Building a marketing analytics roadmap that actually gets used

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Most marketing analytics roadmaps have the same fate: they're built with good intentions, presented in a slide deck, and then slowly forgotten as day-to-day priorities take over. After 15+ years of building these roadmaps for organizations large and small, we've identified the patterns that separate ones that drive change from ones that gather dust.

The problem with most roadmaps

The typical analytics roadmap is built top-down: leadership identifies the metrics they want, an analyst builds a dashboard, and the work is declared done. The problem is that a roadmap built this way rarely accounts for the competing priorities of different stakeholders, the real cost of data infrastructure work, or the gap between what people say they want to measure and what they'll actually use to make decisions.

Start with decision-making, not data

The most effective roadmaps start from a simple question: what decisions do you need to make, and what information would change those decisions? This reframe shifts the conversation from "what can we measure?" to "what do we need to know?" — and it's a more honest accounting of where analytics actually creates value.

For each decision, we work through:

• Who owns this decision and how often is it made?
• What information do they currently use?
• What information would improve the decision?
• What's the realistic business impact of a better decision?

Prioritize by impact and effort — together

Once you have a list of analytic opportunities, the next trap is prioritizing purely by business impact. High-impact analytics work is often high-effort too, and it's easy to build a roadmap that's technically ambitious but practically impossible to execute given real resource constraints.

We use a simple 2x2 of business impact vs. implementation effort to create a balanced roadmap — one that includes some quick wins (high impact, low effort) alongside longer-horizon strategic investments.

Plan for the infrastructure, not just the analysis

The single most underestimated cost in any analytics roadmap is data infrastructure. The analysis itself is often the easy part. The hard part is getting clean, reliable, connected data in the first place — and that work rarely fits neatly into a project plan.

A realistic roadmap accounts for data ingestion, hygiene, and governance work alongside the analytics deliverables that depend on it.

Build in a feedback loop

Finally, the best roadmaps aren't static documents — they're living plans that get reviewed quarterly and updated as priorities shift, new data becomes available, and you learn what's actually working. Building a feedback loop into the process from the start is what separates a roadmap from a PowerPoint.