Best Practices for Digital Marketing Analytics

Yesterday the Head of Partnerships at a $200M health-tech company asked me how to take their partner program from being a C-suite afterthought to a mission-critical GTM strategy. My answer was simple... Data. Let me explain. Partnerships are fluffy. At least that’s what most Boards, C-suites, and Executives think. Why? Because most partner teams struggle with data. Due to unrealistic revenue targets, timelines and limited resources, partnership leaders are often scrambling from day 1. To catch up, they often skip the most important step: Setting up solid processes, KPIs and the mechanisms to track them. So when an important stakeholder asks them for a QUANTITATIVE justification for their activities they either stare back blankly or slap together some unconvincing back-of-the-napkin math. And forget about realistically forecasting more than a quarter out. This is virtually impossible for most partner teams. How can you become a mission-critical GTM strategy if your leadership can’t clearly understand what you’re doing, why you’re doing it, and what value it’s going to drive for the business. This is not the way. Partnership leaders need to start being meticulous about data. We need to take the time to set up good processes and tracking mechanisms. You must measure and track everything! - Partner lifecycle - Sourced deal funnels - Influenced deal funnels - Partner marketing outcomes - Integration adoption - Partner ROI - Revenue by partner - Revenue by partner manager - And a dozen other things The value of this should not be underestimated. Only by measuring and tracking will you be able to understand what’s working and what’s not. When you take the time to do this right, you’ll be able to prove to your C-suite the impact your partnerships strategy has driven for the business and what impact it *will* drive looking forward. You’ll be able to show the leaders of Sales, Marketing, and Customer Success how you’ve made them and their teams more successful. You’ll be able to forecast, budget, and scale a predictable partner program. As partnerships leaders we understand the value of partnerships in our blood. But up until now, we’ve lacked the operational rigor to prove it out. Let’s become data-driven operators and make partnerships an undeniable, mission-critical GTM strategy. Not just an afterthought.

In #datastorytelling, you often want a specific point to stand out or “POP” in each data scene in your data stories. I’ve developed a 💥POP💥 method that you can apply to these situations: 💥 P: Prioritize – Establish which data point is most important. 💥 O: Overstate – Use visual emphasis like color and size as a contrast.   💥 P: Point – Guide the audience to the focal point of your chart. The accompanying illustration shows the progressive steps I’ve taken to make Product A’s Q3 $6M sales bump stand out. Step 1️⃣: Add headline. One of the first things the audience will attempt to do is read the title. A descriptive chart title like “Products by quarterly sales” is too general and offers no focal point. I replaced it with an explanatory headline emphasizing the increase in Product A sales in Q3. The audience is now directed to find this data point in the chart. Step 2️⃣: Adjust color/thickness I want the audience to focus on Product A, not Product B or Product C. The other products are still useful for context but are not the main emphasis. I kept Product A’s original bold color but thickened its line. I lightened the colors of the two other products to reduce their prominence. Step 3️⃣: Add label/marker I added a marker highlighting the $6M and bolded the label font. You’ll notice I added a marker and label for the proceeding quarter. I wanted to make it easy for the audience to note the dramatic shift between the two quarters. Step 4️⃣: Add annotation You don’t always need to add annotations to every key data point, but it can be a great way to draw more attention to particular points. It also allows you to provide more context to help explain the ‘why’ or ‘so what’ behind different results. Step 5️⃣: Add graphical cue (arrow) I added a graphical cue (arrow) to emphasize the massive increase in sales between the two quarters. You can use other objects, such as reference lines, circles, or boxes, to draw attention to key features of the chart. In terms of the POP method, these steps align in the following way: 💥 Prioritize – Step 1 💥 Overstate – Step 2-3 💥 Point – Step 4-5 Because data stories are explanatory rather than exploratory, you need to be more directive with your visuals. If you don’t design your data scenes to guide the audience through your key points, they may not follow your conclusions and become confused. Using the POP method, you ensure that your key points stand out and resonate with your audience, making your data stories more than just informative but memorable, engaging, and persuasive. So next time you craft a data story, ensure your data scenes POP—and watch your insights take center stage! What other techniques do you use to make your key data points POP? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7

