UK

United Kingdom · Marketing · Principal/Manager (12-16 years)

Manager, Marketing Analytics

Here is the whole job, in plain words. What it is, a real day, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

  • Experience bandPrincipal/Manager (12-16 years)
  • Reports toDirector of Marketing Analytics & Insights
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Head of Marketing Analytics · Marketing Analytics Lead (Manager) · Analytics Manager - Marketing

Built on an analysis of 43,079 real UK job descriptions · grounded in qualifications employers recognise

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1What this role really is

You'll be leading our Marketing Analytics team, which means you're the person making sure we actually understand what's working (and what's not) across all our marketing efforts. You'll set the direction for how we measure success, coach your team, and translate complex data into clear, actionable advice for marketing leadership. Honestly, it's about making sure we're spending our money wisely and always learning.

2What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Google Analytics (GA4)Strategic

Governing GA4 usage across your team and the marketing department, making decisions on sub-property/rollup strategy, and evaluating its role versus other product analytics tools. You'll ensure your team is extracting maximum value and data quality from GA4.

SQL (Snowflake)Strategic

Designing the marketing data mart schemas, making strategic build vs. buy decisions for ETL/ELT pipelines, and managing data governance and access control for marketing data within Snowflake. You'll set the technical standards for your team's SQL work.

TableauStrategic

Owning the enterprise BI strategy for marketing, managing Tableau Server/Cloud resources for your team, establishing best practices for dashboard development, and championing data literacy across the marketing organisation through effective visualisations.

Python (pandas, scikit-learn)Strategic

Setting the technical direction for the data science function within marketing, evaluating MLOps platforms for model deployment, and determining where to apply advanced modeling versus simpler heuristics. You'll guide your team's use of Python for complex analyses.

Segment (CDP)Strategic

Owning the Customer Data Platform strategy for marketing, leading vendor selection/renewal, integrating the CDP into the broader enterprise data strategy, and ensuring compliance with privacy regulations. You'll ensure Segment is the single source of truth for customer data.

Salesforce Marketing CloudStrategic

Partnering with Marketing Operations to architect the data flow between SFMC, the CDP, and the data warehouse to create a 360-degree customer view. You'll ensure your team can extract and analyse data from SFMC effectively.

3What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing inWhere you are nowThe step above
Team Project PrioritisationExecutes tasks assigned by manager, no prioritisation authority.Proposes prioritisation for their own tasks within a project, consults with manager.Prioritises tasks and small projects within their workstream, consults with Lead/Manager on conflicts.
Analytical Methodology & Tool Selection (within team scope)Uses pre-defined methodologies and tools.Suggests alternative methodologies or tools for specific problems, seeks manager approval.Selects and implements appropriate methodologies/tools for their projects, gets manager sign-off for significant changes.
Hiring & Performance ManagementNo involvement.May participate in interview panels as a peer.Interviews junior candidates, provides feedback to hiring manager.
Strategic Recommendations (Marketing Spend)Provides data points for others' recommendations.Recommends tactical optimisations for specific campaigns.Develops data-backed recommendations for workstream-level strategy, presents to Marketing Leads.

4How you'll be judged

The scoreboard, honestly: the hard targets, how often each one is actually looked at, and the quiet human signals that never make it onto a dashboard.

Marketing Budget Reallocation Influence
The percentage of the total marketing budget that is reallocated based on your team's incrementality and MMM findings.
Target · Influence a 15% reallocation of the marketing budget towards higher-performing channels annually.

Your team's Q2 analysis showed that Channel X was underperforming by 20% on an incremental basis, leading to a £500K shift to Channel Y. This counts towards your 15% target.

Blended Customer Acquisition Cost (CAC) Reduction
The year-over-year reduction in our overall cost to acquire a new customer, directly attributable to optimisations driven by your team's insights.
Target · Deliver insights that contribute to a 10% reduction in blended Customer Acquisition Cost (CAC) year-over-year.

Through A/B test analysis and audience segmentation, your team identified strategies that reduced paid social CAC by 12%, contributing to the overall 10% target.

