20/32proof of conceptSocial Listening & Reputation Intelligence
Social Listening & Customer Intelligence Platform
Public customer conversations in one view, with customer voice kept apart from owned content.
What it solves
Customer conversation is spread across platforms, and a company’s own posts distort the sentiment figures. This puts the mentions in one view and keeps customer voice apart from owned content.
How it runs
Six steps, input to output
Goes inThe organization to monitor: its names, handles, locations and keywords
- 01
Collect
Public conversations come in from social, video comments, reviews, professional networks and the web.
- 02
Match
Each mention is matched to the organization by name, handle, location and keyword.
- 03
Split voice
Customer voice is separated from the company’s own captions and owner replies.
- 04
Analyse
AI-assisted analysis tags sentiment, topic, intent, risk and relevance, and drafts a summary.
- 05
Review
Reviewers approve or flag each analysis without overwriting it.
- 06
Report
Reports carry the window, sample size, source coverage and collection status beside each figure.
Comes outOne view of public customer conversation, with reviewed analysis and reporting
What it does
Built a social listening workspace that pulls public customer conversations from social platforms, video comments, business reviews, professional networks, and public web results into one view, matches each mention to the monitored organization by name, handle, location, and keyword, and separates customer voice from the company's own captions and owner replies so sentiment reporting is not distorted.
AI-assisted analysis tags sentiment, topic, intent, risk, and relevance and drafts summaries and recommended actions; reviewers approve or flag each analysis without overwriting it, and every reanalysis is versioned.
Reporting shows positive mention share separately from net sentiment, always with the reporting window, sample size, source coverage, and collection status alongside, so a real shift in customer conversation can be told apart from a source that has stopped collecting.
Three decisions
What shaped the build
Customer voice is kept apart
The company’s own captions and owner replies are separated out, so sentiment reporting is not distorted.
Review never overwrites
Reviewers approve or flag an analysis without replacing it, and every reanalysis is versioned.
A figure never travels alone
Positive mention share is shown separately from net sentiment, always with the window, sample size, source coverage and collection status. A real shift can then be told apart from a source that has stopped collecting.
Built with
8 parts
- Collection
- social · video · reviews · web
- Relevance
- names, handles, locations, keywords
- Voice split
- customer vs owned content
- AI analysis
- sentiment · topic · intent · risk
- Human review
- approve / correct, versioned
- Issue queue
- open · monitoring · resolved
- Reporting
- window, sample size, coverage
- Source health
- collection + backlog status