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Cross-Department Performance & Collaboration Review

Captures how internal stakeholders experience working with a specific department — responsiveness, quality, communication, and reliability — plus a trade-off exercise on what to fix first. An AI follow-up interview reconstructs a specific recent interaction behind the numbers so leaders get concrete, fixable detail instead of vague ratings.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

12 questions · ~7 min
Q01
Message

Thanks for taking a few minutes to share feedback on a department you work with. This helps that team understand what's working and what to improve. Your responses are completely confidential and anonymized. About 5 minutes, and your honest take matters most.

Q02
Multiple ChoiceRequired

Which department are you evaluating in this survey?

  • (Replace with Department A, e.g. IT)
  • (Replace with Department B, e.g. Finance)
  • (Replace with Department C, e.g. HR)
  • (Replace with Department D, e.g. Marketing)
Q03
Multiple ChoiceRequired

In the last 3 months, how often have you interacted with this department (requests, projects, meetings, etc.)?

  • Not at all
  • A few times total
  • A few times a month
  • Weekly
  • Almost daily
Q04
MatrixRequired

Thinking about your recent experience, how would you rate this department on each of the following?

5 rows × 5 columns
  • Communication clarity
  • Responsiveness to requests
  • Quality of deliverables
  • Reliability in meeting deadlines
  • Collaboration across teams
Columns: Poor · Below average · Average · Good · Excellent
Q05
Opinion ScaleRequired

How likely are you to recommend working with this department to another team or colleague?

Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q06
Best–Worst Trade-off (MaxDiff)Required

Which of these matters most for this department to improve, and which matters least right now?

  • Faster response times
  • Higher quality of deliverables
  • More proactive communication
  • Better cost or budget management
  • Stronger cross-team collaboration
  • More consistent deadline delivery
  • Deeper technical expertise
  • Greater adaptability to change
Pick best & worst per setBest:Most important to improveWorst:Least important to improve
Q07
Multiple ChoiceRequired

Compared to 6 months ago, how has this department's performance changed?

  • Improved a lot
  • Improved somewhat
  • Stayed about the same
  • Declined somewhat
  • Declined a lot
  • Not sure / too new to say
Q08
AI Interview

Reconstruct the most recent specific interaction the respondent had with this department (a request, project, or handoff): what they needed, what actually happened, and what went well or fell short. Anchor closely on the reason behind their recommendation score and the trade-off item they picked as most important to improve. If they rated performance as declining, probe what changed and when; if it's their first real interaction, ask what would build confidence going forward.

Q09
Long Text

Is there anything specific this department could start, stop, or keep doing to work better with your team?

Q10
Multiple Choice

Which department do you primarily work in?

  • (Replace with Department A)
  • (Replace with Department B)
  • (Replace with Department C)
  • (Replace with Department D)
  • Prefer not to say
Q11
Multiple Choice

How long have you been at the organization?

  • Less than 6 months
  • 6 months to 2 years
  • 2 to 5 years
  • More than 5 years
  • Prefer not to say
Q12
Message

Thank you for the detailed feedback! Your responses will be compiled into a department performance report shared with team leads to guide concrete improvements.

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • Goes beyond static ratings with an AI follow-up interview that reconstructs the specific recent interaction behind a respondent's scores, giving leaders concrete, fixable detail instead of vague averages.
  • Includes a trade-off exercise (max-diff) that forces respondents to prioritize what to fix first versus rating everything as equally important.
  • Combines quantitative tracking (matrix ratings, recommend-likelihood, change-over-time question) with qualitative depth (open-ended start/stop/keep and the AI-reconstructed interaction) in one flow.
  • Segments feedback by requester department, interaction frequency, and tenure, so results can be sliced by who is experiencing the friction and how often.

SurveyMonkey

Department Performance Survey Template

A static, fielding-ready template built specifically for rating how stakeholders experience working with an internal department, making it the most directly comparable template found. It relies on fixed rating-scale and open-text questions rather than any adaptive interviewing. As with most SurveyMonkey templates, it's meant to be customized before use rather than run as-is.

What it does well

  • Purpose-built for the same core use case: internal stakeholders rating a department's performance
  • Backed by a large, well-known survey platform with broad distribution and reporting tooling
  • Likely quick to deploy given SurveyMonkey's drag-and-drop template editor

Where it falls short

  • No adaptive AI follow-up interview to reconstruct the specific recent interaction behind a low or high score — respondents just leave ratings
  • No built-in trade-off/prioritization exercise (like max-diff) to surface what to fix first versus what's merely nice-to-have
  • No transparent, publishable interview prompts or automated per-response quality scoring — quality control is left to the researcher

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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