Marketing Data Literacy Score Improvement
The increase in the organisation's understanding and effective use of marketing data, as measured by internal surveys and adoption rates of self-service tools.
Target · Increase the organisation's data literacy score (via survey) by 20% through training and self-service analytics initiatives.

After your team launched a new self-service dashboard and ran two training sessions, the Q4 Marketing Data Literacy survey showed a 25% improvement in confidence levels among marketing managers.

Team Productivity & Project Delivery Rate
The percentage of agreed-upon analytical projects and recurring reports delivered on time and to a high standard by your team.
Target · Maintain a 90%+ on-time delivery rate for all critical analytical projects and recurring reports.

Your team completed 18 out of 20 planned Q3 projects by their deadlines, including the complex attribution model update and the new CLV segmentation, hitting 90%.

Strategic Influence & Credibility
How often marketing leadership proactively seeks your team's input on strategic decisions and trusts your recommendations.
  • Your team is regularly invited to strategic planning meetings (not just to present numbers), marketing VPs quote your insights in their presentations, and other teams (e.g., Product, Finance) consult you on marketing-related data questions. People actually listen when you say 'the data suggests...'
Team Development & Mentorship
The growth and skill development of your direct reports, and your effectiveness in coaching them.
  • Your direct reports show clear progression in their technical and soft skills, they feel supported and challenged, and you're regularly providing constructive feedback and development opportunities. We'd expect to see at least one analyst mentored for promotion every 18-24 months.
Data Quality & Governance Leadership
Your proactive approach to ensuring the accuracy, reliability, and privacy compliance of marketing data.
  • You're not just reacting to data quality issues
  • you're anticipating them. This means leading initiatives to improve UTM hygiene, working with MarTech to fix tracking gaps, and ensuring our data practices align with GDPR and other privacy regulations. Essentially, you're the champion for clean, trustworthy data.
Cross-Functional Collaboration
Your ability to build strong working relationships and drive alignment with other teams, especially Product, Sales, and Finance.
  • You're seen as a trusted partner, not just a data provider. This looks like joint projects with Product on experimentation, regular check-ins with Sales on lead quality, and proactive discussions with Finance on budget forecasting. You're helping everyone get on the same page with the numbers.

5Would you like it

The honest version. What people enjoy, and what grinds them down.

What people enjoy
Driving Strategic Impact

You'll get a real buzz from seeing your team's analysis directly influence a major marketing budget decision or a new campaign launch. It's about knowing your work isn't just a report, but a catalyst for change.

Leading a project that results in a 20% shift in media spend, directly increasing ROI, and seeing the CMO reference your team's work in an executive meeting.

Building & Developing a High-Performing Team

You'll enjoy mentoring junior analysts, helping them solve tricky problems, and seeing them present their findings confidently. Your satisfaction comes from fostering a team that consistently delivers excellent work and grows their skills.

Coaching a Senior Analyst through a complex attribution model build, then seeing them successfully present it to the VP of Performance Marketing with confidence.

Solving Complex, Ambiguous Problems

You thrive when faced with a messy, ill-defined business question that requires creative data approaches. You're not afraid to tackle problems where there isn't a clear-cut answer, and you enjoy the process of bringing clarity to chaos.

Taking on the challenge of measuring the 'brand uplift' from a new TV campaign, developing a novel methodology, and getting buy-in from leadership on its validity.

