Innovation in Product Development

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

Why do some companies struggle to go from 1 highly successful product to multiple highly successful products? The need for great operations is a common disease in companies that are scaling, especially companies that are going from 1-2 successful products to multiple products that are sold/adopted separately from the core product. Once a company reaches a certain scale, its senior management implicitly begins to view great operations as the most reliable marker of a given team’s (and its leader’s) competence. And they accordingly create incentives for operational excellence, uniformly across all teams. These incentives do tend to produce better results for the teams working on the core product. But these same incentives tend to produce worse results for the teams working on newer products. It is only a really shrewd senior leader who says to an early stage team at a QBR or product review: “it is fine that your team isn’t firing on all cylinders on operations. that is to be expected at this stage. the main & only priority right now is to gain customer insight & creatively build the right things that create differentiation for us in this market.” When senior leaders don’t say this, and when they instead fixate on the operations optics of early stage teams, it makes it nearly impossible for the company to replicate its initial success for its newer products.

I have made the mistake of seeing myself as a typical user of my product too many times. Why is it bad? How to avoid it? As the hilarious picture shows, the product creators may understand their market, but still not capture the real needs of the users. You, the Product Manager, are to be the user and business ambassador, not the actual user. Why is your perspective likely wrong? • Lack of diverse point of view: single perspective • You can't possibly know all your users' challenges • Being in tech gives you instincts no tech users miss • You will miss innovation that only users can uncover • You carry inherent biases and assumptions, as any individual • Being a product expert makes you blind to beginner challenges    ...and likely more. "Ok, smart guy, - you may say - but how do you ensure you design the right product for your users?" Way ahead of you! Here are a few actions a typical Product Manager can invest in, to ensure it's the users's perspective driving product development, not the limited PM ones: 1) KYC (Know Your Customer) client research With the work done by a dedicated company, you will deeply understand your users' needs, behaviors, and motivations. 2) Building user personas Create detailed profiles representing different user types to guide design and development decisions. Use data to identify usage patterns that can be labeled as specific types of users and polish them in a dedicated workshop. Speaking of which: 3) "Jobs to be Done" workshop With this you will identify the tasks users aim to accomplish, focusing on their goals rather than features. This is the ultimate way for PMs to identify the right problems to solve! 4) Dealing with data, not opinions Goes without saying, base decisions on analytics and user data instead of personal hunches. Especially your own. 5) Quantitative discovery (polls and surveys) Use surveys to gather measurable user insights. If you ask the right questions, you will get a representable number. You can also look for those in your reporting suite. 6) Introducing MVP quickly to understand users' reactions You can always launch a Minimum Viable Product early to collect feedback and iterate. Even embed some polls with it to gather live feedback! 7) Qualitative discovery (user interviews and observations) Engage directly with users to gain an in-depth understanding of their experiences. They will tell you whether your prototype resonates with them and they can complete assigned tasks easily. There you have it, many ways to keep your opinion away from good Product decisions. So, have you ever assumed you knew what your users wanted, only to be surprised by their actual needs? How do you get to understand your users? Sound off in the comments! #productmanagement #productmanager #userexperience P.S. To become a Product Manager who truly understands and serves your users, be sure to check out my courses on www.drbartpm.com :)

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

🎨🖊️ "Draw two circles under a rectangle…" "Now, make the circles connect to the rectangle" - some of the instructions that were given to me by our Head of Architecture during a recent offsite. We engaged in an exercise that underscored the importance of clear and effective communication. Each participant paired up, with one partner facing a screen displaying an image and the other facing a blank wall with a pen and paper. The challenge? The partner facing the screen had to guide their teammate in drawing the image using only directional and descriptive language. This exercise was a powerful reminder of how crucial it is to be clear, descriptive and thoughtful when sharing requirements, feedback or instructions. In the world of technology, we often fall into the trap of using complex language, acronyms, and omitting details we assume are "obvious." This can lead to confusion, misunderstandings, rework, and ultimately, wasted time. The key takeaway? Being specific doesn't always mean being overly detailed or long-winded. There's a beautiful balance between being specific and descriptive. It's about conveying the right amount of information in a way that's easily understood. Here are some common pitfalls to avoid when striving for specificity in communication: - Overloading with Details: Focus on the most relevant information to avoid overwhelming your audience. - Using Jargon and Acronyms: Consider your audience and provide explanations when necessary. - Assuming Shared Knowledge: Provide necessary context to ensure understanding. - Being Vague: Use precise language to prevent misunderstandings. - Neglecting the Audience's Perspective: Tailor your communication to the needs and understanding of your audience. I am reminded of a quote by Mark Twain: "I apologize for such a long letter - I didn't have time to write a short one." Concise communication takes time and effort, but it's always worth it. In our fast-paced world, mastering the art of effective communication is essential. It not only enhances collaboration but also drives efficiency and innovation. #Communication #Leadership #EffectiveCommunication

