Free Leadership Effectiveness Survey Template | Excel with Auto-Scoring
Free Leadership Effectiveness Survey Template | Excel with Auto-Scoring
This scorecard-style survey helps you turn ai interview questions for marketing leaders into consistent hiring decisions. Instead of “Do you use ChatGPT?”, you get clear ratings on brand safety, GDPR judgement, measurement discipline, and cross-functional collaboration—so you can spot risk early and hire with fewer surprises.
Survey questions
Closed questions (Likert scale, 1–5)
Scale: 1 = Strongly disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly agree.
- Q1. The candidate can explain how they use AI to explore positioning without copying competitors.
- Q2. The candidate protects brand tone by using explicit voice guidelines in AI workflows.
- Q3. The candidate can show how they validate AI-generated claims before publishing.
- Q4. The candidate can localize messaging for DACH audiences without stereotypes or clichés.
- Q5. The candidate can describe a process to prevent brand-unsafe outputs (e.g., taboo topics, legal claims).
- Q6. The candidate uses AI to speed research, but keeps final narrative choices human-owned.
- Q7. The candidate can explain how they handle sensitive topics with extra review steps.
- Q8. The candidate can share examples of prompts or templates that improve messaging quality.
- Q9. The candidate uses AI to generate campaign variants with a clear hypothesis per variant.
- Q10. The candidate avoids vanity metrics and defines success in pipeline, revenue, or retention terms.
- Q11. The candidate can explain how AI supports channel mix decisions, not just content output.
- Q12. The candidate can describe how they prevent “volume spam” in email or paid campaigns.
- Q13. The candidate has a repeatable experimentation cadence (weekly/biweekly) supported by AI.
- Q14. The candidate can explain how they use AI to improve landing page relevance and clarity.
- Q15. The candidate can describe how they use AI to identify audience segments without over-targeting.
- Q16. The candidate can show how they document learnings and roll them into playbooks.
- Q17. The candidate can clearly explain what data they will not enter into public AI tools.
- Q18. The candidate uses Datenminimierung as a default when designing AI-enabled workflows.
- Q19. The candidate can explain how consent impacts targeting, enrichment, and personalization in DACH.
- Q20. The candidate can collaborate with Legal/Datenschutz to set practical “green/yellow/red” rules.
- Q21. The candidate can explain how they prevent accidental leakage of customer or pipeline data.
- Q22. The candidate can describe how they handle cookie loss and tracking gaps without “dark patterns”.
- Q23. The candidate understands that tool convenience does not equal compliance readiness.
- Q24. The candidate can explain how they align AI use with a Betriebsrat or Dienstvereinbarung process.
- Q25. The candidate can distinguish correlation from causation in AI-generated insights.
- Q26. The candidate uses experiments or holdouts when the decision is high-stakes.
- Q27. The candidate can explain attribution limits and avoids false precision in dashboards.
- Q28. The candidate can describe how they validate model outputs against ground truth data.
- Q29. The candidate can explain how they forecast demand with assumptions and confidence ranges.
- Q30. The candidate can describe a process to detect overfitting in targeting or creative optimization.
- Q31. The candidate can explain how they handle small samples and noisy data responsibly.
- Q32. The candidate can translate measurement into actions for brand, demand, and revenue teams.
- Q33. The candidate can describe a structured prompting approach (context, constraints, examples, checks).
- Q34. The candidate maintains a prompt library or templates for recurring marketing tasks.
- Q35. The candidate can show how they review AI drafts for accuracy, tone, and bias.
- Q36. The candidate can explain when not to use AI because risk outweighs speed.
- Q37. The candidate can design AI-supported briefs for creative, agencies, or internal teams.
- Q38. The candidate can explain how they operationalize AI for reporting without “copy-paste dashboards”.
- Q39. The candidate can describe how they keep human creativity central in brand work.
- Q40. The candidate can describe quality controls that reduce hallucinations and errors.
