Multi-Touchpoint Analysis

Is your multi-touch attribution data lying to you? Your MT reporting is probably making everything look good. Here's why: Most companies attribute pipeline/revenue to ALL touchpoints from ALL contacts under an account. Then look at the total # and $ value of opportunities influenced. The result? • High-volume channels look amazing (even when they're not) = volume bias • Every marketing activity appears to influence deals =  if everything is working, is anything 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 working? There's a better way to analyze MT data (see image): Look at win rates relative to channel/campaign touchpoints. This strips out volume bias and shows you what's moving deals forward vs generating noise. Example: Paid Search: • Influenced ~1400 deals BUT the average win rate of those deals is 20% C-suite dinner: • Influenced 300 deals BUT the average win rate is 40% If you just looked at total influence, you'd think that the dinners are underperforming paid search. But when you look at influence conversion, it tells you the opposite. Linkedin influencers will tell you MT sucks. But it's more nuanced than that. It's actually the way most companies set up their reports misleads them. We need to be smarter about how we leverage the data. ______________ p.s. also worth saying no attribution model, report, or dashboard will be perfect. Each version has pros/cons and tells a different story. The goal is to leverage multiple methods to help triangulate what is working to help make better decisions going forward.

No oncologist changes practice after one conversation. So why are we still measuring Medical Affairs impact like they do? You can look at claims data and see diagnosis rates go up. But can you ever fully attribute that change to a single Med Edu session… or one field interaction? The truth: MA impact is a multi-touch story. Here’s why. 1. Attribution in healthcare is complex. Population health data like claims, diagnostics, treatment patterns can highlight important shifts. But no oncologist changes practice after just one touch. Expecting “one-touch RoE” sets MA up for failure. 2. Medical Affairs works in a multi-touch education model. A Med Edu program raises awareness of new diagnostic criteria. A field engagement clarifies nuanced biomarker testing protocols. Congress presentations highlight trial data among academic leaders. Publications reinforce adoption pathways. It’s the accumulation of these touches that empowers clinical change. Not one channel. Not one moment. 3. The role of MA is to connect the dots. Instead of over-attributing impact to a single activity, MA should show how integrated evidence across field, Med Edu, and congresses aligns to strategic objectives—and creates the environment for better decisions. Illustrative example: An oncology drug launches with strong academic adoption. At top cancer centers, KOLs quickly integrate biomarker testing and new treatment protocols. But in the community, uptake lags -- oncologists are slower to test, diagnose, and treat. Change doesn’t come from one touchpoint. It happens when: - Field teams educate community oncologists on testing guidelines, - Med Edu sessions provide case-based learning on diagnostic pathways, and - Congress symposia showcase real-world evidence that validates earlier adoption. The result? Claims data eventually shows uplift in testing and diagnosis in community settings. But the impact belongs to the multi-touch work of Medical Affairs, not a single session or interaction. That’s the real story of MA impact: Not anecdotes. Not activity counts. But multi-touch journey that empowers clinical change.

