Understanding the Role of Attribution in Ecommerce Analytics

Revenue attribution is not about marketing taking credit for sales. It’s about accountability and its worth investing in. There’s been so much noise about marketing attribution – whether it’s “sourced,” “influenced,” or “contributed.” And way too many influencers suggesting that marketers should not even try to attribute revenue to campaigns/programs/tactics. But here’s the real issue: the problem isn’t the effort, technology, the processes, or even tracking mechanisms. Attribution, at its core, is a critical tool for marketers. It’s not about taking credit for a sale. It’s about helping us understand whether our investments are performing and having an impact. Every year, marketing leaders manage an investment portfolio: people, time, tech, data, and programs. We operate like business leaders—making strategic decisions about where to put our resources. Attribution is meant to show whether or not those investments made an impact. Did the investment make it easier for sales to close more deals faster? What would happen if we didn’t continue or repeat the investment? Here’s where it gets tricky: Too often, we drag attribution into executive meetings and misrepresent its role. Marketers—stop positioning attribution as your ticket to taking credit. Instead, use it for what it’s meant for: to know what’s working and what’s not, so you can make better decisions next time period / next year. My Advice for Marketing Leaders: 1. When you talk about attribution, make it clear: you’re not trying to claim credit. Your goal is to ensure that your investments are making sales’ job easier. 2. Use attribution as an internal marketing tool to figure out which strategies are working, which ones aren’t, and where to double down. Ask yourself, “If I don’t do this next year, will it make a difference?” 3. Be a business leader, not just a marketer. Attribution helps you answer many questions: Did this investment accomplish its goal (audience growth, building trust or driving engagement, monetizing engaged audiences, etc)? Did the dollars you spent actually contribute to the business? If not, reallocate the funds. 4. There is NO such thing as one perfect attribution model - the perfection is having an infrastructure that supports multiple models, enabling you to choose the right model for the question you’re trying to answer. If the sales team resists attribution, ask them this: If I can’t track the impact of my efforts, why should I keep investing in them? If we’re not seeing results, I’ll stop investing in those areas so we can invest in strategies that will help them sell more, faster. Let’s change the way we talk about Revenue attribution. It’s not about taking credit. It’s about managing marketing like a business. At the end of the day, it’s simple: Attribution helps marketers ensure their investments deliver impact. If something isn’t working, we need to know so we can pivot. And if it is? We double down. #cmo #b2b #revenueattribution

If you make large marketing strategic and budget decisions based on one attribution view, you will miss or cut critical work contributing to revenue. Attribution models are meant to be guides, not deciding factors. Yet, we see them as the end-all-be-all and the answer to our questions to decide. The marketing (and GTM) world has long debated the right attribution model. First touch. Last touch. Conversion touch. Multi-touch. Time decay. W-shaped. Custom weighted. (The list goes on.) None of these will be the right model by themselves, nor will they ever give you 100% of the right answer. ✨The magic happens when you use multiple, depending on the answer you’re looking to solve. ✨✨And the even bigger performance magic happens when you *consult* the data, with multiple views, to make an educated decision instead of trying to be “data-driven” off singular views. Disclaimer: I’m NOT saying attribution is bad; you shouldn’t use it, or you shouldn’t be making data-informed decisions. The power of marketing comes from the fundamentals: reaching the right audience with the right message and creating a memorable experience so the audience purchases (hopefully again and again). Some interactions will never be seen in tracking, regardless of all the sophisticated martech software. And these interactions need to happen frequently before most marketing data points even show up. I know I’m preaching to the choir here, so what’s the solution? 1. Change your mindset to outline data discovery questions vs. finding one view 2. Use the right attribution model to answer the question you’re looking to solve. ● What brings our audience to us for the first time? ⋆ First touch ● How does our audience remember us for the first time? ⋆ Self-reported/sales self-reported/surveys ● What trackable actions happen in the buying journey? ⋆ Multi-touch (etc.) 3. Be able to compile all these different data points, analyze them, and make *educated* decisions based on them. 4. Have program-level performance tracking 5. Know when to look at performance in a micro and macro view. 6. Be able to answer the CEO/board questions of ● How much revenue do we generate for every $1 marketing spend? ● How efficient are we? ● If we spend X, what can we expect? ● How much marketing fuel do we need to hit our growth goal of X? ● How is marketing doing? ● What’s working? What should we do more of? ● What’s not working? What should we do less of? ● What are we learning from marketing for the overall business? ● How can we help? 7. Be able to coach and educate your team on how you grade performance, analyze the data, view attribution, and how you make your strategic/budget decisions. 8. Accept it will never be 100% a mathematical equation, and marketing is equal parts science and math - this type of analysis takes business acumen, marketing knowledge, and experience. At the end of the day, attribution models are all about gathering insights.

Many people get 'tipping point' attribution wrong. Here's why: They think it's how you perfectly assign marketing attribution credit. They think it's trying to be a perfect model to measure every type of marketing activity. (Note: 'tipping point' attribution is tagging what the signal/source was that triggered sales to engage with the buyer. Could be a demo request, account-based signal, partner lead, a specific outbound sales play, etc. It's NOT what most people think of as first touch or last touch or multi-touch) Reporting on the tipping point is best at understanding what you're sending to sales and if it's worth their time and worth investing more into.   And "worth their time" means are these sources/signals turning into meetings/pipeline/revenue. You send 1000 demo requests to sales and 80% convert into deals You get sales to follow up on 500 event attendees and 20% convert into deals Sales follows up on account intent data and prospects into 1000 buyers and 5% convert into deals You get 100 partner referrals and 30% convert into deals This is helpful data to dig into to try and understand what sales should be focused on to make them more efficient. And what marketing can do better to drive the right conversations. The goal isn't to prove marketing's value across the full buyer's journey. The journey is too complex for one model to do it all. The key is to know the model's strengths and weaknesses. And tipping point can: Identify which signals are worth sales' time Stop wasting sales' time on low-converting activities Scale what is working at this stage of the buying journey