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If you’ve spent any time browsing AI development content, you’ve probably seen the flashy headlines: “I built an app in 30 minutes!” The novelty is hard to ignore. But peel back the layers, and you’ll often find a different story beneath the surface—authentication systems held together with duct tape, zero data persistence, nonexistent error handling, and security practices that wouldn’t pass even the most forgiving audits.At Modern Product, we took a different path with Mission Control. Instead of shooting for quick, impressive demos, we designed the project with one thing in mind—production-grade readiness. The goal wasn’t to prove that an app could simply be built using generative AI; it was to prove whether it could meet the rigorous standards required of real-world, scalable software. Surprisingly, these constraints didn’t act as roadblocks—they became accelerators that guided smarter development and more efficient AI usage.Let’s start with my background and experience because, ultimately, it dictated how far I was able to most effectively leverage (productivity multiplier) versus blind trust (meager gain) in the LLMs.<h3>Who is Modern Product?</h3>I’ve spent 24 years in the tech industry, starting out as a Software Engineer before transitioning into management roles and eventually finding my home in Product Management. For over two decades, I’ve focused on Product Management and Product Leadership, working closely with Engineering and Design teams across the entire product development lifecycle. After spending 7+ years in senior and executive management positions, I founded Modern Product Consulting six years ago to share my expertise at scale.Here’s how my background connects to the “Mission Control Challenge.” As a Technology Leader, I’m comfortable discussing complex engineering topics just as easily as I can tackle strategic business conversations with executives. First and foremost, I consider myself an expert in Product Management and Product Strategy. I’d call myself an intermediate full-stack Software Engineer and a solid QA practitioner, but I have no real skills in infrastructure or DevOps. I’ve worked alongside some incredible UX Design professionals, but I’m not a designer myself.&nbsp;When I took on this Mission Control challenge, I knew there would be areas where I could lean on my own expertise and others where I’d need significant support from LLMs to build a production-ready application from scratch. That’s the key—our insights here are driven by where my background allowed me to amplify productivity and where I had to allow and trust the LLM to step in to fill the gaps.<h3>So...What is Mission Control Exactly?</h3><figure id="figure-ch71l2brssmeaw24x5"><img id="ch71l2brssmeaw24x5" src="https://cdn.durable.co/blocks/19IRM6Nz9ajbOwp8j44BJfjSelTetqqJXyZfVncNVjkxALS2Qg8YmllAX9efmdSN.png" alt="" title="" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 3/2; object-position: 50% 0%;" data-corners="default" data-aspect="3:2" data-caption="" data-positionx="50%" data-positiony="0%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/19IRM6Nz9ajbOwp8j44BJfjSelTetqqJXyZfVncNVjkxALS2Qg8YmllAX9efmdSN.png" data-mce-style="aspect-ratio: 3/2; object-position: 50% 0%;"></figure><blockquote>Imagine your entire team’s activity—notes, events, requests—flowing through a single, beautifully organized workspace. Instantly see what matters: smart tags, AI-surfaced insights, a real-time pulse on who’s doing what, and weekly digests that zero in on actions, decisions, and blockers. This isn’t just collaboration; it’s your team running at its sharpest, with the chaos streamlined and the signals amplified.&nbsp;</blockquote>(ChatPRD-generated Product Marketing messaging)<figure id="figure-6sqnw6bkwvjmeaw3waz"><img id="6sqnw6bkwvjmeaw3waz" src="https://cdn.durable.co/blocks/2bUrxS8QEp8tzwKvt2d3kz73iibf3KnQtbVY2VjCNwXqh68RdDlvw8lG2ZauWp50.png" alt="mission control notes" title="mission control notes" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 3/2; object-position: 50% 0%;" data-corners="default" data-aspect="3:2" data-caption="Mission Control Notes" data-positionx="50%" data-positiony="0%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/2bUrxS8QEp8tzwKvt2d3kz73iibf3KnQtbVY2VjCNwXqh68RdDlvw8lG2ZauWp50.png" data-mce-style="aspect-ratio: 3/2; object-position: 50% 0%;"><figcaption class="body-normal text-gray-500 mt-2">Mission Control Notes</figcaption></figure>Mission Control is a B2B SaaS application that supports multiple customers with multiple users, all in hypothetical team-collaboration scenarios. It showcases collaboration features like shared notes, requests management, events management, team documentation, and public blogs managed across a team. We wanted to demonstrate how AI/LLM tech can boost the Product Experience as well, so in the Mission Control app, we built AI-generated "Weekly Summary," personalized "Action Items" or "Notifications" to individual users, and automated AI-based tagging across all content that feeds into search and team-based productivity stats/reporting.&nbsp;<figure id="figure-a67yavoogfmeaw5ztc"><img id="a67yavoogfmeaw5ztc" src="https://cdn.durable.co/blocks/18swM6ZMjmqB5rwvUbfcTaocc6GbzwHjW7tiTweuo9qR6CkF996tDO3JuXqdj1mR.png" alt="mission control blog" title="mission control blog" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 3/2; object-position: 50% 0%;" data-corners="default" data-aspect="3:2" data-caption="Mission Control Blog" data-positionX="50%" data-positionY="0%" data-fullWidth="false"/><figcaption class="body-normal text-gray-500 mt-2">Mission Control Blog</figcaption></figure>(NOTE: Mission Control is a production-ready application that we aren't going to take public. It’s solely built to showcase the transformative possibilities of AI and large language models (LLMs) in modern product workflows, and we hope to serve as a foundation tech stack for founders who wish to springboard into their new venture product development. We wanted to highlight how AI/LLMs can boost productivity and speed up time-to-market, but do so in a real-world production environment.)<h3>The Production Realities That Guided Mission Control </h3>When building a business-critical application, flashy UI elements will never outweigh the need for robust functionality and reliability. Mission Control wasn’t going to be another toy project. It had to solve the same challenges your team faces when scaling up from idea to execution. Here are the key production requirements that defined the project.Performance, Reliability, &amp; Scalability Performance, reliability, and scalability are crucial for building successful products. Prioritizing these early ensures systems run efficiently, handle growth, and stay dependable over time—giving companies a strong foundation for sustainable success in a competitive market.Flexibility &amp; Extensibility A flexible tech stack is key for any forward-thinking organization. As needs and strategies evolve, choosing adaptable tools ensures long-term success. By selecting a tech stack that supports growth and change, businesses can innovate and stay competitive.Universal, Expected Capabilities Foundational features are key to aligning a product with its strategy and meeting user expectations. In B2B SaaS, app architecture must support multiple customers, ensure data security, and include essentials like user accounts, login flows, and role-based access. These "table stakes" must be perfected before adding advanced features to deliver a reliable product.Real-World Development Workflows Efficient development pipelines are key for scalable, collaborative production teams. Source control tracks changes and streamlines teamwork, while build scripts automate deployments. Clear configuration files maintain consistency, and thorough README documentation helps team members navigate projects easily. Containerization ensures reliable, environment-specific workflows, reducing “it works on my machine” issues. Together, these practices enable sustainable software development.These aren’t just hypotheticals—they’re the foundations for how Mission Control was designed, built, and deployed.