- Q41. The candidate can align AI-enabled marketing with Sales/RevOps lead definitions and SLAs.
- Q42. The candidate can explain how they protect CRM hygiene when AI touches lead scoring or routing.
- Q43. The candidate can run joint planning with Sales to avoid “marketing-only” optimization loops.
- Q44. The candidate can handle conflict between growth pressure and compliance constraints.
- Q45. The candidate can explain how they collaborate with IT on tool access, security, and permissions.
- Q46. The candidate documents decisions so Legal and leadership can audit what changed and why.
- Q47. The candidate can explain how they communicate AI limits to stakeholders without defensiveness.
- Q48. The candidate can describe escalation paths when an AI workflow creates risk.
- Q49. The candidate can introduce AI without undermining junior talent or learning opportunities.
- Q50. The candidate can explain how they build psychological safety around AI experimentation.
- Q51. The candidate can describe how they upskill the team with role-specific training and practice.
- Q52. The candidate sets clear expectations for disclosure and review of AI-assisted work.
- Q53. The candidate can explain how they prevent bias in AI-assisted audience or content decisions.
- Q54. The candidate can describe how they handle mistakes openly and improve the process.
- Q55. The candidate can define “human in the loop” in practical, day-to-day workflow terms.
- Q56. The candidate can explain how they measure productivity gains without rewarding unsafe shortcuts.
- Q57. The candidate can evaluate AI/martech vendors with a clear requirements checklist.
- Q58. The candidate can compare platform vs point tools based on integration, governance, and cost.
- Q59. The candidate asks for data residency, access controls, and auditability in vendor reviews.
- Q60. The candidate can explain how they would run a low-risk pilot before full rollout.
- Q61. The candidate can describe how they prevent shadow AI by offering safe default tools.
- Q62. The candidate can explain how they negotiate ownership of prompts, outputs, and data retention.
- Q63. The candidate can define exit criteria if a vendor fails on quality or governance.
- Q64. The candidate can explain how they keep vendor choices aligned with company strategy and RevOps.
Overall rating (0–10)
- Q65. How confident are you that this leader will use AI safely and effectively in your marketing org? (0–10)
Open-ended questions
- What evidence most increased your confidence in the candidate’s AI judgement?
- What is the biggest AI-related risk you see with this candidate, and why?
- If we hire them, what is the first AI policy or workflow you want them to implement?
- What would you ask in a follow-up interview to close remaining gaps?
| Question(s) / area | Score / threshold | Recommended action | Responsible (Owner) | Target / deadline |
|---|---|---|---|---|
| Brand & messaging (Q1–Q8) | Average <3,5 | Add a 30-minute case: “DACH product launch messaging”; require tone + claim checks. | Hiring Manager + Brand Lead | Schedule within ≤7 days |
| Demand gen & campaigns (Q9–Q16) | Average <3,5 | Run a mini planning exercise: channel mix + 2 experiments + success metrics. | Growth Lead + RevOps | Complete within ≤7 days |
| Data, privacy & tracking (Q17–Q24) | Any item ≤2 | Escalate to Legal/Datenschutz review; clarify “do-not-enter” data rules and tool boundaries. | HRBP + DPO/Privacy | Decision within ≤5 days |
| Measurement & experiments (Q25–Q32) | Average <3,0 | Require a measurement deep-dive: attribution limits, holdouts, forecast assumptions. | Analytics Lead + Hiring Manager | Schedule within ≤10 days |
| Workflow & prompt design (Q33–Q40) | Average <3,5 | Ask for 2 real prompts/playbooks; assess quality controls and review steps. | Hiring Manager | Collect within ≤72 h |
| Collaboration (Q41–Q48) | Average <3,5 | Add joint panel with Sales + RevOps; test SLA, CRM hygiene, escalation paths. | CRO/Head of Sales + RevOps | Run within ≤10 days |
| Team enablement & culture (Q49–Q56) | Average <3,5 | Ask for an onboarding plan: upskilling, psychological safety, junior development safeguards. | Hiring Manager + People Partner | Draft within ≤7 days |
| Vendor & ecosystem (Q57–Q64) | Average <3,0 | Run a vendor selection scenario: requirements, pilot design, data residency, exit criteria. | IT/Security + Marketing Ops | Schedule within ≤14 days |
Key takeaways
- Turn subjective AI interviews into comparable scores across every panelist.
