Director | Marketing

Head of Marketing Analytics & Insights

"The budget gets set in a room where my analysis is not open on anyone's screen."

Quick Facts

Role

Director | Marketing

Level

Director

Dept

Marketing

Industry

Marketing

Env

Cloud warehouse + martech

Tools

Snowflake, GA4, Looker

Sound familiar?

Marketing data is fragmented across too many platforms for a unified, trusted view of performance to exist

Analytics outputs are technically sound but are not embedded in the planning, optimisation, and budget decisions they were intended to inform

Measurement infrastructure is not keeping pace with privacy changes, signal loss, AI-powered search, and increasingly fragmented customer journeys

The commercial value of marketing analytics is hard to prove because influenced pipeline, revenue contribution, and spend efficiency are not shown in terms Finance accepts

Analytics expertise remains concentrated in a specialist team, limiting self-service, consistent measurement, and adoption across marketing

Experimentation is inconsistent across channels, so budget decisions rely on correlation and platform attribution rather than credible evidence of incremental impact

You are not alone

56%

AI adoption rate in marketing analytics, marking a tipping point in 2026 (Improvado, 2026).

44%

of marketing functions have formalised analytics frameworks despite 73% increasing budgets (Improvado, 2026).

39%

of marketing functions are planning labour reductions amid structural budget pressure (Gartner, 2025 CMO Spend Survey).

7.7%

of company revenue is the level at which marketing budgets have flatlined, with 59% of marketing functions reporting insufficient budget (Gartner, 2025 CMO Spend Survey).

Join those who are leveraging data to move from financial stewardship to strategic business leadership.

How is AI raising the stakes

The data fragmentation problem is compounding.

The average martech stack now runs 17-20 platforms according to MarTech 2025 State of Your Stack research, with each platform producing its own version of performance metrics, using its own attribution logic, and claiming its own share of the credit for outcomes that happened once. The Head of Marketing Analytics who has not yet built the integrated data layer that resolves these conflicts - bringing all platform data into a common schema with agreed attribution methodology - is producing analytics from a data environment that is structurally designed to produce contradictory results.

The adoption problem is the underrated challenge.

Analytics functions that produce technically rigorous outputs that the marketing team does not use or trust are delivering a fraction of their potential value. When marketing managers continue to use platform-native dashboards rather than the unified analytics infrastructure the team has invested in building, the investment is not delivering the decision quality improvement it was supposed to produce. Building the adoption infrastructure - the workflow integration, the training, the trust - is as important as building the analytical capability itself, and it is the dimension that most analytics functions underinvest in.

The Head of Marketing Analytics and Insights is managing the most rapidly shifting measurement landscape in the discipline's history.

AI search is restructuring how brand visibility is created and measured. Privacy regulations are eliminating the data signals that attribution models depended on. AI content tools are accelerating campaign production faster than governance frameworks can keep pace. And through all of this, the marketing analytics function is expected to produce the credible, Finance-accepted measurement that justifies every investment decision the CMO makes. The measurement infrastructure that was adequate 24 months ago is not adequate for the environment the function is operating in today.

Director | Marketing

How Bronson can help

Fractional Data and AI Services

For functions that need specialist data and AI capability without the timeline and cost of permanent recruitment, Bronson.AI provides experienced fractional professionals who integrate directly with the internal team, accelerating delivery while building internal capability in parallel.

  • Fractional data engineers who build and maintain the data pipelines and integration infrastructure the function depends on.
  • Machine learning and AI specialists who design, validate, and deploy analytical models to production standard.
  • Analytics translators who bridge the gap between technical outputs and the business decisions they are designed to inform.

Cloud and Application Migration

Bronson.AI helps modernise the underlying technology infrastructure, migrating legacy systems to cloud platforms that integrate cleanly, scale with the organisation, and support the analytics and AI capabilities the function requires.

  • Cloud migration strategy assessing current systems and sequencing the transition to minimise operational disruption.
  • Application rationalisation identifying which systems can be consolidated onto modern platforms.
  • Data migration and validation programme ensuring historical data is preserved and accessible in the new environment.

AI Readiness and Data Management Assessment

Bronson.AI assesses the data foundation and process maturity that determines whether AI investments will deliver. Before committing to AI tools, the function needs an honest picture of where the data actually stands, and a prioritised roadmap for closing the gaps.