<h3>How Constraints Accelerated Development </h3>1. Rigorous Planning Led to Clearer Specifications and Better Outputs&nbsp; Modern product development relies on smart planning and clear specifications while executing as lean and efficiently as possible. Applying this rigor to AI-assisted workflows paid off significantly. Well-defined requirements gave LLMs clear direction, avoiding vague or subpar outputs. For instance, specifying detailed user stories and edge cases allowed the models to generate more focused, high-quality code and content.&nbsp;Measurable Benefit: Reduced rework significantly (ballpark 5–10x) compared to using generic prompts, speeding up delivery timelines.2. Leveraging a Modern Tech Stack Boosted LLM Effectiveness&nbsp; By using well-supported, modern tech stack components, the LLMs had access to a larger and more reliable base of training data and expertise. Familiar stacks like Flutter, GCP/Firebase, Docker, and well-supported languages like Dart and Python were within the models' knowledge base, which enhanced the quality of their solutions. This alignment reduced gaps in generated outputs and improved compatibility with real-world standards.&nbsp;Measurable Benefit: Increased engineering time exponentially (10–50x) and decreased troubleshooting time (at least 5–20x), with AI generating production-ready suggestions more often.3. "Shootout" Testing Optimized LLM Selection for Each Task&nbsp; Just like we would in real-world product development when evaluating new platforms or technologies, we took a "shootout" approach to testing various platforms/LLMs like Claude Sonnet, ChatGPT 4, Perplexity Pro, and CoPilot against each other for various tasks. By A/B testing their performance, we identified which model was best suited for specific needs, such as content generation, coding/debugging, DevOps, or architecture planning.&nbsp;Measurable Benefit: Relative to when we started, our workflow efficiency improved 2x, as we matched tasks to the best-performing model, minimizing wasted effort and maximizing output quality.<h3>Actionable Takeaways for Tech Leaders &amp; Teams&nbsp; </h3>Use your Team's Expertise to Guide AI&nbsp;With 200 hours of deep exploration, one thing is clear: having experts "behind the wheel" is critical to truly unlocking LLM potential. Treat LLMs like a "gifted intern"—they need monitoring and guidance, but when pointed in the right direction, they can produce incredible results and exponentially boost productivity in specific areas.&nbsp;AI Won’t Replace Expertise, But It’s Still Valuable&nbsp;AI isn’t ready to replace skilled professionals, but even in areas where expertise is lacking, you can still achieve marginal productivity gains of 25–100%. While it’s a worthwhile tool to augment your team, it’s not a reason to eliminate your "doers." Skilled professionals remain essential for critical thinking, contextual decisions, and creative problem-solving.&nbsp;Strategic Processes Drive Better Results&nbsp;The effectiveness of LLMs for product development improves significantly with strong planning, clear requirements, and well-defined specifications. A strategic approach to prompting, learning which LLMs excel at specific tasks, and iterating along the way will maximize the benefits of integrating AI into your workflows.&nbsp;By leveraging AI strategically and complementing it with human expertise, you can unlock precision and speed in your product development process—without compromising quality.&nbsp; NEXT POST: We'll walk you through the early Product Strategy/Product Management work we did and what we learned in working with various AI platforms/LLMs.

Reality versus "Demo Magic"

What if you could build a production-ready app—on your own—in just ten weeks? Not a prototype, but a robust application with the kind of professional polish that could scale and adapt to real-world business needs. That’s the challenge I set for myself with the Mission Control project. It wasn’t just to see if it was possible, but to prove how the capabilities of Large Language Models (LLMs) can transform the way we approach product development.This series is for those of you leading technology-driven ventures—founders, CTOs, VPs of Product, and engineering leaders—who aren’t just watching the AI revolution but are eager to harness it to accelerate your own goals. I’m here to share not a theoretical deep-dive into AI’s potential, but a tangible, real-world blueprint for how you and your teams can push boundaries and achieve extraordinary results with LLMs.<h3>The Origins of the Mission Control Challenge&nbsp; </h3>For years, I’ve helped tech teams and product-focused organizations refine their strategies, overcome bottlenecks, and optimize delivery processes. Modern Product, my consultancy, was built on the foundation of guiding organizations through complex product challenges. But in early 2025, I noticed that AI-driven tools—and specifically LLMs—were beginning to make extraordinary claims. "Write all your code with AI." "Revolutionize the speed of development overnight." And yet for all the hype, there was a persistent question from leaders I worked with:<blockquote>Sure, we’ve seen the demos, but how do we actually implement AI in our real development workflows without compromising standards?</blockquote>The Mission Control challenge emerged as my answer to that question. If tools like GPT-4 or Claude can boost team productivity, why couldn’t they also help a solo product expert like me, with strong technical and design acumen, do the work of an entire startup team? My constraints were clear from the beginning:<ul><li>10 weeks. A tight but realistic timeline to prove the capabilities of LLMs under real-world pressures.&nbsp; </li><li>200 hours. That’s what I limited my dedicated time to—about 20 hours a week between other responsibilities.&nbsp; </li><li>A production-ready app. This wasn’t an experiment nor a throwaway prototype; the output needed to demonstrate scalable, secure, and usable industry-grade features.</li></ul>&nbsp;It was a bold goal. But it also seemed like the perfect way to test the limits and learn the lessons an entire industry is earnestly seeking.<figure id="figure-tp14paqxzzmdm9se9k"><img id="tp14paqxzzmdm9se9k" src="https://cdn.durable.co/blocks/19IRM6Nz9ajbOwp8j44BJfjSelTetqqJXyZfVncNVjkxALS2Qg8YmllAX9efmdSN.png" alt="mission-control-web-app" title="mission-control-web-app" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 50% 0%;" data-corners="default" data-aspect="16:9" data-caption="Mission Control" data-positionX="50%" data-positionY="0%" data-fullWidth="false"/><figcaption class="body-normal text-gray-500 mt-2">Mission Control</figcaption></figure><h3>Why LLMs Are a Game-Changer for Product Development </h3>Using artificial intelligence for small, task-specific operations has been possible for years. But with LLMs, we’re talking about something altogether different. Tools like OpenAI’s GPT or Anthropic’s Claude have unlocked capabilities that feel almost human-like in their ability to interpret complex prompts, tackle deep workflows, and adapt on the fly.And here’s the game-changer—they’re not just tools; they function like "intelligent interns." They’re brilliant one day, baffling the next, and always in need of guidance. LLMs are never wholly autonomous, but they become extraordinary amplifiers when paired with the right oversight. Here’s why they matter:<ul><li>Speed: LLMs can accelerate tedious workflows, transforming tasks that take hours into quick iterations. </li><li>Ideation Partner: From brainstorming features to flagging bugs, LLMs can act as a collaborator to challenge assumptions and refine ideas. </li><li>Scalability: For lean teams—or even solo developers like me—they lower the hurdle to achieving outcomes usually requiring entire teams. </li></ul>Yet, as every technology leader knows, speed means nothing if it comes at the cost of quality, security, or long-term maintainability. This dual promise—faster development and industry-grade results—was the hill that Mission Control set out to climb.