Dear Product Managers, Always remember this (warning: this post contains shocking statistics) Users are bad at telling you what they want but they are very good at showing you what matters to them. After 12 years in product, I’ve learned that the fastest way to uncover what users don’t like isn’t asking them directly but it’s choosing the right research method. Here’s the truth about accuracy: Surveys: 20 to 40% accuracy People guess, answer aspirationally or try to be polite. Good for signals, not truths. Interviews: 50 to 70% accuracy Better context, deeper insights but still influenced by memory, bias, and social pressure. Usability Testing: 70 to 90% accuracy Users won’t say something is confusing, they’ll show you. Watching real behavior is 10x more honest than any spoken answer. Analytics + Experiments (A/B tests): 90 to 100% accuracy The highest truth signal. When users abandon, rage-click, or drop off… that’s real feedback. No opinions. No filters. Just behavior. So if your goal is to understand what users don’t like: 👉 Focus on behavior-based methods. Usability tests Heatmaps Session recordings Funnel analytics A/B tests Ask users what they love but watch them to discover what they hate. That’s where the real product opportunities live.

Some innovations start with technology. The most important start with bravery. This week, Dagens Industri highlighted the journey of Saba Atefyekta, a scientist who came to Sweden from Tehran, studied at #Chalmers, and is now building a company that could save millions of lives threatened by antibiotic-resistant infections. Together with Professor @Martin Andersson and Anand Kumar Rajasekharan, she co-founded Amferia kills bacteria and transformed a patented research insight into a marketed medical product — already used in eight European countries in veterinary care, with human health and FDA approval next. She has now been awarded “Årets Nybyggare Årets Nystart”, a national prize under the patronage of @His Majesty the #KingofSweden. The jury’s motivation captures the essence: deep scientific expertise, transformed into commercialization and international partnerships, tackling one of the world’s greatest health challenges. Saba’s story reflects something crucial about #Sweden and about Chalmers University of Technology : innovation happens when global talent, scientific excellence and entrepreneurial courage meet, and are trusted. Technical universities are not only producing research. They are building the launchpads where ideas become companies, and where people from every corner of the world can create impact. The real question for Europe is this: How do we empower thousands more researchers to make the same leap — from publication to product, from insight to impact? Picture: Henrik Garlöv/Kungl. Hovstaterna/ Kungahuset.se

New streaming data sources and AI’s use of them have revitalized the real-time event stream processing market and boosted revenue. Product leaders can use this research to assess how real-time data, analytics and AI can enhance and differentiate their offerings and adjust their roadmaps to leverage this potential. Gartner recommends that product leaders: 🔵 Allocate a portion of the engineering budget to evaluate the accessibility and applicability of real-time data and analytics that can impact desired business outcomes. Do so by experimenting with new data streams and event logs to understand their ability to inform and adapt products and services. 🔵 Work with engineering teams to design an architecture that can leverage real-time event stream data by identifying technology and requisite technology partnerships to consume the data within the reasonable confines of your product’s existing architecture. 🔵 Demonstrate the positive effect on decision quality and outcomes that result from including real-time contextual data in your products and services. Do so by measuring the accuracy of models that either predict outcomes or recommend actions, as well as embedding the best models in decision workflows. I asked Kevin R. Quinn, Vice President, Analyst - Technical Product Management, Gartner why he believe this research matters: 💡 "AI is accelerating every aspect of business. Decisions can’t just be based on what happened, but need to account for what is happening right now." 💡"Real-time data enables timely decision-making, enhances responsiveness, improves operational efficiency, and provides a competitive edge in rapidly changing environments." Our research shows how the market for real-time streaming data is changing, and how it is more accessible and relevant for providers and end-users, than ever before. Check out the insights from Kevin R. Quinn and myself (David Pidsley) which is exclusively available to Gartner clients who are product leaders subscribed to our "Emerging Technologies and Trends Impact on Products and Services" research. ▶️ "Emerging Tech: Revolutionize Your Products With Real-Time Data and AI" [Published 31 January 2025] 🔗 https://lnkd.in/ev7nk82R (requires client login) #DecisionIntelligence #RealTime #Data #AI #RealTimeData #StreamingData #StreamingAnalytics #StreamAnalytics #EventStream #EventStreamProcessing

