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 which campaigns used emotion signals (sentiment scoring, Hume or Affectiva outputs, dynamic creative) and what shifted: lift in engagement, retention, unsubscribe rate, or brand sentiment deltas.
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 emotion-led campaigns with baseline and post numbers, and separates gains from personalization versus creative quality.
02
Evaluation factor
Audience and segmentation
25% weight
Probe how they build emotional segments beyond demographics: journey-stage sentiment, churn-risk tone signals, voice-of-customer coding, and where empathy modelling misread an audience.
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 segments built from real customer language and admits at least one case where inferred emotion proved wrong and was corrected.
03
Evaluation factor
Measurement and testing
30% weight
Test measurement rigour on soft signals: how they validate emotion scores against behaviour, run holdouts, avoid overfitting to sentiment proxies, and set guardrails for creepiness or consent.
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
Validates emotion models against actual conversion or retention data, uses holdout groups, and names consent and GDPR limits they enforced.
04
Evaluation factor
Working with the business
15% weight
Check collaboration with data science, legal, and brand teams: who signed off on emotional targeting rules, how they briefed engineers, and how ethical objections were resolved.
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
Cites concrete review processes with legal and data science partners, and an instance where they killed a tactic on ethical grounds.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
