Touchpoint Effectiveness Measurement

Brand marketing used to make me nervous. But then I found a way to show it led to 3x pipeline. Here’s how: As a data-driven marketer, I always leaned into performance: ✅ ROI is clearer ✅ Attribution is cleaner ✅ You can scale what works Brand? Not so much. Even though I knew brand mattered (as a consumer), I struggled to prove it as a marketer. 💡 How much should you invest? 💡 When do you scale back or double down? 💡 What even counts as a win? At my last startup, I made it my mission to crack the code — to measure brand like a performance channel. And I figured out a way that even made our CFO smile. Here are 5 principles that helped us get there: 1️⃣ Treat brand like performance — by running experiments. Just like paid campaigns, brand efforts need hypotheses, tests, and measurement. Even if you believe brand marketing works, that’s not enough — you need to answer: → How much should I invest? → What’s the expected impact? 2️⃣ Build your target audience. Create a list of potential buyers who share similar attributes: • Job titles • Seniority • Industry • Company size Use your CRM, LinkedIn, or a custom audience list. 3️⃣ Split that audience into test + control groups. It doesn’t have to be 50/50 — just ensure both groups are large enough and evenly matched. 🔁 Pro tip: If you’re targeting multiple people at the same company, keep them in the same group to avoid cross-contamination. 4️⃣ Expose the test group to your brand campaigns. This is the fun part: • LinkedIn ads • Newsletter sends • YouTube pre-rolls Keep your messaging focused on the problems you solve. And remember: No CTAs needed. You’re not measuring clicks — you’re measuring impact. 5️⃣ Track lift in pipeline — not attribution. Run your campaign for 30–90 days. Then compare pipeline creation between your control and test groups. 🛑 Ignore CRM attribution — your brand touchpoints won’t show up there. Instead, measure the difference in pipeline. That’s your brand lift. That's it. Here's how it looked for us: We ran a LinkedIn brand campaign for 3 months. • The control group saw nothing • The test group was targeted with brand campaigns and thought-leadership content including customer quotes and case studies • Attribution said “organic” and “direct” were driving pipeline But when we compared the groups in our CRM, we saw a 3x lift in pipeline from those who were exposed to the campaign. Once I showed the data to our CFO, she immediately greenlit more brand investment. Brand isn’t fluff. It’s just harder to measure — until you treat it like performance. Curious to try this at your startup? Want templates or more detail? 👇 Drop a comment — happy to share. #B2BMarketing #BrandMarketing #StartupGrowth #DemandGen

Your donor just gave $100 after clicking your #GoogleAd. So Google Ads deserve all the credit? (Maybe you stop here and report the good news if you're the person running the ads ;) Let's talk about the reality of digital impact measurement. That $100 donation? It's actually the result of: • 3 Instagram post views • 2 email newsletter opens • 1 word-of-mouth conversation • 4 website visits • And finally, that Google Ad click But most #ga4 analytics only show you the last touch out-of-box. At Whole Whale, we've analyzed millions in nonprofit donations across 100+ organizations, and here's what we know: The average donor needs 7-13 meaningful touchpoints before converting over a fix period. This is why we help nonprofits build holistic digital strategies that: ✓ Track the full donor journey ✓ Value each marketing channel appropriately ✓ Optimize based on real impact (not just last-click data) ✓ Build meaningful, long-term engagement Because when you understand true attribution, you make better decisions. And better decisions = bigger impact. Don't let the last-click fallacy sink your marketing strategy. Want to dive deeper into multi-touch attribution? Let's talk. #analytics #attribution

𝗜𝗱𝗲𝗮 #𝟭𝟯:  𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆:  𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆, 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻, 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗽𝗿𝗼𝗳𝗶𝘁. Business leaders are increasing their focus on understanding and improving the profitability of digital marketing. But too often this leads to an overly simplistic diagnosis.  There are four critical questions to answer to truly understand marketing performance. 1. Incrementality. Did the sale happen 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 of your marketing?  Big brands should expect to get lots of traffic and orders “for free”.  Much online marketing acts as paid navigation for orders you would have got anyway. For example, bidding on brand-related terms on Google will typically look profitable but may or may not be incremental. Tip:  Always evaluate brand-related keywords independently of generics. And look at the mix of customers acquired via different sources (a high mix of existing customers is not a good sign).   2. Attribution. How should we credit each touchpoint on the customer journey?  The typical online order has multiple marketing touches. The challenge is to allocate credit for each order across the touchpoints based on their relative influence.  Assigning all credit to the last touch will erode efficiency over time.  But relying on a single “magic” black box attribution model will give a false sense of certainty.  Tip:  Understand the sensitivity of marketing decisions to different attribution logic.   Always look at last touch, first touch and any touch to truly understand where a particular marketing source fits into the journey.  3. Productivity. Is each dollar working as hard as possible? Every channel gives significant (and different) scope for optimisation:  bidding logic, targeting, modifiers, advertising copy, call to action, landing pages etc.  It’s critical to ensure that adverts are well optimised before concluding that they are unprofitable.  Tip: look at the site funnel by marketing source and slice by actionable dimension (device, browser, geo, day part etc.) to understand the opportunities for optimisation.  4. Profitability.  What is the business objective? ROAS (sales/ad spend) is easy to measure but is often misleading. It ignores cost of goods, cost to serve, sales tax, returns, cancellations etc. so strong ROAS can mask weak profitability.  And aggregate ROAS also hides poor marginal ROAS —the last dollar spent may actually destroy value.  It’s important to define a marketing objective that aligns to business value - are you focused on acquiring profitable transactions, leads which may turn into customers, or new customers whose value will increase over time.  See table for examples of different performance archetypes.  𝗧𝗶𝗽:  Be deliberate in defining your marketing objective. Always measure both averages and distributions, and remember that scaling spend inevitably means trade-offs of growth, profit, cash payback and risk.

