Best Practices for Digital Marketing Analytics

I spent over $3M on Meta ads in January. And I use 3 attribution models: Ad platforms are notorious for taking credit for view-through conversions they didn't drive. They do it to bait you into spending more. The issue is that your top 1-2% of ads should drive ~50% of your spend and revenue. If you're relying on bad attribution, you won’t be able to find them. This is why 8-9 figure brands (that NEED their tracking to be faultless), use 3 attribution models: 1. Multi-touch attribution (MTA) - for ad and campaign level optimization. This is your Triple Whale. Great for knowing which ads are performing best, which ones to scale, which to cut. Not as good for comparing channel to channel. It also will overcount total revenue, which you need to be careful about. To make sure your account is well optimized, plot CPA vs Spend on a scatter plot. The top ads should be in the low CPA, high spend zone. 2. Post-purchase survey - for channel level allocation. Get a 35%+ response rate, extrapolate to all new customers, and calculate your cost per new customer response per channel. This tells you which channel to push into. Click-based attribution overvalues lower-funnel performance by up to 250%. Post-purchase surveys catch what click attribution misses - top-of-funnel creative can drive 13X more incremental acquisitions than bottom-of-funnel. 3. Marketing Mix Model (MMM) - for validating direction. You can't use this daily, but it confirms your post-purchase survey is sending you the right way. Then you use post-purchase on a daily basis to optimize channel allocation. Some channels drive low-quality customers that look good on ROAS but don't stick around. MMM helps you optimize for 12-month profit as opposed to just immediate return. The other thing to know is that view-through attribution is poor signal. Make sure your attribution is set up for 7 or 14 day click, depending on your purchase funnel. One day view will overcount. Here's what this gives you: When performance drops, you know exactly where to pull budget to create the smallest impact on revenue while keeping the company profitable. When things are going well, you know exactly where to push budget to scale effectively. Bottom line: -> Use MTA for ads and campaigns.  -> Use post-purchase surveys for channel allocation.  -> Use MMM to validate you're heading the right direction. This is how 8-9 figure brands figure out where every dollar should go.

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.

Google Ads : $180,000 in revenue Meta Ads : $160,000 in revenue Actual company revenue: $220,000 According to your dashboards, your ad channels generated 154% of their total sales. So where did this extra revenue come from? Thin air? This isn't a simple pixel issue. It's the last-click attribution model that's completely broken. A customer sees your Meta ad on Monday, Googles your brand on Tuesday, and clicks a retargeting ad on Wednesday. Both platforms take 100% of the credit for the same purchase. When CACs were low, we could live with this. Today, it's a budget-killer. You're in a planning meeting trying to decide between Google and Meta using data that double-counts everything. This is the exact problem I sat down to discuss with measurement expert Jim Gianoglio on the 10xMarketer Podcast. The brands that are scaling profitably right now have stopped trusting platform dashboards as their source of truth. They're using entirely different, more sophisticated measurement: Marketing Mix Modeling (MMM): A statistical method that looks at all your marketing (paid, organic, email, even offline) and correlates it to your actual sales. It shows you the overlaps and true contribution. Incrementality Testing: Running controlled experiments (like turning off a channel in a specific geo) to see what really happens to sales. This proves if a channel is adding new revenue or just taking credit for sales you would have gotten anyway. It's the only way to move from "Meta claims we made money" to "I can prove that every $1 invested in Meta generated $2.50 in incremental revenue." Here's the uncomfortable truth: If your ad reports suggest you're profitable but your bank account says otherwise, trust your bank account. You don't have a cash flow issue; you have a measurement problem. In our full conversation, Jim and I go deep on how to get started with this. It's time to stop grading the platforms' homework and start building a measurement system you can actually trust. Listen to the full deep-dive with Jim Gianoglio for more details. Link in first comment. How are you handling the attribution crisis in your own team or agency? Let's discuss.