What frustrates people
  • The War Against Last-Click: Constantly fighting an uphill battle to convince stakeholders that the easiest-to-measure metric (last-click) is also the most misleading. You'll be the one explaining incrementality for the tenth time.
  • "Just Pull the Numbers": Receiving vague, urgent requests that massively underestimate the work required, treating your team like a vending machine for data points. You'll need to push back and clarify the actual business question.
  • Political Pressure for "Good News": Being implicitly or explicitly pressured to frame results in a positive light, especially when a senior leader's pet project is underperforming. You'll need a strong backbone to present the truth.
  • Garbage In, Garbage Out: Spending 60% of your team's time cleaning, validating, and stitching together messy data from 15 different marketing platforms that were never designed to work together. It's a constant battle for data quality.
  • Chasing the Tracking Ghost: A platform update (e.g., iOS 14, GA4 migration) breaks all your tracking and attribution models overnight, forcing your team to rebuild and re-validate everything. It's a reactive nightmare.
  • The Unmeasurable Campaign: The brand team launches a massive, expensive 'awareness' campaign with no clear KPIs, then asks you to 'prove the ROI' three months later. You'll need to set expectations early.
  • Tool Sprawl & Budget Fights: Arguing for budget for critical data infrastructure (like a CDP or ETL tool) which is seen as a 'cost centre,' while other teams get budget for shiny new martech. You'll need to build a strong business case.
What this role does not give you
  • A purely technical, heads-down role: You'll spend a significant amount of time managing people and stakeholders, not just writing code.
  • A predictable, routine environment: Expect frequent shifts in priorities, urgent requests, and the need to adapt your team's plans.
  • A clean data playground: You'll be dealing with messy, incomplete, and sometimes contradictory data most of the time. Perfection is the enemy of good here.
  • A 'set it and forget it' leadership style: Your team will need active coaching, guidance, and support. You're hands-on with their development.

6Who you work with

This role directly influences how we allocate our marketing budget, optimise campaigns, and understand customer behaviour across the entire organisation. Your team's work underpins strategic decisions that can shift millions of pounds in spend, directly impacting customer acquisition cost (CAC), customer lifetime value (CLV), and overall marketing ROI. You're essentially the guardian of our marketing investment's effectiveness.

Inside the business
  • Director of Marketing Analytics & Insights
  • VP of Performance Marketing
  • Head of Brand & Creative
  • Product Marketing Leads
  • Sales Leadership
  • Finance Business Partners
Outside the business
  • Marketing Technology (MarTech) Vendors
  • External Agencies (Media, Creative)
  • Industry Bodies (e.g., IAB, DMA)

7What you need before you start

Not a wish list. The things you would be expected to already have.

  • Proven experience (8-12 years) as a Lead Marketing Analyst or similar role, demonstrating a track record of delivering high-impact analytical projects.
  • Significant experience managing or mentoring junior analysts, including conducting performance reviews and fostering career development.
  • Expert-level proficiency in SQL for complex data manipulation and modelling, ideally within a cloud data warehouse like Snowflake.
  • Expert-level proficiency in a BI tool like Tableau, including building and managing complex, interactive dashboards and data sources.
  • Advanced proficiency in Python (or R) for statistical modelling, data science applications (e.g., MMM, MTA), and data automation.
  • Demonstrable experience in designing and executing incrementality tests and interpreting their results.
  • Strong understanding of modern marketing measurement frameworks, including MMM, MTA, and CLV modelling.
  • Excellent communication and presentation skills, with a proven ability to translate complex data into clear, actionable insights for senior leadership.

8What to practise next

Where the job is going, and what to do about it starting this week.

Advanced MLOps for Marketing Models

Critical within 12-18 months. As our marketing models (MMM, MTA, CLV) become more sophisticated and move into production, robust MLOps practices for model versioning, monitoring, and retraining will be essential to ensure their ongoing accuracy and reliability. You'll need to drive this standardisation.

Model Registry & Versioning · Automated Model Retraining Pipelines · Model Performance Monitoring · Containerisation (Docker) & Orchestration (Kubernetes)

  • This quarter: Evaluate current MLOps practices within your team and identify key gaps or areas for improvement.
  • Next quarter: Research and recommend an MLOps platform or set of tools that could streamline model deployment and management.
  • Month 6-9: Pilot a new MLOps pipeline for one critical marketing model (e.g., CLV prediction) with your team.
  • Ongoing: Collaborate with Data Engineering/ML Engineering teams to align on best practices and shared infrastructure.

Quick win: Start by implementing strict version control for all your team's model code and datasets using Git. It's foundational.

Real-time Analytics & Streaming Data

Important within 18-24 months. The demand for immediate insights, especially for optimising live campaigns or personalising customer experiences, is growing. Moving beyond batch processing to real-time data streams will unlock new opportunities for rapid decision-making. You'll need to understand the strategic implications.

