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Consumer Insight Synthesizer

Turn messy research into a crisp, action-ready insight.

What is the Consumer Insight Synthesizer?

The Consumer Insight Synthesizer is a free AI skill that turns scattered consumer research into a single, action-ready insight for food and beverage teams. You paste in your raw material — survey verbatims, product reviews, interview notes, social comments — and it distills the recurring themes, separates what consumers say from what they do, names the core insight in one sharp line, and lays out the 'so what' and the recommended actions. It is built for insights, research, and brand teams who are drowning in qualitative input and need to brief a stakeholder by the end of the day, not the end of the month. Rather than a flat summary, it pushes to the tension underneath the data and frames it so a team can act. It works from the inputs you give it and flags where evidence is thin. Pair it with broader food and beverage demand and conversation data to confirm a theme is real and not just loud.

Who it's for

  • Insights and research managers synthesizing study results
  • Brand teams digesting reviews and social feedback
  • Innovation teams turning research into a concept input
  • Anyone briefing stakeholders from messy qualitative data

What you get

  • The recurring themes across your inputs
  • A 'say vs do' read on the data
  • One sharp insight statement
  • The 'so what' for the business
  • Prioritized recommended actions
  • A flag on themes with thin evidence

How to use it

  1. 1Paste the prompt, then paste your raw research below it.
  2. 2Tell it the business question you're trying to answer.
  3. 3Review the insight and ask it to push deeper if it's surface-level.
  4. 4Ask for a one-slide summary for stakeholders.

See it in action

Example input

Business question: why is repeat purchase low on our fictional 'CrispRoot' veggie chips? Inputs (pasted): ~40 reviews and survey comments. Common phrases: 'great flavor', 'bag is mostly air', 'too expensive for the amount', 'kids loved them', 'wish the big bag existed'.

Example output

Insight synthesis: CrispRoot repeat purchase

Recurring themes:
1. Taste is a clear win — flavor and kid-approval come up repeatedly.
2. Value perception is the problem — "mostly air," "too expensive for the amount."
3. Latent demand for a larger / family format.

Say vs do:
Consumers SAY they love the taste (genuine, repeated). But the value complaint is what shows up alongside non-repeat — the barrier isn't the product, it's the perceived value-for-money at the current pack size.

The insight (one line):
CrispRoot wins on taste but loses on value perception — shoppers love the chip and resent the bag.

So what:
The repeat problem is a packaging-and-value problem, not a flavor problem. Fixing fill level or offering a family size likely unlocks the repeat that the taste has already earned.

Recommended actions (prioritized):
1. Test a larger / family pack to address the value complaint directly.
2. Review fill level and slack-fill perception on the current bag.
3. Reframe value messaging around per-serving cost, not bag price.

Evidence flag: "family size demand" appears but in fewer comments — validate with a quick sizing test before committing.

Want a one-slide version for the team?

The prompt

Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a senior consumer insights analyst for food & beverage brands. You don't just summarize research — you find the tension underneath it and turn it into an action. You distinguish what people SAY from what they DO.

# Context I'll provide
- Business question: [WHAT I'M TRYING TO ANSWER]
- Raw inputs (I'll paste them below): [VERBATIMS / REVIEWS / NOTES / COMMENTS]
- Audience for the output (optional): [STAKEHOLDER]

# Your task
1. If the business question or the raw inputs are missing, ask for them before analyzing.
2. Read across all inputs and pull out the recurring themes (cluster, don't list every comment).
3. Separate what consumers SAY from what their behavior implies they DO.

Frequently asked questions

What does it mean to synthesize consumer insight?
Synthesis means turning many scattered inputs — reviews, survey verbatims, interviews — into a single, clear insight you can act on. It goes beyond summarizing by clustering themes, separating what people say from what they do, and naming the underlying tension. This skill produces that insight plus a 'so what' and recommended actions.
Will it make up data or quotes?
No. The prompt instructs the model to work only from the inputs you paste in and never to invent quotes, percentages, or themes that aren't present. If the evidence is thin or contradictory, it's told to say so plainly rather than force a tidy conclusion, which protects you from over-rotating on a weak signal.
What's the 'say vs do' part and why does it matter?
Consumers often state one preference but behave differently — they say they want healthy but buy indulgent, or praise taste while not repurchasing. The skill explicitly separates stated from revealed preference, because the gap between them is usually where the real insight and the real business lever live.
Can it handle qualitative and quantitative inputs together?
Yes. Paste in a mix of verbatims, review snippets, interview notes, and any numbers you have, and tell it the business question. It clusters the qualitative themes and weighs them against whatever quantitative signal you provide, then flags where you'd want more validation before acting.

Related skills

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