Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Campaigns that performed
30% weight
Ask for copy they shipped with LLM assistance: email subject lines, landing pages, ad variants. Probe open rates, CTR, conversion lift, and how much output was human-edited.
Evidence to listen for
Names campaigns or programmes they ran, with the metric they moved and the baseline it moved from
Distinguishes their own work from the team's or the agency's
Knows the audience and the offer, not just the channel
Can describe a campaign that failed and why
Five-point scoring guide
1
Poor
No numbers; describes activity rather than results.
2
Needs Improvement
Vanity metrics only; attribution to their own work is unclear.
3
Satisfactory
Real campaigns with some numbers; baselines often missing.
4
Very Good
Named campaigns with metric, baseline, and clear personal ownership.
5
Excellent
Names specific assets and their numbers, for example a paid social variant lifting CTR from 0.9 to 1.6 percent.
02
Evaluation factor
Audience and segmentation
25% weight
Test how they adapt tone for distinct segments and channels: enterprise buyers versus self-serve trials, cold email versus lifecycle nurture. Look for brand voice guides or style docs they built.
Evidence to listen for
Describes real customer segments and what differs between them, not demographic guesses
Has built lifecycle stages, journeys, or nurture logic that reflect actual behaviour
Knows where the audience data comes from and its limits
Writes for the segment rather than for everyone
Five-point scoring guide
1
Poor
One message for everyone; no segmentation thinking.
2
Needs Improvement
Segments by demographics only; no behavioural insight.
3
Satisfactory
Workable segmentation; lifecycle logic is basic.
4
Very Good
Behaviour-driven segmentation and lifecycle design with evidence behind it.
5
Excellent
Rewrites the same offer for two segments on the spot and explains which objection each version answers.
03
Evaluation factor
Measurement and testing
30% weight
Probe their testing habits: subject line A/B splits, sample sizes, holdout groups, and tools like Optimizely, HubSpot, or Google Ads experiments. Check they detect AI-generated hallucinations before publish.
Evidence to listen for
Tests deliberately rather than changing everything at once
Understands attribution limits and does not overclaim
Knows what sample size and duration a test needed
Can name a test whose result contradicted what they expected
Five-point scoring guide
1
Poor
No testing; claims credit for correlation.
2
Needs Improvement
Runs tests but reads them badly; overclaims attribution.
3
Satisfactory
Tests sensibly; rigour drops under deadline pressure.
4
Very Good
Disciplined testing with honest attribution and a result that surprised them.
5
Excellent
Describes a test where the AI draft lost to the human version, and what they changed in the prompt afterwards.
04
Evaluation factor
Working with the business
15% weight
Judge how they work with product marketing, SEO, and legal: intake briefs, review cycles, prompt libraries shared with the team, and handling subject matter expert corrections.
Evidence to listen for
Works with sales, product, or operations rather than throwing campaigns over a wall
Briefs designers and writers well enough to get usable work
Manages agencies or freelancers to a standard
Reports results to leadership without spin
Five-point scoring guide
1
Poor
Works in isolation; reports only flattering numbers.
2
Needs Improvement
Limited coordination; briefs are thin.
3
Satisfactory
Coordinates adequately; reporting is honest if basic.
4
Very Good
Strong partner to sales and product; briefs and reports clearly.
5
Excellent
Shows a reusable prompt or brief template adopted by teammates, and cites feedback that reshaped a campaign.
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