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 launches, hackathons, or conference talks they drove: API adoption numbers, sign-ups from a keynote, GitHub stars, or docs traffic lifted after a tutorial series.
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 launches with adoption figures: SDK installs, active API keys, repo forks, or talk-attributed trial conversions.
02
Evaluation factor
Audience and segmentation
25% weight
Probe how they segment developer audiences: backend versus mobile, hobbyist versus enterprise architect, and how their demo, sample repo, and talk depth changed 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
Distinguishes practitioner personas clearly and shows content reshaped for each, citing where a message failed with the wrong crowd.
03
Evaluation factor
Measurement and testing
30% weight
Test measurement habits: attribution from DevRel activity to signups, funnel tracking in Amplitude or Mixpanel, docs analytics, community sentiment, and how they judged a talk that flopped.
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
Tracks activation and retention beyond vanity metrics, runs content experiments, and states honestly which channels showed no measurable return.
04
Evaluation factor
Working with the business
15% weight
Check how they fed developer feedback into product roadmaps, worked with engineering on breaking changes, and defended community interests against product marketing pressure.
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 product or docs changes they triggered from community signals, plus evidence of engineers trusting their technical judgement.
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