Product Reviews

Product Reviews surface what real users are saying about software vendors on G2 — complaints, switching intent, quantified business impact, and decision-maker frustrations that signal active displacement opportunities.

We analyze G2 reviews across 2M+ companies, scanning for 9 signal types that matter for sales intelligence. Our model extracts switching intent phrases ("we're evaluating alternatives"), quantified impact ("cost us $50K in lost pipeline"), and decision-maker complaints (filtering for VP/Director/C-level titles). Multiple reviews mentioning the same issue get aggregated into a single signal with supporting quotes from each reviewer.

Each signal includes structured evidence — reviewer quotes, titles, star ratings, and review URLs — so you can validate the intelligence and use it directly in outreach.

Reviewers are anonymous — like Reddit, you get unfiltered sentiment without individual identity. We're working to de-anonymize where possible.

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See real delivered data → Sample Files

Subtypes represent the specific pattern detected across product reviews — from competitive displacement to feature gaps to pricing concerns.

Categories group subtypes into higher-level themes — a common way to filter signals by sales motion or use case.

Example Signal

What a single entry looks like in a delivered signal file:

{
  "signal_id": "1cb8bd74-e7c9-4c41-b7aa-9c6f19f72e5d",
  "batch_id": "2026-03-01-00-00-00",
  "signal_type": "g2-product-review",
  "signal_subtype": "CompetitorMentions",
  "detected_at": "2026-02-28T09:22:47.312Z",
  "association": "company",
  "company": {
    "name": "6sense",
    "domain": "6sense.com",                   // match on domain
    "linkedin_url": "linkedin.com/company/6sense",  // or match on LinkedIn URL
    "industries": ["Software Development", "Marketing Technology"],
    "employee_count_low": 1001,
    "employee_count_high": 2000
  },
  "contact": [],
  "data": {
    "headline": "Director of Demand Gen Switching from 6sense to Demandbase, Cites 70% of Intent Data Unactionable",
    "summary": "Multiple mid-market marketing leaders report 6sense intent data quality has degraded since their last funding round. Director of Demand Gen at a 500-person SaaS company actively migrating...",
    "product_name": "6sense Revenue AI",
    "source_page_url": "https://www.g2.com/products/6sense-revenue-ai/reviews",
    "relevance": 92,                          // 0-100; 90+ = decision-maker + switching intent
    "switching_intent": {
      "detected": true,
      "urgency": "immediate",
      "signal_phrase": "We started our Demandbase POC last month and are not renewing 6sense"
    },
    "quantified_impact": {
      "has_numbers": true,
      "metrics": [
        "70% of intent signals unactionable",
        "$120K annual contract",
        "3 months to get onboarding support"
      ]
    },
    "decision_maker_complaint": {
      "is_decision_maker": true,
      "title": "Director of Demand Generation"
    },
    "competitors_mentioned": [
      "Demandbase",
      "Bombora",
      "ZoomInfo"
    ],
    "evidence": [
      {
        "quote": "70% of the intent signals 6sense surfaces are unactionable — accounts that have zero fit or timing. We started our Demandbase POC last month and are not renewing...",
        "reviewer_name": "Michael T.",
        "reviewer_title": "Director of Demand Generation @Mid-Market SaaS (400-600)",
        "review_date": "2026-02-18",
        "review_url": "https://www.g2.com/products/6sense-revenue-ai/reviews/6sense-review-9851432",
        "star_rating": 2.0
      },
      {
        "quote": "Took 3 months to get proper onboarding support after signing a $120K contract. By then half our team had lost confidence in the platform...",
        "reviewer_name": "Sarah P.",
        "reviewer_title": "VP Marketing @Series C Startup",
        "review_date": "2026-02-10",
        "review_url": "https://www.g2.com/products/6sense-revenue-ai/reviews/6sense-review-9847891",
        "star_rating": 1.0
      }
    ]
  }
}

Field Reference

Standard envelope and entity fields are shared across all signals — see Schema and Resolution. The fields below are specific to this signal:

Signal-Specific Fields

The data object contains everything unique to this signal type — the intelligence extracted from aggregated G2 reviews.

Switching Intent

The switching_intent nested object captures whether reviewers are actively moving away from the product.

Quantified Impact

The quantified_impact nested object captures specific numbers mentioned in reviews.

Decision Maker Complaint

The decision_maker_complaint nested object identifies whether the complaint comes from someone with purchasing authority.

Evidence Array

The evidence array contains individual review quotes that support the signal.

Timing & Delivery

• detected_at is when we processed the review batch. Use evidence[].review_date for when individual reviews were posted.
• One signal per subtype per product per 30-day window. The same product can have multiple signal subtypes active simultaneously (e.g., both PricingConcerns and ActiveChurn), but each subtype fires only once per cycle.
• Each delivery arrives in a timestamped folder. Treat all signals in a new folder as recent — no need to diff against prior deliveries.

Coverage

• Refresh: Monthly
• Coverage: 2,000,000+ companies
• Best for: Competitive displacement, churn prevention, market intelligence, ABM triggers for companies losing customers to you

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