In today’s hyperconnected world, understanding your customers no longer means tracking clicks or counting conversions - it means decoding the full narrative of how people move, decide, and connect across every channel. Customer Journey Analytics turns fragmented data into a unified, behavioral map that reveals the true flow of experience behind every purchase, sign-up, or interaction. Journey analytics follows behavior as it unfolds - how someone discovers a brand on social media, compares options on mobile, signs up through an email, and completes a purchase in-store. Each of these steps reflects both data and intention, and when linked together, they reveal the underlying logic of decision-making. This clarity allows organizations to see where attention drifts, where delight occurs, and where friction stops momentum. At the heart of the practice is journey mapping - the process of visualizing the full customer lifecycle from awareness to advocacy. By combining behavioral data with emotional and contextual signals, teams can understand what customers feel at each stage and design experiences that match those expectations. Touchpoint analysis adds another layer of insight by evaluating which interactions truly drive engagement and which need rethinking. The modern customer journey is fluid. People start on one device, switch to another, and complete their actions elsewhere. Cross-channel optimization connects those pathways, merging data from social, web, mobile, and physical environments. Machine learning models can then detect patterns and predict what happens next, empowering teams to act at the right moment with precision and empathy. Path and attribution analysis refine this even further. Rather than crediting the last click, advanced models assign value across every contributing touchpoint - ads, emails, search, and referral traffic- clarifying which combinations of actions actually lead to conversion or retention. But data alone isn’t enough. The most effective journey analytics strategies blend quantitative patterns with qualitative understanding - surveys, interviews, and sentiment analysis that explain the emotional “why” behind behavioral “what.” A drop-off on a checkout page might be clear in the numbers, but only customer feedback reveals whether it’s caused by confusion, lack of trust, or poor usability. Leading organizations already use journey analytics to bridge this gap between insight and action. Retailers link online behavior to in-store experiences, streaming services personalize recommendations in real time, and airlines trace the entire travel journey to enhance loyalty. Each case demonstrates how connecting data and human understanding reshapes the way companies anticipate needs, reduce friction, and build stronger relationships.

💡 Why is everyone so hooked on First-Touch Attribution (aka “Lead Source”)? Sure, it’s tempting (and easy) to give 100% credit to that first touch that brought in a lead. But if you're only looking at the "Lead Source," you’re probably doing your marketing team a disservice—and missing out on true funnel optimization insights. First-Touch is like a snapshot of your funnel’s entryway. But what about all the steps that happen _after_ that? If you’re not tracking the full journey, you’re missing what actually drives conversions in the middle and bottom of the funnel. ⚠️ What you’re missing with just First-Touch Attribution: - Key touchpoints that nurture leads into actual buyers - A balanced look at which channels deliver conversions at different stages, not just leads - Strategic insights to optimize your channel mix, budget, and targeting 🔍 Enter Multi-Touch Attribution: Instead of a single snapshot, you’re capturing the entire movie. Multi-touch attribution gives credit to all touchpoints, showing how your channels and interactions work together to drive conversions. With a multi-touch approach, you can: - Identify _exactly_ which channels drive meaningful engagement at each funnel stage - Invest where it truly matters by optimizing spend across the journey, not just at the top - Refine targeting to better resonate with prospects from awareness to decision - First-Touch is only part of the story. Get the full picture with Multi-Touch, and watch your marketing (and budget!) become smarter and more impactful. #MarketingOps #RevOps #MultiTouchAttribution #ChannelOptimization

Your customer saw a Google ad. Then an Instagram reel. Then a friend shared your link. Then they Googled your brand. Then they clicked a retargeting ad. Then they bought. So... which touchpoint gets the credit? That's the problem Multi-Touch Attribution (MTA) solves. Instead of giving all the credit to the last click (or the first), MTA distributes credit across every interaction that influenced the purchase. Here's how the most common models work: 𝗟𝗶𝗻𝗲𝗮𝗿 → Every touchpoint gets equal credit. Simple, but treats a random banner ad the same as the demo that closed the deal. 𝗧𝗶𝗺𝗲 𝗗𝗲𝗰𝗮𝘆 → Touchpoints closer to conversion get more credit. Makes sense for short sales cycles, but undervalues the awareness that started it all. 𝗨-𝗦𝗵𝗮𝗽𝗲𝗱 → First touch and last touch get 40% each, the rest share 20%. Rewards both discovery and conversion, but the middle of the funnel gets squeezed. 𝗪-𝗦𝗵𝗮𝗽𝗲𝗱 → First touch, lead creation, and last touch each get 30%, the rest share 10%. Better for longer B2B journeys with a clear consideration stage. 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 → Algorithms assign credit based on actual conversion patterns in your data. Most accurate, but needs volume and clean data to work. Here's what I've learned building and scaling businesses: No model is perfect. The goal isn't mathematical precision; it's making better decisions about where your next dollar goes. Start with a model that matches your sales cycle. Revisit it quarterly. And never let "last-click" thinking kill a channel that's silently filling your top of funnel. The brands winning today aren't the ones with the biggest budgets. They're the ones who understand how their customers actually buy. What attribution model has worked best for your team? I'd love to hear what's actually working in practice. Express Analytics #MultiTouchAttribution #MarketingAttribution #GrowthMarketing

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.

