marketing growthcustomer data platformexperimentationpersonalizationrecommendation engines
Complete evaluation framework
What to assess and how to score it
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 personalization programmes they owned end to end: recommendation modules, onboarding flows, dynamic pricing or lifecycle messaging, plus uplift in conversion, AOV or retention.
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 personalized experiences shipped, the surfaces they ran on, and measured lift with baseline and time period attached.
02
Evaluation factor
Audience and segmentation
25% weight
Probe how they built audiences: CDP segments in Segment or Tealium, behavioural traits, propensity scores, and how they avoided over-fragmenting into unusable micro-cohorts.
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 segment logic tied to observed behaviour and value, and explains when broad rules beat narrow one-to-one targeting.
03
Evaluation factor
Measurement and testing
30% weight
Test experiment discipline: Optimizely or Adobe Target setup, holdout groups, sample sizing, primary versus guardrail metrics, and how they handled flat or negative results.
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
Runs holdouts routinely, sizes tests before launch, distinguishes personalization lift from novelty effects, and reports losing variants openly.
04
Evaluation factor
Working with the business
15% weight
Check how they worked with product, engineering and legal: backlog negotiation for content variants, consent and GDPR constraints, and roadmap trade-offs against core product work.
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 negotiations with engineering on variant tooling and shows respect for consent rules without abandoning personalization goals.
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