How to Drive Action Using Data Insights

Your research findings are useless if they don't drive decisions. After watching countless brilliant insights disappear into the void, I developed 5 practical templates I use to transform research into action: 1. Decision-Driven Journey Map Standard journey maps look nice but often collect dust. My Decision-Driven Journey Map directly connects user pain points to specific product decisions with clear ownership. Key components: - User journey stages with actions - Pain points with severity ratings (1-5) - Required product decisions for each pain - Decision owner assignment - Implementation timeline This structure creates immediate accountability and turns abstract user problems into concrete action items. 2. Stakeholder Belief Audit Workshop Many product decisions happen based on untested assumptions. This workshop template helps you document and systematically test stakeholder beliefs about users. The four-step process: - Document stakeholder beliefs + confidence level - Prioritize which beliefs to test (impact vs. confidence) - Select appropriate testing methods - Create an action plan with owners and timelines When stakeholders participate in this process, they're far more likely to act on the results. 3. Insight-Action Workshop Guide Research without decisions is just expensive trivia. This workshop template provides a structured 90-minute framework to turn insights into product decisions. Workshop flow: - Research recap (15min) - Insight mapping (15min) - Decision matrix (15min) - Action planning (30min) - Wrap-up and commitments (15min) The decision matrix helps prioritize actions based on user value and implementation effort, ensuring resources are allocated effectively. 4. Five-Minute Video Insights Stakeholders rarely read full research reports. These bite-sized video templates drive decisions better than documents by making insights impossible to ignore. Video structure: - 30 sec: Key finding - 3 min: Supporting user clips - 1 min: Implications - 30 sec: Recommended next steps Pro tip: Create a library of these videos organized by product area for easy reference during planning sessions. 5. Progressive Disclosure Testing Protocol Standard usability testing tries to cover too much. This protocol focuses on how users process information over time to reveal deeper UX issues. Testing phases: - First 5-second impression - Initial scanning behavior - First meaningful action - Information discovery pattern - Task completion approach This approach reveals how users actually build mental models of your product, leading to more impactful interface decisions. Stop letting your hard-earned research insights collect dust. I’m dropping the first 3 templates below, & I’d love to hear which decision-making hurdle is currently blocking your research from making an impact! (The data in the templates is just an example, let me know in the comments or message me if you’d like the blank versions).

Dashboards should be designed for action, not data. Most dashboards contain plenty of data. Dozens of metrics and pretty charts. We've been taught that data drives action, but in practice, it rarely does. As you build your dashboards & and reports, consider the question: What is the user's "next best action"? Then, build solutions to prompt (or enable) that action. Some examples of "next best action": 1.) More Data Sometimes, the user will have more questions. That's ok! We build in self-service filters, segments, and drill-downs to dive in deeper. Self-service > fewer questions for the data team > faster time to action. 2.) Related Data Most businesses will have dozens of reports, often fragmented and disjointed. We can build links to bridge between the reports. Additionally, those links can be dynamic to carry through important filters (date ranges, segments applied) and help users keep their contextual flow. Less time hunting for reports > faster action. 3.) Sharing the data Once users find interesting data, they want to save it or send it to a coworker or client. Enable sharing via email, slack, raw export, etc. Sharing > More distribution > more action. 4.) Actions in another platform (Shopify, Meta, Salesforce, etc) Based on the data, users will need to make a change in another tool. Take someone in merchandising. They see product reports showing that certain products have low conversion rates, likely due to dwindling inventory levels. We can build a link in the dashboard that takes them DIRECTLY to the Shopify admin portal to the product setup and re-merchandise their collection. With one click, they've gone from data > to action. Fewer clicks > faster action. 5.) Alerts Users may see a number and wish they knew about it sooner. For this we setup alerts (email, slack, sms, webhook, etc.) Faster alerts > faster action. Our goal is to transform data-heavy dashboards into tools for action. Consider: - Can we make them more self-service? - Can users set up alerts? - Can they export and share the data easily? - Can we link tools and reports together to avoid context switching? - Can we automate the data to drive action? Are there any tricks you're using to make your dashboards more actionable? #businessintelligence #looker #ecommerceanalytics #measure