𝗪𝗲 𝗷𝘂𝘀𝘁 𝗰𝗹𝗼𝘀𝗲𝗱 𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗶𝗻𝗯𝗼𝘂𝗻𝗱 𝗱𝗲𝗮𝗹 𝗲𝘃𝗲𝗿—$3B+ ARR, 20,000+ employees. 𝗕𝗿𝗮𝗻𝗱 𝗸𝗲𝘆𝘄𝗼𝗿𝗱 𝗴𝗼𝘁 𝘁𝗵𝗲 𝗰𝗿𝗲𝗱𝗶𝘁, 𝗯𝘂𝘁 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵. When I saw this deal come through on Slack, I was pumped. The last touch attribution said: Brand Keyword. Most B2B companies would stop there, assume the deal came from a Google search, and pour more budget into branded keywords. But here’s the thing: that’s NOT what actually happened. 𝗪𝗵𝗲𝗻 𝗜 𝗱𝘂𝗴 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮, 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗳𝗼𝘂𝗻𝗱: → 21 unidentified visitors from the account → 4 identified visitors with 10+ web visits → 5 visits to our case study page → 1,000+ LinkedIn impressions with 100+ engagements over the past year This deal wasn’t the result of one touchpoint. It was the culmination of countless interactions across multiple channels over time. 𝗬𝗲𝘁, 90% 𝗼𝗳 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝘁𝗶𝗹𝗹 𝗿𝗲𝗹𝘆 𝗼𝗻 𝗳𝗶𝗿𝘀𝘁 𝗼𝗿 𝗹𝗮𝘀𝘁 𝘁𝗼𝘂𝗰𝗵 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻. In 2025, with tighter budgets and growing pressure to deliver more with less, that’s a dangerous game. Because if you don’t see the full buyer journey, you’ll end up misallocating resources—like pumping 90% of your budget into branded keywords while ignoring the touchpoints that actually influenced the deal. Here’s the takeaway: People don’t make decisions because of one touchpoint. They make decisions because of many. The question is: do you have visibility into those touchpoints? What’s your approach to mapping the full buyer journey?

Data in isolation vs. data from multiple touchpoints: the power of a holistic perspective! 5 years ago, we analysed purchase patterns and digital conversations for denim category in India. Since May 1873, men’s denims have dominated the market.  Data suggested that Women were buying male denims. As a single data point, the initial analysis was women were buying for the men in the house However, by connecting insights from retail trends, behavioural data and social media conversations, we discovered that women were buying men’s jeans for themselves due to inconvenient smaller pockets in women's collection. This insight led to the #PowerPockets range with a small design change for Flying Machine, addressing a fashion bias, by introducing denims with deeper pockets for women - an idea that consumers loved. At GIPSI, Tonic Worldwide’s research arm, we connect fragmented data to uncover meaningful insights. Deep listening often leads to strategies that address real consumer needs. Anjali Malthankar #GIPSI #ConsumerInsights #DataMatters #PowerPockets

