What's Your Edge?

In this episode, we’re joined by Stacie Feller, President of Kanga Roof. Stacie’s journey is a powerful illustration of how the absence, or presence, of customer data can dramatically impact business decisions. We’ll go behind the scenes to explore how a lack of customer insights created missed opportunities, and how a renewed focus on data has since transformed her decision-making. Today’s conversation takes us from instinct to insight and ultimately to impact by exploring how data transforms business decisions into measurable growth. Crises Create Opportunities for Learning and for Growth Stacie, at the outset of the pandemic, uncertainty was the order of the day. Like many leaders, you and your team anticipated a sharp decline in business. Yet, the reality was the opposite. Instead, you experienced a surge in demand for roofing services. Despite this unexpected opportunity, Kanga Roof was unable to fully capitalize. The supply chain faltered, and your organization was caught off guard. This wasn’t simply a story of external disruption, but a wake-up call for the importance of customer data. This scenario is not unique. According to Forrester, “insights-driven businesses” are growing at more than 30% annually—far outpacing their peers.  The difference? These organizations don’t rely on gut instinct; they invest in data-driven decision-making. Let’s start at that critical moment. What were the early warning signs that your assumptions about customer demand might be off? How did you recognize that the root issue was a lack of actionable customer data? During the early days of the pandemic, we assumed business would slow down like it did for most industries. But when the phone started ringing off the hook, we realized our assumptions were off. Homeowners were home, noticing roof issues they hadn’t before, and investing in their property instead of travel. The problem was that we didn’t have good data on who our customers really were, how they found us, what services they needed, or how their priorities were shifting. We were reacting instead of planning. That’s when I realized we needed a stronger system for capturing and using customer insights to guide decisions instead of relying on instinct. Every Delay in Understanding Customers Costs Time and Revenue What you described is something we see across many organizations: decisions made from instinct or urgency rather than insight. We often refer to these as “random acts”. Random acts of marketing, planning, selling, or even customer engagement. They feel productive in the moment, but often lead to inefficiencies or missed opportunities. Once you recognized that pattern, what steps did you take to move from these random acts to more deliberate, insight-driven actions? We had to get intentional. We started by documenting every customer touchpoint—how they heard about us, what problems they were trying to solve, and what factors drove their buying decision. Then we implemented tools to track trends in lead sources, service types, and timing. Instead of reacting to what we thought was happening, we began making decisions based on what the data told us. It wasn’t just marketing; it was operations and staffing.  For example, we have trained our appointment coordinators to gather data that will help them determine who is the best roofing advisor to send to them.  That shift from gut feeling to evidence was a real turning point. Your experience highlights a common pitfall: acting on assumptions rather than evidence. As you reflect, what was the impact of not having real-time customer insights during that period? What decisions do you wish you could have made if you’d had better data? Not having real-time data caused us to miss opportunities. We couldn’t predict demand surges, so we were short on materials and labor when business spiked. Looking back, I wish we’d had a clearer view of customer intent, like knowing who was in research mode versus ready to buy. If we’d had better insights then, we could’ve staffed more efficiently, adjusted pricing based on material shortages, and prioritized the right jobs. It taught us that every delay in understanding our customers costs both time and revenue. Treat Data Like an Ongoing Process to Capture Valuable Insights Many organizations realize they need better insights—but struggle to know where to start. We have found it is helpful to have a practical framework organizations can use to move beyond intuition and experience. That’s why we developed our  8 Steps for Applying Business Data-Derived Insights approach that transforms data into a disciplined decision-making process. The framework offers a systematic way to recognize, validate, and apply insights from quantitative and qualitative data. It emphasizes that data-derived insights, when leveraged through a disciplined, eight-step process, enable more objective, scalable, and predictive decision-making, leading to above-market growth and significant EBITDA gains, somewhere between 15-25%.  Stacie, what were your first steps in building a more robust customer data foundation? Did you focus on capturing customer feedback, transaction data, market trends, or something else? Our first step was organizing the data we already had. We pulled customer information from estimates, service calls, and follow-ups into one system so we could see patterns. Then we started collecting feedback after every job and tracking reviews to understand what customers valued most. We also began layering in operational data—things like lead times, call volumes, and job profitability. It wasn’t just about collecting data; it was about connecting it so we could make better business decisions across departments. Download the CC Worksheet I know you are very familiar with the value of the supply chain.  When it comes to insights, there is a supply chain as well. The insights supply chain entails five critical stages: plan, source, make, deliver, and return. By mapping and continuously optimizing this “insights supply chain,” organizations can efficiently transform vast amounts of data into timely, relevant insights distributed across business functions. Proactive management of this process is essential for maximizing customer value, sustaining competitive advantage, and supporting long-term business success. How do you ensure that your data collection and analysis processes keep pace with evolving customer needs and market conditions? What systems or routines have you put in place to keep your “insights supply chain” flowing and relevant? What obstacles did you encounter as you tried to close the gaps in your data supply chain? We treat data like an ongoing process, not a one-time project. Every quarter, our leadership team reviews trends in customer behavior and operations. We look for shifts—like a rise in roof repairs versus replacements—and adapt our marketing and staffing accordingly. One obstacle early on was data accuracy. We learned quickly that bad data leads to bad decisions. So, we trained our team on why clean data matters, built checks into our CRM, and made data quality part of everyone’s responsibility. Make Smarter Customer Decisions from Trends in Your Data Because effective business planning and decision-making require more than intuition. It demands a disciplined approach to identifying trends and patterns within both internal and external data. By understanding and leveraging these trends (general directions over time) and patterns (repeatable occurrences), organizations can anticipate customer needs, allocate resources strategically, and drive future growth. The real advantage comes from combining data trends and patterns to inform forward-looking, customer-centric business strategies. With a stronger data foundation, how did your decision-making evolve? Can you share specific examples of business decisions—whether about staffing, marketing, or operations—that you can now make with confidence thanks to data? How do you distinguish between a trend and an anomaly when interpreting your data? Today, data drives a lot of our major decisions. For example, we track which zip codes convert best and adjust marketing budgets based on that. We analyze job types to know when to add or cross-train crews. Even our customer service scripts are refined from feedback trends.  We created a dynamic internal webpage for our appointment coordinators to follow that helps with this. To tell a trend from an anomaly, we look for consistency over time and across multiple data points. One busy week doesn’t mean a market shift…but three quarters of the same pattern does. That discipline has made our decisions much more confident and less reactive. Measuring the Value of Data: Time and Speed to Action I can see how important it is to take the right action at the right time. Stacie, a lot of people find it difficult to justify investing in data initiatives. We have found that two key metrics for evaluating the ROI of data are time to insights (TTI) and time to decisions (TTD). By tracking how quickly an organization can transform raw data into actionable insights (TTI) and then convert those insights into informed business decisions (TTD), leaders can quantify the productivity and profitability gains from their data investments. Accelerating both TTI and TTD is essential for gaining a competitive edge, improving operational efficiency, and maximizing the value derived from data-driven strategies. How do you measure the value of your customer data today? What metrics or business outcomes do you track to ensure your insights are driving results? How do you connect your data efforts to tangible business value? We measure success by looking at conversion rates, job profitability, and customer satisfaction.  We also monitor how fast we move from data to action—if it takes weeks to act on insights, that’s a lo