Decision
Anton Buchner, Founder, Front Foot Marketing Last updated 8 August 2026
What happens when the numbers disagree?
The question behind the question
Which number is right?
What can each number actually tell us, and what can't it?
The straight answer
Don't automatically believe the number that looks most precise.
Marketing attribution says one thing. Sales says another. Customer research says something else. Finance sees no obvious margin improvement. The dashboard says performance is up. The business says it doesn't feel like it.
The deeper answer
This is where judgement matters.
Different evidence answers different questions. Attribution can help explain observed journeys. Customer research can explain motivations. Sales data can show commercial behaviour. Financial data can show economic consequences. None of them necessarily tells the whole story.
The answer isn't to pick your favourite dataset. It's to understand what each piece of evidence can, and can't, tell you.
The Customer Value Diagnostic and its companion, the Customer Value Risk Index, exist for exactly this reason, to give you a structured read that sits alongside the numbers you already have, not instead of them.
The uncomfortable bit
The number your team is most confident in is often the number built with the fewest assumptions questioned, not the fewest assumptions made.
What we know
- What each individual source, attribution, sales, research, finance, is currently reporting
- Roughly how each of those numbers is built
What we don't
- Which source is actually closest to the truth for this specific question
- Whether the disagreement is a measurement problem or a real signal
What we need to decide anyway: Which evidence to weight most heavily for this particular call, and why that one.
What I'd challenge
I'd challenge the instinct to trust the most precise-looking number. Precision and accuracy aren't the same thing, and attribution tools are very good at being precisely wrong.
The Anton Test
Which of these numbers would I trust with my own money, and why that one specifically?
What I'd do next
I'd ask what specific question each dataset was actually built to answer, then use the one built to answer the question I'm asking, not the one that sounds most confident.
The decision
This is ultimately a decision about which evidence to trust for this specific call.
This connects to the broader decision on Marketing effectiveness, if you want the fuller picture.
Keep asking
Related answers
Decision
How much evidence is enough?
You rarely get perfect information. The goal isn't certainty. It's confidence good enough to act.
Effectiveness
What should our marketing dashboard actually tell us?
A dashboard should help you make a decision. If it doesn't, it's probably just reporting.
Value
Are we growing the right customers?
Not every customer is equally valuable, and not every source of growth deserves more investment.
Still working out what the real question is?
Sometimes the hardest part isn't finding the answer. It's knowing which question to ask.