Creating a Feedback Loop for Tech Innovations

Your Product Managers are talking to customers. So why isn’t your product getting better? A few years ago, I was on a team where our boss had a rule: 🗣️ “Everyone must talk to at least one customer each week.” So we did. Calls were scheduled. Conversations happened. Boxes were checked. But nothing changed. No real insights. No real impact. Because talking to customers isn’t the goal. Learning the right things is. When discovery lacks purpose, it leads to wasted effort, misaligned strategy, and poor business decisions: ❌ Features get built that no one actually needs. ❌ Roadmaps get shaped by the loudest voices, not the right customers. ❌ Teams collect insights… but fail to act on them. How Do You Fix It? ✅ Talk to the Right People Not every customer insight is useful. Prioritize: -> Decision-makers AND end-users – You need both perspectives. -> Customers who represent your core market – Not just the loudest complainers. -> Direct conversations – Avoid proxy insights that create blind spots. 👉 Actionable Step: Before each interview, ask: “Is this customer representative of the next 100 we want to win?” If not, rethink who you’re talking to. ✅ Ask the Right Questions A great question challenges assumptions. A bad one reinforces them. -> Stop asking: “Would you use this?” -> Start asking: “How do you solve this today?” -> Show AI prototypes and iterate in real-time – Faster than long discovery cycles. -> If shipping something is faster than researching it—just build it. 👉 Actionable Step: Replace one of your upcoming interview questions with: “What workarounds have you created to solve this problem?” This reveals real pain points. ✅ Don’t Let Insights Die in a Doc Discovery isn’t about collecting insights. It’s about acting on them. -> Validate across multiple customers before making decisions. -> Share findings with your team—don’t keep them locked in Notion. -> Close the loop—show customers how their feedback shaped the product. 👉 Actionable Step: Every two weeks, review customer insights with your team to decipher key patterns and identify what changes should be applied. If there’s no clear action, you’re just collecting data—not driving change. Final Thought Great discovery doesn’t just inform product decisions—it shapes business strategy. Done right, it helps teams build what matters, align with real customer needs, and drive meaningful outcomes. 👉 Be honest—are your customer conversations actually making a difference? If not, what’s missing? -- 👋 I'm Ron Yang, a product leader and advisor. Follow me for insights on product leadership + strategy.

Innovation within an organization is tough—it doesn’t just happen by accident. You have to be intentional about how you collect and apply insights, whether they come from market research, customer feedback, or even new R&D in areas outside your core market. The real challenge lies in figuring out how to take those insights and spread them throughout the rest of the organization in a way that drives meaningful change. At ZoomInfo Labs, one of our core frameworks is built around this very idea. Our job isn’t just to innovate in a vacuum. 1/ It's about going out into the market. 2/ Listening to our customers. 3/ Exchanging best practices. We want to hear what’s working for them, what isn’t, and how they’re going to market alongside us. But here’s the key: it’s not enough just to gather these insights. We need to bring them back into our organization and use them to drive real progress. That could mean pushing our product innovation and roadmap forward, or it could mean applying those insights to fuel our own internal go-to-market strategies. At the end of the day, what we’re really doing is creating a continuous loop—an innovation flywheel. We gather insights from the market, feed them into our product development, and then use those improved products to deliver even more value back to the market. It’s a constant cycle of innovation, ensuring that we’re always improving both for our customers and for ourselves. The takeaway? Innovation isn’t a one-time event; it’s an ongoing process. You need to keep that flywheel spinning, making sure that you’re delivering maximum value to your customers while constantly evolving in response to what you learn from them. That’s how you stay ahead—by ensuring that every insight, every piece of feedback, and every bit of innovation gets fed back into the system to create something even better. 💡 How are you doing this at your organization today?

Feedback loops are AI’s compound interest engine.. if you skip them and your AI performance will just erode over time. Too many roadmaps punt on serious evals because “models don’t hallucinate as much anymore” or “we’ll tighten it up later.” Be wary of those that say this, they really aren't serious practitioners. Here is the gold standard we run for production AI implementation at Bottega8: 1. Offline evals (CI gatekeeper): A lightweight suite of prompt unit tests, RAGAS faithfulness checks, latency, and cost thresholds runs on every PR. If anything regresses, the build fails. 2. RLHF, internal sandbox: A staging environment where we hammer the model with synthetic edge cases and adversarial red team probes. 3. RLHF, dogfood: Real users and real tasks. We expose a feedback widget that decomposes each output into groundedness, completeness, and tone so our labelers can triage in minutes. 4. RLHF, virtual assistants: Contract VAs replay the week’s top workflows nightly, score them with an LLM as judge, and surface drift long before customers notice. 5. Shadow traffic and A/B canaries: Ten percent of live queries route to the new model, and we ship only when conversion, CSAT, and error budgets clear the bar. The result is continuous quality and predictable budgets.. no one wants mystery spikes in spend nor surprise policy violations. If your AI pipeline does not fail fast in code review and learn faster in production, it is not an engineering practice, it is a gamble. There's enough eng industry best practice now with nearly three years of mainstream LLM/GenAI adoption. Happy building and let's build AI systems that audit themselves and compound insight daily.

Generative AI surveys: where your feedback is interactive, valued, and promptly discarded. But hey, at least it’s efficient! Sorry, I know it’s a bit early to be snarky. Seriously though, closing the loop with your customers on their feedback - solicited or unsolicited - is a game changer. Start by integrating customer signals/data into a real-time analytics platform that not only surfaces key themes, but also flags specific issues requiring follow-up. This is no longer advanced tech. From there, create a workflow that assigns ownership for addressing the feedback, tracks resolution progress, and measures outcomes over time. With most tech having APIs for your CRM, also not a huge lift to set up. By linking feedback directly to improvement efforts, which still requires a human in the loop, and closing the loop by notifying customers when changes are made, you transform a simple data collection tool into a continuous improvement engine. Most companies are not taking these critical few steps though. Does it take time, effort, and money? Yes it does. Can it help you drive down costs and drive up revenue? Also, a hard yes. The beauty of actually closing the loop is that the outcomes can be quantified. How have you seen closing the loop - outer, inner, or both - impact your business? #cx #surveys #ceo

One of the most common misconceptions in early-stage startups is that if you build something technically extraordinary with a talented team, success will naturally follow. The reality is far more nuanced. Yes, building a complex product under tight resource constraints is challenging. The trade-offs alone can feel insurmountable. But the most critical—and often overlooked—challenge at this stage is constructing a feedback loop while the product is being developed. For engineers-turned-founders, this is especially dangerous. The instinct to focus solely on technical execution, what I call “engineering in the closet,” can doom even the most innovative startups. Without input from potential users or customers, you risk building a product that solves a problem no one has—or in a way no one values. The truth: 👉 Building doesn’t truly begin until the feedback loop is in place. 👉 Early validation ensures you’re creating the right solution, not just a technically impressive one. 👉 Regular feedback forces you to align your product with real-world needs—long before it’s too late. A practical approach: Create a simple demo to gather feedback early. This doesn’t require a fully functioning product—mocked or simulated backends are perfectly fine. A demo not only highlights your value proposition and product experience but also compels you to practice articulating its benefits. These early iterations are invaluable. They help you refine your direction, strengthen your messaging, and ensure that your efforts are aligned with real demand. Founder-led sales are critical through the seed stage, and this process builds the muscle of selling early and often. By the time the product is ready for market, founders will already have a head start, both in refining the pitch and in building relationships that can drive adoption. #Startups #EngineeringLeadership #ProductDevelopment #FounderInsights