PromptDeck
MarketingCustomer Feedback Synthesis#positioning#message#marketing

Feedback Synthesis · Marketing Lens · Positioning Check

Synthesize feedback through marketing lens: is positioning still right? Is messaging landing? Which new use cases emerged?

Pain addressed · After a campaign ships, marketers rarely know if users 'got the message'. Feedback is scattered across tickets / G2 / Twitter — synthesis is the only way to get a positioning signal.

Feedback Synthesis · Marketing Lens · Positioning Check

Prompt·Advanced
Also available in 中文 →
You're my marketing feedback synthesis partner. Turn 50 feedback items into a marketing-actionable brief.

# Raw feedback (mix of G2 / Twitter / tickets / podcast mentions / sales relays)
{paste_feedback}

# Current positioning + headline messaging
{positioning_and_messaging}

# Output four sections

## 1. Positioning check (80 words)
- Does what users say we are match what we want to be? Match score 1-5.
- Cite 1-2 direct quotes as evidence.

## 2. Message gaps (3, 40 words each)
- The messages we WANT users to remember vs what they actually mention. Where's the gap?
- e.g. we push "save 10x time", users say "love the integrations". That's the gap.

## 3. Emerging use cases (2-3, 60 words each)
- What are users doing with our product that we didn't anticipate?
- Could any of these become the next campaign theme?

## 4. 3 marketing actions (30 words each)
Based on the prior sections:
- One on positioning (tweak / re-anchor)
- One on messaging (add / change / cut a line)
- One on campaign (new theme or channel)

# Boundaries
- "Users are happy" isn't a conclusion. Cite specific match scores and gaps.
- Quote users in their actual voice; don't polish.
- Each action must be launchable within a week.
Saves ~75 min per use

Open directly on your favourite AI

One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V).

Workflow · 5 steps

  1. 1

    Pull 30 days of G2 reviews, Twitter mentions, and sales relays — 50 items mixed — into one block.

  2. 2

    Run in Claude (strongest at synthesis). Fill the 2 placeholders.

  3. 3

    Audit the positioning match score. If the model says 4-5 but you suspect 2, push back: 'give me an honest read'.

  4. 4

    The message-gap section is gold — extract specific examples of 'we said X, users heard Y'.

  5. 5

    Bring the 3 actions to the next marketing standup as proposals. Ship one this week.

Verify before you ship

AI's "almost right" is the #1 pain. Tick these off so reviewers can't find the obvious holes. (Checkbox state stays local — we don't track it.)

Done 0 / 5 checks

Why this prompt works

This prompt isn't a generic “write me a Customer Feedback Synthesis”. It folds in the concrete context a Marketing actually faces (target reader, info density, deliverable format). LLMs deliver an order-of-magnitude better answer when they get role × scenario × constraints together — they can make the right tradeoffs about what to spotlight and what to cut.

When to use

Best moment: you know roughly that you need to do a Customer Feedback Synthesis, but you're stuck on “how do I even ask the AI?” Muscle memory has no template. Copy this card, tweak 2-3 fields per the workflow, and get a first revisable draft in 30 seconds.

Common pitfalls

  • Send without tweaking placeholders · the draft reads as default-GPT and your reviewer spots it in 3 seconds.
  • Skip the verify checklist · “almost right” failures usually hide in a thing you assumed the model handled but it didn't.
  • Stop after the first answer · a single follow-up like “make section X tighter / more formal” usually pushes a 70 → 90.

Variants & extensions

Want to use this card more aggressively? Three extensions:

  • Swap languages: both EN/ZH prompts ship in the card — use the EN version when shipping to a Western audience.
  • Swap platforms: the same prompt has slightly different taste on ChatGPT / Claude / Kimi. The recommended-platform pill marks the best fit.
  • Stack the checklist: paste the verify checklist back into the chat and ask the AI to self-audit — error rate drops sharply.
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