SaaS Voice-of-Customer Battlecard

Convert customer-review datasets into traceable product and competitive intelligence. The Actor accepts reviews directly or reads another Apify Actor's dataset; it does not scrape review sites itself and ignores reviewer identities.

Outputs

Structured theme rows contain:

• theme and category
• frequency and severity
• sentiment and date-aware trend
• evidence count and short representative evidence
• explicit competitor mentions

The final battlecard row groups the strongest findings into strengths, weaknesses, common complaints, missing features, switching reasons, competitive opportunities, and product opportunities.

Why deterministic first

The Actor uses transparent phrase dictionaries, ratings, dates, and user-supplied custom themes. It does not send review text to an LLM, so there is no model/API key requirement, no invented quote, and no uncontrolled token bill. Evidence excerpts come from the supplied review text and remain short.

Quick start

{
  "productName": "Acme Cloud",
  "reviews": [
    { "text": "Easy to use, but reports are slow and sometimes crash.", "rating": 3, "date": "2026-07-01" },
    { "text": "Please add a native Salesforce integration.", "rating": 3, "date": "2026-08-01" }
  ],
  "competitorNames": ["RivalCo"]
}

Pricing

The intended model is low-cost pay per review analyzed, with platform usage included. Theme rows and the battlecard are included. Current exact prices appear on the Store page. If a maximum run charge stops processing, the battlecard is marked partial.

Limitations

Keyword analysis is explainable but cannot understand every idiom, language, sarcasm, or implicit product comparison. Trend requires enough dated reviews and reports unknown on small samples. Frequency reflects the supplied sample, not the entire customer base. Do not use the output to profile individuals or make automated high-impact decisions.