Product usage data is one of the best signals available to GTM teams today. 🚩 Problem: Your data is trapped in analytics tools while your go-to-market teams are flying blind in HubSpot. → Marketing sees email metrics but has no insight into what drives user engagement. → Sales spots expansion opportunities too late. → CS identifies churn risks after companies have already switched to an alternative. Product analytics tools are great for understanding what users do. But are useless if your go to market teams can’t act on those insights. That’s where integrating product data captured in Amplitude with custom events in HubSpot becomes a powerful combination. → Product teams use Amplitude to identify predictive user behavior → GTM teams can use HubSpot to build lifecycle campaigns to influence that behavior Let’s use a practical example that identifies accounts ready for team expansion (PQL) based on behavioral signals and proactively loops in the sales or AM team. 💡 Adding custom events to HubSpot health scores is a great way to make them more visible in account records. Here’s how it to works: 1. Define your core product events in Amplitude → Created Project → Invited Collaborator → Integrated Slack 2. Map your product data to an active list in HubSpot using custom events from Amplitude as filters. → List Name: Product Qualified Leads ⚡️ → Filters: Users who created 5+ projects in first 14 days → AND invited 3+ collaborators → AND integrated with Slack/MS Teams → Within accounts < 10 seats (assuming team plan > 10 seats) 💡 This behavior pattern indicates a power user who would benefit from a team plan. 3. Create a contact based workflow in HubSpot ⚡️ Trigger Criteria: Is member of list → List is Product Qualified Leads ⚡️ 4. 🤖 Action 1: Send Slack notification → Channel: expansion-opps → Message: 💰 New PQL identified {{ company name }} → Properties to include: ARR, Health Score, Renewal Date 5. ✅ Action 2: Create task (if AM assigned) → Name: Send upgrade notification → Type: Email → Associate to: Deal & Contact records → Assign to: Existing sales owner 5a. Automate upgrade email (for low touch accounts → Use a template with HubSpot personalization tokens → Send to associated account contacts → Association labels: Account admin/billing contact 6. 📊 Track your results back in Amplitude → Conversion rate from PQL to expansion → Time to conversion → Revenue impact → Cohort retention post-expansion If you’re looking for ways to more deeply segment your product users to send more effective emails, definitely give Amplitude a look. https://hubs.la/Q02X3fP50 Ultimately, the tools individually are great but alignment between teams is what drives results. Give everyone access to the same data and watch your metrics improve. Fun story - Yes, I’m posting this as part of a paid partnership with HubSpot, but I remember first using Amplitude way back in 2014 as a PM - cool to be leveraging it today in a GTM context.

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.

Let me share a personal story that changed my perspective on data's role in decision-making. Picture this: I'm on the New York subway platform, staring at the digital display. "Next train: 6 minutes." Useful? A bit. But I've already swiped my card and committed to this train line. All I can do is figure out how to best use the wait time. This is classic Business Intelligence (BI) - information that's useful but not action-oriented. Now, fast forward a few years. The MTA installs displays outside the stations. Seeing a 6-minute wait for the local train, I now have a choice. It's a 4-minute walk to the express station. Stay or go? This is Decision Intelligence (DI) - the power of right place, right time delivery. The same principle applies to our role as CDOs. We often pour resources into creating insights, reports, and metrics, but then neglect that crucial last mile - getting the right information to the right person at the right time. Here's how we can shift from BI to DI in our organizations: 1. Identify Key Decision Points Where in the business cycle are your stakeholders making critical decisions? That's where your data products need to be integrated and ready to use. 2. Focus on Actionable Insights Don't just report what happened. What's relevant to the decision-maker? Is your insight in the "good to know" category or the "option A is vastly better" category? 3. Optimize the Last Mile Think about how you're delivering insights. Are they embedded in the decision-making process or sitting in a separate report? This shift isn't just about technology - it's about positioning data as a profit enabler, not a support function - from data aware to data driven. This is how we move from being seen as a cost centre to becoming a strategic partner directly contributing to the core objectives of the business. *** 2500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organisational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.

Do you want your data to make a difference? Transform your numbers into narratives that drive action—follow these five key steps: 📌 STEP 1: understand the context Before creating any visual, ask: - Who is your audience? - What do they need to know? - How will they use this information? Getting the context right ensures your message resonates. 📊 STEP 2: choose an appropriate graph Different visuals serve different purposes: - Want to compare values? Try a bar chart. - Showing trends? Use a line graph. - Need part-to-whole context? A stacked bar may work. Pick the right tool for the job! 🧹 STEP 3: declutter your graphs & slides More isn’t better. Remove unnecessary elements (gridlines, redundant labels, clutter) to let your data breathe. Less distraction = clearer communication. 🎯 STEP 4: focus attention Not all elements on your graphs and slides are equal. Use: ✔️ Color ✔️ Annotations ✔️ Positioning …to guide your audience’s eyes to what matters most. Help them know where to look and what to see. 📖 STEP 5: tell a story Numbers alone don’t inspire action—stories do. Structure your communication like a narrative: 1️⃣ Set the scene 2️⃣ Introduce the conflict (tension) 3️⃣ Lead to resolution (insight or action) Make it memorable! THAT'S the *storytelling with data* process! ✨ Following these five steps will help you create clear, compelling data stories. What's your favorite tip or strategy for great graphs and powerful presentations? Let us know in the comments!

