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⚡ Source: ReedRef: 56868249

Marketing Analyst

Harnham - Data & Analytics Recruitment·London·Posted 3 weeks ago
🏢 On-site💰 £40-50k/year
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Job description

Original text imported from Reed

Marketing Analyst

London (4 days onsite) | £45-50k + bonus


Data-driven marketing business working with large retail and consumer brands to improve customer targeting, campaign performance, and commercial outcomes through data, analytics, and technology.

The Role

You'll join a small analytics team supporting multiple client accounts, focusing on turning customer, campaign, and behavioural data into actionable insights.

Work includes:

  • Campaign and customer data analysis to understand performance drivers
  • Audience and segmentation analysis (including high-value customers)
  • Ad hoc deep dives on large transactional datasets
  • Supporting insight delivery alongside senior team members
  • Contributing to client-facing outputs (with support)
Requirements
  • 1-3 years' experience in analytics / marketing / data role
  • Strong SQL skills
  • Exposure to CRM, digital, or marketing data desirable
  • BI tool experience (e.g. Tableau) a plus
  • Strong communication and stakeholder skills
  • Curious, commercially minded, and keen to develop
Interview Process
  • 45-min intro interview
  • Task/presentation (in-person)
  • Final interview with senior stakeholder


Find out more and apply via the link below.

SpeedCV AI

Key skills

AI-extracted from the job advert

Must-have skills
SQLAnalytics experienceMarketing data analysisCommunication skillsCommercial mindset
Nice-to-have
CRM systemsDigital marketing dataTableauBI toolsData visualisation
Soft skills
CommunicationCommercial mindsetCuriosityStakeholder managementAnalytical thinkingAttention to detail
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight your SQL skills prominently as this is explicitly mentioned as a strong requirement for the role

2

📊 Quantify your campaign analysis experience: 'Analysed 15 retail campaigns, improving CTR by 23%'

3

🎯 Emphasise any CRM or digital marketing data exposure as this is specifically mentioned as desirable

4

🔍 Showcase experience with large transactional datasets and customer segmentation projects

5

🤝 Demonstrate your commercial mindset with examples of how your analysis drove business outcomes

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Suggested CV bullets

3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.

How to tailor your CV

Add these 3 bullets under your most recent experience:

  • Analysed customer segmentation data using SQL for 12 retail campaigns, identifying high-value segments that increased conversion rates by 28%
  • Built Tableau dashboards tracking campaign performance across 8 consumer brands, reducing reporting time by 3 hours weekly
  • Conducted deep-dive analysis on £2.4M transactional dataset, uncovering behavioural patterns that improved customer targeting accuracy by 35%

Free to copy — tailoring requires a 30-sec CV upload.

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Letter preview — tailored to Harnham - Data & Analytics Recruitment

Dear Hiring Manager,

Harnham's client opportunity as Marketing Analyst combines my passion for SQL-driven insights with retail campaign analysis — exactly the direction I want my analytics career to take. My experience with customer segmentation and CRM data analysis aligns perfectly with your focus on improving targeting and commercial outcomes for major brands.

My background in marketing analytics has equipped me with the technical SQL skills and commercial mindset needed to turn transactional data into actionable insights that drive campaign performance improvements.

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SpeedCV AI

Interview questions

10 questions generated from this advert.

Technical

  • Walk me through how you would analyse campaign performance for a retail client
  • How would you approach customer segmentation for high-value customers?
  • Describe your experience with SQL and give an example of a complex query you've written
  • How would you handle analysis of large transactional datasets?
  • What BI tools have you used and how do you decide which visualisation to use?

Behavioural

  • Tell me about a time you had to present complex data insights to stakeholders
  • Describe a situation where your analysis led to a commercial decision
  • How do you handle working with multiple client accounts simultaneously?
  • Give an example of when you had to dig deeper into data to find the real story
  • Tell me about a time you had to learn a new analytical technique or tool quickly
SpeedCV AINEW

STAR answer examples

Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.

1Question

Tell me about a time you had to present complex data insights to stakeholders

During a quarterly review, I needed to present campaign performance analysis to 6 senior marketing managers who had varying levels of data literacy. I analysed 3 months of customer journey data showing a 15% drop in conversion rates. Rather than overwhelming them with technical details, I created a simple narrative showing how customers were dropping off at the email stage. I used clear visualisations highlighting the 23% email open rate decline and proposed 4 specific improvements. The presentation led to immediate budget reallocation of £12,000 towards email optimisation, which recovered conversion rates to previous levels within 6 weeks.
2Question

Describe a situation where your analysis led to a commercial decision

While analysing customer purchase patterns for a retail client, I discovered that 18% of customers who bought premium products also purchased complementary items within 48 hours, generating £180,000 additional revenue quarterly. However, our recommendation engine wasn't capturing this behaviour. I presented findings to the commercial team showing the missed opportunity was worth £720,000 annually. Based on my analysis, they invested £45,000 in upgrading the recommendation algorithm. Within 3 months, complementary product sales increased by 31%, exceeding the projected ROI by 40% and validating the data-driven approach to commercial decisions.

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