- Spot GDPR and brand-safety risk before it becomes a public incident.
- Separate “prompting skill” from judgement, measurement discipline, and collaboration.
- Use thresholds to trigger follow-ups, not endless debate.
- Convert results into a 30–60–90 day AI enablement plan after hire.
Definition & scope
This survey measures how safely and effectively a marketing leader uses AI across brand, demand, data, measurement, workflows, collaboration, team culture, and vendor decisions. It’s designed for interview panels (CEO/CRO/CMO, RevOps, Legal/Datenschutz, HR) and supports hiring decisions, onboarding priorities, and targeted development plans linked to your skill management approach.
How to run this as a hiring scorecard
Use the survey right after the AI interview block, while details are fresh. Each rater scores based on evidence they heard or saw, not on “confidence vibes”. If a topic didn’t come up, score 3 and flag it for follow-up.
If you see one “critical miss” in privacy, treat it differently than weak prompting. A candidate can learn better prompts fast, but weak data judgement is harder to fix.
- HRBP sends the survey to panelists and sets a completion SLA of ≤24 h.
- Each panelist adds 1–2 evidence notes per low score within ≤24 h.
- Hiring Manager reviews domain averages and triggers follow-ups within ≤3 days.
- Legal/Datenschutz reviews any Q17–Q24 item scored ≤2 within ≤5 days.
- RevOps validates Q41–Q48 concerns and proposes a test scenario within ≤7 days.
| Rater | What they should focus on | Evidence they should capture |
|---|---|---|
| Hiring Manager (Marketing) | Brand, demand, workflows, team enablement | Examples, playbooks, review steps, leadership trade-offs |
| RevOps / Sales leader | Lead handover, SLAs, CRM hygiene, revenue alignment | Definitions, escalation paths, anti-gaming controls |
| Legal/Datenschutz / DPO | Consent, data boundaries, Datenminimierung, governance | Tool/data rules, retention thinking, risk handling |
| Analytics lead | Measurement, experiments, forecasting discipline | Holdout logic, assumptions, limits, data validation |
How to pair this with ai interview questions for marketing leaders
The survey works best when your panel asks consistent questions, then scores consistently. Use it as the “answer key”: pick the domains you need, run structured probes, then rate Q1–Q64 based on what the candidate actually did.
If the role is more brand-heavy, weight Q1–Q8 and Q49–Q56 more. If it’s performance-heavy, weight Q9–Q16 and Q25–Q32 more, but keep the privacy domain non-negotiable.
- HR chooses 3 domains as “must-pass” and shares them with the panel within ≤5 days of kickoff.
- Hiring Manager assigns domain ownership to panelists (1–2 domains per person) within ≤48 h.
- Panelists use shared probes and capture evidence notes during the interview within the same day.
- HRBP collects scores and flags domain averages <3,5 within ≤24 h after the interview.
- Hiring Manager runs a 15-minute calibration huddle within ≤3 days to align interpretations.
| Interview panel blueprint | Timebox | Use these question ranges | Output you should get |
|---|---|---|---|
| Marketing Manager / Team Lead AI block | 20 minutes | Q9–Q16, Q33–Q40, Q49–Q52 | Evidence of safe execution and basic workflow discipline |
| Head of Marketing AI + governance deep-dive | 30–40 minutes | Q17–Q24, Q25–Q32, Q41–Q48 | Judgement under constraints, measurement discipline, cross-functional alignment |
| CMO strategy + governance block | 30 minutes | Q1–Q8, Q17–Q24, Q57–Q64 | Strategic direction, guardrails, vendor approach, operating model |
Governance, brand safety and GDPR guardrails (DACH lens)
In DACH, “Can we do this?” matters as much as “Can we ship this?”. You want leaders who can move fast inside constraints, involve Datenschutz early, and work with a Betriebsrat when AI changes how people work.
