How to Measure Marketing Effectiveness Beyond Last-Click
Let’s conduct a thought experiment. Imagine you saw a TV commercial for a shoe brand last week. Today, during your lunch break, you are scrolling through Instagram and see an ad for the same brand. You don’t click. Tonight, before going to bed, you search for the brand name on Google, click on the search ad, and make a purchase.
Who gets credit for the sale? With a last-click model, it is Google Search. 100% of the credit goes to the final touchpoint. TV? Zero. Instagram? Zero. And yet, without those two touchpoints, the Google search would never have happened.
Why Last-Click Persists (and Why It Is Dangerous)
Last-click has one undeniable merit: it is simple. It works with any analytics tool, requires no modeling, and produces numbers that appear clean and well-defined. But this apparent clarity is precisely the problem.
When a CMO or Head of Marketing allocates budget following a last-click logic, they inevitably end up over-investing in channels that capture existing demand (search, retargeting, brand keywords) and under-investing in those that generate it (TV, video, social awareness, content). The short-term result seems positive: ROAS on conversion channels is high. However, in the medium-to-long term, the funnel narrows, organic demand drops, acquisition costs rise, and everyone will push for new Brand Awareness activities—a scenario we have seen play out many times in the real world.
A Three-Level Framework as a Viable Alternative to Last-Click Only
There is no single tool that replaces last-click for everything. The solution is a multi-level approach, each with a specific role:
Level 1: Marketing Mix Modeling (Strategic Vision)
MMM evaluates the contribution of all channels—online and offline—to total sales. It does not depend on individual tracking, works on aggregated data, and is immune to privacy limitations. It is the right tool for macro-level budget allocation decisions: how much to invest in TV vs. digital vs. promotions.
Level 2: Incrementality Testing (Causal Validation)
Incrementality tests—such as geo-lift experiments—measure the causal effect of a channel or campaign. A group of exposed geographic areas and a control group are selected, and the difference in sales is measured. The result is robust data that is defensible before any CFO: ‘This campaign generated +X% in incremental sales.’
Level 3: Multi-Touch Attribution (Tactical Optimization)
MTA models distribute conversion credit among the digital touchpoints of the customer journey. They remain useful for tactical optimization within the digital ecosystem, provided one accepts that their coverage is partial and subject to tracking limitations.
Triangulation: Making Them Work Together
The real breakthrough occurs when the three levels feed into each other. MMM results define the strategic framework. Incrementality tests validate (or disprove) the model’s results on specific channels. MTA optimizes the tactical execution of the digital plan. At Core Analytics, we systematically integrate MMM and Incrementality Studies with two distinct products: CoreMMM & CoreLift, creating a coherent measurement ecosystem from strategic allocation to operational optimization.
Where to Start
If your team is still using last-click as a compass for budget decisions, the first step is not to change tools: it is to change mindset. The goal is not to have a perfect number for every channel. It is to have a vision accurate enough to make better decisions than the competition.
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