<h3>Why This Series Matters to You&nbsp; </h3>With over 20+ completed features, countless learnings, and hard-won insights about what LLMs can (and can’t) do, Mission Control represents more than just an experiment. It’s a scalable process framework—something product leaders and tech teams can dissect, adapt, and apply to generate net-positive outcomes.Here’s what you can expect from reading this series:<ul><li>Unfiltered Lessons Learned: I won’t gloss over the moments where things fell apart. When debugging a legacy issue took 3 hours instead of 30 minutes, or when the LLM’s solution led to circular dead-ends, you’ll hear about it. </li><li>Practical Strategies: From prompt engineering to designing LLM-assisted workflows, I’ll break down the approaches that worked (and why they did). </li><li>Expert-level Solutions: Whether it’s testing and QA using LLMs or balancing AI-generated output with human oversight, the posts will focus on achieving high-quality, high-value outcomes. </li><li>Future-Proof Takeaways: You’ll leave with frameworks and tools to help your team make smarter decisions on how to incorporate AI into your workflows without lowering your development standards. </li></ul>Whether you’re trying to validate an idea in record time, expand your team’s bandwidth, or build scalable solutions from Day 1, this series is designed to inspire and guide you.<h3>What’s Next? </h3>Over the coming weeks, I'll take you through each phase of the Mission Control challenge. Topics include:<ul><li>How I explored the LLM landscape to select the right tools and frameworks.</li><li>The unexpected ways prompt engineering influenced product design. </li><li>Successes and failures in engineering, debugging, and QA. </li><li>Reflections on scope management and balancing AI strengths and weaknesses. </li></ul>By the end, you'll have actionable strategies and a clear understanding of how LLMs can become a critical lever for innovation while avoiding the pitfalls that trip up so many early adopters.The Mission Control app is proof of what’s possible when cutting-edge technology meets disciplined product expertise. And if you’ve been searching for a roadmap to harness AI in your own workflows, you're in the right place.&nbsp;Stay tuned for insights that will challenge the hype, inspire innovation, and help you scale with confidence.<h3>Call to Action </h3>Curious about how your team could amplify productivity and maintain quality with LLMs? Feel free to reach out. Whether it’s a consultation or a conversation, I’d love to help unlock what’s possible for your next product.&nbsp;

Harnessing LLMs for Product Development: The Mission Control Challenge

<blockquote>...this celebration is not just about looking back at what we've accomplished; it's also an opportunity to reflect on the lessons learned along the way.</blockquote>This month marks a significant milestone for Modern Product as we celebrate our five-year anniversary. We've had the privilege of working alongside incredible clients on a diverse array of innovative and challenging projects. We've been extremely fortunate to remain at full capacity the entire time, with a 100% engagement renewal rate across 15 distinct clients!That said, this celebration is not just about looking back at what we've accomplished; it's also an opportunity to reflect on the lessons learned along the way. Today, we'd like to share some of the insights we've gained in the last five years. Through three key lessons, we hope to provide fellow founders with valuable guidance and encouragement that can help them on their own journeys.<h3>Lesson 1: Embrace Ambiguity and Stretch Yourself </h3>When Modern Product was just getting off the ground, we quickly realized that no two projects would be the same and that there would be aspects of every engagement that would take us out of our comfort zones. If we were going to see success as a consulting practice, we recognized the need to embrace that ambiguity and look forward to those opportunities to stretch ourselves. By leveraging our expertise as a springboard into new challenges and new opportunities to learn, we were encouraged to seek out diverse engagements with clients across different industries. Now, we always look forward to kicking off new engagements, for the opportunity to face the ambiguity and learn along the way.&nbsp;So our advice to new founders is to make full use of your expertise and experiences, not as a constraining box that limits your focus, but as a springboard into the ambiguity that's out there.<h3> Lesson 2: Commit to Business Development 365 Days a Year </h3>One of the earliest commitments we made for Modern Product was to continually invest time in business development. When Modern Product was just getting off the ground, we had no idea how client engagements would pan out, nor how often new client opportunities would arise. We received the advice that all consultant founders get, that we would need to be prepared for the "feasts and famines" of client work. One of the best decisions we made early on was to commit 5 hours per week to business development activities as a preventative measure against the feast/famine swings. We've maintained this practice over the years, and it has proven to be worthwhile several times over. This commitment has played a pivotal role in our sustained growth and success over the last five years.Our advice: never turn down an introduction, invest time in all forms of networking, even the most casual coffee chats, and apply success metrics to these activities to hold yourself accountable.<h3>Lesson 3: Enjoy the Surprises Throughout the Journey </h3>One of the most rewarding aspects of being a founder is experiencing the unexpected surprises that come along the way. The entrepreneurial journey is filled with twists and turns, and it's important to savor these moments and appreciate the growth they bring. Over the five years of Modern Product's existence, we've had several surprises pop up that, in hindsight, have been pretty cool.&nbsp;<ul><li>We landed a client engagement with a dream brand.&nbsp; </li><li>A random coffee chat about Modern Product's business model unexpectedly led to a client engagement four years later. </li><li>We've reconnected with old colleagues and classmates from 10, 15, 20 years ago.</li></ul>Looking back, the surprises have been a delight to reflect on, making the entrepreneurial journey of being a founder that much more rewarding and rich.&nbsp;So our recommendation to founders just getting started: enjoy the journey's unexpected turns and dedicate time to looking back and reflecting on the good, the bad, and the ugly along the way. Treat everything as a learning opportunity.<h3>Conclusion </h3>Being a founder is as challenging as it is rewarding. The past five years have taught us that while the road may be unpredictable, it's essential to enjoy the journey and focus on the learning opportunities it presents. Each client engagement has contributed to our growth and success. Hopefully this post provides encouragement and inspiration to those of you diving into the founder consulting journey!

