B2B Competitor Review Battlecard Monitor

B2B Competitor Review Battlecard Monitor

Convert Trustpilot, G2, Capterra, and other B2B review records into battlecard-ready objections, competitor strengths, review snippets, and sales actions.

This Actor is for product marketing, sales enablement, RevOps, and agencies that already collect competitor reviews but need a repeatable way to turn them into competitive intelligence.

Workflow Hub

See the public review intelligence workflow for the scraper dataset -> analyzer path, demo story, and links across the review-intelligence Actors. Product marketing teams can start from the competitor review battlecards use case. For the first run, use the B2B competitor battlecard demo script or the B2B Competitor Review Battlecard alternatives page. The proof GIF shows mixed competitor reviews becoming an objection-handling battlecard row.

What You Learn

• Which competitor reviews mention pricing, support, reliability, integrations, implementation, ease of use, or missing features
• Which negative reviews should become battlecard objection rows
• Which positive reviews reveal competitor strengths your sales team should counter
• Which snippets are worth saving for enablement
• What action to take for each competitor review

Use Cases

• Monthly sales battlecard refreshes
• Competitor-review monitoring for SaaS categories
• Pricing objection mining
• Churn and switching-trigger detection
• Product marketing research from Trustpilot, G2, Capterra, and similar datasets

Input

Provide reviews inline or pass an Apify datasetId from another review scraper.

{
  "defaultCompetitor": "Acme CRM",
  "productCategory": "CRM",
  "reviews": [
    {
      "competitorName": "Acme CRM",
      "rating": 2,
      "text": "The pricing got expensive at renewal and support took days to respond.",
      "source": "Trustpilot"
    }
  ],
  "maxReviews": 100
}

Output

Each dataset item is one analyzed competitor review:

{
  "status": "succeeded",
  "recordIndex": 1,
  "billingEventName": "battlecard-review-analyzed",
  "competitorName": "Acme CRM",
  "productCategory": "CRM",
  "sourceName": "Trustpilot",
  "rating": 2,
  "sentimentLabel": "negative",
  "objectionThemes": ["pricing_objection", "support"],
  "urgencyScore": 75,
  "battlecardSnippet": "The pricing got expensive at renewal and support took days to respond.",
  "salesAction": "Position against Acme CRM with transparent pricing, ROI proof, and renewal-risk messaging."
}

The run also writes a SUMMARY key-value-store record with objection and praise theme counts.

FAQ

Does this scrape review sites?

No. It analyzes B2B review records you provide inline or through an Apify dataset from another scraper. It is designed to turn review text into battlecard material.

What input do I need for the first run?

Use the Store example with defaultCompetitor, productCategory, and a few review records. Ratings and source labels help, but review text is the key field.

What do I get back?

One dataset item per analyzed review, including objection themes, urgency, battlecard snippet, and a suggested sales action. The run also writes a SUMMARY record.

Who is this for?

Product marketing, sales enablement, RevOps, and agencies that need recurring competitor-review intelligence rather than raw review exports.

When is it commercially chargeable?

The configured paid event is battlecard-review-analyzed at $0.03, scheduled for 2026-05-26T21:05:10Z. Run a small paid smoke after activation before promoting high-volume use.

Pricing

Default monetization model: pay per event.

Recommended chargeable event:

• Event name: battlecard-review-analyzed
• Event meaning: one successfully analyzed competitor review
• Store price: $0.03 per analyzed review
• Pricing activation: scheduled for 2026-05-26T21:05:10Z

Successful rows are pushed only after the charge path allows the event. Run a paid smoke after the scheduled activation time before promoting the listing.

Limitations

• This MVP analyzes review records; it does not scrape Trustpilot, G2, or Capterra directly.
• Source schemas vary. The Actor recognizes common fields such as text, reviewText, rating, competitorName, productName, source, and date.
• The taxonomy is deterministic and explainable, built for monitoring and sales workflows.

Automation And Agent Use

• Run a review scraper first, then pass its dataset ID to this Actor.
• Schedule monthly battlecard refreshes by competitor.
• Send pricing/support objections to a CRM note, Slack channel, or battlecard document.
• Export battlecardSnippet and salesAction to Google Sheets for sales enablement.

Local Development

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