G2 Review Battlecard Monitor

G2 Review Battlecard Monitor

Convert G2 review records into field-aware objection themes, praised features, evidence quotes, sales talk tracks, and battlecard row candidates. This Actor analyzes G2 review records collected by another Apify Actor or supplied inline; it does not scrape G2 directly.

This Actor is built for product marketing, sales enablement, competitor intelligence, and agencies that already collect G2 reviews and need a repeatable way to turn buyer language into sales-ready competitive intelligence.

Workflow Hub

See the public review intelligence workflow for the scraper dataset -> analyzer path and links across the review-intelligence Actors. For the first run, use the G2 battlecard demo script, the G2 Review Battlecard alternatives page, or the competitor review battlecards use case. The proof GIF shows reviews becoming a battlecard row.

What You Learn

• Which G2 reviews mention pricing, support, reliability, integrations, implementation, usability, reporting, or missing features
• Which negative reviews should become objection rows in a competitor battlecard
• Which positive reviews reveal competitor strengths your sales team must counter
• Which snippets are worth saving for enablement and win/loss research
• Which proof asset is needed to answer each objection
• Which aggregate battlecard row candidates deserve PMM review
• What action to take for each G2 review

Use Cases

• Monthly G2-based battlecard refreshes
• Competitive messaging research for SaaS categories
• Pricing and renewal objection mining
• Product marketing proof-point planning
• Agency reporting for competitive intelligence retainers

Input

Provide G2 reviews inline or pass an Apify datasetId from another Actor. The analyzer recognizes common G2-style fields such as productName, rating, title, pros, cons, reviewText, marketSegment, reviewDate, and reviewUrl.

{
  "defaultCompetitor": "Acme CRM",
  "productCategory": "CRM",
  "reviews": [
    {
      "productName": "Acme CRM",
      "rating": 2,
      "title": "Good CRM, hard renewal",
      "pros": "The Salesforce integration is useful.",
      "cons": "The pricing got expensive at renewal and support took days to respond.",
      "marketSegment": "Mid-market"
    }
  ],
  "maxReviews": 100
}

Output

Each dataset item is one analyzed G2 review:

{
  "status": "succeeded",
  "recordIndex": 1,
  "billingEventName": "g2-review-analyzed",
  "competitorName": "Acme CRM",
  "productCategory": "CRM",
  "sourceName": "G2",
  "rating": 2,
  "sentimentLabel": "negative",
  "complaintThemes": ["pricing", "support"],
  "praisedFeatures": ["integrations"],
  "objectionType": "pricing_objection",
  "riskScore": 85,
  "priority": "high",
  "battlecardSnippet": "Good CRM, hard renewal The Salesforce integration is useful. The pricing got expensive at renewal and support took days to respond.",
  "proofAssetNeeded": "ROI calculator, transparent packaging page, and renewal-risk proof.",
  "battlecardRow": {
    "objection": "pricing_objection",
    "buyerQuote": "The pricing got expensive at renewal and support took days to respond.",
    "competitorWeakness": "pricing objection backed by pricing, support language.",
    "repTalkTrack": "When G2 buyers question Acme CRM pricing, lead with transparent packaging, ROI proof, and renewal-risk controls.",
    "priority": "high"
  },
  "salesTalkTrack": "When G2 buyers question Acme CRM pricing, lead with transparent packaging, ROI proof, and renewal-risk controls.",
  "recommendedAction": "Add a high-priority pricing_objection row to the Acme CRM battlecard and alert sales enablement."
}

The run also writes a SUMMARY key-value-store record with analyzed review counts, top objections, top themes, top praised features, battlecard row candidates, summary bullets, and the charge event name.

FAQ

Does this scrape G2?

No. It analyzes G2-style review records you provide inline or through a dataset from another Actor. It is built for analysis and battlecard generation.

What input do I need for the first run?

Use the Store example with a defaultCompetitor, productCategory, and G2-style fields such as title, pros, cons, rating, and marketSegment.

What do I get back?

One dataset item per analyzed review with complaint themes, praised features, objection type, proof asset, sales talk track, and battlecard row candidate. The run also writes a SUMMARY record.

Why use this instead of a raw G2 scraper?

Raw review exports tell you what people said. This Actor turns the review into sales enablement and product marketing actions.

How much does it cost?

The configured paid event is g2-review-analyzed at $0.006 per successfully analyzed G2 review.

Pricing

Default monetization model: pay per event.

Chargeable event:

• Event name: g2-review-analyzed
• Event meaning: one successfully analyzed G2 review
• Store price: $0.006 per analyzed review
• Pricing status: active from 2026-05-13T19:04:23Z; verified by private and public paid smokes

Successful rows are pushed only after the charge path allows the event. If Actor.charge fails, the Actor fails closed before returning paid output.

Limitations

• This MVP analyzes supplied G2 review records; it does not scrape G2 directly.
• Source schemas vary. The Actor recognizes common fields such as title, pros, cons, reviewText, rating, productName, competitorName, marketSegment, reviewUrl, and reviewDate. pros and cons are classified separately so positive integration language does not become a complaint theme.
• The taxonomy is deterministic and explainable. It does not call external AI APIs.

Automation And Agent Use

• Run a G2 review scraper first, then pass its dataset ID to this Actor.
• Schedule monthly battlecard refreshes by competitor.
• Send high-risk pricing, support, or reliability objections to sales enablement.
• Export battlecardSnippet, salesTalkTrack, and recommendedAction to Google Sheets or a battlecard document.

Local Development

python3 -m pip install -r requirements.txt
ACTOR_TEST_PAY_PER_EVENT=true apify run --purge --input-file examples/smoke-input.json