Streaming Data Platforms (e.g., Kafka, Kinesis) · Real-time Personalisation Engines · Low-Latency Data Warehousing · Event-Driven Architectures

  • This quarter: Identify one marketing use case where real-time insights would provide a significant competitive advantage (e.g., bid optimisation, churn prevention).
  • Next quarter: Research available real-time analytics solutions and assess their feasibility for our current tech stack.
  • Month 6-9: Work with Data Engineering to explore a small-scale pilot for a real-time data pipeline for a specific marketing event.
  • Ongoing: Educate your team on the potential and challenges of real-time analytics.

Quick win: Start by identifying existing near real-time dashboards and exploring ways to reduce their refresh rates. Understand the current data latency.

9Staying current once you are in

What people here do to keep up
  • Regularly attending industry conferences (e.g., Marketing Analytics Summit, Data & AI in Marketing) to stay abreast of trends and network.
  • Participating in online courses or bootcamps focused on advanced machine learning, MLOps, or AI applications in marketing.
  • Engaging with relevant professional communities and forums (e.g., Measure Slack community, local analytics meetups) to share knowledge and learn from peers.
  • Mentoring junior professionals outside your direct team to hone your leadership and coaching skills.
  • Reading key industry publications and academic papers on marketing science and econometrics.

10How the AI economy is changing work like this

Before we ask anything of you, here's what we can already say about AI and work of this kind:

The new skill this role is being asked for: Prompt Engineering & LLM Integration for Analytics

Critical within 6-12 months. Competitors are already using Large Language Models (LLMs) to draft reports in minutes that used to take hours, or to summarise complex research. Analysts who figure this out will outproduce peers significantly, and as a manager, you need to guide this adoption.

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

Your PlanIllustration

Built for Manager, Marketing Analytics

4 units that map to this job, from the qualifications that cover it.

  1. Data AnalyticsPearson Education Ltd · covers 3 of 10 standardsLevel 5
  2. Data analysis and designPearson Education Ltd · covers 2 of 10 standardsLevel 5
  3. Introduction to Data Science and Big DataNCC Education Limited · covers 2 of 10 standardsLevel 5
  4. Digital StrategyChartered Institute of Marketing · covers 1 of 10 standardsLevel 6
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration for Analytics

Critical within 6-12 months. Competitors are already using Large Language Models (LLMs) to draft reports in minutes that used to take hours, or to summarise complex research. Analysts who figure this out will outproduce peers significantly, and as a manager, you need to guide this adoption.

  • Context Windows & Token Limits
  • RAG (Retrieval Augmented Generation)
  • Output Validation & Hallucination Detection
  • Prompt Chaining for Complex Analysis

Data Ethics & Responsible AI in Marketing

Important within 12-18 months. As AI becomes more embedded in marketing, ethical considerations around data privacy, bias in algorithms (e.g., audience targeting), and transparent use of AI will become paramount. Regulators and consumers are paying more attention, and you need to lead your team responsibly.

  • Algorithmic Bias Detection & Mitigation
  • Privacy-Preserving Analytics (PPA)
  • AI Explainability (XAI)
  • Ethical AI Frameworks

What you’ll use

Skills this role draws on

Technical

  • Marketing Mix Modeling (MMM)
  • Multi-Touch Attribution (MTA)
  • Incrementality Testing & Measurement
  • Customer Lifetime Value (CLV) & Propensity Modeling
  • Data Governance & Privacy Compliance

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Lead Marketing Analyst

    3-5 years in a Lead role

    Skills to master

    • Deep expertise in a specific analytical domain (e.g., attribution, MMM), proven ability to lead complex projects end-to-end, informal mentorship of junior colleagues, strong stakeholder management skills.

