UK

United Kingdom · Marketing · Senior (5-8 years)

Senior Predictive Analytics Director

Here is the whole job, in plain words. What it is, 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.

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  • Experience bandSenior (5-8 years)
  • Direct reportsNo direct reports
  • Reports toLead Predictive Analytics Director
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Senior Marketing Data Scientist · Analytics Lead (Marketing) · Predictive Modeller (Marketing) · Marketing Science Specialist

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 4 of 7 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 7 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 7 standards
Pearson BTEC Level 5 Higher National Diploma in Business Pearson Education Ltd · Level 5 · covers 1 of 7 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 7 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Business Data Analytics and Insights Pearson BTEC Level 5 Higher National Diploma in Business
Data-led Decision Making Level 6 Award in Data-led Decision Making
Apply the Concepts of Data Science to Computer Engineering NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science)

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Senior Predictive Analytics Director

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

  1. Introduction to Data Science and Big Data NCC Education Limited · covers 4 of 7 standards Level 5
  2. Data Analytics Pearson Education Ltd · covers 4 of 7 standards Level 5
  3. Business Data Analytics and Insights Pearson Education Ltd · covers 1 of 7 standards Level 5
  4. Data-led Decision Making Institute of Sales Professionals · covers 1 of 7 standards Level 6
  5. Apply the Concepts of Data Science to Computer Engineering NOCN · covers 1 of 7 standards Level 5
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.

2AI and this work

Will AI change your work as a Senior Predictive Analytics Director?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration for Analytics
  • Privacy-Preserving Measurement Techniques
Why each one is rising

The future skills this job leans on

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

What happens when you start

Next, a free account. We build your Senior Predictive Analytics Director plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

This job involves building predictive models that show where marketing budgets should go and who to target. It means turning raw, often messy, marketing data into forecasts that shape strategy. That covers churn prediction, customer lifetime value forecasting, and working out what drives a sale rather than just a click. It is hands-on work, with models and code that feed into large spending decisions.

4What you'd actually use

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

Python (pandas, scikit-learn, statsmodels) Expert

Your primary tool for data cleaning, feature engineering, building, training, and evaluating all predictive models (churn, CLV, propensity, MTA, MMM). You'll write production-level code and mentor others.

SQL (PostgreSQL, BigQuery SQL) Expert

Querying our data warehouses (Snowflake, BigQuery) to extract, transform, and prepare data for your models. You'll write complex, optimised queries and help junior analysts with theirs.

Snowflake / Google BigQuery Advanced

Working directly with our data warehouse to understand schemas, extract raw data, and sometimes even design new data models or tables to support your analytical needs.

Tableau / Looker / Power BI Advanced

Building and maintaining interactive dashboards to monitor model performance, track key marketing KPIs, and visualise complex attribution paths. You'll need to make these easy for non-technical users to understand.

Google Analytics 4 (GA4) Advanced

Extracting raw event-level data, understanding its structure, and ensuring proper tracking for downstream modeling. You'll often need to troubleshoot GA4 implementation issues.

Databricks / Kubeflow / Airflow Intermediate

Using Databricks for model training and deployment, or Airflow for orchestrating data pipelines that feed your models. You'll be involved in ensuring your models run reliably in production.

5What 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 in Where you are nowThe step above
Model Selection & Methodology Follows prescribed methods; escalates any deviation. Chooses appropriate models from a defined set; consults on novel approaches. Designs and justifies novel model architectures; full autonomy on technical approach within project scope.
Project Prioritisation (within your workstreams) Works on tasks as assigned; no prioritisation authority. Can re-prioritise minor tasks within a project; escalates conflicting priorities. Manages and prioritises tasks for your owned workstreams; negotiates timelines with stakeholders, escalating only major conflicts to Lead Director.
Data Source Integration (for your models) Uses existing data sources; requests new data via supervisor. Identifies and requests new data sources; works with Data Engineering to integrate. Defines data requirements for new models; influences Data Engineering roadmap to ensure data availability and quality for your projects.
Budget for Tools/Resources (project-specific) No budget authority; requests everything. Can recommend tools up to £2K; needs approval. Recommends and justifies tools/resources up to £10K for specific projects; requires Lead Director approval for anything above.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Model Prediction Accuracy
How close your predictive models (e.g., churn, CLV) are to the actual outcomes.
Target · Achieve 80%+ precision and recall for key predictive models (e.g., identifying at-risk customers).

