Customer Intelligence Executive

A Customer Intelligence Executive analyses data about a company's customers — who they are, what they buy, when they stop buying — and turns it into findings that marketing, sales or product teams can act on. The work sits between raw data and business decisions: pulling figures from customer databases, spotting patterns, and explaining what they mean to people who are not analysts. It's most common in retail, financial services, telecoms, utilities, media and marketing agencies, where there are large customer bases and a constant need to understand them.

Starting salary: £25k–£32k
Live graduate jobs: 2 open now
Main way in: Direct entry as an analyst or executive after graduating
They usually ask for: A degree is usually expected, commonly a 2:1

Approximate graduate salary

Typically somewhere around £25,000–£32,000 to start, though this varies widely. London and financial services tend to sit at the higher end, while agencies, smaller firms and roles outside the South East often start lower. Salaries tend to rise noticeably once you have a couple of years of solid SQL and reporting experience.

What you'd actually do

Writing queries (usually SQL) against a customer database or data warehouse to pull lists and figures — for example, customers who bought in the last quarter but not this one, or how a particular product sells across different age groups and regions.
Building and updating dashboards and regular reports in tools such as Power BI, Tableau or Looker, so that marketing and commercial teams can see customer numbers, retention and campaign performance without asking an analyst each time.
Analysing how a marketing campaign performed: who was targeted, who responded, what they spent, and whether the campaign was worth the money compared with a control group who weren't contacted.
Building or refreshing customer segments — grouping customers by behaviour, value or lifecycle stage — and producing the selection files that the marketing team uses to send emails or direct mail to the right people.

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Cleaning and sense-checking data: chasing down why two systems report different customer counts, fixing duplicate records, and flagging data quality issues to the data engineering or CRM team.
Presenting findings in short decks or written summaries to non-technical stakeholders, and sitting in planning meetings where the question is usually 'what do we do next with this group of customers?'
Pulling in other sources alongside internal data — survey responses, market research panels, geodemographic or postcode-level classification data, web and app analytics — to build a fuller picture of a customer group.

How graduates get in

Direct entry as an analyst or executive after graduating — this is the most common route. Job titles vary enormously for essentially the same work: Customer Insight Analyst, CRM Analyst, Marketing Analyst, Data Analyst (Customer), Insight Executive.
Graduate schemes in marketing, data/analytics or commercial functions at large retailers, banks, insurers, telecoms firms and utilities. These typically rotate you through several teams, with customer insight as one placement.
Starting in a broader marketing, CRM or campaign executive role and moving sideways into the analytical side once you've shown you can handle data — very common, particularly at smaller employers.
Agency route: data-driven marketing, CRM and insight agencies hire graduates in volume and train them on client data. The pace is faster and you'll work across several clients at once.

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Market research route: starting as a research executive on quantitative projects, then moving into customer intelligence where behavioural data matters more than survey data.
Via a master's in business analytics, data science, marketing analytics or similar — useful but by no means required, and more of a conversion route for graduates from non-numerate subjects.

What employers ask for

A degree is usually expected, commonly a 2:1, though 2:2 with relevant experience is accepted in plenty of places — especially at agencies and smaller companies.
Subject matters less than numeracy. Economics, maths, statistics, business, marketing, geography, psychology and the sciences are all common. Humanities graduates do get in, but you'll normally need to show some evidence of working with data.
Some employers ask for A-level maths or a strong GCSE maths grade as a numeracy screen; others don't mention it at all. This varies a lot.
SQL is the single most commonly requested technical skill. Some employers expect it on day one, others will teach you — read adverts carefully, as this is the biggest dividing line between roles.

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Excel to a good standard (pivot tables, lookups, handling large files) is near-universal. Experience with a dashboard tool, and increasingly Python or R, is a bonus rather than a requirement at entry level.
No professional qualification is required. Some people later take courses or certifications in analytics tools, or qualifications through marketing and research professional bodies, but these are optional and usually employer-funded.

Skills that matter

SQL and data querying

Almost all customer data sits in databases, and being able to extract exactly the right records yourself — rather than waiting for someone else to — is what makes the role workable.

Translating a vague business question into an analysable one

Stakeholders ask things like 'why are our best customers leaving?'; your job is to turn that into a defined question about a defined group over a defined period that the data can actually answer.

Statistical judgement

You need to know when a difference between two groups is meaningful and when it's noise, and to resist presenting a coincidence as a finding.

Data visualisation and clear reporting

Most of your audience will look at a chart for a few seconds, so the chart has to make the point on its own without a paragraph of explanation.

Scepticism about your own numbers

Customer data is messy — duplicates, test accounts, changed definitions — and the analysts people trust are the ones who check their figures before someone else finds the error.

Commercial understanding of how the business makes money

An insight is only useful if it connects to something the company can change, like pricing, a campaign budget or which customers get contacted.

Putting these on a CV? We counted which skills 12,914 live graduate adverts ask for, and what to do with the answer — part of our application advice.

Where it leads

Executive or junior analyst: you're mostly given defined questions and asked to answer them, with your work checked by someone more senior. Typically a couple of years, though this varies.
Analyst or Senior Insight Analyst: you own areas of analysis, set your own approach, and present directly to marketing or commercial managers. Many people start specialising here — customer value and retention, segmentation, campaign measurement, or pricing.
Insight Manager or Lead Analyst: managing one or two analysts, shaping what the team works on, and acting as the main analytical contact for a business area. Timelines to this point vary widely — faster in agencies and fast-growing businesses, slower in large established organisations.
From there the paths genuinely diverge: Head of Customer Insight or Head of CRM on the leadership track; a move into data science if you've built up Python and modelling skills; a move into marketing, product or commercial management if you prefer owning decisions to analysing them; or consultancy and agency-side work.

What people get wrong

“It's a customer-facing job — you'll be talking to customers.”

You'll rarely speak to a customer. 'Customer' describes the subject of the analysis, not the audience. Your actual stakeholders are internal marketing, commercial and product colleagues.

“The hard part is the analysis.”

A large share of the time goes on finding the data, working out which of three conflicting sources is correct, and cleaning it. The analysis itself is often the shortest stage, and the most valued skill is explaining the result simply.

“You need to be a programmer or data scientist.”

Most customer intelligence work is SQL, Excel and a dashboard tool. Predictive modelling and machine learning exist in some teams but are far from universal at this level — many roles never touch them.

“Findings automatically get acted on because they're backed by data.”

Plenty of good analysis gets ignored. Getting a recommendation adopted depends on timing, on framing it around a decision someone is already trying to make, and on having built credibility with that person — which is why the influencing side of the job matters as much as the technical side.

Where this varies

The title covers quite different jobs depending on employer. In a large retailer, bank or telecoms firm you'll likely sit in a dedicated insight team with proper data infrastructure, specialise fairly narrowly, and have clear training and progression. At a smaller company you may be the only person doing this work, which means broader exposure but also doing your own data wrangling and building things from scratch. At a marketing or data agency you'll work across multiple clients to tight deadlines, with more emphasis on campaign measurement and targeting. Some roles labelled 'customer intelligence' lean heavily towards market research and survey work rather than behavioural database analysis — worth checking in the advert, because the skill sets only partly overlap. Note also that 'customer intelligence' occasionally appears in a competitive-intelligence or sales-prospecting sense, meaning researching prospective client companies rather than analysing your own customer base; that is a different job with the same words in the title.

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General guidance about the role across the UK market, not about any specific employer. Entry routes and requirements vary — always check the individual job advert.