Attribution is HARD AF; but doesn't make it any less impactful

Marketing attribution is like having a secret recipe that makes the best dish ever, but no one believes it until they take a bite. They’ll think you’re full of BS because it is a stat the can be spun however way you want it. You can back into the answer you’re looking for. That’s what makes it lose its credibility to key decision makers. Yet without it, I’d challenge you to be able to consistently build a meaningful marketing machine. You need the data to make persuasive arguments to your peers on why you should resource share. You need the data to make compelling investment theses in order to secure proper discretionary budgets.

But it is so darn challenging. It’s shake-your-fist-to-the-sky type of maddening.

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Why Attribution Matters

Attribution connects marketing efforts to revenue outcomes by identifying which channels and touchpoints drive conversions. For B2B organizations with 6-9 month sales cycles, it reveals how early-stage activities (e.g., LinkedIn ads, blog posts) contribute to deals closed months later. This visibility enables:

• ROI Optimization: Allocate budgets to high-performing campaigns.

• Cross-Team Alignment: Reduce friction between sales and marketing by clarifying lead sources.

• Accurate Forecasting: Understand how today’s efforts impact future revenue.

Without attribution, companies risk misjudging channel effectiveness or undervaluing brand-building activities.

Why Attribution Is Difficult to Measure

1. Complex Customer Journeys
Buyers interact with multiple touchpoints (ads, content, events) across devices and channels, creating a "messy" path to purchase. No model perfectly captures nonlinear behavior.

2. Data Limitations

• Offline Conversions: In-person events or word-of-mouth referrals often go untracked. Hello dark social!

• Privacy Constraints: Regulations limit cross-platform tracking.

3. Statistical Calibration
Marketing Operations can improve accuracy using regression analysis to weigh lead-scoring factors (e.g., webinar attendance, whitepaper downloads). For example:

• Metrics like R Square (model fit) and P-values (significance) help prioritize behaviors correlated with conversions

• Regular calibration ensures scores reflect evolving buyer patterns, though even robust models have gaps.

But let’s be frank. Raise your hands if you’re in Markerting Operations and you are really handy with statistical analysis!

Not saying that stats is the answer either. One of my good friends was on a data science team at Target Corp. Over drinks she told me that bananas were correlated with most purchase baskets. Correlation doesn’t mean causation comes to mind. I’m thinking great! Let’s just place bananas everywhere and you’ll purchases up.

I’m sure attribution works the same way. The most frequented touchpoint cannot possibly be the cause of purchase, but it does say something about how important it is to have that touchpoint or bananas in the store.

Don’t sleep on bananas. They’re low key the MVP of retail.

Why Attribution Is Hard to Articulate

• Model Bias: Single-touch models (e.g., last-click) oversimplify, while multi-touch models overwhelm stakeholders with complexity.

• Internal Politics: Teams often debate "credit" for leads, especially when quotas and commissions are at stake.

• Directional vs. Definitive: Attribution highlights trends (e.g., “LinkedIn drives pipeline”) but rarely offers absolute answers.

Explaining Attribution’s Impact by Persona

Depending who you’re talking to within the business you may find our point not coming across confidently. This is why I suggest preparing a reframing of attribution by persona. Here’s how you might consider doing so.

So many attribution models, so little time

Lead attribution models are essential for understanding which marketing efforts drive conversions, but their implementation varies widely. Here's a breakdown of key model types:

Attribution models come in two forms: single touch vs multi-touch. For single touch there are two models:

• First touch

• Last touch

First Touch

In a first touch model the Credit Distribution goes 100% credit to the initial interaction. This is often best for short sales cycles, lead generation. The downside of this model is that it ignores the impact of mid and late-stage touchpoints, leading to an incomplete view of the buyer's journey.

Last-Touch

In a last touch model the Credit Distribution goes 100% credit to the final interaction. This is often best for bottom-funnel optimization. The downside of this model is that it overlooks the importance of early-stage interactions that contribute to customer engagement and nurturing.

For the multi-touch models there are several you may look at but the most common are linear, time decay, U-shaped, and W-shaped.

Linear

In a linear touch model the Credit Distribution is equal credit across all touchpoints. This is often used for a simplistic, holistic view. I think it’s downright lazy but that’s just me. Because this model treats all touchpoints as equally valuable, it may not accurately reflect their true impact on conversions.

Time Decay

In a time decay model the Credit Distribution gives more credit to recent interactions. This is often best for long sales cycles, late-stage influence. The disadvantage of this model is that it undervalues early interactions that might have played a crucial role in initial awareness and engagement.

Position-Based (U-Shaped)

In a U-shaped model the Credit Distribution might look this: 40% first/last touch, 20% middle. This is often best for balancing awareness/conversion focus. The disadvantage of this model is that it assumes first and last touchpoints are most critical, potentially under-crediting valuable middle interactions.

W-Shaped

In a W-shaped model the Credit Distribution might look this: 30% first, lead, and opportunity touchpoints. This is often best for mid-funnel emphasis (e.g., lead qualification) The disadvantage of this model is that it overemphasizes specific touchpoints while potentially missing other influential interactions in the journey..

Insane right? No matter which model you choose you’re both right and wrong.

Risks of Over-Indexing on One Model

It’s little wonder that believing an exact ROI on marketing is hard to believe. Depending on which model you select, you downplay certain touchpoints while highlighting others. If I was a CFO my head would be spinning.

• Last-Touch Bias: Overvaluing final interactions risks underfunding top-of-funnel efforts (e.g., SEO, content).

• First-Touch Blindness: Ignoring mid-funnel nurturing (e.g., email follow-ups) that move leads toward closure.

• Multi-Touch Complexity: Overly granular models may stall decisions due to analysis paralysis.

• Pragmatic Approach: Use a hybrid model (e.g., 40% first-touch, 30% mid-funnel, 30% last-touch) to balance simplicity and insight. Regularly review with sales to validate assumptions.

Take the time to do the analysis and add value as a strategic partner