Your churn model correctly identifies 85% of customers who actually churn within the next quarter, allowing us to intervene proactively.

Incremental Lift from Model-Driven Campaigns
The measurable uplift in key marketing KPIs (e.g., conversion rate, revenue) directly attributable to campaigns driven by your models.
Target · Deliver a statistically significant 5-10% incremental lift in conversion rates or revenue for campaigns using your models compared to control groups.

A campaign targeting 'high-propensity to buy' customers (identified by your model) shows a 7% higher conversion rate than a randomly selected control group.

Project Delivery & Timeliness
Your ability to deliver complex analytical projects and models on time and within scope.
Target · Complete 90% of assigned model builds and analytical projects within agreed timelines, with less than 5% scope creep.

You committed to deploying the new CLV model by end of Q2, and it was live and generating predictions by 28 June.

Data Quality Improvement (Indirect)
While not directly responsible for data quality, your models will highlight issues. This measures your ability to identify and help fix them.
Target · Identify and document at least 3 critical data quality issues per quarter, leading to a measurable improvement in data completeness or accuracy within the relevant source system.

Your attribution model flagged inconsistent UTM tagging, leading the Marketing Ops team to implement a new tagging standard that reduced errors by 30%.

Stakeholder Adoption & Trust
How much the Marketing team trusts and uses your models and insights in their day-to-day decision-making.
  • Marketing VPs proactively seek your input on strategic questions
  • your models are consistently referenced in planning meetings
  • you're seen as a trusted advisor, not just a data provider. They actually implement your recommendations, rather than just nodding politely.
Mentorship & Knowledge Sharing
Your effectiveness in guiding and upskilling junior analysts and sharing your expertise across the team.
  • Junior analysts regularly seek your advice
  • you lead internal training sessions or workshops
  • you contribute actively to our internal knowledge base (e.g., best practices for model deployment, code review feedback). You'll know you're doing well when someone you've mentored starts teaching others.
Proactive Problem Identification
Your ability to spot potential analytical gaps or business problems before they become critical and propose solutions.
  • You're often the first to flag a new market trend that needs analysis, or you propose a new model to address an emerging business challenge (e.g., a shift in customer acquisition costs). You don't wait to be asked
  • you bring ideas to the table.
Clarity of Communication
Your skill in translating complex technical findings into clear, concise, and compelling stories for non-technical audiences.
  • Your presentations to leadership are easy to understand and lead to clear actions
  • you can explain the 'so what' of a p-value without resorting to jargon
  • stakeholders consistently tell you your explanations are helpful and easy to follow.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Building and Seeing Impact

You'll spend your days coding, building models, and then seeing those models actually get deployed and influence real marketing campaigns. The satisfaction comes from knowing your work isn't just a report, but a driver of change.

Your CLV model identifies a new high-value customer segment, and the Marketing team uses it to launch a targeted campaign that boosts revenue by 10%.

Solving Complex Puzzles

Marketing data is a messy, multi-faceted puzzle. You'll love the challenge of stitching together disparate data sources, cleaning up inconsistencies, and finding the hidden patterns that unlock new insights. Every day brings a new analytical problem to crack.

You figure out how to accurately attribute conversions across 'walled gardens' like Google and Meta, a problem that's stumped the team for months.

Mentoring and Sharing Knowledge

You'll get a real kick out of helping junior analysts get unstuck, reviewing their code, and explaining complex statistical concepts in a way that clicks for them. Seeing others grow because of your guidance will be a big motivator.

A junior analyst you've been mentoring successfully builds and presents their first end-to-end attribution model to a senior stakeholder.

