Visualizing Customer Experience Data

If you’re still looking at channels separately, you’re missing out on real ROI. Here’s how to think about measurement loops instead: What’s a measurement loop? Feedback flowing between channels instead of a straight line of clicks. Why does linear attribution fail? It focuses on the channel and the last click, ignoring the influence of everything else in the journey. Closed-loop feedback works. Search informs email. Email informs social. Social fuels search again. Cross-platform tracking is key. Continuous data flow prevents drop-offs when people switch apps. The loop in motion combines channel, here are some of my favorites: - Simple but goody, Social Impressions / Landing Page Clicks - Tracking Topical Authority: A Simpler Way to Monitor a Complex KPI Topical authority is tricky but it’s one of the most useful signals you can track. Here's one way to break it down. - Start by calculating total reach across both SEO and organic social. You can do this combined or separately by SEO and social search. - Then stack that against key outcomes: -- Primary KPIs like conversions or lead volume -- Secondary KPIs like product detail views or email signups - Now take all of that and map it out in a simple waterfall-style diagram for each topic cluster weekly or biweekly, depending on how fast your content is moving. - Once you look at it this way, you’ll start to see patterns in behavior. The momentum becomes clearer. Other KPIs to track? Not last-click. Look at social-to-form starts, search-to-email reopens, and re-engagement conversions. Multi-channel measurement loops don’t just give cleaner reports. They compound impact. ------------------------ Find this insightful? ♻️ Repost it to your network and follow Ryan Edwards for more. Join our newsletter to get tips and tricks to help you turn data to insights and insights into strategy. Join 3,000+ other marketers https://lnkd.in/gyrXK4mf

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

As I deliver #datastorytelling workshops to different organizations, I encounter a common misconception about how you should approach telling stories with data. To use a Lord of the Rings (LOTR) movie analogy, some #data professionals appear more focused on creating behind-the-scenes documentaries than actual narratives. They want to show the steps, methodologies, and approaches they used during their analysis rather than crafting a concise, compelling narrative. As a LOTR geek, I have watched many behind-the-scenes featurettes. However, I recognize that most people have only watched the LOTR movies and none of the documentaries. They're interested in compelling narratives--not the nitty-gritty of how the movies were made. When it comes to data stories, audiences are more interested in hearing an insightful narrative about a business problem or opportunity than an explanation of how you performed your analysis to assess the problem or opportunity. Taking a documentary approach with your data stories will introduce the following problems: ❌  Added complexity as you go into details that don’t matter to your audience (data collection/preparation, methodology, technical aspects, etc.). ❌  Loss of attention or interest as the audience waits to hear something meaningful. ❌  Less focused or clear communication as insights become buried in minutiae. ❌  Less time to discuss conclusions and determine next steps. ❌  Reduced actionability as extraneous details sidetrack the narrative and obscure the key takeaways. The only people who will get value from a behind-the-scenes documentary will be fellow data professionals. This is a much narrower audience than a broader business audience that is seeking insightful narratives about the business. I recommend delivering the narrative first and having your documentary ready in an appendix (if needed). Most of the time, no one will ask how you performed your analysis (unless they have questions about your numbers). With this approach, the audience will be focused on understanding your insight, implementing your recommendations, and taking action. That's a win-win. How do you avoid telling documentaries instead of narratives? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my brand new newsletter today: https://lnkd.in/gRNMYJQ7