AI prototyping has changed what it means to be a PM, designer, and engineer in forward-thinking organizations. Here's how: The Old Way Here’s what most product development lifecycles look like: 1. Ideation Most teams barely prototype at the idea stage. A rare few exceptional designers and PMs do (~5%) 2. Planning Here, more teams use prototypes, but it still is an exception few (~15%), while sketches and mockups are much more common (>75%) 3. Discovery For many in feature factories, this step is skipped completely. But in more empowered companies, many teams would test prototypes in the discovery phase (~50%) 4. PM Handoff It's rare for PMs to include prototypes in their PRDs—often met with side-eyes from designers who prefer sketches or plain descriptions (5%). 5. Design Exploration This is the most common spot where prototypes come into the picture, and most product teams looking to reduce risk of big features prototype (~75%) 6. Engineering Start: Because many prototypes are not engineered, engineers would start from scratch with a final design. This has been steady for the past several years. The New Way But something is changing amongst forward-thinking teams. PMs are moving beyond documents and closer to the “bare metal” of the pixels that actually define a product. Prototypes have become the new way to communicate your ideas - at all stages: 1. Ideation They’re using prototypes to work out product problems. 2. Planning They’re pairing roadmap discussions, pre-PRD, with prototypes. 3. Discovery They’re putting these prototypes in front of real potential customers to explore solution spaces. 4. PM Handoff Then they’re attaching working prototypes to their PRDs—turning ideas into clickable clarity. 5. Design Exploration Designers are going from a PM-fidelity prototype to a design-level prototype (often in Figma) that fits into the design systems and goals of the team. 6. Engineering Start Engineers may even start with the latest prototype from a tool to get a headstart on their code. Then they’ll use AI coding tools like Cursor or Windsurf to get it ready for production. In other words: All 3 of PM, design, and engineering are now using these prototyping tools.

𝗙𝗿𝗼𝗺 𝗥𝗼𝘄𝘀 𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗧𝗵𝗿𝗲𝗲 𝗔𝗰𝘁𝘀 Most enterprises don’t fail at collecting data. They fail at turning it into impact. Confusion between data sets, data models, and data products is one of the biggest hidden taxes on transformation programs. Let’s break it down. 𝗧𝗵𝗲 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀, 𝗥𝗲𝗰𝗶𝗽𝗲, 𝗮𝗻𝗱 𝗦𝗮𝘂𝗰𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 Data Set (The Ingredient): Rows, columns, logs, and transactions. They provide visibility but are meaningless without context. Data Model (The Recipe): Structures data into meaning - predicting churn, segmenting customers, optimizing supply chains. Intelligence, but abstract unless operationalized. Data Product (The Sauce): What users consume - a pricing dashboard, fraud detection tool, or recommendation engine. It drives action by solving business problems. Taking an example of revenue growth management - The data set has outlet details, shipments, price lists, and promotions. The model translates this into elasticity curves, promo effectiveness, and pack architecture. The product delivers actionable guidance: which packs to push, discounts to drop, promotions to double down on. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟭: 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗨𝗻𝗹𝗼𝗰𝗸𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 Data products need dedicated owners like product managers who bridge business and technical teams. They validate use cases, ensure business alignment, and champion adoption. Ownership accelerates decisions and keeps products impactful. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟮: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝗲𝗿-𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗠𝗼𝗱𝗲𝗹 Treat data products like commercial offerings. Producers focus on quality, documentation, and compliance; consumers discover and use products independently. Catalogs, self-service tools, and governance enable delivery at business velocity without sacrificing standards. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟯: 𝗖𝗿𝗼𝘀𝘀-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 Components like models, platforms, and APIs often sit in siloed teams. Leading companies form cross-functional teams that own data products end-to-end, reducing friction, accelerating innovation, and balancing enterprise consistency with business agility. 𝗧𝗵𝗲 𝗧𝗿𝘂𝗲 𝗨𝗻𝗹𝗼𝗰𝗸 When raw data, robust models, impactful products, and analytics align, data stops being a cost center and becomes a growth engine. What’s your view? Does your organization clearly differentiate between data sets, models, products, and analytics? Where are the biggest gaps or opportunities today? #DataStrategy #DataProducts #AI #Analytics #Transformation