Should you retarget by intent? We ran the test... Most B2B retargeting looks something like this: Someone visits your site, any page at all…and immediately: they’re getting hit with “Book a demo” or “Start your free trial” ads. No nuance. No context. Just one-size-fits-all messaging chasing every visitor around the internet. It’s simple. It’s easy. But also pretty broken. Here’s why: > Not everyone on your site is in the same headspace. > Blog readers aren’t ready to talk to sales. > Product page visitors are curious but not convinced. And people on the demo page? They’re this close but something’s holding them back. Treating all three the same? That’s how you burn ad dollars without actually building pipeline. So we ran a test. One of our clients had a basic retargeting setup. One campaign. One CTA. One generic message. We broke it apart and rebuilt it based on intent. ___________________________ Here’s how we segmented it: Blog readers Top-of-funnel folks in research mode. → We showed them value-first content: guides, checklists, downloads. Product & feature page visitors Mid-funnel visitors sniffing around the solution. → We served ROI calculators, interactive tools, and “how do you stack up” style CTAs. Pricing/demo page visitors Bottom-of-funnel leads with real buying signals. → They saw direct “Book a demo” and “Start your trial” ads with tons of social proof. ___________________________ Here’s what happened over 60 days: Old campaign (one-size-fits-all): > Low click-through rates (~0.4%) > Modest form fill volume > Demo-to-close rates hovering around 17% New segmented retargeting: > 3.1x higher CTR > 2.4x more total form fills > 29% increase in demo-to-close conversion from high-intent segments ___________________________ Better message-match. Cleaner funnel transitions. Better results.

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!

Understanding your customers’ behaviors and responding accordingly is key to sustained business success. In yesterday’s post, I introduced the Recency-Frequency Matrix, a powerful tool for customer segmentation that helps businesses identify and prioritize their most valuable customers. Today, I want to take it a step further by showcasing how this analysis can inform targeted marketing strategies to drive engagement and growth. Strategic Actions Based on the Recency-Frequency Matrix: Champions: These are your top-tier customers who purchase frequently and recently. To maintain their loyalty, consider offering early access to new products or services, implementing a robust loyalty rewards program, and sending highly personalized communications. Loyal Customers: Customers in this segment are consistent buyers but with slightly less frequency. Encourage more frequent purchases through special incentives, reminders of your product or service benefits, and targeted re-engagement campaigns. Needs Attention: These customers have shown steady engagement but may need a prompt to stay active. Reactivation campaigns with tailored offers, requesting feedback, and exclusive deals can help prevent potential churn. Churn Risk: These customers are at risk of disengagement. Win them back with significant incentives, reminders of positive past experiences, and personalized offers designed to reignite their interest in your brand. Already Churned: For customers who have not engaged for a while, occasional check-ins or updates, targeted ads for reintroduction, and a focus on acquiring new customers might be the most efficient use of resources. Leveraging a Recency-Frequency Matrix not only provides a clear view of where your customers stand but also empowers you to implement highly tailored strategies that maximize both engagement and ROI. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Partnerships have a honeymoon period. But you can't build a successful partnership strategy that way. A successful partnership strategy can't survive on starry-eyed excitement. It needs consistent tracking, review, and adjustment. Setting up a routine for regular partnership reviews helps ensure that every partner continues to contribute value and align with your goals. Here’s a straightforward guide to establishing an effective review cadence: DURING MONTHLY CHECK-INS: Monitor Engagement and Pipeline Health: - Partner Engagement: Are partners actively promoting your solutions? Monitor how frequently partners engage, share leads, or collaborate on content. - Pipeline Health: Review the current status of partner-sourced leads. Are they progressing through the pipeline or stalling? This provides a pulse on lead quality and pipeline velocity. (Pro Tip: Use CRM dashboards to quickly visualize monthly trends. A partner falling behind in engagement or lead generation can be flagged for extra support before the issue impacts quarterly goals.) DURING QUARTERLY CHECK-INS (Quarterly Business Reviews or QBRs): Assess KPIs and impact: - Revenue Contribution: Track revenue from partner-sourced leads. Are partners contributing to target revenue goals? Compare this against previous quarters to detect any patterns. - Deal Velocity: Examine the average time for partner-sourced deals to close. Faster deal cycles may indicate strong alignment with your audience, while slower cycles could highlight areas for enablement improvement. - Retention and Renewals: Review retention rates for customers acquired through each partner. Higher retention often suggests the partner is bringing well-aligned, high-value leads. (Pro Tip: Share a summary of the QBR data with the broader team and executives. Keeping everyone informed boosts alignment across departments and reinforces the value of your partnerships.) DURING ANNUAL CHECK-INS (Annual Pipeline Audit): Evaluate & adjust long-term strategy - Trend Analysis: Review metrics like partner-sourced revenue, pipeline growth, and retention over the year. Look for trends that show which partnerships delivered consistent value and which may need reevaluation. - Resource Allocation: Identify high-impact partners and consider how to deepen those relationships. This could mean exclusive training, co-marketing, or more dedicated support to further accelerate growth. - Forecasting and Goal Setting: Use annual metrics to set achievable targets for the coming year. Which partner types or industries contributed the most? (Pro Tip: Use insights from the annual audit to adjust your Ideal Partner Profile and refine your partner strategy. Trends from a full year’s data will guide resource allocation and pinpoint where to focus for maximum impact.) Anything you'd add?