    You're ready to move on when

    • Consistently delivers high-impact analytical projects that drive measurable business outcomes.
    • Proactively identifies and solves complex, ambiguous marketing measurement problems.
    • Is sought out by senior stakeholders for their analytical expertise and recommendations.
    • Demonstrates strong communication skills, translating technical findings into business language.
    • Has informally mentored junior analysts and shown an interest in their development.
  2. 2

    Senior Data Scientist (Marketing Focus)

    3-5 years in a Senior Data Scientist role

    Skills to master

    • Advanced machine learning and statistical modelling techniques, experience deploying models into production (MLOps), strong programming skills (Python/R), and a good understanding of marketing domain challenges.

    You're ready to move on when

    • Has built and deployed advanced predictive models that have driven business value.
    • Demonstrates strong coding practices and an understanding of scalable data solutions.
    • Is able to translate complex data science problems into clear project plans.
    • Shows an interest in the commercial application of their models and influencing business strategy.
    • Has experience collaborating with cross-functional teams (e.g., engineering, product).
  3. 3

    Analytics Manager from a different domain (e.g., Product, Finance)

    5+ years as an Analytics Manager in another domain

    Skills to master

    • Proven managerial experience (team leadership, performance management), strong stakeholder management, robust analytical fundamentals, and a keen interest in learning the nuances of marketing data and business problems.

    You're ready to move on when

    • Has successfully led and developed an analytics team in a different business function.
    • Demonstrates strong transferable skills in data strategy, governance, and stakeholder influence.
    • Shows a genuine passion for marketing and a willingness to quickly get up to speed on marketing-specific methodologies.
    • Has a track record of driving business impact through data in their previous roles.

11Where this role leads

The long view:This role is a fantastic stepping stone to significant leadership within marketing, data, or even general management. The key is to continuously learn, build strong relationships, and always focus on how data can drive tangible business outcomes. The path is yours to shape.

Pay & demand

Pay and demand for this role will appear here, each figure traced to a named authoritative source (e.g. the ONS Annual Survey of Hours and Earnings, under the Open Government Licence). We don’t show numbers we can’t attribute.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Manager, Marketing Analytics is actually changing. In about two minutes, the free confidence check asks where you stand on each of the ten. That's the whole check, and it's what makes the plan yours rather than generic.

Where it leads

Roles this leads to

Also named as a next step, with no page in the catalogue yet:

  • Head of Customer Analytics (cross-functional)

12The team that's yours

No two people are taught the same way. This is one-to-one, not one-to-many.

Zavmo is a hyper-personalised AI learning platform. Twelve virtual tutors, each with a different way of teaching, and one orchestration agent that picks the right one for the moment. So every single lesson is shaped around you, your role, and the way you learn. Not a course everyone sits through. A conversation built for you, and no one else.

…and nine more, matched to you after your first chat. Meet all twelve

Start free and meet yours

13What it feels like

A conversation, not a course

Because your tutor knows your role, your projects and your last session, learning sounds like this. And it's different for every single person:

Data AnalyticsLevel 5

Applied to your work in Manager, Marketing Analytics

The objective of this unit is to equip learners with the knowledge and skills to apply data analytics techniques in decision-making processes. Learners will be able to utilise descriptive, statistical, predictive, and prescriptive analytic methods to transform data into actionable insights, forecast future events, and determine optimal solutions for a given situation.

How the thinking builds
  1. Remember
  2. Understand
  3. Apply
  4. Analyse
  5. Evaluate
  6. Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

  1. Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
  2. What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
  3. The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
  4. How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
  5. Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

DemonstrateIllustration

Evidenced on your work in Manager, Marketing Analytics

You do not finish by watching something. You finish by showing it on the work you already do, against the measures this job is judged on.

