Marketing

Review Voice-of-Customer Miner

Hear the market in customers own words.

Turns public review data into product and marketing insight so teams can sharpen positioning, improve onboarding, and prioritize fixes.

Best forEcommerceProfessional ServicesSaaS

Agents7 required

Duration5-10 minutes

Collects review text from public sources through Apify, clusters themes, and translates customer language into product, support, and marketing recommendations.

How it runs

Multi-agent orchestration — here's the flow, step by step.

Call verslay_recall to retrieve prior VoC mining results, known customer themes, top pain points, and brand sentiment baselines from memory. Run verslay_web_search and verslay_news_search for recent reviews, case studies, and customer feedback articles mentioning the brand and its category. Compile a freshness brief and pass to Phase 2.

web researcher

Consume Phase 1 freshness brief. Call verslay_exclusive_run_actor with actor 'yin/g2-reviews-scraper' on the brand's and top 3 competitors' G2 profiles to extract: star distributions, most recent review texts, top praised features, and most common complaints. In parallel call verslay_exclusive_reddit_scrape on brand-adjacent subreddits to capture organic user discussions — unfiltered praise, complaints, workarounds, and feature requests. Produce a raw review and community feedback dataset.

sentiment analyst

In parallel with sentiment-analyst, call verslay_exclusive_twitter_scrape on the brand's handle and competitor handles to capture recent customer mentions, support complaints, and product praise in real-time Twitter conversations. Call verslay_reddit_search on the brand name and product category to surface recent discussion threads and any reputation incidents. Produce a social feedback intelligence report.

competitive intel

Consume Phase 2 G2/Reddit raw dataset and Phase 2 social feedback report. Cluster feedback into themes using natural language pattern matching. For each theme: count mention frequency, score sentiment intensity, tag the data source (G2/Reddit/Twitter), and identify verbatim quotes that best represent the theme. Compare themes against competitor review data to identify relative strengths and weaknesses. Call verslay_chart_create to produce a VoC theme frequency and sentiment heatmap.

data analyst

Consume Phase 3 VoC theme clusters and competitor comparison. Translate the top customer language patterns into actionable outputs: marketing copy hooks using verbatim customer phrases, objection-handling scripts addressing the top 5 complaints, product positioning opportunities based on underrated strengths, and customer success early-warning signals. Produce a VoC-to-action recommendations brief.

conversion optimizer

Consume Phase 3 VoC heatmap, Phase 4 action recommendations, and all source data. Produce a comprehensive Voice of Customer mining report: executive summary of top themes, verbatim quote bank by category (praise/pain/request), VoC sentiment heatmap, competitor comparison, marketing copy hooks, objection-handling scripts, and product development signals. Call verslay_memorize to store the customer theme clusters, verbatim language bank, and sentiment baselines in memory.

executive briefing writer

Receive the VoC mining report from executive-briefing-writer. Deliver it to the configured Slack channel or email recipient specified by the user. Confirm delivery and log send timestamp.

distributor

Required Agents

web-researcher
sentiment-analyst
competitive-intel
data-analyst
conversion-optimizer
executive-briefing-writer
distributor

Connections

Required

verslay_exclusive

What it does

Review collection
Theme clustering
Sentiment scoring
Quote extraction
Messaging recommendations

Example prompt

Analyze public reviews for our product category. Extract common complaints, desired outcomes, exact customer phrases, and messaging opportunities.

Ready to deploy Review Voice-of-Customer Miner?

Start free. One click, full agent orchestration.

Get Started Free →

All Use Cases

Marketing

Review Voice-of-Customer Miner

Hear the market in customers own words.

Turns public review data into product and marketing insight so teams can sharpen positioning, improve onboarding, and prioritize fixes.

Best forEcommerceProfessional ServicesSaaS

Agents7 required

Duration5-10 minutes

Collects review text from public sources through Apify, clusters themes, and translates customer language into product, support, and marketing recommendations.

How it runs

Multi-agent orchestration — here's the flow, step by step.

Call verslay_recall to retrieve prior VoC mining results, known customer themes, top pain points, and brand sentiment baselines from memory. Run verslay_web_search and verslay_news_search for recent reviews, case studies, and customer feedback articles mentioning the brand and its category. Compile a freshness brief and pass to Phase 2.

web researcher

Consume Phase 1 freshness brief. Call verslay_exclusive_run_actor with actor 'yin/g2-reviews-scraper' on the brand's and top 3 competitors' G2 profiles to extract: star distributions, most recent review texts, top praised features, and most common complaints. In parallel call verslay_exclusive_reddit_scrape on brand-adjacent subreddits to capture organic user discussions — unfiltered praise, complaints, workarounds, and feature requests. Produce a raw review and community feedback dataset.

sentiment analyst

In parallel with sentiment-analyst, call verslay_exclusive_twitter_scrape on the brand's handle and competitor handles to capture recent customer mentions, support complaints, and product praise in real-time Twitter conversations. Call verslay_reddit_search on the brand name and product category to surface recent discussion threads and any reputation incidents. Produce a social feedback intelligence report.