Is the era of the Google search results page fading for discovery? The data suggests a rapid shift is underway. We're seeing a seismic shift in how information is discovered - moving rapidly from keyword searches yielding lists of links towards a conversational AI delivering direct answers (ChatGPT, Perplexity, Google Gemini, etc.). This isn't a slow evolution; it's happening fast. Consider this: Adobe Analytics recently reported a 1200% YoY increase in traffic from gen AI sources to US retail websites. While this is retail data, view it as a leading indicator. This behavioral change - seeing answers, not just links - will ripple across all customer journeys, B2C and B2B alike. I've heard from multiple B2B startups that they are getting more inbound from ChatGPT than Google Search. What does this mean for the customer journey? * Discovery & Search: Buyers won't just browse websites. They'll increasingly ask AI models for comparisons, summaries, and recommendations. * From Answer to Action: This isn't just about information retrieval. With AI agents like OpenAI's Operator, Google's Project Mariner, and Amazon's "Buy with Me", we're seeing the potential for AI to move directly from discovery to research to purchase. The Implications: * For Marketers: The traditional customer journey playbook needs a significant update. How do you ensure your solution is surfaced, understood, and trusted by the AI? Getting discovered in an answer-first world requires new strategies, likely involving structured data on both 1st party and 3rd party sites taking advantage of protocols like the emerging Model Context Protocol (MCP) to effectively communicate to the models. * For Founders: There's an opportunity to build the next generation digital experience platform. We need solutions purpose-built for this new reality across Customer Experience technology categories - consider that in a post AI world the next customer may by AI, which means leveraging conversational interfaces, agent capabilities, and protocols like MCP. It isn't just about new features; it's about rethinking the entire go-to-market motion in an AI-world.