Keep this non-legal and practical: define what data is allowed, where it’s allowed, and who approves exceptions. When in doubt, default to Datenminimierung and human review.
- DPO/Privacy defines “do-not-enter” data examples for marketing within ≤14 days of process start.
- Marketing Ops documents approved tools and access rules (RBAC) within ≤21 days.
- Legal + HR draft a simple AI usage guideline for marketing within ≤30 days.
- Works council/Betriebsrat touchpoint is scheduled by HR within ≤30 days when required.
- Hiring Manager adds a “risk scenario” probe to interviews within ≤7 days.
| Topic | Green (ok without escalation) | Yellow (needs review) | Red (stop) |
|---|---|---|---|
| Customer/prospect data in prompts | Fully anonymized, minimal fields | Pseudonymized with business justification | Names, emails, phone numbers, deal notes |
| Brand claims and compliance | AI drafts + human fact-check | Regulated claims with extra approvals | Publishing unverified claims |
| Targeting and personalization | Consent-aligned segments | New enrichment sources | Manipulative or discriminatory targeting |
| Employee impact | Training + clear expectations | Monitoring concerns raised by Betriebsrat | Opaque scoring of employees by AI |
Domain insights you can act on (without re-litigating the interview)
After scoring, you want fast decisions: hire, no-hire, or “hire with conditions”. Use domain averages to drive that, and use single-item lows (≤2) as risk flags. This keeps the conversation factual and reduces bias from the loudest voice in the room.
A helpful rule: if privacy (Q17–Q24) is weak, pause. If prompting/workflow (Q33–Q40) is weak but judgement is strong, you can coach it.
- HRBP produces a 1-page score summary with domain averages within ≤48 h.
- Hiring Manager writes “hire conditions” for any domain average 3,0–3,4 within ≤3 days.
- Analytics Lead proposes one validation task when Q25–Q32 average <3,5 within ≤7 days.
- RevOps proposes a joint KPI/SLA proposal when Q41–Q48 average <3,5 within ≤7 days.
| Domain (questions) | What “strong” usually looks like | What you should verify | Fast follow-up if needed |
|---|---|---|---|
| Privacy & tracking (Q17–Q24) | Clear boundaries, escalation, Datenminimierung | Tool choices, data examples, works council awareness | Run a 15-minute “what data goes where” scenario |
| Measurement & experiments (Q25–Q32) | Holdouts, assumptions, limits, decision focus | How they handle noisy data and attribution gaps | Ask for a one-page experiment plan with success metrics |
| Collaboration (Q41–Q48) | Shared definitions, SLAs, CRM hygiene ownership | How conflict gets resolved under pressure | Add a Sales/RevOps panel and test the handover flow |
| Team enablement (Q49–Q56) | Upskilling plan, psychological safety, guardrails | How juniors learn, not just produce more output | Request a 30–60–90 day enablement plan draft |
Using results for onboarding and development after hire
The survey isn’t just for selection. It gives you an onboarding backlog: what to standardize, what to train, and what to govern. If you already run structured development cycles, tie the weakest domain to the leader’s first 30 days goals and check progress in 1:1s.
A talent platform like Sprad Growth can help automate check-ins, capture action items, and keep follow-ups from disappearing. For broader people systems, align actions with your talent management routines so AI capability becomes measurable over time.
- Hiring Manager drafts a 30–60–90 plan covering the weakest 2 domains within ≤10 days of offer acceptance.