I just reviewed a $50M company's marketing stack and found the same red flag I see everywhere. When I see a B2B company relying almost entirely on Google Analytics for attribution, I know their growth strategy is headed for trouble. Don't get me wrong - Google Analytics is brilliant. It's comprehensive, feature-rich, and lethal for B2C companies tracking quick purchase decisions. But here's where it falls apart for B2B In B2C, a customer sees your ad, clicks, and buys your sneakers in 20 minutes. Google Analytics captures that journey perfectly - from first touch to purchase. In B2B? Your prospect downloads a whitepaper in January, attends your webinar in March, gets nurtured through 47 emails, has 12 sales calls, involves 6 decision-makers, and finally signs in September. Google Analytics sees the whitepaper download and... that's pretty much it. It's like trying to track a 9-month relationship through text messages alone. What B2B attribution needs: → Multi-touch tracking: Every interaction across the entire buyer journey → Account-level insights: Understanding how multiple stakeholders engage → Intent signal detection: Knowing when accounts are in-market → Cross-platform visibility: Connecting LinkedIn, email, events, and sales calls → Long sales cycle mapping: Attribution windows that match your actual sales process Using Google Analytics for B2B attribution is like using a tennis racket in a cricket match - wrong tool, wrong game. Smart B2B companies layer their attribution stack: > Foundation: Dedicated B2B attribution platform (like Factors) > Enhancement: CRM integration for sales activity tracking > Intelligence: Intent data for account prioritization > Validation: Regular attribution model testing and refinement This isn't just about better reporting - it's about understanding which marketing activities drive pipeline and revenue. With economic headwinds, every marketing dollar needs to prove its worth. Companies using tennis-racket attribution are losing deals to competitors with cricket-bat precision. Your sales team deserves to know which leads are ready to buy. Your marketing team deserves credit for the pipeline they're driving. Your board deserves accurate ROI data for marketing investments. Curious about what red flags might be lurking in your attribution setup? I'd love to hear what challenges you're facing with tracking your B2B marketing performance. #GTM #B2B

We inherited a Google Ads account burning £4K/month with zero attribution. Here's what we found. A B2B technology company came to us, spending £4,000 per month on Google Ads. They had been running the same campaigns for 14 months. When we asked what pipeline those campaigns had generated, the answer was: we don't know. Here is what the audit uncovered: 1. 62% of spend was going to broad match keywords that attracted unqualified traffic 2. Landing pages had no clear call to action for enterprise buyers 3. The same ad copy was shown to every visitor regardless of company size, industry, or buying stage 4. No remarketing sequences for accounts that showed initial interest 5. Zero integration between Google Ads data and their sales pipeline The total spend over 14 months: £56,000. The attributable pipeline from that spend: £0 confirmed. Not because Google Ads does not work for B2B. It does. But only when it is built into a system that targets the right accounts and tracks the right outcomes. The ABM Paid Media Restructure (what we built in 30 days): 1. Replaced broad keywords with intent-based search terms mapped to their target account list 2. Built account-specific landing pages with messaging aligned to each stakeholder's priorities 3. Created remarketing sequences triggered by account engagement signals, not just page visits 4. Integrated Google Ads conversion data directly into CRM pipeline stages 5. Set up weekly pipeline attribution reports so every pound of spend was accountable The lesson is consistent across every account I audit. Paid media in B2B is not a lead generation tool. It is a pipeline acceleration tool. And it only works when it is connected to named accounts, personalised messaging, and closed-loop attribution. If your Google Ads or LinkedIn Ads spend cannot be traced to specific pipeline, you have an attribution problem before you have a performance problem. DM me "PAID" and I will run a 15-minute review of your paid media setup and tell you exactly where the leaks are. --------------------------------------------------------------------------- Who am I I'm Lukas, founder of LDS Digital. What I do I help businesses build steady lead and revenue systems. What LDS Digital does We turn interest into real enquiries and booked calls using account-based marketing and AI automation. Who we help B2B operators who want growth without guesswork. The outcome A clearer pipeline, better lead quality, and more predictable revenue. Why this works This approach works because it focuses on fundamentals, clean execution, and systems that keep performing over time. If this resonates, feel free to DM me.

"If I only looked at last-touch attribution, I would have killed everything driving our growth." Kacie Jenkins 🎁 uncovered a scary truth about B2B marketing metrics: Sendoso's best-performing channel is direct website traffic. But traditional attribution missed that those "direct" visitors had already: + Interacted with partners + Opened nurture emails + Seen organic content + Taken a product tour + Engaged at events + Received a gift The pipeline was there. The attribution wasn't. If you saw their multi-touch data, you'd see something fascinating about these "direct" visitors... Most of them had interacted with the exact channels that looked like they were failing. The same channels a finance team would have flagged for cuts. This pattern kept showing up: High-intent buyers were consuming 7-8 different marketing touches. None of them showed up in pipeline reports. Then they'd visit the website directly and convert. Without multi-touch analytics, every investment driving those conversions looked worthless. That's when they made a radical change to their attribution model. The results transformed not just their pipeline reporting, but their entire relationship with finance. Your "worst performing" marketing channels might actually be your best. Most CMOs get forced cut them before they ever find out. If you're looking to transition away from being a lead-gen machine, this is the way.