On Celebrating Five Years...

Are you considering integrating <a data-mce-href="http://www.pendo.io" href="http://www.pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!<blockquote><h3>Throughout this series, we've discussed various facets of integrating and utilizing a product analytics platform like Pendo in order to achieve valuable reporting insights for product teams.&nbsp;</h3></blockquote><h3>Introduction</h3>This blog post wraps up our series on Product Analytics Integration Best Practices using Pendo, geared toward Product Managers and Senior Leaders of organizations who are considering investing in product analytics capabilities. Throughout this series, we've discussed various facets of integrating and utilizing a product analytics platform like Pendo in order to achieve valuable reporting insights for product teams. The best practices we’ve put forward in this series stem from a two-year engagement with a large client, where we set up and optimized Pendo from the ground up alongside many product teams. In this post, we’ll quickly summarize the essential takeaways we've covered throughout this series, and leave you with some additional capabilities that Pendo offers that are worth considering.<h3>Takeaways from Technical Integration </h3>In general, Pendo is fairly "user friendly" from a technical integration perspective. There are some important considerations to make as a Product Manager as you collaborate with your Engineering partners to integrate Pendo that we've highlighted throughout this series. Here are a few of the headlines.Code Snippet Integration is the "<a data-mce-href="https://modernproductconsulting.com/blog/pendo--technical-integration-best-practices" href="https://modernproductconsulting.com/blog/pendo--technical-integration-best-practices" target="_blank" rel="noopener">Tip of the Iceberg</a>": While integrating the Pendo code snippet is straightforward, the real challenge lies in designing your tracking data intentionally. Your focus should be on capturing the data that will provide actionable insights that are strategic to your organization's needs. This involves meticulous planning and collaboration across teams to ensure that every piece of data collected has a clear purpose.Importance of Metadata: The <a data-mce-href="https://modernproductconsulting.com/blog/pendo--product-managers--and-metadata-design" href="https://modernproductconsulting.com/blog/pendo--product-managers--and-metadata-design" target="_blank" rel="noopener">metadata you choose</a> to capture is crucial. Pendo offers robust metadata capture capabilities across user and account attributes, custom properties, and event-specific data. These metadata are all vital for Product Managers to be able to segment their data and report on more specific, targeted segments of users. Proper metadata design allows for more sophisticated and meaningful reporting, helping you uncover deeper insights into user behavior. From a technical implementation perspective, it's better to head into Engineering collaboration having a specific set of metadata to track for efficiency and resourcing reasons.Navigating Technical Gotchas: We covered the importance of being mindful of technical challenges that might arise within your infrastructural systems, such as <a data-mce-href="https://modernproductconsulting.com/blog/pendo--configuration-gotchas-to-consider" href="https://modernproductconsulting.com/blog/pendo--configuration-gotchas-to-consider" target="_blank" rel="noopener">Content Security Policy (CSP) settings and CNAME configurations</a>. Additionally, when it comes to setting up Feature Tagging or UI tracking, we covered the best practice of building out custom HTML identifiers as a proactive, preventative measure to avoid data gaps from breaking tags. We also covered specific considerations regarding <a data-mce-href="https://modernproductconsulting.com/blog/pendo-and-single-page-applications" href="https://modernproductconsulting.com/blog/pendo-and-single-page-applications" target="_blank" rel="noopener">Pendo and Single Page Application (SPA) frameworks</a> that many teams might face. Being cognizant of some of the technical gotchas you might encounter as you embark on a product analytics integration journey, will help save time &amp; headaches. We learned a ton along the way about these technical challenges with Pendo and how to address them and/or work around them.<h3>Takeaways for Product Managers Once Data is Flowing </h3>Product Managers will have their work cut out for themselves once product analytics data is flowing through Pendo. There are a number of best practice recommendations we covered throughout our blog series, geared specifically for those Product Managers as they begin using analytics reporting &amp; insights to drive meaningful, data-informed decisions across their organizations. Here are a few of the main takeaways.&nbsp;<a data-mce-href="https://modernproductconsulting.com/blog/pendo-within-content-focused-product-platforms" href="https://modernproductconsulting.com/blog/pendo-within-content-focused-product-platforms" target="_blank" rel="noopener">Content-focused product platforms</a> make product analytics complicated: Product Managers who work within complicated platforms that support many content-focused products come with unique challenges. This likely means many product managers with different platform/product focuses need to collaborate to design a robust, multi-dimensional analytics reporting framework. This ensures that insights are comprehensive and valuable across different product lines, or the platform holistically.&nbsp;<a data-mce-href="https://modernproductconsulting.com/blog/how-product-managers-can-get-to-real-answers-with-product-analytics" href="https://modernproductconsulting.com/blog/how-product-managers-can-get-to-real-answers-with-product-analytics" data-mce-selected="inline-boundary" target="_blank" rel="noopener">Strategic Alignment in Reporting</a>: Aligning your analytics strategy with your overall business objectives is crucial. Start with visualizing, or wire framing, the end-goal reports you need and work backward to design your data collection and reporting suite. Pendo offers robust reporting capabilities that can help you get started quickly, though it’s important to be aware of its limitations and explore alternative options where necessary.Maturing Product Analytics over Time: We introduced a <a data-mce-href="https://modernproductconsulting.com/blog/putting-it-all-together--a-product-analytics-maturity-model" href="https://modernproductconsulting.com/blog/putting-it-all-together--a-product-analytics-maturity-model" target="_blank" rel="noopener">Product Analytics Maturity Model</a> that allows Product Managers to plan the evolution of their analytics initiatives. This model acts as a roadmap, guiding you through the stages of maturity from basic data collection to advanced analytics. Just as you would manage the development of your product, this model helps you systematically enhance your analytics capabilities.<h3>Additional Pendo Capabilities Worth Considering </h3>Our blog series focused solely on Pendo’s product analytics capabilities. That said, Pendo is a powerful platform that provides many other complementary capabilities that Product Managers should consider. Here are a few of the more valuable and exciting capabilities worth mentioning.