- HRBP schedules a governance kickoff with Legal/IT/DPO within ≤14 days of start date.
- Marketing Ops creates a shared prompt/playbook space within ≤21 days of start date.
- Leader runs a team “safe AI ways of working” session within ≤30 days of start date.
- HRBP reviews progress against domain targets at day 45 and day 90 within ≤7 days of each checkpoint.
Scoring & thresholds
Use a 1–5 scale for Q1–Q64 (1 = Strongly disagree, 5 = Strongly agree). Treat Average <3,0 as critical, 3,0–3,9 as needs improvement, and ≥4,0 as strong. Convert scores into decisions by applying thresholds: privacy items ≤2 trigger escalation, domain averages <3,5 trigger a follow-up case, and “must-pass” domains decide hire/no-hire.
Follow-up & responsibilities
Follow-up only works when it has owners and deadlines. Keep a simple routing: Hiring Manager owns brand/demand/workflow gaps, RevOps owns revenue alignment gaps, Legal/Datenschutz owns data boundary gaps, and HR owns process discipline and fairness checks.
Response times keep momentum: react within ≤24 h to any privacy red flag (Q17–Q24 item ≤2), create a follow-up plan within ≤7 days for any domain average <3,5, and close the loop with a written decision within ≤14 days. HR documents actions and keeps an audit trail for consistency.
- HRBP requests missing evidence notes from panelists within ≤24 h after scoring.
- Hiring Manager confirms whether a follow-up interview is needed within ≤3 days.
- Legal/Datenschutz provides a risk opinion on privacy concerns within ≤5 days.
- RevOps proposes SLA/KPI alignment checks within ≤7 days when collaboration scores are low.
- HRBP finalizes the decision log (scores, evidence, actions) within ≤14 days.
Fairness & bias checks
Run fairness checks so you don’t penalize candidates for style, accent, or “AI buzzword fluency”. Compare results by interviewer group (Marketing vs Sales/RevOps vs Legal/HR), location, and seniority of raters. Use minimum-group rules for reporting patterns: if fewer than 3 raters scored a domain, treat it as directional and gather more evidence.
Typical patterns and what to do: (1) Marketing rates high, Legal rates low on Q17–Q24—run a focused privacy scenario and decide with explicit data examples. (2) One rater scores consistently 1–2 below everyone—HR runs a 10-minute calibration to align anchors. (3) Remote candidates score lower on “collaboration”—add a structured cross-functional case instead of relying on “presence” cues.
- HRBP checks rater variance per domain and flags gaps ≥1,0 points within ≤48 h.
- Hiring Manager runs a calibration huddle when variance ≥1,0 within ≤5 days.
- HRBP audits language in open comments for bias signals within ≤7 days.
- Panel repeats one standardized probe when evidence is missing within ≤10 days.
Examples / use cases
Use case 1: Strong growth operator, weak privacy judgement. Scores were high on Q9–Q16 and Q25–Q32, but Q17–Q24 included two items scored ≤2. The team paused the process and ran a 15-minute “what data goes where” scenario with the DPO. The decision: proceed only if the candidate accepted strict tool boundaries and an escalation workflow, documented for the first 30 days.
Use case 2: Great brand leader, fuzzy measurement discipline. Q1–Q8 and Q49–Q56 averaged ≥4,2, but Q25–Q32 averaged <3,5. The decision: hire, with a condition. The leader’s first 60 days included an experimentation cadence, clear KPI definitions with RevOps, and a basic holdout test. Progress was reviewed at day 45 with the analytics lead and hiring manager.
Use case 3: Solid all-rounder, weak cross-functional alignment. Most domains were 3,6–4,1, but Q41–Q48 averaged <3,5. The panel added a joint Sales/RevOps interview focused on SLAs, lead definitions, and CRM hygiene ownership. The decision: proceed after the candidate proposed a shared dashboard, a weekly pipeline review ritual, and escalation rules for lead quality disputes.
