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 specific AI-assisted content they shipped: blog series, short-form video, newsletters. Look for named tools (ChatGPT, Claude, Midjourney, ElevenLabs, Descript) and traffic or engagement numbers attached.
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 published pieces with organic sessions, watch time or signups, and explains which parts were AI-generated versus human rewritten.
02
Evaluation factor
Audience and segmentation
25% weight
Probe how they adapt tone and format per channel: LinkedIn thought leadership versus TikTok hooks versus lifecycle email. Check whether they build prompt libraries or brand voice guides per segment.
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
Describes distinct voice and format choices for named personas, backed by a documented brand voice prompt or style guide they maintain.
03
Evaluation factor
Measurement and testing
30% weight
Test their testing: headline and thumbnail variants, hook A/B runs, GA4 or Search Console tracking, and how they detect AI content that underperforms or gets flagged.
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
Cites concrete tests with before and after metrics, and describes killing or rewriting AI drafts that failed retention or ranking checks.
04
Evaluation factor
Working with the business
15% weight
Assess how they handle SME reviews, legal or factual accuracy checks, and disclosure policy on AI use. Look for evidence of working inside a content calendar with designers and product teams.
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 fact-check and SME approval workflow, clear stance on AI disclosure, and steady delivery against an agreed publishing calendar.
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