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From Instinct to Customer Insights for Better C-Suite Decision Making | WYE?
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 lost opportunity. The faster we turn information into decisions, the more value we get from our data investment.
Advice for the Journey: Getting Started and Leveling Up
Simply accumulating more data does not guarantee better business insights or outcomes. Throughout our conversation, you’ve mentioned that it is important for organizations to establish a disciplined process for data management. You brought up a great point about the quality of data, its relevance, and the need for structured analysis to extract actionable insights that drive strategic decisions. As you mentioned, value comes not from volume, but from a systematic approach that transforms the right data into meaningful intelligence for business growth. For leaders just starting their data journey, what advice would you offer? What are the biggest lessons learned about collecting, analyzing, and applying customer data? For those further along, what best practices or next-level challenges should they anticipate?
Start small and stay consistent. Don’t get overwhelmed by collecting every piece of data—focus on what helps you serve your customers better. For us, that was understanding where leads came from and why customers chose us. Once we had that, we could build from there.
The biggest lesson is to actually use the data. Too many businesses collect it but never act on it. Create a rhythm of reviewing, learning, and applying what you see. That’s where the real growth happens.
One of the biggest hurdles we see is that many organizations operate through what we call “random acts”. These are isolated efforts not anchored to a clear data-driven strategy. Avoiding these requires discipline, process, and focus. For leaders just starting out, how do you recommend they prevent these random acts from creeping into their data and decision-making processes?
Random acts happen when there’s no clear strategy or accountability. We prevent that by setting measurable goals and reviewing performance regularly. Every initiative—marketing, hiring, or operations—must be tied to a specific data point or outcome.
That discipline keeps us focused and ensures that everyone understands why we’re doing something, not just what we’re doing.
See Ongoing Success with a Data-Driven Culture
Stacie, it’s clear you and your team have found actionable customer insights to be a critical driver of business growth, enabling your organization to anticipate needs, personalize offerings, and build stronger relationships. We believe that by systematically collecting and analyzing customer data, you can see patterns and preferences that inform strategy and foster innovation. And your story seems to bear that out. By leveraging customer insights, companies can differentiate themselves, improve customer value, and accelerate sustainable growth. How do you ensure that customer insights are systematically collected, analyzed, and translated into strategies that deliver measurable value and sustainable growth?
We’ve built a rhythm of gathering and sharing insights across the company. Our CRM gives every department visibility into customer trends. We use that data to forecast sales, plan staffing, and even improve training programs.
When people see how their input feeds into better decisions, they take ownership of the data. That’s how we’ve sustained a culture of continuous improvement.
As we wrap up, let’s talk about building a data-driven culture, which requires more than technology. It takes leadership commitment, clear processes, and continuous investment in data literacy across the organization. It is critical to align people, practices, and incentives to ensure that data and insights are consistently used in decision-making at every level. The key is to foster collaboration, encourage curiosity, and make data accessible and actionable, so organizations can embed a culture where insights drive performance, innovation, and long-term growth. What are the most important steps leaders can take to embed a truly data-driven mindset across the organization, ensuring that customer insights consistently inform strategy and innovation?
As a leader, I try to model curiosity—asking questions like “what does the data say?” before making decisions. We include data reviews in leadership meetings and align our KPIs around both customer experience and profitability.
The key is making data part of the conversation, not an afterthought. When leaders value it, everyone follows suit.
How do you, as President, ensure that data accountability is part of leadership discussions—perhaps even boardroom conversations—so that insight-driven decision-making becomes part of your organization’s governance?
Data accountability starts at the top. We are transparent about what’s working and what isn’t. We discuss metrics so everyone understands how their numbers tie to the company’s goals.
Over time, that’s built trust and alignment. People know decisions are made based on facts, not feelings, and that’s changed how we lead.
Stacie, thank you for sharing your story and the lessons learned from your journey and highlighting how moving from instinct to actionable insights fundamentally transforms C-Suite decision-making. Through your real-world experience, we see the tangible impact of building disciplined data processes, investing in data literacy, and cultivating a culture where insights drive strategy, operations, and growth. Our conversation underscores that sustainable business success depends on embedding data-driven thinking at every level of leadership.
Ready to take your organization from instinct to insight? Connect with us to discover practical steps you can implement today for a more resilient, customer-centric, and competitive future.
FAQ:
(written by Penn at Sintra.ai)
FAQ: From Instinct to Customer Insights for Better C-Suite Decision Making
Instinct and experience are valuable, but on their own they can lead to “random acts” of strategy, marketing, and operations—activities that feel productive but are not grounded in evidence. In volatile markets, decisions based solely on gut feel increase the risk of misallocating resources, missing shifts in customer behavior, and being outpaced by competitors who use data-driven insights to guide their choices.
Actionable customer insights are findings derived from customer data—quantitative and qualitative—that directly inform a decision or action. They go beyond raw data or simple reporting to answer questions such as: Which customers are most valuable? What drives their decisions? Which offers, channels, or experiences are most effective?Actionable insights are specific, relevant, and tied to a clear business outcome.
