Talk me through your 2026 marketing plans
We’re deep in planning season, and it’s time to define what data initiatives are needed to make your marketing ideas a reality.
Every year around this time, someone on the team created a “2026 Planning” Google Doc and suddenly all the ideas that felt exciting in July show up at once.
Maybe this is the year you finally test out-of-home.
Or build a real ABM program.
Or scale Meta more aggressively.
Or give Google’s AI Max a try.
Or increase your experimentation velocity across creative, channels and audiences.
All totally reasonable things to want. But here’s the thing I see over and over:
The marketing plan gets written long before anyone maps what needs to happen in the data layer to support it.
That’s usually when problems appear. Not because the initiative was wrong, but because the tracking or measurement foundation wasn’t ready for it.
A few examples I’ve seen:
Testing OOH without a way to measure lift or model the impact
Scaling Meta when conversion signals aren’t attributing enough to support optimization
Running AI-led Google campaigns (either PMax or AI Max) without signal engineering/conversion values
Trying to run more experiments when the reporting layer can’t judge them properly (or fast enough)
Starting ABM with no scoring, routing or funnel measurement structure
So this year, I’m doing something new. Bare in mind, this is a sales initiative for me.
I’m opening a small set of free 30-minute planning calls to help marketers connect both sides of their plan:
The ambition + the data required to make it work.
What we can cover in the call
Your 2026 initiatives (big bets, small bets, channel changes, experiments)
The data, tracking and measurement requirements behind each idea
What needs to happen first so initiatives perform as planned AND you’re able to measure their impact
Where you might need specialist help vs what your team can handle internally
The kinds of projects I support
If you’re newer to my work, here’s where I typically help teams:
FixMyTracking: reliable conversion signals for Meta, Google, TikTok and attribution
Attribution and measurement strategy beyond “why don’t GA4 and Meta match”
Creative and channel reporting systems that actually guide decisions
Data foundations for scaling: event design, server-side tracking, CDPs, analytics architecture
Experimentation frameworks: uplift tests, incrementality, structured creative testing
Who I work with
I don’t deliver everything alone. I have my set of trusted coworkers that include:
Marketing scientists for lead scoring, predictive models, synthetic conversions
Marketing analysts for debugging, QA, analytics troubleshooting + supporting much of the work I do
Data engineers for server-side tracking, pipelines, BigQuery, CDPs and integrations
If you’re shaping your 2026 marketing plan and want help making sure the data side isn’t a bottleneck, grab a slot here:
Bring the wishlist. The bigger the better.