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

If you are looking for a roadmap to master data storytelling, this one's for you Here’s the 12-step framework I use to craft narratives that stick, influence decisions, and scale across teams. 1. Start with the strategic question → Begin with intent, not dashboards. → Tie your story to a business goal → Define the audience - execs, PMs, engineers all need different framing → Write down what you expect the data to show 2. Audit and enrich your data → Strong insights come from strong inputs. → Inventory analytics, LLM logs, synthetic test sets → Use GX Cloud or similar tools for freshness and bias checks → Enrich with market signals, ESG data, user sentiment 3. Make your pipeline reproducible → If it can’t be refreshed, it won’t scale. → Version notebooks and data with Git or Delta Lake → Track data lineage and metadata → Parameterize so you can re-run on demand 4. Find the core insight → Use EDA and AI copilots (like GPT-4 Turbo via Fireworks AI) → Compare to priors - does this challenge existing KPIs? → Stress-test to avoid false positives 5. Build a narrative arc → Structure it like Setup, Conflict, Resolution → Quantify impact in real terms - time saved, churn reduced → Make the product or user the hero, not the chart 6. Choose the right format → A one-pager for execs, & have deeper-dive for ICs → Use dashboards, live boards, or immersive formats when needed → Auto-generate alt text and transcripts for accessibility 7. Design for clarity → Use color and layout to guide attention → Annotate directly on visuals, avoid clutter → Make it dark-mode (if it's a preference) and mobile friendly 8. Add multimodal context → Use LLMs to draft narrative text, then refine → Add Looms or audio clips for async teams → Tailor insights to different personas - PM vs CFO vs engineer 9. Be transparent and responsible → Surface model or sampling bias → Tag data with source, timestamp, and confidence → Use differential privacy or synthetic cohorts when needed 10. Let people explore → Add filters, sliders, and what-if scenarios → Enable drilldowns from KPIs to raw logs → Embed chat-based Q&A with RAG for live feedback 11. End with action → Focus on one clear next step → Assign ownership, deadline, and metric → Include a quick feedback loop like a micro-survey 12. Automate the follow-through → Schedule refresh jobs and Slack digests → Sync insights back into product roadmaps or OKRs → Track behavior change post-insight My 2 cents 🫰 → Don’t wait until the end to share your story. The earlier you involve stakeholders, the more aligned and useful your insights become. → If your insights only live in dashboards, they’re easy to ignore. Push them into the tools your team already uses- Slack, Notion, Jira, (or even put them in your OKRs) → If your story doesn’t lead to change, it’s just a report- so be "prescriptive" Happy building 💙 Follow me (Aishwarya Srinivasan) for more AI insights!

I had the opportunity to meet and spend some time talking to expert data storyteller Brent Dykes at the recently concluded 2nd IP Analytics Community of Practice Annual Symposium in Rio de Janeiro and took away some great insights. As we were chatting before his keynote address on data storytelling, he said, data stories are not stories with data. Wait, what? Seeing the confusion on my face, he went on. A data story begins with the data. The narrative is based on what the data is telling you. A story with data already has a narrative that does not necessarily originate in the data, but rather the data is shown to support the story. I looked back to the time I was working as a scientist, the insights from the data generated from my experiments would form the story and get written up as a journal paper. When I was working as a science journalist, these journal papers along with interviews with scientists would form the story, the data coming in indirectly. This very fine distinction between the play of words made absolute sense. If you are someone who investigates data and are called upon to present your insights, it is very important to make this distinction. Here are some tips that might help: ✅ View the data without any preconceived assumptions. Any initial bias you have will color the story you are trying to find. ✅ Statistics are not data stories. They are the “what” you are seeing. Ask yourself, “why” am I seeing these numbers. The real story is in the why. ✅ Remove the noise. If there are too many threads to your data story, it confuses the audience who will stop paying attention. Think of the times when you put down a novel half way because of too many subplots. ✅ Not all data have stories to tell. Great data stories shift perspectives and change how we see the world. If yours doesn't create that "aha!" moment, consider it supporting material instead. Do you have more tips for data storytelling? Do share in the comments. #DataStory #DataStorytelling

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.

Data without a story is just… numbers. And numbers don’t make decisions. People do. Stakeholders want a narrative that moves them to act. Here are 5 storytelling hacks in data that stakeholders love (and that drive real impact): 1. Lead with the punchline     Don’t warm them up with a 20-slide build-up. Start with the big reveal: “If we fix onboarding, churn drops 20%, that’s $3M saved annually.” Stakeholders love clarity upfront. Then you can unpack the details. 2. Make the data human     Percentages are forgettable, people aren’t. Instead of “25% of users churn after week one”… Say: “1 out of every 4 new users walks away before they even meet us.” Suddenly, the problem feels real. 3. Use contrast for drama Great stories need tension. Data storytelling is no different. “We spent $1.2M on marketing last year… but only $200k of that actually drove conversions.” Contrast makes people lean in. 4. Translate everything into money or time Metrics are nice. Impact is better. “Efficiency up 10%” sounds good… But “This saves 40 engineering hours a month” makes people care. Dollars and hours are universal languages. 5. End with the action shot Never leave them wondering, “So what?” Finish with the next step: “Here are 2 experiments we can run next month to fix this.” Stories without a call to action die in the room. Remember: Data storytelling isn’t dumbing it down. It’s leveling it up so the right people act on it. Because the chart doesn’t create impact. The story does. If you want to read stories about how other data professionals are getting interviews consistently and how they convert them into an offer, visit our website. If you found this post valuable, follow me, Jaret André and DataShip for more.