Start by clarifying the business questions you need to answer, then identify the data you already have that can help answer those questions. Focus on a few high-impact areas—such as lead sources, win/loss drivers, customer satisfaction, or product/service profitability—rather than trying to capture everything at once. From there, establish simple routines for collecting, reviewing, and acting on the data.
An insights supply chain describes the end-to-end process that converts raw data into insights and then into decisions and actions. It typically includes five stages: plan, source, make, deliver, and return. When this supply chain is well-designed and continuously optimized, organizations can move faster from data to insight to action, reduce rework, improve data quality, and ensure that insights are delivered to the right people at the right time.
Meaningful trends show consistent patterns over time and across multiple data points or segments. Anomalies are short-lived spikes or drops that don’t repeat or align with other indicators. Leaders should look at data over multiple periods, compare across segments (e.g., customer type, region, product line), and validate findings with both quantitative data and qualitative feedback before making major decisions.
Time to insights (TTI) measures how long it takes to convert raw data into actionable insights. Time to decisions (TTD) measures how long it takes to turn those insights into implemented decisions. Together, they reveal how effectively and efficiently an organization is using its data. Shorter TTI and TTD typically correlate with greater agility, faster response to market changes, and higher ROI from data and analytics investments.
Avoiding random acts requires a clear strategy, defined outcomes, and accountability. Every initiative should be tied to specific objectives, metrics, and customer insights. Leaders can reduce random acts by:
Culture is the multiplier. Technology and tools are necessary but not sufficient. A data-driven culture is one where leaders model curiosity, regularly ask “what does the data tell us?”, and reward evidence-based decisions. It also requires data literacy, transparency around metrics, and making data accessible so teams can confidently use insights in their day-to-day work.
Leaders can embed data accountability by integrating key metrics and insights into regular leadership and board discussions, tying strategic decisions to evidence, and clearly linking data to business outcomes such as growth, profitability, and customer value. Over time, this shifts governance conversations from opinion-based debates to fact-based dialogue and reinforces the expectation that decisions are grounded in insights.
A practical first step is to select one critical decision area—such as resource allocation, customer acquisition, or retention—and map the current path from data to decision. Identify where data is missing, delayed, or unused, and define one or two changes that would shorten the path from insight to action. Use this as a pilot to build momentum, demonstrate value, and then scale the approach across the organization.
View all episodes
By Laura Patterson-VisionEdge Marketing PresidentIn 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 lost opportunity. The faster we turn information into decisions, the more value we get from our data investment.
Advice for the Journey: Getting Started and Leveling Up
Simply accumulating more data does not guarantee better business insights or outcomes. Throughout our conversation, you’ve mentioned that it is important for organizations to establish a disciplined process for data management. You brought up a great point about the quality of data, its relevance, and the need for structured analysis to extract actionable insights that drive strategic decisions. As you mentioned, value comes not from volume, but from a systematic approach that transforms the right data into meaningful intelligence for business growth. For leaders just starting their data journey, what advice would you offer? What are the biggest lessons learned about collecting, analyzing, and applying customer data? For those further along, what best practices or next-level challenges should they anticipate?
Start small and stay consistent. Don’t get overwhelmed by collecting every piece of data—focus on what helps you serve your customers better. For us, that was understanding where leads came from and why customers chose us. Once we had that, we could build from there.
The biggest lesson is to actually use the data. Too many businesses collect it but never act on it. Create a rhythm of reviewing, learning, and applying what you see. That’s where the real growth happens.
One of the biggest hurdles we see is that many organizations operate through what we call “random acts”. These are isolated efforts not anchored to a clear data-driven strategy. Avoiding these requires discipline, process, and focus. For leaders just starting out, how do you recommend they prevent these random acts from creeping into their data and decision-making processes?
Random acts happen when there’s no clear strategy or accountability. We prevent that by setting measurable goals and reviewing performance regularly. Every initiative—marketing, hiring, or operations—must be tied to a specific data point or outcome.
That discipline keeps us focused and ensures that everyone understands why we’re doing something, not just what we’re doing.
See Ongoing Success with a Data-Driven Culture
Stacie, it’s clear you and your team have found actionable customer insights to be a critical driver of business growth, enabling your organization to anticipate needs, personalize offerings, and build stronger relationships. We believe that by systematically collecting and analyzing customer data, you can see patterns and preferences that inform strategy and foster innovation. And your story seems to bear that out. By leveraging customer insights, companies can differentiate themselves, improve customer value, and accelerate sustainable growth. How do you ensure that customer insights are systematically collected, analyzed, and translated into strategies that deliver measurable value and sustainable growth?
We’ve built a rhythm of gathering and sharing insights across the company. Our CRM gives every department visibility into customer trends. We use that data to forecast sales, plan staffing, and even improve training programs.