What frustrates people
  • The 'Tyranny of the Urgent': Your quarterly plan to build a sophisticated CLV model is constantly derailed by 'urgent' requests from the demand gen team to pull a list for a campaign launching tomorrow.
  • Garbage In, Garbage Out: Being held accountable for the accuracy of a forecast built on CRM data that you know is incomplete and riddled with human error from the sales team.
  • The Privacy Treadmill: Apple's ITP, Google's cookie deprecation... major platform changes constantly move the goalposts, forcing you to rebuild foundational measurement frameworks every 12-18 months. It's a never-ending cycle.
  • 'Creative vs. Quants' Cold War: The subtle (and sometimes not-so-subtle) tension with the creative/brand team, who may view your data-driven recommendations as an attack on their artistic intuition. It's like speaking different languages sometimes.
What this role does not give you
  • A perfectly clean, well-structured dataset from day one. You'll be doing a lot of data wrangling.
  • An environment where every single model you build gets celebrated and immediately deployed. Some will fall by the wayside.
  • Complete autonomy over the entire data pipeline. You'll need to work closely with Data Engineering, and sometimes wait for them.
  • A role where you only focus on technical execution. You'll spend a significant amount of time communicating and influencing.

8Who you work with

Your work directly influences how we allocate our marketing budget – we're talking millions of pounds here. Get it right, and we grow faster and more profitably. Get it wrong, and we could be pouring money down the drain. You'll be a critical voice in strategic planning, making sure our decisions are driven by evidence, not just assumptions.

Inside the business
  • Marketing VPs (especially Demand Gen and Brand)
  • Head of Marketing Operations (they'll use your outputs)
  • Product Marketing team (for customer insights)
  • Data Engineering team (you'll need clean data from them)
  • Sales Leadership (your models will help them prospect better)
Outside the business
  • Marketing technology vendors (e.g., CDP providers)
  • External data partners (for enrichment or new data sources)
  • Industry peers (for benchmarking and best practices)

9What you need before you start

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

  • Proven experience (5+ years) in a dedicated data science or predictive analytics role, specifically within a marketing context.
  • A strong portfolio of deployed predictive models (e.g., churn, CLV, propensity) that have driven measurable business impact.
  • Expert-level proficiency in Python (with relevant data science libraries) and SQL for complex data manipulation and model building.
  • Demonstrable experience with experimental design and robust A/B testing methodologies, including statistical significance testing.
  • Experience working with cloud-based data warehouses (Snowflake, BigQuery) and marketing platforms (GA4, Salesforce Marketing Cloud).
  • The ability to clearly communicate complex analytical concepts and findings to non-technical business stakeholders, both verbally and in writing.
  • Experience mentoring junior analysts or contributing to a team's technical best practices.

10What to practise next

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

Advanced MLOps & Model Governance

As our reliance on predictive models grows, ensuring their reliability, scalability, and ethical use becomes paramount. We can't just 'build and forget'; models need to be monitored, re-trained, and governed properly.

Model Versioning & Experiment Tracking · Automated Model Retraining & Deployment · Model Drift Detection & Alerting · Explainable AI (XAI)

  • This month: Deep dive into Databricks MLflow or Kubeflow documentation and tutorials.
  • Month 2: Implement model versioning for one of your existing models using a chosen MLOps tool.
  • Month 3: Develop a basic model drift detection system for a production model, sending alerts when performance drops.
  • Month 4: Research and apply an XAI technique to explain the predictions of a complex model to a non-technical audience.

Quick win: Start documenting your model assumptions and limitations more rigorously. This is the first step towards better model governance and explainability.

11Staying current once you are in

What people here do to keep up
  • Regularly contributing to open-source data science projects or maintaining a public GitHub repository of your work. This is a great way to showcase your skills.
  • Attending industry conferences (e.g., Data Science Festival, Marketing Analytics Summit) or webinars to stay current on new techniques and trends.
  • Participating in online courses or bootcamps on advanced machine learning topics, causal inference, or privacy-preserving AI.
  • Subscribing to leading data science and marketing analytics journals or blogs to keep your finger on the pulse of the latest research.
  • Actively seeking out mentorship from senior leaders within the company or in the wider industry.

