/startup-competitors

Wave 2: Customer Sentiment Mining

Read research-principles.md first.

Agent B1: Review Mining

Research task: Mine customer reviews for competitors in {product category}
Context: {product summary from intake}
Competitors to analyze: {list from Wave A}

Review mining reveals what customers actually experience — not what competitor marketing promises. The gap between promise and reality is where opportunities live.

HANDLING SCARCE REVIEWS:
Some markets (especially B2B, niche, or emerging categories) have very few formal reviews on G2/Capterra. When this happens:
1. Expand to additional platforms: App Store, Play Store, Trustpilot, Google Maps reviews (for physical products/services), industry-specific review sites
2. Search for case studies and testimonials on competitor websites — extract the language used
3. Search for "{competitor name} experience" or "{competitor name} honest review" on blogs and YouTube
4. Lean more heavily on forum mining (Wave B2) for unfiltered opinions
5. Declare the scarcity explicitly in Data Gaps — "only X reviews found across platforms" is valuable information itself (it signals market immaturity or low switching)
Always include at least 2-3 verbatim quotes per competitor, even if they come from blog posts, tweets, or forum comments rather than formal review platforms.

RESEARCH PROTOCOL:

ROUND 1 — Aggregate review platforms (2 searches per competitor):
- "{competitor name} reviews G2"
- "{competitor name} reviews Capterra" OR "Trustradius"
- "{competitor name} reviews Product Hunt"
- "{competitor name} app store reviews" (if mobile product)

ROUND 2 — Negative review deep-dive (1-2 searches per competitor):
- "{competitor name} complaints"
- "{competitor name} problems reddit"
- "{competitor name} worst things"

ROUND 3 — Feature request patterns (1-2 searches):
- "{competitor name} feature request"
- "{competitor name} missing features"
- "{product category} wishlist"

For EACH competitor, extract:

## {Competitor Name} — Review Analysis

### Review Volume & Ratings
- **G2:** {count} reviews, {avg} stars
- **Capterra:** {count} reviews, {avg} stars
- **TrustRadius:** {count} reviews, {avg} stars
- **Product Hunt:** {upvotes}, {comment sentiment}
- **App Store / Play Store:** {if applicable}

### What People Love (top 3-5 themes)
For each theme:
- **Theme:** {e.g., "Easy onboarding"}
- **Frequency:** mentioned in ~{X}% of positive reviews
- **Verbatim quotes:**
  - "{exact quote}" — {source, date}
  - "{exact quote}" — {source, date}

### What People Hate (top 3-5 themes)
For each theme:
- **Theme:** {e.g., "Pricing feels unfair at scale"}
- **Frequency:** mentioned in ~{X}% of negative reviews
- **Severity:** annoyance / blocker / deal-breaker
- **Verbatim quotes:**
  - "{exact quote}" — {source, date}
  - "{exact quote}" — {source, date}

### Most Requested Features
- {feature 1} — mentioned {X} times
- {feature 2} — mentioned {X} times
- {feature 3} — mentioned {X} times

### Churn Signals
Reasons people leave this competitor:
- {reason 1 — with evidence}
- {reason 2 — with evidence}
- {reason 3 — with evidence}

---

After all competitors:

## Cross-Competitor Pain Patterns
| Pain Theme | {Comp 1} | {Comp 2} | {Comp 3} | {Comp 4} | Opportunity |
|-----------|----------|----------|----------|----------|-------------|
| {pain 1} | severity | severity | severity | severity | {implication} |
| {pain 2} | ... | ... | ... | ... | ... |

Pains shared across multiple competitors = structural market problems = biggest opportunities.

## Data Gaps
- [Competitors with few reviews]
- [Platforms not checked]

Save to: {project-name}/raw/review-mining.md

Agent B2: Forum & Community Mining

Research task: Mine forums and communities for customer voice about {product category}
Context: {product summary from intake}
Competitors: {list from Wave A}

Forum mining captures unfiltered opinions that people won't write in formal reviews. It also reveals the exact language customers use — gold for positioning and copywriting.

RESEARCH PROTOCOL:

ROUND 1 — Reddit (3-4 searches):
- "site:reddit.com {product category} recommendations"
- "site:reddit.com {competitor name} alternative"
- "site:reddit.com {problem statement} tool"
- "site:reddit.com switching from {competitor name}"

ROUND 2 — Indie communities (2-3 searches):
- "site:indiehackers.com {product category}"
- "site:news.ycombinator.com {product category}"
- "{product category} forum discussion"

ROUND 3 — Q&A and niche (2 searches):
- "site:quora.com best {product category}"
- "{product category} community Slack Discord"

ROUND 4 — Migration stories (1-2 searches):
- "switched from {competitor name} to"
- "migrating from {competitor name}"
- "why I left {competitor name}"

OPTIONAL — Reviewed X/Twitter source packet:
If the user already has an approved X/Twitter export, search result, reply
thread, account summary, or monitor report from a tool such as TweetClaw, treat
it as an extra evidence packet for language mining. Do not ask for cookies,
sessions, API keys, screenshots, or raw account access. Extract only quoted
customer language, URLs, timestamps, and source labels, then mark the packet as
user-provided evidence in Data Gaps and Sources.

OUTPUT FORMAT:

## Forum & Community Findings

### Discussion Themes
For each major theme found:
- **Theme:** {what people are discussing}
- **Volume:** {approximate number of threads/comments}
- **Sentiment:** positive / negative / mixed
- **Key threads:**
  - [{thread title}]({url}) — {key takeaway}

### Language Map
The exact words customers use — organized for reuse in positioning and copy:

**To describe the problem:**
- "{exact phrase}" — used in {X} threads
- "{exact phrase}" — used in {X} threads

**To describe desired solution:**
- "{exact phrase}"
- "{exact phrase}"

**To describe frustrations with competitors:**
- "{exact phrase}" — about {competitor}
- "{exact phrase}" — about {competitor}

**To describe switching triggers:**
- "{exact phrase}"
- "{exact phrase}"

### "What do you use for X?" Threads
| Thread | Top Recommended | Runner Up | Common Criteria |
|--------|----------------|-----------|-----------------|
| {title} | {tool} ({why}) | {tool} | {what people care about} |

### Migration Stories
For each migration story found:
- **From:** {competitor} → **To:** {competitor}
- **Why they switched:** {reason}
- **What they gained:** {benefit}
- **What they lost:** {trade-off}
- **Would they switch again?** {yes/no and why}

### Churn Signal Summary
Aggregated reasons people leave competitors:
| Reason | Competitors Affected | Frequency | Severity |
|--------|---------------------|-----------|----------|
| {reason} | {which ones} | common / occasional | high / medium |

## Data Gaps
- [Communities not found or not active for this category]
- [Competitors with no forum presence]

Save to: {project-name}/raw/forum-mining.md