Marketing Customer Data Strategy Lead

Vertu Motors plc
Gateshead
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Consultant, Data Science (Customer Data)

Head of Data Analytics & Marketing Insights

Senior CRM Data Analyst

Senior CRM Data Analyst

Lead Data Analyst

Senior Data Analyst

Vertu Motors - Marketing Customer Data Strategy Lead

Location: Rotterdam House, Gateshead


Salary: Up to £40,000, depending on experience


Summary
The Marketing Customer Data Strategy Lead is responsible for defining, governing, and maximising the value of marketing data across Vertu Motors. This role leads the Data Strategy & Intelligence workstream within Marketing Automation, ensuring data-driven insights, segmentation, and predictive models are delivered to fuel customer engagement and optimise marketing performance.


As a highly analytical, innovative, and business-focused storyteller, the Data Design & Intelligence Lead combines technical expertise (data modelling, SQL, BI tools, CDP integration) with commercial acumen to translate complex data into actionable strategies. The role works in close, continuous partnership with the Head of Automation (Technology) and the Customer Engagement Senior Lead, with ongoing communication and collaboration between them forming a closed-loop operating model of insight → enablement → execution → measurement → optimisation.


The successful candidate will have 8–10 years’ experience in marketing, data, analytics, and reporting functions, with a proven record of building segmentation strategies, predictive models, and insight dashboards that drive measurable business outcomes.


Key Purpose

  • Data Strategy Development: Define and own the Marketing Data Framework, segmentation approach, and predictive models to maximise customer lifetime value (CLV), order-to-business (OTB), and retention.
  • CDP Business Transformation: Lead the Data Mastery pillar, using the CDP to identify process deviations and improve data accuracy. Build stakeholder networks to embed better standards and highlight opportunities to improve the accuracy of marketing data and operational effectiveness. Drive cultural adoption of data-led decision making across Vertu.
  • Governance & Standards: Establish and maintain data quality, compliance, and integration standards across platforms (CDP, CEP, DMS, Showroom, MarTech).
  • Insight Generation: Deliver actionable dashboards (CDP SCV), reports, and business intelligence that enable data-led decision-making and accountability.
  • Optimisation & Measurement: Build feedback loops and KPIs into campaigns, ensuring ongoing improvement in funnel efficiency and campaign ROI.
  • Collaboration: Partner with Technology (Automation/IT) and Customer Engagement leaders to integrate insights into orchestrated journeys and measurable outcomes.
  • Innovation: Introduce advanced analytics and modelling (LTV, churn, propensity) to scale personalisation and predictive engagement.

Essential Criteria
Technical & Analytical Skills

  • Expert in data modelling, and BI tools.
  • Proven experience integrating and operationalising Customer Data Platforms.
  • Strong capability in segmentation design, predictive modelling, and lifecycle analytics.
  • Ability to translate data into clear, compelling business stories.

Experience

  • 8–10 years’ experience in marketing data, analytics, or reporting roles.
  • Demonstrable success in delivering commercial outcomes through data-led strategies.
  • Experience designing and implementing dashboards for senior commercial stakeholders.
  • Deep understanding of data governance, compliance, and first-/zero-party data strategy.

Core Marketing Skills – Authoritative Level

  • Data-led, innovative, customer-focused.
  • Strategic thinking, commercial awareness, and ability to align data to business KPIs.

Leadership & Collaboration

  • Experienced in leading a data workstream or team.
  • Able to influence and collaborate cross-functionally with Marketing, IT, and Operations.
  • Strong communicator with the ability to simplify complexity for varied audiences.

Desirable Criteria

  • Automotive sector experience.
  • Experience with advanced analytics tools.
  • Knowledge of AI-driven modelling and orchestration.
  • CIM qualification.
  • Degree in Data Science, Marketing Analytics, or a related field.

Personality & Behavioural Qualities Needed

  • Analytical, precise, and innovative thinker.
  • Commercially focused storyteller who translates insight into action.
  • Strong attention to detail without losing sight of strategic outcomes.
  • Positive, solutions-focused, with a continuous improvement mindset.
  • Ambitious for both business success and personal growth.

What you can expect

We are proud to be the Motor Retailer who invests more in our colleague's personal development than any other, so if you are successful, you can look forward to on-going training opportunities that provide you with the right career path, career progression and a range of benefits you would expect from an employer of choice which includes:



  • 25 days holiday rising with length of service plus bank holidays
  • Access to our online rewards platform giving you cash back and discounts for multiple retailers
  • Preferential Service Rates
  • Colleague Purchase Scheme
  • Share Incentive Scheme
  • Pension
  • Enhanced Maternity & Paternity
  • Hybrid working

If you are interested in joining our brilliant team, please apply now!


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.