  • Marketing Budget Reallocation InfluenceThe percentage of the total marketing budget that is reallocated based on your team's incrementality and MMM findings.Your team's Q2 analysis showed that Channel X was underperforming by 20% on an incremental basis, leading to a £500K shift to Channel Y. This counts towards your 15% target.Influence a 15% reallocation of the marketing budget towards higher-performing channels annually.
  • Blended Customer Acquisition Cost (CAC) ReductionThe year-over-year reduction in our overall cost to acquire a new customer, directly attributable to optimisations driven by your team's insights.Through A/B test analysis and audience segmentation, your team identified strategies that reduced paid social CAC by 12%, contributing to the overall 10% target.Deliver insights that contribute to a 10% reduction in blended Customer Acquisition Cost (CAC) year-over-year.
  • Marketing Data Literacy Score ImprovementThe increase in the organisation's understanding and effective use of marketing data, as measured by internal surveys and adoption rates of self-service tools.After your team launched a new self-service dashboard and ran two training sessions, the Q4 Marketing Data Literacy survey showed a 25% improvement in confidence levels among marketing managers.Increase the organisation's data literacy score (via survey) by 20% through training and self-service analytics initiatives.
  • Team Productivity & Project Delivery RateThe percentage of agreed-upon analytical projects and recurring reports delivered on time and to a high standard by your team.Your team completed 18 out of 20 planned Q3 projects by their deadlines, including the complex attribution model update and the new CLV segmentation, hitting 90%.Maintain a 90%+ on-time delivery rate for all critical analytical projects and recurring reports.
These are this job's own measures, with its own targets. Nothing is marked evidenced, because nobody has started this yet. Yours would fill in from the work you bring.

Your passport

This isn't a certificate you file away. It's a passport to the life you're designing.

Every credit you earn and every fluency you build adds up: evidence where it counts, carried with you. Zavmo keeps the map: where you are, where you're heading, and the next step, at your pace, around your life. From Manager, Marketing Analytics to Director of Marketing Analytics & Insights, and whatever you decide comes after.

Level 5 · in progressAI Fluency→ Director of Marketing Analytics & Insights→ your design
Where this takes you

This role is a fantastic stepping stone to significant leadership within marketing, data, or even general management. The key is to continuously learn, build strong relationships, and always focus on how data can drive tangible business outcomes. The path is yours to shape.

See Your Progress GrowIllustration
Manager, Marketing Analytics
  • Marketing Mix Modeling (MMM)
  • Multi-Touch Attribution (MTA)
  • Incrementality Testing & Measurement
  • Customer Lifetime Value (CLV) & Propensity Modeling
  • Data Governance & Privacy Compliance
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

14The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Manager, Marketing Analytics is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Director of Marketing Analytics & Insights

    3-5 years as Manager, Marketing Analytics

    L6

    • Enterprise Data Strategy: Integrating marketing data into broader company-wide data initiatives and infrastructure.
    • Vendor Management (strategic): Leading complex vendor negotiations and partnerships for major MarTech and analytics platforms.
    • M&A Due Diligence: Assessing the data and analytics capabilities of potential acquisition targets.
    • Advanced Risk & Compliance: Navigating complex regulatory landscapes and ensuring the entire function adheres to the highest standards.

  2. Head of Customer Analytics (cross-functional)

    4-6 years as Manager, Marketing Analytics

    L6

    • Advanced Segmentation & Personalisation: Developing sophisticated customer segments and driving personalised experiences across all channels.
    • Churn Prediction & Retention Strategies: Leading the analytical efforts to identify at-risk customers and develop effective retention programmes.
    • Customer Experience (CX) Measurement: Defining and tracking key CX metrics and linking them to business outcomes.
    • Experimentation at Scale: Designing and overseeing a comprehensive experimentation roadmap across the entire customer journey.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, a big chunk of marketing analytics can feel a bit repetitive or time-consuming. Imagine if you could offload some of that grunt work, freeing up your team to focus on the truly strategic, high-impact problems. That's where AI comes in. We're not talking about replacing your brilliant analysts; we're talking about giving them superpowers.

As a Manager, Marketing Analytics, you're constantly balancing team workload, stakeholder demands, and the need for deeper insights. AI isn't just a buzzword here; it's a practical way to boost your team's output, accelerate insights, and ensure you're always ahead of the curve. Think of it as an extra pair of hands (or brains) for your team.

Automated Commentary Generator

Use a fine-tuned Large Language Model (LLM) to analyse weekly performance data from your dashboards (e.g., via API) and generate a first draft of the 'key takeaways' and 'what this means' commentary for stakeholder emails and presentations. Your team then refines and adds strategic context, saving hours on initial drafting.