12The rising capability

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

Essential for future readiness in this role.

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

Privacy-Preserving Measurement Techniques

Essential for future readiness in this role.

  • Differential Privacy
  • Synthetic Data Generation
  • Clean Rooms & Data Collaboration Platforms
  • Probabilistic Attribution Models
  • Server-Side Tracking & First-Party Data Strategies

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Marketing Mix Modeling (MMM)
  • Multi-Touch Attribution (MTA)
  • Customer Lifetime Value (CLV) & Propensity Modeling
  • Uplift Modeling (Causal Inference)
  • Experimental Design & A/B/n Testing
  • Identity Resolution & Graphing

14The pathway

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

    From Marketing Data Analyst (L2)

    2-3 years as an L2 Analyst

    Skills to master

    • Moving beyond descriptive analytics to building your first predictive models (e.g., simple churn models), mastering experimental design, and improving your data storytelling for non-technical audiences.

    You're ready to move on when

    • You've independently built and deployed at least two predictive models that have shown initial business impact.
    • You're regularly asked to lead the analysis of A/B tests and can confidently explain statistical significance.
    • You're comfortable presenting complex data to mid-level marketing managers and can handle their questions.
    • You've started mentoring new joiners or junior analysts informally.
  2. 2

    From Data Scientist (Generalist)

    3-5 years as a Data Scientist in a non-marketing domain

    Skills to master

    • Deep diving into marketing-specific domain knowledge (MMM, MTA, CLV), understanding marketing data nuances (UTM chaos, walled gardens), and translating general data science skills into marketing strategy.

    You're ready to move on when

    • You've actively sought out projects with a commercial or customer focus in your previous role.
    • You've independently researched and understood key marketing analytics concepts.
    • You can articulate how your general data science skills would directly apply to marketing challenges.
    • You've demonstrated an interest in the business impact of your models, not just their technical elegance.
  3. 3

    From Research/Academia (PhD/Postdoc)

    1-2 years post-PhD/Postdoc with some industry exposure

    Skills to master

    • Translating theoretical statistical and machine learning knowledge into practical, deployable business solutions. Learning to work with messy, real-world data and communicating findings to non-technical stakeholders. Understanding commercial drivers.

    You're ready to move on when

    • You've completed an industry internship or a commercial project during your academic career.
    • You've actively sought to simplify complex research findings for broader audiences.
    • You're proficient in Python/R for data manipulation and modeling, beyond just academic use.
    • You're genuinely excited about applying your rigorous analytical skills to real-world business problems with tangible outcomes.

15Where this role leads

The long view: This work can lead to director-level roles in marketing analytics, or into wider positions such as VP of marketing science or chief data officer.

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 Senior Predictive Analytics Director is actually changing.

Where it leads

Roles this leads to

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

  • Manager, Predictive Analytics (L5)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Introduction to Data Science and Big Data, worked against Senior Predictive Analytics Director.

Introduction to Data Science and Big Data Level 5

Applied to your work in Senior Predictive Analytics Director

The objective of this unit is to provide learners with a systematic understanding of Data Science and Big Data concepts, including their characteristics and applications. Learners will develop proficiency in data collection, design, and modelling techniques, and will be able to select appropriate tools for data pre-processing and apply analytical techniques to generate insights from data.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 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.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

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See Your Progress Grow Illustration
Senior Predictive Analytics Director
  • Marketing Mix Modeling (MMM)
  • Multi-Touch Attribution (MTA)
  • Customer Lifetime Value (CLV) & Propensity Modeling
  • Uplift Modeling (Causal Inference)
  • Experimental Design & A/B/n Testing
  • Identity Resolution & Graphing
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.

16The 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

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

  1. Lead Predictive Analytics Director (L4)

    2-4 years in this Senior role

    This is the natural next step for someone who excels at building and delivering. You'll move from owning workstreams to architecting entire solutions and potentially managing a small team.