I've come to understand that the real magic happens when you can transform raw data into actionable insights. Now this logic probably won't work in your relationships, but ... you'll most likely find more success at work. 😆 Achieving this requires more than just intuition; it demands a rigorous, strategic approach to data analysis, especially critical during those pivotal monthly and quarterly reviews, and some great debate conversational skills-you'll see why. What revenue leaders need—and what new marketing leaders must learn—is the importance of grounding their strategies in solid, data-driven evidence. *Read THAT AGAIN. To navigate those conversations, one must rely on reports(data), meticulously tailored to various segmentations such as persona and use cases. This is how one navigates from the ASK -> ACTION. The sales funnel is your beacon in navigating the complex journey of #demandgeneration. It offers a detailed view into the genesis of revenue, tracking Closed Won (CW) opportunities by pipeline source (PS), and dissecting metrics such as Average Annual Recurring Revenue (ARR) and sales cycle lengths. This analysis extends to the creation and conversion rates of qualified opportunities, providing a clear picture of your marketing effectiveness. The #attribution analysis is essential for understanding the impact of our marketing efforts. By categorizing qualified opportunities and high-intent submissions through self-reported attribution (SRA), we can pinpoint the most effective channels and "touchpoints," guiding our investment strategies. This one pains me sometimes; investment insights. We examine everything from total marketing spend to Customer Acquisition Cost (CAC) and the payback periods, ensuring every dollar is accounted for and aimed towards maximizing ROI. For new marketing leaders, here's my advice: Live in the Data. Use these reports as lenses through which to view the entire marketing landscape. Each campaign, whether it be a podcast series or paid media, should be meticulously tracked and analyzed. This not only provides a roadmap for navigating through the complexities of marketing strategies but also acts as a powerful mentorship tool, enabling your team to quickly identify and capitalize on opportunities for improvement. In essence, the arsenal of reports and analytical tools we've developed are more than a collection of data points. It's a strategic asset that enables us to continuously refine our approach, ensuring our marketing efforts are not just efficient but strikingly effective. By embracing a data-first mentality, we navigate the competitive digital landscape with confidence, driving growth and success through informed, evidence-based strategies. This is the new paradigm for marketing leadership, one where data and action converge to create tangible results. #digitalmarketing #dataanalytics #growthmarketing #marketinginsights

To move from insights -> action requires the CX practicioner to frame the problem to be solve so the business cares about it. Here is a simple framework I love: Every [ frequency ], at least [ reach ] of our [ customers / employees / stakeholders ] experience [ pain point tied to CX, e.g., delivery delays, inconsistent support, poor communication ], resulting in [ measurable CX loss, e.g., lapsed customers, increased churn, or loss revenue ]. This leads to [ implication #1, e.g., loss of repeat purchases, higher acquisition costs to replace churned customers, etc ]. If this is not resolved by [ timeline, e.g., Q2 ], we risk [ implication #2, e.g., further erosion of brand loyalty, compounding operational inefficiencies, or falling behind revenue target by $$]. The framework is a simple way to connect the dots from insight to P&L impact. Execs don't speak customer, they speak P&L. Your role is to connect the dots to inspire action. If you want a copy of the 1-page business case for CX practitioners, comment below and I will get it over to you!

Anyone can analyze data. But the best analysts don't just present insights. They craft compelling stories that drive action. Story-driven data analysts transform complex numbers into narratives that resonate with decision-makers. Here are 20 signs of a story-driven data analyst 👇 1. They start with the business question, not the data ↳ "What decision are we trying to make?" comes before opening any dataset 2. They identify the protagonist in every analysis ↳ Whether it's the customer, employee, or product, someone's journey drives the narrative 3. They establish a context before diving into metrics ↳ Paint the landscape before introducing the numbers 4. They create a narrative arc with data points ↳ Build tension through problem metrics before revealing solution insights 5. They use analogies to explain complex patterns ↳ Make the unfamiliar relatable through everyday comparisons 6. They highlight conflicts in the data ↳ The tension between metrics creates compelling narratives that demand resolution 7. They humanize data with real examples ↳ Turn anonymous segments into specific user stories 8. They craft headlines that capture key insights ↳ Distill complex findings into memorable phrases that stick with stakeholders 9. They use visualization as a narrative element ↳ Each chart serves a specific role in advancing the story 10. They eliminate noise that distracts from the core narrative ↳ Ruthlessly remove data and clutter that doesn't support the central story 11. They time their reveals strategically ↳ Build to key insights rather than leading with them 12. They connect data points with transitional language ↳ "This led to..." and "As a result..." bridge insights into coherent stories 13. They incorporate stakeholder perspectives ↳ Weave different viewpoints into a narrative 14. They use white space in presentations ↳ Give powerful insights room to breathe and resonate 15. They create data-driven characters ↳ Transform segments into memorable personas with specific traits 16. They anticipate and address plot holes ↳ Proactively explain anomalies before they break the narrative spell 17. They balance quantitative evidence with qualitative context ↳ Numbers tell what happened, stories explain why it matters 18. They craft different versions of insights for different audiences ↳ Technical depth for analysts, executive summaries for leadership 19. They end with clear calls to action ↳ Every story concludes with specific next steps 20. They use metaphors consistently throughout the analysis ↳ Thread recurring imagery to reinforce complex concepts Data analysis isn't about displaying numbers. It's about crafting narratives that inspire people to take action. Which of these storytelling skills are you developing as an analyst? ♻️ Repost to help your network build data storytelling skills 🔔 Follow for daily insights on transforming data into compelling business narratives