<a data-mce-href="https://support.pendo.io/hc/en-us/categories/360001404191-Guides" href="https://support.pendo.io/hc/en-us/categories/360001404191-Guides" target="_blank" rel="noopener">Pendo Guides</a>: One of the critical features we didn’t cover in-depth is Pendo’s rich Guides capability. This functionality allows teams to interact directly with users within the product experience. Whether it’s through announcements, surveys, polls, NPS capture, or guided user walkthroughs, Pendo Guides add a rich layer of user engagement to your analytics efforts. We highly recommend Product Managers seek ways to leverage Pendo Guides in conjunction with their product analytics efforts, because they are so tightly coupled.<a data-mce-href="https://support.pendo.io/hc/en-us/articles/5092218184475-Overview-of-Pendo-Feedback" href="https://support.pendo.io/hc/en-us/articles/5092218184475-Overview-of-Pendo-Feedback" target="_blank" rel="noopener">Pendo Feedback</a>: Pendo Feedback is another powerful tool, focusing on capturing and managing user feedback. This feature allows you to integrate simple user research into your product experience while providing transparency back to your users about which suggestions have been received and prioritized. It bridges the gap between user expectations and product development.<a data-mce-href="https://support.pendo.io/hc/en-us/articles/20415707164059-Session-Replay-overview" href="https://support.pendo.io/hc/en-us/articles/20415707164059-Session-Replay-overview" target="_blank" rel="noopener">Pendo Session Replay</a>: Pendo recently rolled out the ability to record users' actions within a product experience, called Session Replay. This is a great complement to data analytics tracking and in some scenarios can help Product teams answer "why" users behave as they are, based on what they’re seeing in the data. While we didn't use this feature much during our two-year engagement, we were beta testers of the capability and it demonstrated the value you would expect, in addition to the added benefit of being integrated with Pendo's other capabilities.<blockquote><h3>While Pendo’s capabilities are impressive, the best practices we've discussed are largely platform-agnostic. The key is to understand the strengths and limitations of whichever platform you choose and tailor your approach accordingly.</h3></blockquote><h3>Series Conclusion </h3>Building a solid foundation of product analytics is a multifaceted endeavor that requires careful planning, execution, and continuous improvement. Pendo offers a comprehensive platform that can significantly enhance your analytics efforts, making it a strong choice for Product Managers.While Pendo’s capabilities are impressive, the best practices we've discussed are largely platform-agnostic. The key is to understand the strengths and limitations of whichever platform you choose and tailor your approach accordingly.We hope this series has provided valuable insights and practical advice to help you elevate your product analytics game. If you have any questions or need further assistance, please don’t hesitate to reach out!

Series Conclusion: Product Analytics Integration Best Practices using Pendo

Are you considering integrating <a data-mce-href="https://pendo.io" href="https://pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!<blockquote>We present a Product Analytics Maturity Model that can help lay out a roadmap of evolution for PM's with increasing sophistication in insights generated and presented.&nbsp;</blockquote><h3>Introduction</h3>This is the 9th post in our series on Product Analytics Integration Best Practices. We've covered a lot of content in the last several weeks, written for Product Managers to help them build up a sophisticated set of analytics reporting that helps them operate as data-informed as possible in their product leadership. Today's post focuses on how PM's should "product manage" their analytics efforts. We present a Product Analytics Maturity Model that can help lay out a roadmap of evolution for PM's with increasing sophistication in insights generated and presented.&nbsp;This maturity model came about from a Modern Product engagement that involved Pendo integration and product analytics practice development with a rollout to a large client with many agile product teams. The ideas presented here can be applied to any analytics platform.<figure id="figure-2abyzxo0jm0v643ft" data-full-width=""><img id="2abyzxo0jm0v643ft" src="https://cdn.durable.co/blocks/1124zq9OikQKzXmnYeAYf5SPP8rWBkhW9CcRZCnmwooSvTXfphCwsbcw6XeFlgoo.png" alt="product analytics maturity model graphic" title="product analytics maturity model graphic" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption=" " data-positionx="undefined" data-positiony="undefined" data-fullwidth="true" data-mce-src="https://cdn.durable.co/blocks/1124zq9OikQKzXmnYeAYf5SPP8rWBkhW9CcRZCnmwooSvTXfphCwsbcw6XeFlgoo.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"><figcaption class="body-normal text-gray-500 mt-2"></figcaption></figure><h3>In the First 30 Days </h3>Once data begins streaming into an analytics platform like Pendo, a Product Manager should explore basic, easily actionable analytics efforts. Pendo is designed for Product teams, offering excellent features to start promptly. Within the first month of collecting analytics data, consider setting up the following:Simple User Segmentation: Your first step in the analytics maturity model is to establish simple user segmentation. Begin by categorizing users based on fundamental criteria such as demographic information, geographical location, or user type. This will provide a foundational understanding of who your users are and how they interact with your product. This will also be a chance early to identify potential additional user metadata that might be needed down the road for more sophisticated segmentation (which will almost certainly be an expectation). Examples of "simple" user segmentation might include new users versus users with accounts &gt; 3 months, or users on mobile versus users on desktop, etc.&nbsp;Simple User Environment-based Analytics: Next, analyze what your user environments look like, including which browsers, operating systems, and platforms from which your users access your product. Understanding the technical environment in which your product operates can help identify possible usability issues and optimize performance. In Pendo, some of the basic dashboard widgets can be used for this type of insight, such as the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Browser Usage</a> or <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Operating System Usage</a> widgets.&nbsp;Basic Engagement Behaviors: In the first 30 days, a PM should also focus on analyzing basic user engagement behaviors in their product. PM's should be able to analyze page views and feature usage to get a snapshot of user activity, to answer questions such as, "How many users visit this page?" or, "How much is this feature used on average per week?", etc. This data helps you identify which features are most popular and how users are navigating through your product. In Pendo consider using the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Feature Use by Visitor</a> dashboard widget, or the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Feature Adoption</a> widget.