  • Data maturity assessment evaluating completeness, consistency, quality, and governance readiness across relevant systems.
  • AI use case prioritisation identifying which applications the current data foundation can support now versus after remediation.
  • Prioritised roadmap sequencing the data and process work that AI adoption requires.

Unlock your potential

Unlock the Power of Data in Marketing Analytics

Data is the backbone of a high-impact marketing analytics function. For the Head of Marketing Analytics and Insights, building a unified, governed, and future-proof measurement infrastructure is what enables the function to deliver the attribution credibility, AI search visibility measurement, and marketing mix intelligence that the CMO and CFO need to make confident investment decisions.

Overcome Data Challenges Effortlessly

One of the primary challenges facing marketing analytics leaders is building a unified measurement layer across a martech stack that was never designed for integration - producing a fragmented data environment where platform-native metrics contradict each other and every attribution conversation becomes a negotiation rather than a shared view of truth. Building the integrated measurement layer that resolves these conflicts is the foundational investment the function requires.

The Promise of Data, Analytics, and AI Advancements

Imagine a marketing analytics function with a trusted, unified data layer that Finance accepts, measurement infrastructure that captures AI search visibility alongside traditional performance, and analytics adoption that means the marketing team is making decisions from evidence rather than from platform dashboards and gut instinct. This is not just a vision but the very real value proposition that our Data, Analytics, and AI Consulting and Solutions offer.

Realize the Value of Advanced Data Solutions

Our services are designed to guide Heads of Marketing Analytics through:

  • Unified Marketing Data Layer: Integrated, governed measurement infrastructure across all channels with consistent attribution methodology.
  • Advanced Marketing Analytics: Multi-touch attribution, AI search visibility, and marketing mix modelling capabilities at production grade.
  • Analytics Adoption: Workflow integration and capability building that connects analytics outputs to marketing team decisions.

See Results

4x ROI

payback with AI is guaranteed

90 DAYS

to a funded, board-ready AI roadmap

18 MONTHS

from pilots to
AI-centric enterprise

Get started today!

Frequently asked questions

The fix is a governed foundation that unifies the marketing data across the platforms, because a unified, trusted view of marketing is impossible while the data sits fragmented across too many platforms each holding its own slice, and connecting them is what makes that view achievable.

Establish secure, well governed data management as the unifying foundation, because a single trusted view of marketing depends on consistent, connected, governed data, and the fragmentation across platforms is exactly what prevents it. The work is connecting the platforms' data into a common environment, aligning definitions so metrics are consistent across platforms, and governing the result so it stays reliable as the platform landscape changes. The effort is in the alignment and governance, because data spread across many platforms each measuring its own way cannot become a trusted unified view until the definitions agree and someone owns keeping them consistent.

The reason fragmentation is so limiting for a marketing analytics function is that its entire purpose is to provide a unified, trusted view of marketing performance, and a fragmented platform landscape makes that impossible, producing instead a collection of platform-specific views that do not reconcile and that nobody fully trusts. The function ends up reconciling data rather than analysing it, and its outputs carry the uncertainty of inputs that were never properly aligned.

The payoff is the unified, trusted view of marketing that the analytics function exists to provide, replacing the fragmented platform-by-platform picture. When the marketing data is connected and governed, the function can analyse marketing as a whole, produce numbers that reconcile and that the organisation trusts, and build the more advanced measurement and analytics that depend on a sound data foundation. Fixing the fragmentation through a governed foundation is what turns marketing analytics from a function fighting a fragmented platform landscape into one providing the unified, trusted view of marketing that lets the organisation make decisions on consistent data, which is the core value the function is supposed to deliver.

Modernising measurement means building it on a foundation that adapts to privacy and AI search changes rather than depending on the tracking those changes are breaking, because measurement built on third-party tracking and traditional search metrics is being undermined by privacy regulation and the shift to AI search, and modernising it means rebuilding on approaches that survive those changes.

Turn the data you can durably collect into modern measurement insight, because measurement that keeps up with privacy and AI search rests on first-party data, modelled measurement, and new metrics for the AI search era rather than on the tracking that is being deprecated, and that requires a deliberate rebuild on a durable foundation. The work is establishing measurement based on the data you can reliably collect under tightening privacy rules, complementing it with modelled measurement where direct tracking is gone, and developing the new metrics that capture visibility in AI search, all built on a data foundation designed to adapt rather than one tied to the tracking that is disappearing.

