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Grok Bot+18 agentsCustomer Review Objection Analyzer
Analyze hundreds of customer reviews, rank recurring purchase objections, separate real product weaknesses from messaging problems, and generate evidence-backed sales copy and counterarguments.
Free
Customer Review Objection Analyzer
Example session with this skill installed
Analyze reviews.xlsx for our subscription meal-planning app.
There are about 1,800 reviews from the last 12 months.
I want to understand the biggest objections that could stop a prospect from buying.
Please
- normalize and deduplicate the reviews;
- separate purchase objections from real product defects, service issues, and feature requests;
- group similar objections;
- rank the objections by frequency and severity;
- cite the review IDs supporting every major cluster;
- draft truthful counterarguments only when supported by product facts;
- mark any unsupported response as PROOF_REQUIRED;
- create product-page copy, FAQ answers, ad angles, and a separate list of product-improvement issues.
Do not invent guarantees, statistics, or product capabilities.
- Read your context and instructions
- Compiled the customer review objection
- Generated the data export
Review objection analysis complete
Reviews processed: 1,842
Duplicate reviews removed: 37
Meaningful objection clusters: 11
Product-defect / improvement signals: 6
Major objections with sufficient proof for messaging: 7
Objections requiring additional proof: 4
Top purchase objections
| Rank | Objection | Review count | Share | Severity |
|---|---|---|---|---|
| 1 | “I won't have time to set this up” | 214 | 11.6% | High |
| 2 | “Will this work for picky eaters?” | 168 | 9.1% | High |
| 3 | “The subscription feels expensive” | 143 | 7.8% | High |
| 4 | “I don't want another complicated app” | 96 | 5.2% | Medium |
Example objection
Objection:
“I won't have time to set this up.”
What customers are really worried about:
The recurring concern is not the app itself—it is the perceived effort required before the user receives value.
Evidence:
214 reviews mention setup time, onboarding effort, configuration, or difficulty getting started.
Supporting review IDs include
REV-00194, REV-00308, REV-00472, REV-00631, REV-00944
Truthful response angle
The supplied product documentation confirms that the app includes a guided onboarding checklist and default meal-plan templates.
That supports messaging around reducing initial setup effort.
Suggested product-page copy
Start with a plan—not a blank screen.
Use the guided setup and ready-made meal templates to build your first plan without configuring everything from scratch.
Suggested FAQ
How long does it take to get started?
You can begin with the guided onboarding flow and pre-built meal templates instead of creating a plan from zero.
PROOF_REQUIRED: Add a quantified setup-time claim only if the product team provides validated timing data.
Product issue separated from messaging
Signal: Recipe filtering becomes slow on older devices.
This appeared in 61 reviews and was classified as a product-performance issue, not a sales objection.
Recommended action: route to Product / Engineering rather than attempting to counter the concern with marketing copy.
Files generated
- normalized_reviews.csv
- objection_clusters.json
- objection_matrix.csv
- evidence_audit.csv
- sales_copy.md
- faq_objection_handlers.md
- counterarguments.md
- product_improvement_signals.md
- summary.json
normalized_reviews.csv
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Customer reviews contain the exact reasons buyers hesitate—but reading hundreds of reviews manually makes it easy to overfocus on a few loud complaints or miss recurring patterns.
Generic AI summaries are often too vague to turn into useful sales messaging.
What it does
Customer Review Objection Analyzer converts large review datasets into a structured objection intelligence package.
- Normalizes CSV, XLSX, JSON, TXT, and Markdown review exports
- Removes duplicate review text from frequency counts
- Groups recurring purchase objections into source-backed clusters
- Separates objections from real product defects, delivery issues, and feature requests
- Quantifies frequency and severity
- Links every major finding back to review IDs
- Identifies where proof is required before a counterargument can be used
- Drafts product-page copy, FAQ answers, checkout reassurance, ad angles, and sales rebuttals
- Creates a separate product-improvement signal report
Why this beats prompting it yourself
A generic prompt can summarize reviews, but it may lose track of which customers said what, inflate a pattern from a handful of comments, or create counterarguments that are not supported by product facts.
This skill forces a staged workflow:
Reviews → Signals → Objection Clusters → Evidence Validation → Prioritization → Sales Messaging
Every major objection remains tied to its supporting review IDs.
It doesn't hide real product problems
Not every negative review should become marketing copy. Genuine defect, reliability, delivery, or feature problems are separated into a product-improvement report instead of being disguised with persuasive messaging.
Evidence-backed copy
Counterarguments are generated only when supported by known product facts, policies, or reliable evidence.
If proof is missing, the skill outputs PROOF_REQUIRED rather than inventing a guarantee or claim.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Requires
Python