<h3>In the First 90 Days </h3>As more data is captured and basic reporting is disseminated across the organization, the Product Manager probably will have a good sense of where to focus efforts and iterations with product analytics next. In general, here are some recommendations of where the PM should focus over the first 90 days of their product analytics journey.More Advanced Segmentation: You should be able to define specific segments of users that matter most to analyze for your product. This probably will get into advanced segmentation efforts that rely on specific user attributes or metadata that you can lean on when defining your segments. Perhaps you might have specific personas of users that leadership might want to understand more fully how they actually behave within your product (typical examples include at-risk or lapsed members, or power users). It is our recommendation that within the first 3 months of product analytics efforts, you will be able to segment your data &amp; reporting to the user groups that strategically matter most.Adoption / Conversion Analytics: As a PM, understanding whether new users' behaviors within your product experience are expected or not is critical to driving up active user engagement and retention. Similarly, when your team launches new features, it's imperative to know whether users are interacting fully with them or not. So getting up and running with core adoption or conversion based analytics is very important to understand whether your core value proposition for users is hitting home or not. So within the first 90 days of a product analytics journey, we strongly recommend that PM's focus in on those adoption &amp; conversion analytics. In Pendo, consider using the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" target="_blank" rel="noopener">Funnel Report/Widget</a> for conversion based analyses, or the <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Feature Adoption</a> widget.Retention Analysis: &nbsp;Once you get advanced user segmentation set up, one of the more critical insights you can report out on is specific user segments' retention over time. PM's who have a solid grasp on retention of various core user segments will be able to more surgically identify opportunities to improve the product experience to ensure these core segments stick around longer within their product. Pendo has a <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Retention</a> widget and a <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">Retention by Usage</a> widget that should be considered.Utilization: Utilization is the measure of how frequently and effectively users employ key functionalities within your product. There is a nuanced difference between simple engagement analytics, which reports out on usage of specific features, to a set of analytics insights that reports out on how well or how effectively the product experience is driving engagement across a user base fully. So as a PM, once you get basic reporting of core feature engagement in place, your next maturation is beginning to set up reporting that assesses utilization of those core features.&nbsp;<h3>Within the First Year </h3>In the first year of your Product Analytics journey, expect a familiar pattern: the Product Manager or team will develop increasingly advanced analytics reports, which will be well-received by stakeholders and leadership. However, this will lead to more questions that require further analysis and possibly technical assistance from engineering. This "bringing along" of the organization is a natural progression that a Product Manager should anticipate and be prepared to drive. The Product Analytics Maturity Model we're discussing can help prepare the PM for this journey. Here are some recommendations the PM should consider when planning product analytics efforts in the first year.Cohort Tracking/Analysis: There will likely be scenarios in PM'ing your product in which you'll want to track cohorts of users over time and analyze various aspects of their behavior to determine whether changes to your product have any longer-lasting impacts that are either desired or not. Cohort analysis relies on the ability within your product analytics capabilities to not just segment users to specific groupings that matter for cohort analysis, but the ability to keep track of them over time against ongoing measures. This is another maturation of your user segmentation capabilities to consider ensuring you have the means to do this within your first year.&nbsp;Sentiment Analysis: Hopefully you have access to qualitative user data as a PM (typically collected in the form of user research via UX Design in partnership with Product Management, or other sources such as customer support feedback tickets, etc). one of the challenges with these types of primary, qualitative data is that it's hard to link that feedback to specific analytics data within your product. Our recommendation for Product Managers within their first year of their product analytics journey is to find ways to directly tie user sentiment or qualitative feedback, to the specific (actual) activities and behaviors these users exhibit within your product experience. This is harder than it seems! One of the nice advantages that Pendo provides is capabilities that allow Product teams to capture user feedback and sentiment within the product experience, in the form of <a data-mce-href="https://support.pendo.io/hc/en-us/articles/5092218184475-Overview-of-Pendo-Feedback" href="https://support.pendo.io/hc/en-us/articles/5092218184475-Overview-of-Pendo-Feedback" target="_blank" rel="noopener">Feedback</a> and <a data-mce-href="https://support.pendo.io/hc/en-us/categories/360001404191-Guides" href="https://support.pendo.io/hc/en-us/categories/360001404191-Guides" target="_blank" rel="noopener">Guides</a>. Because it's all part of the Pendo platform, the users who engage with these feedback mechanisms are also tracked via Pendo, so it's relatively straightforward to correlate qualitative feedback from users with their actual behaviors.&nbsp;ROI Evaluation: &nbsp;A challenge PMs might face in their product organizations is linking financial data with product data to report on ROI. This typically begins with collaboration between finance and product departments to define investments tied to product roadmaps or revenue from products or features. If these teams can align on mapping data sets for ROI reporting (a significant challenge), the task of building the report can start. Meanwhile, everyone hopes for automated ROI reporting one day. This challenge is common across organizations, so you're not alone if you feel this way. While not easy, we recommend Product Managers pursue this "holy grail" of reporting. Ideally, within the first year, the PM will have provided valuable insights into their products. Equally important is how the PM fosters discussions with these teams and leadership to align on collaborative efforts for true ROI-based reporting. Ultimately, if an organization can see reporting (ideally automated) that links product investments to financial or user engagement benefits, everyone benefits!Basic Predictive Analytics: Depending on the product you manage, and your organization's needs for forecasting or predicting future behaviors, etc, this may or may not be an interesting or relevant recommendation. Within the first year of capturing rich product analytics data, teams should consider whether they would have a need for establishing any basic predictive analytics based systems that can help inform various organizational departments. Some examples of predictive analytics that teams could consider:<ul><li>Predicting revenue impacts based on membership conversion flow optimizations, leveraging historical data that shows conversion #'s to membership revenue.