competitive intel

Consume Phase 2 G2/Reddit raw dataset and Phase 2 social feedback report. Cluster feedback into themes using natural language pattern matching. For each theme: count mention frequency, score sentiment intensity, tag the data source (G2/Reddit/Twitter), and identify verbatim quotes that best represent the theme. Compare themes against competitor review data to identify relative strengths and weaknesses. Call verslay_chart_create to produce a VoC theme frequency and sentiment heatmap.

data analyst

Consume Phase 3 VoC theme clusters and competitor comparison. Translate the top customer language patterns into actionable outputs: marketing copy hooks using verbatim customer phrases, objection-handling scripts addressing the top 5 complaints, product positioning opportunities based on underrated strengths, and customer success early-warning signals. Produce a VoC-to-action recommendations brief.

conversion optimizer

Consume Phase 3 VoC heatmap, Phase 4 action recommendations, and all source data. Produce a comprehensive Voice of Customer mining report: executive summary of top themes, verbatim quote bank by category (praise/pain/request), VoC sentiment heatmap, competitor comparison, marketing copy hooks, objection-handling scripts, and product development signals. Call verslay_memorize to store the customer theme clusters, verbatim language bank, and sentiment baselines in memory.

executive briefing writer

Receive the VoC mining report from executive-briefing-writer. Deliver it to the configured Slack channel or email recipient specified by the user. Confirm delivery and log send timestamp.

distributor

Required Agents

web-researcher
sentiment-analyst
competitive-intel
data-analyst
conversion-optimizer
executive-briefing-writer
distributor

Connections

Required

verslay_exclusive

What it does

Review collection
Theme clustering
Sentiment scoring
Quote extraction
Messaging recommendations

Example prompt

Analyze public reviews for our product category. Extract common complaints, desired outcomes, exact customer phrases, and messaging opportunities.

Ready to deploy Review Voice-of-Customer Miner?

Start free. One click, full agent orchestration.

Get Started Free →

All Use Cases

Marketing

Review Voice-of-Customer Miner

Hear the market in customers own words.

Turns public review data into product and marketing insight so teams can sharpen positioning, improve onboarding, and prioritize fixes.

Best forEcommerceProfessional ServicesSaaS

Agents7 required

Duration5-10 minutes

Collects review text from public sources through Apify, clusters themes, and translates customer language into product, support, and marketing recommendations.

How it runs

Multi-agent orchestration — here's the flow, step by step.

Call verslay_recall to retrieve prior VoC mining results, known customer themes, top pain points, and brand sentiment baselines from memory. Run verslay_web_search and verslay_news_search for recent reviews, case studies, and customer feedback articles mentioning the brand and its category. Compile a freshness brief and pass to Phase 2.

web researcher

Consume Phase 1 freshness brief. Call verslay_exclusive_run_actor with actor 'yin/g2-reviews-scraper' on the brand's and top 3 competitors' G2 profiles to extract: star distributions, most recent review texts, top praised features, and most common complaints. In parallel call verslay_exclusive_reddit_scrape on brand-adjacent subreddits to capture organic user discussions — unfiltered praise, complaints, workarounds, and feature requests. Produce a raw review and community feedback dataset.

sentiment analyst

In parallel with sentiment-analyst, call verslay_exclusive_twitter_scrape on the brand's handle and competitor handles to capture recent customer mentions, support complaints, and product praise in real-time Twitter conversations. Call verslay_reddit_search on the brand name and product category to surface recent discussion threads and any reputation incidents. Produce a social feedback intelligence report.

competitive intel

Consume Phase 2 G2/Reddit raw dataset and Phase 2 social feedback report. Cluster feedback into themes using natural language pattern matching. For each theme: count mention frequency, score sentiment intensity, tag the data source (G2/Reddit/Twitter), and identify verbatim quotes that best represent the theme. Compare themes against competitor review data to identify relative strengths and weaknesses. Call verslay_chart_create to produce a VoC theme frequency and sentiment heatmap.

data analyst

Consume Phase 3 VoC theme clusters and competitor comparison. Translate the top customer language patterns into actionable outputs: marketing copy hooks using verbatim customer phrases, objection-handling scripts addressing the top 5 complaints, product positioning opportunities based on underrated strengths, and customer success early-warning signals. Produce a VoC-to-action recommendations brief.

conversion optimizer

Consume Phase 3 VoC heatmap, Phase 4 action recommendations, and all source data. Produce a comprehensive Voice of Customer mining report: executive summary of top themes, verbatim quote bank by category (praise/pain/request), VoC sentiment heatmap, competitor comparison, marketing copy hooks, objection-handling scripts, and product development signals. Call verslay_memorize to store the customer theme clusters, verbatim language bank, and sentiment baselines in memory.

executive briefing writer

Receive the VoC mining report from executive-briefing-writer. Deliver it to the configured Slack channel or email recipient specified by the user. Confirm delivery and log send timestamp.

distributor

Required Agents

web-researcher
sentiment-analyst
competitive-intel
data-analyst
conversion-optimizer
executive-briefing-writer
distributor

Connections

Required

verslay_exclusive

What it does

Review collection
Theme clustering
Sentiment scoring
Quote extraction
Messaging recommendations

Example prompt

Analyze public reviews for our product category. Extract common complaints, desired outcomes, exact customer phrases, and messaging opportunities.

Ready to deploy Review Voice-of-Customer Miner?

Start free. One click, full agent orchestration.

Get Started Free →