Implementation & updates
Start small, then scale. Pilot the survey with one hiring process (one role level), review where raters struggled, and tighten prompts and thresholds. Roll out to all marketing leadership hires once you see consistent scoring and faster decisions. Train hiring managers on evidence-based scoring, and review the question set 1× per year as tools, regulations, and workflows change.
Track a few simple metrics so this doesn’t become “another form”: participation rate (target ≥90 % of panelists), completion time (target ≤10 minutes), follow-up rate triggered by thresholds, follow-up completion rate (target ≥80 % within deadlines), and outcome quality signals (e.g., onboarding issues linked to low-scoring domains within first 90 days).
- HRBP pilots the survey in 1 hiring process within ≤30 days.
- Hiring Manager collects rater feedback and proposes edits within ≤14 days after the pilot.
- HRBP trains all marketing interviewers on scoring anchors within ≤45 days.
- HRBP reviews thresholds and item clarity 1× per year and updates within ≤30 days.
- DPO/Privacy reviews the privacy domain annually within ≤30 days of the review window.
When AI sits inside almost every marketing workflow, you need a hiring process that tests judgement, not tool familiarity. This survey gives your panel shared language, clear thresholds, and a simple path from “I feel” to “we saw evidence”. It also helps you catch risk early—especially around GDPR, consent, and brand safety—before it becomes a costly incident.
To move fast, pick one pilot role, load Q1–Q64 into your survey tool, and assign domain owners across the panel. Name an HR owner for deadlines and a Legal/Datenschutz owner for escalation. After the pilot, tighten any items that created confusion, and keep the decision table as your default playbook for follow-up actions.
FAQ
How often should we use this survey?
Use it for every marketing leadership hire where AI will touch messaging, targeting, measurement, or customer data. If you hire frequently, keep the survey constant for 2–3 months so scores stay comparable, then update quarterly. For lower-volume hiring, review once per year and after any major governance change (new AI policy, new vendor stack, or a new Dienstvereinbarung).
What should we do if scores are very low in one domain?
Don’t debate it in circles—trigger a specific follow-up. If any privacy item (Q17–Q24) is ≤2, escalate to Legal/Datenschutz within ≤24 h and run a concrete scenario. If measurement (Q25–Q32) is <3,0, ask for a one-page experiment plan and discuss assumptions. If collaboration (Q41–Q48) is low, add a Sales/RevOps panel and test SLA thinking.
How do we handle critical open-ended comments from panelists?
Require evidence. HR should ask the rater to add: (1) what the candidate said/did, (2) why it matters, (3) what question would confirm or refute it. Remove speculation and protected-characteristic inferences. If the comment points to compliance risk, route it to Legal/Datenschutz. If it points to style or communication preference, use a structured follow-up task instead of “gut feel”.
How do we avoid turning this into a compliance checklist?
Keep the goal behavioural: can the leader make good calls under constraints and explain trade-offs? Use the privacy domain to set boundaries, then focus on outcomes—better experiments, clearer messaging, cleaner handovers to Sales. Let candidates show artifacts (prompts, playbooks, dashboards) and ask how they review AI output. If you need a policy reference point, keep it high-level and non-binding, such as the European Commission’s AI regulatory framework overview.
How should we update the question bank over time?
Update based on failure modes you see, not on new tool hype. Once per year, review: which items had low agreement between raters, which follow-ups predicted onboarding issues, and which domains changed due to new tracking realities or vendor shifts. Keep at least 70 % of items stable for comparability. Any change should ship with updated scoring anchors and a short rater training refresh.
Jürgen Ulbrich
Jürgen Ulbrich has more than a decade of experience in developing and leading high-performing teams and companies. As an expert in employee referral programs as well as feedback and performance processes, Jürgen has helped over 100 organizations optimize their talent acquisition and development strategies.