When people see how their input feeds into better decisions, they take ownership of the data. That’s how we’ve sustained a culture of continuous improvement.
As we wrap up, let’s talk about building a data-driven culture, which requires more than technology. It takes leadership commitment, clear processes, and continuous investment in data literacy across the organization. It is critical to align people, practices, and incentives to ensure that data and insights are consistently used in decision-making at every level. The key is to foster collaboration, encourage curiosity, and make data accessible and actionable, so organizations can embed a culture where insights drive performance, innovation, and long-term growth. What are the most important steps leaders can take to embed a truly data-driven mindset across the organization, ensuring that customer insights consistently inform strategy and innovation?
As a leader, I try to model curiosity—asking questions like “what does the data say?” before making decisions. We include data reviews in leadership meetings and align our KPIs around both customer experience and profitability.
The key is making data part of the conversation, not an afterthought. When leaders value it, everyone follows suit.
How do you, as President, ensure that data accountability is part of leadership discussions—perhaps even boardroom conversations—so that insight-driven decision-making becomes part of your organization’s governance?
Data accountability starts at the top. We are transparent about what’s working and what isn’t. We discuss metrics so everyone understands how their numbers tie to the company’s goals.
Over time, that’s built trust and alignment. People know decisions are made based on facts, not feelings, and that’s changed how we lead.
Stacie, thank you for sharing your story and the lessons learned from your journey and highlighting how moving from instinct to actionable insights fundamentally transforms C-Suite decision-making. Through your real-world experience, we see the tangible impact of building disciplined data processes, investing in data literacy, and cultivating a culture where insights drive strategy, operations, and growth. Our conversation underscores that sustainable business success depends on embedding data-driven thinking at every level of leadership.
Ready to take your organization from instinct to insight? Connect with us to discover practical steps you can implement today for a more resilient, customer-centric, and competitive future.
FAQ:
(written by Penn at Sintra.ai)
FAQ: From Instinct to Customer Insights for Better C-Suite Decision Making
Instinct and experience are valuable, but on their own they can lead to “random acts” of strategy, marketing, and operations—activities that feel productive but are not grounded in evidence. In volatile markets, decisions based solely on gut feel increase the risk of misallocating resources, missing shifts in customer behavior, and being outpaced by competitors who use data-driven insights to guide their choices.
Actionable customer insights are findings derived from customer data—quantitative and qualitative—that directly inform a decision or action. They go beyond raw data or simple reporting to answer questions such as: Which customers are most valuable? What drives their decisions? Which offers, channels, or experiences are most effective?Actionable insights are specific, relevant, and tied to a clear business outcome.
Start by clarifying the business questions you need to answer, then identify the data you already have that can help answer those questions. Focus on a few high-impact areas—such as lead sources, win/loss drivers, customer satisfaction, or product/service profitability—rather than trying to capture everything at once. From there, establish simple routines for collecting, reviewing, and acting on the data.
An insights supply chain describes the end-to-end process that converts raw data into insights and then into decisions and actions. It typically includes five stages: plan, source, make, deliver, and return. When this supply chain is well-designed and continuously optimized, organizations can move faster from data to insight to action, reduce rework, improve data quality, and ensure that insights are delivered to the right people at the right time.
Meaningful trends show consistent patterns over time and across multiple data points or segments. Anomalies are short-lived spikes or drops that don’t repeat or align with other indicators. Leaders should look at data over multiple periods, compare across segments (e.g., customer type, region, product line), and validate findings with both quantitative data and qualitative feedback before making major decisions.
Time to insights (TTI) measures how long it takes to convert raw data into actionable insights. Time to decisions (TTD) measures how long it takes to turn those insights into implemented decisions. Together, they reveal how effectively and efficiently an organization is using its data. Shorter TTI and TTD typically correlate with greater agility, faster response to market changes, and higher ROI from data and analytics investments.
Avoiding random acts requires a clear strategy, defined outcomes, and accountability. Every initiative should be tied to specific objectives, metrics, and customer insights. Leaders can reduce random acts by:
Culture is the multiplier. Technology and tools are necessary but not sufficient. A data-driven culture is one where leaders model curiosity, regularly ask “what does the data tell us?”, and reward evidence-based decisions. It also requires data literacy, transparency around metrics, and making data accessible so teams can confidently use insights in their day-to-day work.
Leaders can embed data accountability by integrating key metrics and insights into regular leadership and board discussions, tying strategic decisions to evidence, and clearly linking data to business outcomes such as growth, profitability, and customer value. Over time, this shifts governance conversations from opinion-based debates to fact-based dialogue and reinforces the expectation that decisions are grounded in insights.
A practical first step is to select one critical decision area—such as resource allocation, customer acquisition, or retention—and map the current path from data to decision. Identify where data is missing, delayed, or unused, and define one or two changes that would shorten the path from insight to action. Use this as a pilot to build momentum, demonstrate value, and then scale the approach across the organization.