The reason traditional measurement cannot keep up is that it was built for an environment that is ending, third-party tracking that privacy regulation is removing, and traditional search metrics that the shift to AI search is making less meaningful, so measurement anchored to them degrades as that environment changes. Modernising means moving the foundation to what is durable, the first-party data you own, modelled approaches, and metrics suited to how discovery actually works now, rather than patching measurement that depends on a disappearing world.

The payoff is measurement that remains reliable as privacy and search continue to change, rather than measurement that degrades with each new privacy rule and each step toward AI search. A modern measurement foundation built on durable data and adaptable approaches keeps giving the organisation a reliable read on marketing performance through changes that break traditional measurement, which is what lets marketing keep optimising and demonstrating value through the transition. Modernising measurement on a durable, adaptable foundation is what keeps the marketing analytics function able to measure what matters as the environment shifts, rather than perpetually rebuilding measurement that the next change breaks again, which is the trap of anchoring measurement to tracking that is on its way out.

The fix is closing the gap between producing analysis and embedding it in how marketing decisions are actually made, because the problem is rarely the quality of the analytics; it is that the outputs are not reaching the decisions in a form and at a moment that lets them be acted on.

Turn the analysis into clear, comprehensible insight delivered where decisions happen, because analysis that drives decisions has to reach decision-makers in their context and language, at the point the decision is made, not be produced in the analytics team's terms and left to be found. The work is understanding the marketing decisions the analytics should inform, then delivering the insight accordingly, in a form the decision-maker immediately grasps, at the moment and place the decision is made, rather than as standalone outputs the team produces and hopes land.

The reason analytics so often fails to drive decisions is a disconnect between the analytics function and the decision process. The function produces work measured by its analytical quality, while the business measures usefulness by whether it changed a decision, and those are different things. An analysis that is technically excellent but arrives in the wrong form, at the wrong time, or without a clear implication does not drive a decision regardless of its quality, and the function puzzles over why its good work goes unused.

The payoff of closing the gap is analytics that actually influences marketing decisions, which is the only reason the function exists. When insight is delivered where and when marketing decisions are made, in a form that makes the implication clear, it gets used, and the function shifts from producing outputs to shaping outcomes. That also transforms how the function is valued and funded, because demonstrable influence on decisions is a far stronger position than a record of producing sound analysis that sits unused. Fixing the gap between analysis and decision, by embedding insight in the decision process rather than producing it alongside, is what turns a marketing analytics function from a producer of reports into a genuine influence on how marketing is run, which is where its value is realised.

The options are to keep analytics confined to the specialist team, to grow that team slowly and expensively, or to extend analytics capability across the wider marketing function with the right support, and scaling usually means a combination of enabling marketers to use analytics themselves and accessing experienced capability to support the extension.

Draw on the analytics, engineering, and AI support of a full data capability to extend analytics across marketing, because that support is what lets you scale beyond the specialist team without the slow, costly build of a much larger internal one, and capability spreads through enabling and supporting marketers rather than concentrating it in specialists. The approach combines making analytics accessible to the wider marketing team, through the right tools, support, and reusable analytics, with experienced capability that helps the extension succeed rather than leaving marketers to struggle alone with analytics work.

The reason analytics stays trapped in the specialist team is usually that it depends entirely on a few skilled people who become a bottleneck, and scaling by hiring more such specialists is slow and expensive, while simply expecting marketers to pick up analytics without support produces the familiar failure where the capability does not actually spread. Extending it requires both enabling the wider team and supporting them through the transition, which is where experienced external capability helps most.

The payoff is analytics capability spread across the marketing function rather than confined to specialists, which is what lets analytics become part of how marketing works rather than a service a small team provides. When the wider team can apply analytics to their own work, supported by specialists and experienced capability for the harder problems, the function's overall analytics capacity multiplies without a proportional increase in specialist headcount, and the capability becomes more resilient because it no longer depends on a few individuals. Extending analytics across marketing with the right support, rather than concentrating it in or slowly growing a specialist team, is what turns marketing analytics from a bottlenecked specialism into a capability the whole function uses, which is what scaling actually requires and what lets analytics keep pace with the demand marketing places on it.
Marketing