinction is costing companies millions in wasted development: • Airbnb discovered people wanted to belong anywhere 𝘣𝘦𝘧𝘰𝘳𝘦, experimenting with their platform • Slack discovered team communication pain points 𝘣𝘦𝘧𝘰𝘳𝘦, experimenting with features • Zoom discovered simplicity was key 𝘣𝘦𝘧𝘰𝘳𝘦 experimenting with their UI Yet most product teams do it backwards: They jump straight to A/B tests and MVPs before understanding if they're solving the right problem. Here's why this matters and how to think about it. 1. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗮𝘀𝘀 • Focuses on understanding the problem space • Validates if a problem is worth solving • Uses qualitative methods (interviews, observations) • Answers "What should we build and why?" 2. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗟𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘆 • Lives in the solution space • Tests specific ideas and hypotheses • Relies on quantitative data (A/B tests, metrics) • Answers "How should we build it?" 3. 𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Discovery without experimentation = Building the wrong thing confidently Experimentation without discovery = Optimizing the wrong solution perfectly 4. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗣𝗶𝘁𝗳𝗮𝗹𝗹𝘀 • Rushing to experiment before proper discovery • Getting stuck in endless discovery without action • Running isolated tests without a broader strategy • Focusing on minor tweaks instead of real innovation 5. 𝗛𝗼𝘄 𝘁𝗼 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗕𝗼𝘁𝗵 Start with discovery to understand the problem deeply, then use experimentation to validate your solutions. They're complementary, not competitive. The most successful products come from teams that know when to discover and when to experiment. What's your experience with these two approaches? Have you seen teams favor one over the other?