It’s time to stop thinking like it’s 2005. Correlation may flatter your GTM story, but only causation proves impact. More than 80% of companies missed their sales forecast in at least one quarter over the last two years (Gong, 2024). In H1 2024, 49% of companies missed their revenue goals (GTM Partners Benchmark Report, 2024). At the same time, executives keep putting faith in attribution models that only tell a sliver of the story. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: too often, data is interpreted in ways that confirm existing assumptions rather than test them. Harvard Business Review found that sales leaders are frequently blindsided by overinflated forecasts driven by “all-too-human behavior” (Harvard Business Review, 2019). GTM Partners research shows that poor data quality can cost companies up to 25% of annual revenue, yet 60% don’t even measure these costs. That’s value leakage every CFO cares about. It’s time to fix this. Here are 5 ways to make GTM decisions actually data-driven: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗻𝘂𝗹𝗹 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀: Harvard Business Review notes that “consistently accurate sales forecasts are rare because many companies fail to align their sales and marketing departments.” Assume your campaign 𝘸𝘰𝘯’𝘵 work—then try to prove yourself wrong.     2. 𝗥𝘂𝗻 𝗽𝗿𝗼𝗽𝗲𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝘁𝗲𝘀𝘁𝘀: Compare your marketing results to a control group to see the actual lift your efforts create. MIT Sloan warns that confirmation bias leads us to “interpret ambiguous facts in light of preexisting attitudes.” Stop crediting natural growth to your LinkedIn ads.     3. 𝗕𝘂𝗶𝗹𝗱 𝗿𝗲𝗱 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗺𝗮𝗷𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: MIT Sloan recommends bringing together “different perspectives on the same issue” because organizational biases cloud interpretation. Create space for contrarians—the risks of blind spots are too expensive to ignore.     4. 𝗧𝗿𝗮𝗰𝗸 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝙖𝙣𝙙 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Research shows the average B2B buyer has ~31 touchpoints with a brand before deciding (Dreamdata, 2024). Your last-touch attribution is missing most of the story.     5. 𝗣𝗿𝗲-𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀: Record in advance your testing methodology and success criteria. This prevents “analysis after the fact” bias and ensures accountability when results don’t fit expectations. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: If your data never challenges you, it’s not science; it’s storytelling. The companies that break through are the ones willing to let the data argue back. What’s the most obvious confirmation bias you’ve seen in GTM? #GTM #MarketingLeadership #causalinference  

Attribution has never been perfect, but for DTC brands, it has become significantly harder in the past few years. Apple’s iOS14 updates, third-party cookie deprecation, and increased privacy regulations have disrupted traditional attribution models. Brands that once relied on last-click attribution, ad platform reporting, or rule-based LTV calculations now face major blind spots in understanding which marketing efforts drive long-term value. Even those investing in first-party data strategies, post-purchase surveys, and media mix modeling (MMM) struggle to fully connect the dots. The reality is that data is still fragmented across multiple platforms such as Shopify, Klaviyo, Google Analytics, ad networks, and third-party analytics tools. Most solutions focus on aggregating data, but aggregation alone doesn’t tell the full story of how customers move through the funnel and what actually drives retention. Rob Markey - In his article, "Are You Undervaluing Your Customers?" published in the Harvard Business Review, Markey emphasizes the significance of measuring and managing the value of a company's customer base. He advocates for creating systems that prioritize customer relationships to drive sustainable growth. Chip Bell - Recognized as a pioneer in customer journey mapping, Bell has contributed significantly to the field of customer experience. In an interview titled "The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership," he discusses how organizations can co-create with customers to drive innovation and enhance the customer journey. So how do brands solve this? 1. Shift from static LTV models to predictive insights - Traditional LTV calculations are backward-looking, often based on averages that don’t account for future behavior. Predictive analytics, using real-time behavioral and transactional data, can provide a more accurate forecast of customer lifetime value at an individual level. 2. Invest in first-party data strategies that go beyond acquisition - Many brands have adapted to privacy changes by collecting more first-party data, but few are fully leveraging it. Loyalty programs, surveys, and on-site behavioral tracking can provide valuable insights into retention and repeat purchase drivers, helping brands reallocate spend more effectively. 3. Adopt AI-driven segmentation and customer equity scoring - RFM segmentation and standard cohort analysis have limitations. AI-powered models can help identify high-value customers earlier in their lifecycle, predict churn risk, and optimize acquisition based on true long-term value, not just early spend. Markey and Bell have long emphasized that customer loyalty isn’t built on transactions alone, it’s about the entire journey. Brands that can better understand and predict customer value will be the ones that thrive in a world where third-party tracking is no longer a reliable option. #CustomerJourney #Attribution #CustomerEquity