📊💡 From Facts to Feelings: The Real Secret Behind Data-Driven Campaigns We all love numbers. CTR, ROI, conversion rates, bounce rates - they make our dashboards look impressive. But let me ask you this 👉 When was the last time a graph made you feel something? In the race to optimise every campaign, it’s easy to forget that behind every data point is a human being. A customer. A story. A moment of hesitation or delight. And yet, this is exactly where modern marketing is evolving - where data meets emotion, and insight meets storytelling. In our latest deep-dive, "From Facts to Feelings: How to Use Storytelling in Data-Driven Campaigns," we explore: 🎯 Why raw data alone no longer cuts through the noise 🧠 How the brain reacts to stories vs. statistics (yes, there’s science behind this!) 💬 Real-world examples of campaigns that paired data with storytelling - and won big 📚 Practical strategies you can use to craft compelling narratives around your insights This isn’t about abandoning analytics. It’s about bringing your campaigns to life by turning trends into tales, charts into characters, and stats into stories that stick. Because the brands that will thrive aren’t the ones with the most data - they’re the ones that know how to humanise it. 💥 If your marketing reports are impressive, but your audience isn’t moved - it’s time to re-think the approach. 👇 I’d love to hear from marketers, analysts, copywriters, and brand strategists: How do YOU bring emotion into data-led campaigns? Let’s start a conversation. #Storytelling #MarketingStrategy #DataDrivenMarketing #ContentMarketing #BrandStrategy #DigitalMarketing #EmotionalMarketing #MarketingLeadership #LinkedInForMarketers #MarketingTips #CMO #AgencyLife

📊 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗧𝗼𝗽𝗡 + ‘𝗢𝘁𝗵𝗲𝗿𝘀’ 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗺𝗮𝘁𝗿𝗶𝘅 𝘃𝗶𝘀𝘂𝗮𝗹 This visual combines several native Power BI techniques into one flexible view - letting users dynamically control both 𝘄𝗵𝗮𝘁 they see and 𝗵𝗼𝘄 they see it. Users can: ✅ Switch between 𝗠𝗼𝗻𝘁𝗵-𝘁𝗼-𝗗𝗮𝘁𝗲 and 𝗬𝗲𝗮𝗿-𝘁𝗼-𝗗𝗮𝘁𝗲 ✅ Change the 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻 (Country, Business Unit, Segment) ✅ Adjust the 𝗧𝗼𝗽𝗡 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 using a parameter ✅ Automatically group remaining customers into “𝗢𝘁𝗵𝗲𝗿𝘀” 💡 This setup could easily be extended for example with a metric switch or additional breakdown options. This setup brings together multiple techniques I’ve learned through the 𝗗𝗮𝘁𝗮 𝗩𝗶𝘇 𝗙𝗼𝗿𝗴𝗲 workouts by Gustaw Dudek - highly recommended if you want to learn advanced techniques that are immediately applicable in real projects. 💪 ⚙️ 𝗦𝗲𝘁𝘂𝗽 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 ▪️Supplemental Customer table with “Others” group ▪️(Advanced) DAX for dynamic TopN + Others by Revenue calculation ▪️Parameter for TopN selection ▪️Parameter for breakdown (Country / BU / Segment) ▪️MTD/YTD switch for flexible time analysis ▪️Variance data bars using conditional formatting 💬 Would you use a dynamic TopN + Others setup like this in your own reports? Let’s keep pushing the limits of native Power BI 🚀