Marketing analyst CV template and example

This marketing analyst CV template follows an analyst from agency reporting into an in-house retail marketing team. The editable Word example connects SQL, campaign evaluation and customer analysis with practical marketing decisions. The guide explains how to present analytical projects, measurement judgement and the value of your recommendations.

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Content of this CV template

How to write a marketing analyst CV

A marketing analyst CV should connect the data you work with to decisions about customers, campaigns and investment. A dashboard is useful evidence when the reader can see which question it answered, how reliable the measures were and what the marketing team did next.

Lead with your strongest area: acquisition, CRM, customer behaviour, digital journeys, brand measurement or a combination. Then choose examples that show analytical judgement as well as technical delivery.

Set the commercial context

Explain the business model and the decisions your analysis supports. An ecommerce analyst may assess orders, returns and repeat purchase; a subscription business may focus on activation, retention and lifetime value; a B2B analyst may follow leads through a long sales cycle.

State the channels and audiences you know, with enough detail to establish scope. Paid search, paid social, email, direct mail and offline activity have different measurement considerations. If you work across markets, brands or products, explain that breadth without crowding the profile with every campaign type.

Aisha’s example combines agency reporting with an in-house retail role. Her current work links acquisition and CRM activity to order and customer data, so the CV gives more space to commercial interpretation than to producing reports for their own sake.

Build project bullets around a marketing decision

A useful project account has a question, an analytical contribution and an outcome. For example, a channel appeared efficient on cost per order, but analysis of returns and product mix changed the view of customer value. The CV can explain the cohort analysis and the budget decision it informed.

Choose two or three examples that demonstrate different strengths. One might concern spend allocation, another customer targeting and another a tracking or reporting problem. That range helps an employer assess how you handle both strategic questions and the practical work needed to answer them.

Describe your role in the decision. You may have recommended a test, supplied evidence for a reallocation, identified a segment to investigate or helped a campaign manager interpret an unexpected result. The decision can be valuable evidence even when there is no subsequent revenue figure available.

Where a recommendation was implemented, include the result and measurement period if you have them. Distinguish a forecast or modelled opportunity from an observed outcome so the reader can assess the work on its own terms.

Show how you make marketing data usable

SQL experience is strongest when it names the data and the purpose. Explain how you joined campaign spend, web events, CRM records, orders or subscriptions; built customer cohorts; or created reusable datasets for reporting. Relevant detail about joins, window functions or model structure can support the account when the vacancy calls for it.

Include the warehouse and transformation tools you use, such as BigQuery, Snowflake or dbt, alongside your responsibilities. Building a tested data model, writing an analytical query and using an existing reporting table are distinct contributions. Clear descriptions let a recruiter see where you work independently and where you partner with data engineering.

Data quality is part of marketing analysis. Evidence might include reconciling platform totals with order records, identifying duplicate events, checking campaign identifiers or documenting which customers enter a report. A resolved discrepancy can be an achievement when it changed confidence in a decision.

Excel and Power Query remain relevant for reconciliation, budget scenarios and ad-hoc analysis. Describe what you built and who used it. Python or R can be presented through repeatable analysis, statistical evaluation or modelling, where they form part of your work.

Explain measurement judgement, not just metrics

Choose measures that fit the business question. Cost per acquisition, return on advertising spend, contribution, payback, retention and lifetime value can all be useful, but their meaning depends on the customer, cost and revenue definitions behind them.

Your CV can demonstrate that understanding in a short phrase: analysing net sales after returns, comparing acquisition cohorts over a common follow-up period, or agreeing the definition of a qualified lead with sales. This is more informative than listing a string of metric abbreviations.

Attribution work deserves a clear description of the method and your role. Comparing attribution views, maintaining a multi-touch model and designing an incrementality study answer different questions. Explain the insight or decision the work supported, including how you handled a material limitation.

If you have marketing mix modelling experience, state whether you prepared data, built or validated models, interpreted an external supplier’s output or used the results for planning. Senior applicants can show how different evidence sources informed investment decisions rather than relying on one reporting view.

Give experiments enough context

For an A/B test or holdout study, identify the question, your contribution to the design or evaluation, the outcome measure and the decision. Relevant responsibilities might include defining eligible customers, checking allocation, agreeing the analysis period or communicating uncertainty around the result.

A brief account could describe evaluating an email holdout using contribution per eligible customer and recommending which audience segments should remain in the programme. That tells an employer more about your judgement than “conducted A/B testing”.

When quoting a result, give the basis of comparison and make the change understandable. An increase from 4% to 5% is one percentage point; a CV can use that direct wording. Where the evidence was inconclusive, the useful outcome may be a revised test, a narrower recommendation or a decision to gather more data.

For a candidate moving into experimentation, analytical support on a campaign test can still be relevant. Explain the preparation, checks or interpretation you handled and the collaboration with a senior analyst or marketing scientist.

Make customer and funnel analysis concrete

Segmentation experience should explain the basis of the groups and how marketing used them. Purchase behaviour, tenure, engagement, product use and value can support different targeting decisions. State whether you developed the segmentation, implemented an existing definition or evaluated the resulting campaigns.

For B2B roles, show your understanding of the journey from lead to opportunity and won business. Experience reconciling marketing and CRM records, allowing for sales-cycle length or working with sales operations can be particularly relevant. In consumer roles, cohort retention, repeat purchase and the relationship between acquisition cost and customer value may carry more weight.

Funnel analysis benefits from a specific stage and action. Identify where customers dropped out, how you investigated the pattern and the change or test the team prioritised. Device, market and acquisition-source comparisons matter when they explain a finding.

Show what sits behind a dashboard

Name the BI tool, but also describe the dataset, metric definitions, refresh process and users where these demonstrate your responsibility. A report used in a weekly trading review has a clearer purpose than an unspecified “interactive dashboard”.

Explain how you helped people interpret the information. You might have agreed measures with finance, written guidance for channel managers or presented a recommendation to a marketing director. Regular reporting and an occasional deep-dive analysis can show different communication strengths.

Time saved through automation is useful when the earlier and later workload are clear. Other outcomes include fewer reconciliation queries, consistent definitions across teams or a reporting process adopted by additional markets. Choose the result that best explains the value of the work.

Choose evidence for your career stage

Entry-level applicants can use a placement, dissertation, voluntary project or a carefully developed analysis of public data. Present a marketing question, the available evidence, your approach and a recommendation. A small, well-explained project can show more judgement than an elaborate dashboard with no decision behind it.

A portfolio is useful when it adds detail the CV cannot hold. Include a concise case study, readable charts and, where relevant, a query or notebook with an explanation of the data. A GitHub link is optional; the important part is that a recruiter can understand the question and your contribution.

Experienced analysts should prioritise decisions, measurement ownership and stakeholder influence. Senior applicants can add evidence of setting analytical priorities, mentoring colleagues, defining metrics or improving the data foundation used across marketing.

List your degree and focused development with the provider and date. Quantitative, business and marketing backgrounds can support different strengths; use projects and work evidence to show how yours applies. Certifications in analytics or BI tools are useful supporting detail when paired with practical use.

For a broader analytical application, the data analyst CV example gives additional ways to present technical work. Alternative Word designs are in the template library.

Marketing Analyst CV FAQs