    • MLOps Strategy Designing and owning the end-to-end MLOps strategy for the marketing function.
    • Data Architecture Influence Influencing the overall data architecture to support advanced analytical needs.
    • Vendor Management Evaluating and managing relationships with external data/tech vendors.
    • Complex Programme Management Leading multiple, interconnected analytical programmes simultaneously.

  2. Manager, Predictive Analytics (L5)

    3-5 years in this Senior role (or after a Lead role)

    This path is for those who want to build and lead a team. You'll shift from individual contribution to managing people and owning the overall delivery of a function.

    • Capability Building Establishing new analytical capabilities within the team (e.g., a new MMM framework).
    • P&L Ownership Managing a departmental budget (typically £500K-£2M) and ensuring ROI.
    • Cross-Departmental Strategy Aligning predictive analytics strategy with broader company goals (e.g., Product, Sales).
    • Talent Acquisition Leading the hiring process for your team, attracting and retaining top talent.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, a good chunk of your week is probably spent on repetitive tasks or sifting through mountains of information. What if you could get that time back? We're not talking about replacing you; we're talking about giving you a seriously powerful co-pilot.

As a Senior Predictive Analytics Director, your real value is in strategic thinking, complex problem-solving, and communicating insights. AI isn't going to do that for you, but it can absolutely handle the grunt work, freeing you up to focus on the stuff that truly moves the needle for our Marketing team. Think of it as having an army of interns who never sleep and are always learning.

Automated Feature Engineering

Imagine feeding raw customer data into an AI tool (like GPT-4 Code Interpreter or an AutoML library) and having it automatically generate hundreds of potential features for your CLV or churn models. It'll create variables like 'time since last purchase' or 'average order value by day of week' in minutes, saving you hours of tedious manual work.

Hypothesis Generation Engine

Instead of manually poring over dozens of campaign performance reports, you can feed all that raw data into a large language model. Ask it to identify non-obvious patterns and generate 10 testable hypotheses. For example, it might spot that 'campaigns targeting mid-market finance on Tuesdays perform 30% above average' and suggest a hypothesis to test why. This could save you 2-3 hours of manual digging every week.

Research & Technique Summarizer

No more slogging through dense academic papers on the latest in causal inference or privacy-safe measurement. Use an AI research assistant (like Elicit or Scispace) to summarise the key methodologies, assumptions, and limitations of the top 5 papers on a topic in minutes. This can shave 5-8 hours off your research time for each new technique you need to learn.

Executive Summary Drafter

After you've built your complex attribution model and have all your technical results (tables, charts, statistical outputs), paste them into an AI. Prompt it: 'Acting as a marketing strategist, write a 3-paragraph executive summary for a non-technical CMO explaining the key business takeaway, the recommended action, and the expected financial impact.' It's a massive time-saver for presentations, easily an hour or two per major report.

Common questions

Common questions

How do you become a Senior Predictive Analytics Director?

Common routes in include From Marketing Data Analyst (2-3 years as an L2 Analyst), From Data Scientist (Generalist) (3-5 years as a Data Scientist in a non-marketing domain) and From Research/Academia (PhD/Postdoc) (1-2 years post-PhD/Postdoc with some industry exposure). Times vary with prior experience.

Where can a Senior Predictive Analytics Director progress to?

This role can lead on to Lead Predictive Analytics Director (2-4 years in this Senior role) and Manager, Predictive Analytics (3-5 years in this Senior role (or after a Lead role)), depending on the skills you build.

What new skills matter most for a Senior Predictive Analytics Director?

Increasingly, Prompt Engineering & LLM Integration for Analytics and Privacy-Preserving Measurement Techniques. These are the skills our record of this role lists as growing.

Free tools

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17Where 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.

 More jobs in marketing

Stay in the field you know and move sideways rather than up.
If you leave this industry

Skills built in this role, such as predictive modelling, data storytelling and influencing strategy with data, carry across sectors. Similar senior data science or analytics roles exist in fintech, e-commerce, gaming and consulting. Understanding and predicting customer behaviour is a problem common to all of them.

This role profile is © 2026 Growth 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. Start my Senior Predictive Analytics Director plan, free What happens next: a free account, then we build your plan as you set up. 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.