💡 Everyone's measuring #marketing #performance. But I find that a lot of great marketers are not combining the right #measurement protocols to tell the real story behind the numbers. The pressure to prove #ROI is intense. And yet most teams are either drowning in #data they can't action, or relying on metrics that only tell part of the story. The problem isn't effort, but it may be a lack of experience in using the right framework. There are two models that are essential in a marketer's toolbelt - Marketing Mix Modeling (#MMM) and Multi-Touch Attribution (#MTA). They're not competitors. Frankly they solve different problems and together, they give you a more comprehensive understanding of #marketing performance than either can alone. 🧠 Marketing Mix Modeling (MMM) :: Your top-down view. ✨ It uses aggregated data such as spend, revenue, pricing, seasonality, even external economic factors to model how your entire marketing mix drives business outcomes over time. → Mechanics: Statistical regression across channel-level data, typically requiring 2+ years of historical to be reliable. → Use Case: Annual budget planning, scenario modeling, and measuring channels that are hard to track individually. → Primary Limitation: It won't tell you what's happening in your campaigns right now. It's a strategic lens, not a real-time one. 🧠 Multi-Touch Attribution (MTA) :: A bottom-up analysis. ✨ It tracks individual user journeys across digital touchpoints such as impression, clicks, search, conversions and distributes credit across each interaction. → Mechanics: User-level data stitched together across sessions and platforms to map the path to purchase. → Use Case: Real-time digital campaign optimization, creative testing, and understanding which touchpoints are actually moving people through the funnel. → Primary Limitation: It's increasingly fragile in a privacy-first world, and it systematically undervalues anything offline or upper-funnel. As with any valuable framework, there is great benefit in pairing these two models together in partnership as they truly fact check one another. This is what's called a Unified Marketing Measurement, using MMM to set your strategy and allocate budgets at a macro level, while MTA helps you optimize the execution of your digital campaigns week to week. MMM tells you where to invest. MTA tells you how it's performing. One gives you the long-term baseline. The other gives you real-time signal against it. It may sound like a lot, but it doesn't have to be. Start with the #analysis that fits your immediate need and build the other alongside it. Let them inform each other over time. Marketing measurement doesn't need to be perfect from day one. It just needs to be pointed in the right direction. Are you using one, both, or something else entirely? I'd love to hear how your team is approaching measurement right now. #MarketingMeasurement #MMM #MTA #MediaMix #MarketingAnalytics #DataDrivenMarketing

Last year, I asked 200+ marketers one question: "How many of you believe there's a single source of truth that tells you everything about your marketing performance across all channels?" Almost no hands went up. They're right to be skeptical. If there is one thing that I've taken away from managing $2B+ in total marketing budgets, it’s that every measurement method tells a different story. - Google Analytics says one thing.  - Ad platforms say another.  - MMM disagrees with both. And the problem gets worse. - Third-party cookies might disappear. - iOS tracking is limited. - AI bidding systems are black boxes. We're facing unknown returns on ~$700 billion in annual digital ad spend. The result: stakeholders get puzzled and frustrated because they don't know what the real ROI of your marketing campaigns is. The answer I found most effective is not to rely on the 1 “perfect" methodology. It's triangulation - getting 3 methodologies working together in orchestration: Lift Testing - the gold standard for establishing causality. Run geo-experiments on the biggest channels to understand what conversions you wouldn't get without advertising. Statistically accurate and privacy-proof, but difficult to scale with opportunity costs. Marketing Mix Modeling - holistic regression linking all inputs to outputs. Captures seasonality, promotions, pricing, offline media, competitor activity. Gives you baseline conversions if you stopped all marketing. Privacy-proof but limited granularity. Multi-Touch Attribution - user-level methodology tracking touchpoints in conversion paths. Real-time and granular for daily optimization. But myopic - only sees UTM-tracked visits, misses offline marketing, inflates attribution on bottom-of-funnel channels. This is what combining them looks like: - Start with a Bayesian MMM as your absolute framework. - Inject your lift test results as prior knowledge to calibrate the model - this ensures your MMM output stays grounded in reality instead of producing unrealistic attribution from finding a local optimum. - Then layer MTA underneath on a relative basis. Take your realistic channel-level results from the calibrated MMM and use MTA to break them down to campaign-level granularity or below within each channel. This gives you a complete picture: holistic insights for strategic budget allocation and granular data for daily optimization decisions. Most companies pick one methodology and force it to answer every question. That's like using a hammer for surgery. Each method has blind spots. Together, triangulation fulfills most of your marketer needs. I've seen this approach work across industries through my work at Rocket Internet and with @Growth Vision Partners clients. The single source of truth is still a myth, and there is no silver bullet. But triangulation gets you as close as you’ll ever get to knowing your marketing’s real ROI.