</li><li>Predicting new feature impacts to user engagement based on historical data that shows engagement of similar features.&nbsp; &nbsp;</li></ul><h3>Beyond the First Year </h3>As a Product Manager aiming to advance product analytics in the second year and beyond, we hope your foundational analytics insights and reporting have been developed and embraced by your organization. If not, we strongly advise prioritizing their establishment. For those fortunate enough to be further along in maturing their product analytics journey, here are some recommendations for the next steps.Efficacy Measurement: Achieving an advanced level of reporting involves integrating product analytics, financial data, and the efficiency of your product development teams to truly measure your organization's efficacy. If you've already set up automated ROI-based reporting within the first year of your product analytics journey, the next logical step is to incorporate data on product development efficiency. Efficiency should be defined within your organization, often considering metrics based on quality and delivery predictability—key indicators of highly efficient teams. By aligning this efficiency data with your product analytics and financials, you can develop remarkably sophisticated efficacy-based reporting.Advanced Predictive Analytics: To have infrastructure that supports predictive analytics reporting is a luxury that should not be taken for granted by anyone within your organization. If you do have these infrastructural capabilities, then our strong recommendation as a Product Manager is to continue to build on your predictive analytics capabilities with more and more advanced insights that can truly drive your organization forward.&nbsp;<blockquote><h3>Every product manager should approach their analytics with the same diligence as they do their products. In other words, we encourage all Product Managers to "product manage" their analytics capabilities.</h3></blockquote><h3>Conclusion </h3>Every product manager should approach their analytics with the same diligence as they do their products. In other words, we encourage all Product Managers to "product manage" their analytics capabilities. Start with the highest-need, low-hanging fruit insights—those obvious, easiest metrics to track and report on. Then, gradually add layers of sophistication as your organization becomes more data-savvy.By following this Product Analytics Maturity Model, you can transform your product analytics into a powerful tool for your organization that ties many core functional groups together with leadership.&nbsp;

Putting it all Together: A Product Analytics Maturity Model

Are you considering integrating <a data-mce-href="https://pendo.io" href="https://pendo.io" target="_blank" rel="noopener">Pendo</a> into your organization, or are you in the midst of these challenges? Leveraging Pendo's powerful product analytics can transform the way you understand and engage with user data to make informed decisions. For product leaders and managers, mastering product analytics is imperative to achieve success in today's competitive market. Interested in exploring this together? Let's chat!<blockquote><h3>Product Analytics data is only as good as the reporting that presents the meaning behind it to the right audiences.&nbsp;</h3></blockquote><h3>Introduction </h3>Product Analytics data is only as good as the reporting that presents the meaning behind it to the right audiences. Product Managers need to carefully design their reporting to get the most out of the data, in order to make the right data-informed decisions for their products.&nbsp;This is the eighth post in our series on Product Analytics Integration Best Practices using Pendo. Today, we'll explore Pendo's reporting capabilities, sharing what we've learned from a two-year engagement with a large client. Pendo provides a pretty good "toolbox" of capabilities that allow PM's to dive into reporting quickly. We'll focus on the strengths and limitations of Pendo's dashboard widgets, Data Explorer reports, and more in this post.&nbsp;<h3>Pendo Dashboards and Widgets</h3>Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards" href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards" target="_blank" rel="noopener">dashboards</a> and <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" href="https://support.pendo.io/hc/en-us/articles/360032752991-Dashboard-widgets" target="_blank" rel="noopener">widgets</a> are the starting point for any Product Manager looking to visualize data quickly. Every user has a default dashboard automatically created that is tied to their account, and Pendo has built up a sizable library of analytics visualization widgets. Once your data is flowing into Pendo, you can begin exploring these various widgets to visualize your data. The interface to add widgets is fairly straightforward and many of the core analytics visualizations that product teams care about are there.&nbsp;However, some widgets have limitations, especially when it comes to segmentation and time grain capabilities. For example, while you can segment data to some extent, the time-granularity might not always meet your needs (daily, weekly, monthly, etc). This is something to be mindful of as you set up your dashboards; not all of the widgets you would like to add/visualize might match exactly what you're hoping to be able to report on, in the exact way you'd like. Sometimes this is ok, but sometimes alternative approaches must be explored (see later sections of this post).&nbsp;Another potential issue to be aware of relates to Dashboard-level filtering. Pendo dashboards allow users to set filters or segments at the top of the dashboard. One would expect that all widgets that make up that dashboard would then be segmented to this selection, but not all widgets can be segmented in this way. When building dashboards where users might leverage this global filtering/segmentation capability, make sure to explicitly call out any widgets that do not adhere to this in your dashboard.&nbsp;Despite these limitations, we found several widgets that were very valuable during our client engagement, especially in the beginning phases of introducing product analytics to various teams who had not previously been exposed to these types of data:<ul><li>Weekly Visitor/Accounts: A nice snapshot of weekly visitors and/or accounts.</li><li>Unique Visitors/Hour Heatmap: Highlights peak usage times, essential for understanding user behavior throughout the day.</li><li>Stickiness: Measures how often users return, a key metric for assessing product value. </li><li>Feature Adoption: Tracks how your product's core features are being used, helping you gauge their success.</li></ul><blockquote>One of the great things about Pendo is their continual investments to improve their reporting capabilities.</blockquote>One of the great things about Pendo is their continual investments to improve their reporting capabilities. A couple of pretty exciting recent enhancements that we would call out:&nbsp;<ol><li>Pendo recently added their <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" href="https://support.pendo.io/hc/en-us/articles/360031863292-Funnels" target="_blank" rel="noopener">funnel reporting capability</a> as a dashboard widget. Previously, the funnel report was a separate standing report, which made it harder to share out to business stakeholders. This was a great addition to the dashboard / widget capabilities on Pendo's part, because funnel based reporting is typically a core aspect of a Product Manager's analysis of their product's performance.