How to assess · For hiring teams

How to Assess Google Analytics Skills When Hiring

The test formats that actually work for Google Analytics, what a strong answer looks like, sample questions and a scoring rubric you can use as-is.

The short answer

Assess Google Analytics with a task, not a conversation: measurement plan for a site, diagnose a broken report, ai-scored assessment (e.g. cohesyve) or report-to-recommendation. Score it against written criteria you fix before you see any submissions, and weight the criteria that the role actually depends on.

Starts from a measurement plan: what questions, what events, what parameters
Implements tracking through a tag manager with naming conventions and a data layer
Configures conversions and audiences that match business outcomes
Diagnoses data quality: missing events, duplicates, broken sessions, bot traffic

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Google Analytics skill is widely claimed and rarely deep. Many marketers can read a dashboard; far fewer can set up event tracking that measures what the business needs, tell when the data is wrong, or explain why two reports disagree. Since the move to event-based measurement, the gap has widened: the tool assumes a measurement plan, and most implementations do not have one. This page covers how to assess Google Analytics for marketing, growth and web analyst roles: measurement design, implementation and tagging, data quality, and turning reports into decisions.

Why Google Analytics is worth testing

Bad analytics is worse than none, because it produces confident decisions on wrong numbers. Untracked conversions, double-counted events, sessions broken by cross-domain journeys, attribution read as causation — each leads to budget moved for the wrong reason. Testing shows whether a candidate can design measurement and distrust it appropriately, and that determines whether the analytics are an asset.

What strong Google Analytics looks like

Starts from a measurement plan: what questions, what events, what parameters
Implements tracking through a tag manager with naming conventions and a data layer
Configures conversions and audiences that match business outcomes
Diagnoses data quality: missing events, duplicates, broken sessions, bot traffic
Understands attribution models and their limits, and does not read them as causation
Handles consent and privacy requirements in the implementation
Turns reports into a recommendation, not a screenshot

Ways to assess Google Analytics

Measurement plan for a site

Describe a business and its site and ask for the events, parameters, conversions and audiences needed to answer three stated questions. Forty-five minutes.

Pros

Tests design thinking, which most implementations lack.

Cons

Design-based; pair with a diagnosis.

Best for Any level.

Diagnose a broken report

Provide reports showing a conversion count that does not match the CRM, sessions split across a payment domain, and a spike from a referral. Ask what is wrong.

Pros

The real problems; scoreable.

Cons

Needs crafted screenshots.

Best for Mid and senior roles.

AI-scored assessment (e.g. Cohesyve)

Generate a Google Analytics task from the job description — a measurement plan, a data-quality diagnosis, an attribution question — with a rubric. Each candidate receives a different variant; the reasoning is scored alongside the work.

Pros

Asynchronous and consistent across a large pool; a different task per candidate removes shared answers; scores the explanation, which is where judgement shows.

Cons

Cannot run tools on the candidate's behalf; keep a human review for shortlisted finalists.

Best for Screening an applicant pool fairly before interview time is spent.

Report-to-recommendation

Show a set of reports and ask what the business should do.

Pros

Tests the outcome that matters.

Cons

Narrow.

Best for Marketing analyst roles.

Cohesyve

Run a Google Analytics assessment on your next opening

Cohesyve generates a unique Google Analytics task per candidate from your job description, with the scoring rubric attached. Questions are different for every applicant, so they cannot be shared or looked up.

Try it free →Book a 30-min demo

What to test

Measurement design

Whether tracking starts from questions.

Define events and parameters for a sign-up funnelChoose conversions that match revenueWrite naming conventions for a tagging plan

Implementation

Whether tracking works.

Set up an event through a tag manager with a data layerConfigure cross-domain trackingExplain what a consent mode does

Data quality

Whether they can tell when the data is wrong.

Explain why conversions differ from the CRMFind a double-firing tagFilter bot and internal traffic

Interpretation

Whether reports become decisions.

Explain the limits of last-click attributionRecommend a budget change from channel data and say what could be wrongDesign a report for a stakeholder question

Sample Google Analytics questions

What is the difference between an event and a conversion?

Entry

Look for Everything is an event; a conversion is an event marked as an outcome; conversions should match business value.

Conversions in Analytics are 20% higher than in the CRM. Why might that be?

Mid

Look for Double-firing tags, counting method, test traffic, definitional differences; check implementation first.

Sessions break when users go to the payment provider and back. What is happening and how do you fix it?

Mid

Look for Cross-domain tracking not configured; referral exclusions; linker parameters.

Last-click attribution says paid search drives most conversions. Should we move budget there?

Mid

Look for Attribution is not causation; upper-funnel channels are undervalued; suggest incrementality tests.

Design the measurement for a new product launch with a two-week deadline.

Senior

Look for Prioritised event list, conversions, naming, a data layer spec for developers, a QA plan, and consent handling.

Red flags

Cannot describe a measurement plan
Has never used a tag manager
Trusts the numbers without reconciliation
Reads attribution as causation
Reports without recommendations

Scoring rubric

Criterion	Weight	What strong looks like
Measurement design	30%	Tracking follows from business questions.
Implementation	25%	Tag manager, data layer, conventions, consent.
Data quality	25%	Finds and explains discrepancies.
Interpretation	20%	Reports lead to sound decisions with caveats.

Mistakes hiring teams make

Testing interface navigation instead of measurement thinking
Not including a data-quality diagnosis
Accepting attribution reports as fact
Ignoring consent and privacy
Assuming marketing experience includes implementation skill

Roles that need Google Analytics

Marketing AnalystWeb AnalystGrowth MarketerDigital Marketing ManagerProduct AnalystSEO Specialist

Common questions

Should I test the tag manager as well as Analytics?

Yes for implementation roles. Analytics is only as good as the tagging feeding it, and the tag manager is where that lives.

What is the best single Google Analytics question?

Ask why Analytics conversions differ from the CRM. Implementation knowledge and healthy scepticism show together.

Are Google certifications a good signal?

They confirm interface familiarity. Measurement design and data-quality judgement need to be tested directly.

How long should a Google Analytics assessment take?

Forty-five minutes for a measurement plan; thirty for a diagnosis.

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