I’m surprised we are still here. So many marketing measurement conversations still collapse into: “What channel did this deal come from?” “Did sales source it or did marketing?” That is a one-dimensional lens on a multi-dimensional system. Enterprise buying journeys are long, non-linear, and 75% complete before a buyer ever speaks to sales. Dark social is real. Off-platform influence is real. Multiple stakeholders shape the outcome long before an opportunity is created. Yet, I'm learning there are still so many organizations are still trying to force that complexity into a single attribution answer. Here is how I approach it. 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐞𝐱𝐢𝐬𝐭𝐬 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫 𝐭𝐰𝐨 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: 1. What is working and why? 2. What is not working, and what are we doing about it? That's it. 𝐈 𝐠𝐞𝐭 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐨𝐧 𝐚 𝐟𝐞𝐰 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐮𝐩𝐟𝐫𝐨𝐧𝐭: 1. All channels matter. 2. No single model tells the full story. 3. First-touch, last-touch, and multi-touch each have specific use cases. 4. Attribution in enterprise GTM can suggest influence. It cannot prove causality. Without that alignment, every deal becomes a courtroom. Marketing vs sales. Brand vs demand. Channel vs channel. Having a shared understanding of this helps us make decisions based on directional truth, not perfect certainty. 𝐓𝐡𝐞𝐧 𝐜𝐨𝐦𝐞𝐬 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦 𝐥𝐚𝐲𝐞𝐫. I share a clear playbook: 1. What signals we care about and why. 2. When to trust system-level reporting. 3. When to zoom into individual accounts and buyer behavior. 4. Why attribution responsibility is shared across everyone carrying a quota, not just marketing. The goal is more confident judgement, smarter risks and quicker growth. Advanced cross-platform measurement absolutely helps. The ability to understand exposure across environments, link perception to behavior, and see influence before form fill changes the strategic conversation. If you have a good system in place for this, you probably feel like you've found the holy grail 😅 But even without sophisticated tooling, the mindset is available to any team. Assume the journey is complex. Treat attribution as a decision-support system. Optimize for probability, not credit. 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐰𝐡𝐨 𝐬𝐨𝐮𝐫𝐜𝐞𝐝 𝐭𝐡𝐞 𝐝𝐞𝐚𝐥. 𝐈𝐭 𝐢𝐬 𝐰𝐡𝐞𝐭𝐡𝐞𝐫 𝐲𝐨𝐮𝐫 𝐠𝐨-𝐭𝐨-𝐦𝐚𝐫𝐤𝐞𝐭 𝐬𝐲𝐬𝐭𝐞𝐦 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭𝐥𝐲 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐬 𝐭𝐡𝐞 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐝𝐞𝐚𝐥𝐬 𝐜𝐥𝐨𝐬𝐢𝐧𝐠. If we keep asking the wrong measurement question, we will keep optimizing the wrong parts of the system.

Let's talk HubSpot attribution. I've been surprised to see how many partners get this wrong. The number of times I'm asking to build attribution reporting for clients only to discover poor integrations and setup that prevent us from doing so is high at this point. Here is what I see regularly: 📞 APIs set up without passing the Hubspotutk cookie- if you're using an API and not HubSpot forms, necessary for many organizations, it's critical this cookie is passed. Otherwise HubSpot will not populate Original Traffic Source, Latest Traffic Source, Original Traffic Source Drilldown 1 & 2, Latest Traffic Source Drilldown 1 & 2... and essentially the majority of fields in the Web Analytics History section of a Contact record. Let me be clear: UTMs do NOT replace the cookie and are NOT interchangeable. We track both, as there are instances where one can track without the other, but they are NOT the same value and don't populate the same data. Which leads me to... 🔢 UTM data setup and tracking with GTM- Simply passing through UTM data into hidden fields with an API pass or form submission isn't enough. In GTM, you need to set up your UTMs to persist, or with every page change you will lose your UTM data immediately and the data will show as direct instead. If you want to know the UTM data and source from their first page visit, this is critical to getting that data accurate. AND if part or all of your site is SPA (Single Page Application), you need to change UTM data from tracking page changes to history changes since those sites don't have page changes function normally. 📈 Understanding attribution is still messy- even with tracking this as accurately as possible, you will have messy data. Users returning on different devices (more common depending on industry), visitors using cookie or ad blockers (also more common depending on industry), and cookie banners on your site (which allow rejecting cookies more often) can all make your data more messy. Measuring marketing success has to be big picture and look holistically today, but missing out on the data we CAN gather is merely an issue of poor knowledge and implementation. Setting orgs up for success to gather this critical information in understanding marketing campaign efficacy is the foundation all your future marketing will rest on. If an agency doesn't understand what data this populates and doesn't prioritize this data, you need to evaluate how well they understand your needs. #marketingattribution #hubspot #marketinganalytics