STOP evaluating channels in isolation. This is the biggest mistake I see brands making today - judging each marketing channel by its own metrics without understanding how they interact. That’s why we've developed a Total Business Framework that completely transforms how we measure marketing effectiveness. Here's how it works →  When a customer sees your TikTok ad, searches your brand on Google, clicks a shopping ad, but doesn't purchase... then later clicks an email and buys - who gets credit? In most attribution systems, only the email. But that's not the full story. Our framework tracks how Meta, Google, TikTok, and your organic channels interact throughout the entire customer journey. It de-duplicates conversions and creates a holistic view of your marketing ecosystem by: Setting business-level targets first Instead of starting with "What ROAS do we need on Facebook?" we ask "What total revenue do we need to generate this month?" Then, we work backward to determine each channel's contribution. Measuring cross-channel impact We've observed consistent patterns: when you scale paid social, you typically see corresponding increases in email performance, direct traffic growth, and branded search volume. These aren't coincidences - they're predictable interactions. De-duplicating conversion path Using first and last-touch attribution models creates massive blind spots. Our framework uses multi-touch attribution that weights each touchpoint appropriately based on its position in the funnel. This approach has helped brands understand the true ROI of their marketing investments. Some discover that platforms performing "below target" in isolation are actually driving significant revenue through other channels. Others identify underperforming channels that look good on paper but aren't contributing to overall business growth. The framework helps us set monthly goals for EVERY channel, not just the ones we manage. This ensures the entire business grows synergistically - paid drives awareness, email captures leads, SMS converts sales, and retention strategies maximize LTV. In today's fragmented customer journey, looking at channels in isolation is like trying to understand a movie by watching one scene. You need the complete picture to make smart decisions.

Last-touch attribution is the worst way to measure LinkedIn Ads performance: → It favours less effective channels → Paints an incomplete picture → Leads to poor strategy decisions Here's the best way to do it (the exact framework I use): — 1️⃣ 𝗨𝘀𝗲 𝗮 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗼𝗿 𝗵𝘆𝗯𝗿𝗶𝗱 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 Data-driven attribution assigns variable weights to touch points: ↳ First & last get the highest weight ↳ The rest are distributed evenly Example: Imagine there are 10 touchpoints before a sale. A data-driven attribution model would allocate: → 30% credit to the first touchpoint → 30% credit to the last touchpoint → 40% credit to the other 8 You can use an attribution tool like Dreamdata to easily measure how each touchpoint influences pipeline. 𝘏𝘺𝘣𝘳𝘪𝘥 𝘢𝘵𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯 𝘪𝘴 𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯 𝘵𝘶𝘳𝘣𝘰-𝘤𝘩𝘢𝘳𝘨𝘦𝘥. Hybrid attribution means adding self-reported attribution to the mix. It's super easy too, you just ask prospects which channels influenced their decision. This ensures you don't inadvertently miss hard to measure channels like: → organic social media → in-person events → podcasts Add the self-reported touchpoints to contacts in your CRM. This way your attribution software will show a more complete performance picture. — Now your tracking's sorted, you need the right KPIs. I recommend separate KPIs for demand generation and demand capture activity. This helps you understand marketing's impact on out-market and in-market prospects. Both are important. 2️⃣ 𝗠𝗲𝗮𝘀𝘂𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Reach & frequency: ↳ 80% of your target audience ↳ With a 10-20 frequency Engaged accounts: ↳ Increase # of engaged accounts ↳ Aim for a 5-10% increase month-on-month Ad engagement: ↳ Aim for a 0.6% CTR minimum ↳ Engagement rate should be higher Low-intent leads: ↳ Identify content with higher CVRs ↳ Double-down on this ↳ Eliminate less efficient topics and types 3️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 Demos: ↳ Demos are high intent ↳ Track them separate from leads ↳ Expect them to cost 5-10X more Revenue KPIs: ↳ Influenced pipeline ↳ Closed/won ↳ CAC ↳ Close rate ↳ Sales cycle length Set baselines, then aim to increase pipeline, closed/won, and close rate while decreasing CAC and sales cycle length. — Remember, you can't accurately measure a marketing program based on one touchpoint. Switch to data-driven, hybrid attribution and use full funnel KPIs to set yourself up for success in 2025. 🤘 — ♻️ Like, comment, and repost to help out another marketer. Hit follow for more.

For years, marketers have been forced to analyze performance in silos—evaluating Facebook in Ads Manager, Google in GA, TV through post-campaign lift reports. Each platform tells a different story, leaving teams to stitch together a fragmented view of performance. The problem? Siloed measurement doesn’t reflect how consumers actually move through the funnel. A purchase isn’t usually the result of a single channel—it’s the product of multiple touchpoints working together. Relying on platform-specific attribution ignores this complexity, leading to misallocated budgets and missed opportunities. This is where unified measurement comes in. By combining methodologies like Multi-Touch Attribution (MTA), Marketing Mix Modeling (MMM), and incrementality testing, marketers can move beyond siloed analysis and see the full picture. A unified approach ensures: -More accurate decision-making—by accounting for both granular, user-level data and broader, market-level trends. -Better budget allocation—understanding the true impact of each channel instead of over-relying on the last-click or individual platform metrics. -More trust in marketing data—giving finance and leadership a clear, consistent framework for investment decisions. The days of optimizing channels in isolation are over. Marketers who embrace unified measurement gain the clarity and confidence needed to drive real business outcomes. How is your team thinking about breaking down silos in measurement?