I just finished reading Flywheel's "The Big Shift" report on redefining ROI with Return on Consumer, and it crystallized something I've been thinking about for months. Here are my key takeaways and what they mean for Amazon advertisers. While ROAS has served us well for immediate conversion optimization, it falls short in identifying and nurturing long-term customer relationships. What's exciting about Amazon's canvas is the quality of identity resolution we can achieve. When customers interact with ads and make purchases, we can connect those touchpoints with much higher confidence than other platforms. This isn't just about tracking sales – it's about understanding the complete customer journey. Amazon Marketing Cloud: A Bridge to the Future The recent expansion of AMC's lookback window to five years is more than just a feature update. It represents a fundamental shift in how brands can understand and activate their customer data. This unprecedented access to purchase history, combined with privacy-safe behavioral insights, allows brands to: • Measure true customer lifetime value • Identify high-potential audience segments • Optimize point of market entry (POME) • Drive sustainable growth through data-driven decisions Beyond Last-Touch Attribution One of the most common conversations I have with advertisers centers around breaking free from last-touch attribution. The reality is that customer journeys are complex and non-linear. With AMC, brands can now see how different touchpoints – from Sponsored Products to Streaming TV – work together to drive both immediate sales and long-term customer value. Real-World Impact The report illustrates this perfectly: a consumer might enter a brand's portfolio with hand soap one year, then purchase detergent and dryer sheets the next year, followed by air fresheners and storage products in the third year. This insight, only possible through long-term customer journey analysis, completely transforms how we should think about acquisition strategy and budget allocation. Looking Ahead • The future belongs to brands that can effectively: • Verticalize their ROI approach within Amazon's canvas • Focus on customer lifetime value rather than individual transactions • Use behavioral signals to fuel sustainable growth • Balance immediate performance with long-term customer value The Question for Advertisers The shift to ROC isn't just about new metrics – it's about fundamentally rethinking how we measure success. Are you still optimizing for short-term ROAS, or are you building for sustainable customer lifetime value? Want to learn more? Read the report: https://lnkd.in/gd2DNBfT Like to listen? Check out the podcast: https://lnkd.in/gPzvS7ci How is your organization adapting to this evolution in measurement and optimization?

“It’s really hard to understand what sourced, what brought people to our product, what we should do more of and less of,” admitted a highly-accomplished 3X CMO from a billion-dollar SaaS company. If this CMO, with a ginormous tech stack and dedicated data analysts, can’t nail attribution, who can? As we enter 2025, perhaps it’s time to reimagine measurement and KPIs. Multi-touch attribution is the false prophet (or profit) of marketing. Multi-touch attribution starts with a solid premise - that B2B purchase journeys are rarely linear or single-stepped. Instead, they meander. They take time. Like 6 to 18 months. Time for lots of folks to weigh in. According to Forrester’s latest research, these journies involve, on average, 13 internal roles and 9 outside influencers (analysts, friends, rating sites, etc.). Let’s just focus on the 13 internal roles. Most of these folks will do their own homework. For simplicity’s sake, let’s assume each visits five web pages. That’s 65 touches. Which of those mattered in the final decision? Was it the PR-driven story in a trade magazine or the carefully cultivated analyst’s rave? Or the buyer’s guide the content team crafted and the SEO team optimized? Arguably, all of them, since any one of them had the potential to derail the decision. Attribution efforts tend to over-simply purchase journeys Because every CMO needs to show that marketing is having an impact, many deploy multi-touch attribution. Noted a SaaS CMO, “We use the data that we have to convince leadership to get on board, but I don’t always trust the data because we tend to overemphasize first-touch.” In other words, the CMO offers the appearance of cause and effect, knowing that it will only tell part of the story. Attribution efforts don’t often help with spending decisions Marketers, like other departments especially at SaaS companies, aspire to be agile. They want to demonstrate that they can adjust spending to changing market conditions with agility. But this isn't easy. As one SaaS CMO shared, “It’s hard for us to be agile when it comes to marketing decision-making since there’s a lag time between action and result.” So, what’s a data-driven marketer to do? Stop promising direct and short-term cause and effect from specific marketing activities. Sure, that’s what investors and the C-suite want to hear, but it’s a trap without an escape hatch. Stop separating budgets, people, and programs into “performance marketing” or “demand generation” and “everything else.” The implication is that everything else is not performing! Instead, reimagine your metrics dashboard and commitments. Make sure you are monitoring brand health (awareness, reputation), customer satisfaction (rating, recommendations, referrals, retention), and brand velocity (opportunities, win rate, deal size). Measure trends rather than absolutes. Look at campaign performance, not channel performance. How will your metrics change in 2025?