Anomaly Detection Assistant

Implement an AI model (like Prophet or statistical process control) to constantly monitor key metrics like spend, CPA, and conversion rates. It'll automatically flag statistically significant deviations from the norm, sending alerts directly to your team for immediate investigation. No more manually scanning charts for hours.

Research & Methodology Summarizer

Deploy an AI agent to research and summarise the latest academic papers on marketing attribution, distill the pros and cons of a new measurement technique, or extract key points from a 50-page industry report on consumer privacy. This means your team spends less time reading and more time applying cutting-edge knowledge.

Stakeholder Translation Engine

After your team completes a complex analysis, feed the technical findings and charts into an LLM with a prompt like: 'Explain this to a CMO who is smart but not technical. Focus on the business impact and recommended action. Write it in the style of a concise, confident advisor.' This helps your team hone their communication and ensures insights land effectively.

Common questions

Common questions

How do you become a Manager, Marketing Analytics?

Common routes in include Lead Marketing Analyst (3-5 years in a Lead role), Senior Data Scientist (Marketing Focus) (3-5 years in a Senior Data Scientist role) and Analytics Manager from a different domain (e.g., Product, Finance) (5+ years as an Analytics Manager in another domain). Times vary with prior experience.

Where can a Manager, Marketing Analytics progress to?

This role can lead on to Director of Marketing Analytics & Insights (3-5 years as Manager, Marketing Analytics) and Head of Customer Analytics (cross-functional) (4-6 years as Manager, Marketing Analytics), depending on the skills you build.

What level is a Manager, Marketing Analytics in the UK?

This role aligns to RQF Level 5 on the UK framework, a guide to the depth of qualification it maps to, not a hard entry bar.

What new skills matter most for a Manager, Marketing Analytics?

Increasingly, Prompt Engineering & LLM Integration for Analytics and Data Ethics & Responsible AI in Marketing. These are the areas where the higher-paid, future-proof work is heading.

The honest bit

You’ve started things before

Most of them were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

What it costs

Less than one coaching session. Every month.

A single career-coaching hour costs more than a month of this, and it ends when the hour does. Zavmo doesn't. It's £70 a month, about £2.30 a day, for a companion that knows Manager, Marketing Analytics, works on the job you actually do, and keeps going at your pace rather than a timetable's.

  • Searching and planning stay free. You only pay when you start learning.
  • Your credits are yours. Regulated, and they don't vanish when a subscription ends.
  • Cancel any time and billing stops. No notice period, no minimum term.

Your path, personalised

You have the map. Walking it is the part we do together.

This route runs to 10 national skill standards. That is a real journey.

Zavmo shapes a learning experience as unique as you are. It fits how you learn, your pace and the work you already do. Every step stays benchmarked to recognised national standards. That’s the plan for becoming a Manager, Marketing Analytics: personal to you, and it still counts. The first steps are free.

Independent research finds well-designed intelligent tutoring performs nearly as well as one-to-one human tutoring: VanLehn (2011), Educational Psychologist.

A private tutor in the UK averages £35–40 an hour . Zavmo is £70/month.

A real plan on learn.zavmo.ai: Ofqual-regulated units, credits, and a three-month run at your own pace.
Start free No commitment. See your first steps free.

15Where to go from here

Other roles at Level 5

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

Other roles in Marketing

Stay in the field you know and move sideways rather than up.

If you leave this industry

The skills you'll develop as a Manager of Marketing Analytics are highly transferable. You could move into broader data leadership roles (e.g., Head of Data Science, Director of Business Intelligence) in other industries, or specialise further within marketing tech (e.g., Head of MarTech, CDP Product Manager). Your ability to translate data into business value is universally sought after.

Not sure this is the right direction?

Work out what you actually want from work first, then come back and see which roles fit it. Takes about ten minutes.

This role profile is © 2026Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Build my plan, free Here's the first ten minutes: a 2-minute confidence check → your personalised roadmap → meet the tutors matched to you. No card, cancel any time. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.