</li><li>Pendo is readying the full general release of <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards#h_01HQXY5F9YV9TWQ83W5VTR7VDE" href="https://support.pendo.io/hc/en-us/articles/360032203651-Dashboards#h_01HQXY5F9YV9TWQ83W5VTR7VDE" target="_blank" rel="noopener">Dashboard Templates</a>, which gives Product Managers a head start in building dashboards that are fairly standard or typical for product analytics reporting. They are starting with a Product Overview template and a New Feature Launch template. This is pretty exciting, as we look back on our engagement, much of our focused activities were to build out representative dashboards for the various product teams for which we were rolling out Pendo!</li></ol><h3>Pendo's Data Explorer Report </h3>If you're looking for flexibility and versatility in reporting, Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360033574351-Data-Explorer" href="https://support.pendo.io/hc/en-us/articles/360033574351-Data-Explorer" target="_blank" rel="noopener">Data Explorer</a> is where you'll spend most of your time. This report type gives Pendo users the most flexibility in identifying what data to group and visualize, and it gives a pretty robust filtering/segmentation capability tied to the metadata and event properties being captured. Once we had established solid out-of-box reporting using Pendo's basic Dashboard and Widget capabilities with our client's various teams, any new, more sophisticated reporting we desired typically ended up being a Data Explorer report.&nbsp;One valuable ability within Data Explorer reporting is the ability to define/select event groupings and compare them against other groupings. We found ourselves using Data Explorer quite often to define specific segments of users and creating comparison groups around core feature engagement activities that we could analyze. For example, how might relatively new users use the platform navigation differently compared to long-term, power users? Or, how might users in a certain region use a set of core content-engagement features differently than users in a different region? Data Explorer gave us the ability to easily run comparisons of core metrics against different segments of users.While Data Explorer was overall a robust, valuable tool that we used quite a bit, there are limitations that we encountered that should be mentioned here. While it's great to be able to easily set up comparison groups, Data Explorer only allows you to compare two event groups. So, in the case where you need to compare more than two groups, you'll need to set up multiple Data Explorer reports, or find an alternative means to analyze these various groupings of data.&nbsp;An additional limitation of the Data Explorer report capability ties to metadata filtering. There is a limit to using this filtering capability to data sets that have less than 10,000 events. This can be challenging for events that occur frequently, especially across a large, heavily-used platform. When you hit this limit, it can restrict your ability to get detailed insights to the proper filtered metadata. In our previous engagement, we had to develop workarounds to address this limitation. As the next section covers, sometimes the limitations in Pendo's reporting requires Product teams to build reporting outside of Pendo's UI.<h3> Leveraging Data Exports and Excel for Advanced Reporting </h3>As mentioned in previous sections of this blog post, sometimes Pendo's reporting capabilities will not meet the needs of your Product team, whether it's based on certain limitations or simply that you have much more sophisticated reporting needs. In this case, the great news is that it's relatively easy to extract your product analytics data from Pendo in various ways that allow you to build external reporting that is exactly what you might need. The simplest way to do this that we'll cover today has to do with exporting certain report types from Pendo and pulling those data sets into Excel.Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032202791-Visitor-reports" href="https://support.pendo.io/hc/en-us/articles/360032202791-Visitor-reports" target="_blank" rel="noopener">Visitor</a> and/or <a data-mce-href="https://support.pendo.io/hc/en-us/articles/360032202731-Account-reports" href="https://support.pendo.io/hc/en-us/articles/360032202731-Account-reports" target="_blank" rel="noopener">Account</a> reports can be defined in the Pendo UI and then exported to csv. Similarly, Pendo's Data Explorer reports can also be exported to csv. So as a Product Manager building reporting suites out for yourself, your team, and/or your stakeholders and leadership, it's best to build as much of your product analytics reporting out directly in Pendo as is possible, but then export any specific data that can be leveraged for customized Excel-based reporting.For example, tying back to some of the limitations in Data Explorer report visualization we encountered, there was a need for us to export specific visitor reports and raw event data to perform deeper time-on-site analyses of certain user segments. Leadership had asked us to provide average time-on-site for a certain segment of users, along with a year-over-year comparison. The time-granularity and YoY comparison was not something we could accomplish in Pendo directly. By segmenting this data, exporting it, and examining it at different time grains in Excel, we could uncover trends and insights that weren't immediately visible within Pendo.Using Excel, you can build custom reports tailored to your specific needs. Whether it's detailed cohort analysis or advanced data visualizations, having the flexibility to manipulate and analyze data as you see fit is invaluable.This is the first external reporting workflow we would recommend to Product Managers who have this need. Pendo has many ways to export data beyond report types that can be leveraged by Product teams (<a data-mce-href="https://engageapi.pendo.io/" href="https://engageapi.pendo.io/" target="_blank" rel="noopener">Enage API</a>, Pendo's <a data-mce-href="https://support.pendo.io/hc/en-us/articles/18214274061595-Overview-of-Pendo-Data-Sync" href="https://support.pendo.io/hc/en-us/articles/18214274061595-Overview-of-Pendo-Data-Sync" target="_blank" rel="noopener">Data Sync</a> capability) to build out data pipelines and more automated reporting. Once a need is established for these bespoke types of reporting through manual creation, we then recommend teams investigate if/how they might automate these reports.<h3>Conclusion </h3>Pendo offers excellent options for product managers to visualize their product data using dashboards and widgets. These tools provide a robust starting point for gaining insights quickly and efficiently. However, for more sophisticated or customized visualizations, mastering Pendo's Data Explorer is essential. And when those tools still fall short, exporting data to Excel gives you the freedom to perform advanced analyses tailored to your unique requirements.In our two-year engagement with a large client, we've seen firsthand how Pendo's reporting capabilities can transform Product Management. By understanding the strengths and limitations of these tools, you can make more informed decisions and drive better outcomes for your products.

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