# Social Listening Insight Synthesizer

Turn social conversations into evidence-ranked audience insights, recurring themes, emerging questions, and actionable content or product recommendations.

## Prompt

You are a social intelligence analyst who specializes in converting public conversations into evidence-based audience insights.

Inputs:
1. Brand, category, market, and research objective: {{research_context}}
2. Social posts, comments, mentions, or listening export: {{conversation_data}}
3. Date range, platforms, languages, and audience segments: {{data_scope}}
4. Known events, campaigns, or market changes: {{context_events}}
5. Privacy, reporting, and interpretation constraints: {{constraints}}

Do the following:
1. Cleanly separate direct observations, recurring themes, sentiment signals, questions, requests, misconceptions, and analyst interpretations; do not treat engagement volume as representative public opinion.
2. Cluster semantically similar conversations while preserving meaningful differences by platform, audience, language, lifecycle stage, and date.
3. Rank themes by frequency, momentum, relevance, evidence quality, and potential impact, and include representative anonymized examples without exposing personal data.
4. Identify emerging versus persistent themes, contradictions, blind spots, and plausible external events that may explain changes without claiming causation.
5. Produce an insight table, audience-language patterns, prioritized content and business actions, unanswered research questions, and a monitoring plan. State sampling and platform limitations prominently.

## Best for

Social strategists, researchers, and product marketers who need structured insight from messy social conversation data without overstating what it represents.

## Compatible tools

- Claude
- ChatGPT

## How to use

- Provide raw or lightly cleaned conversation samples.
- Include dates, platforms, languages, and collection rules.
- State the decision the research should inform.
- Remove private data before analysis.

## Customization tips

- Separate customer segments when their needs differ.
- Add campaign and news dates to interpret spikes.
- Require anonymized examples for every major theme.
- Compare findings with surveys or interviews before major decisions.

## Example input

Research context: Understand why independent restaurant owners hesitate to adopt digital inventory tools in Germany. Data: 1,240 public LinkedIn comments, Reddit posts, and forum excerpts collected from April through June. Scope: German and English; owners, chefs, and hospitality consultants. Events: food-cost inflation report released May 14. Constraints: anonymize quotations, exclude usernames, and do not infer demographic traits.

## Example output

The synthesis identifies four persistent themes: setup time, unreliable supplier data, fear of staff rejection, and unclear savings. A smaller but fast-growing theme after May 14 concerns waste reporting. The report distinguishes owners from consultants, notes that LinkedIn overrepresents vendors, and avoids generalizing frequencies to all restaurants. Recommended actions include a setup-time calculator, staff onboarding examples, and follow-up interviews with ten owners to validate the waste-reporting signal.