REVIEWSBLENDER - REVIEW ANALYSIS WORKING TEMPLATE
Updated: 1 November 2026
Guide: https://reviewsblender.com/information/how-to-analyse-customer-reviews.html

Purpose: organise a defined review set and record evidence, hypotheses and actions.
Keep personal data to the minimum needed; agree access and handling before sharing.
This is a working template, not automated sentiment analysis or legal advice.

ANALYSIS BRIEF
Management question:
Locations/products:
Included sources and permitted collection route:
Review-publication window:
Known experience-date window:
Languages and translation checks:
Excluded or unavailable records:
Text-review count and denominator definition:
Coding definitions and validation method:

RECORD (repeat for each relevant review)
Record ID:
Source and source reference:
Publication date:
Experience date (only if supplied):
Location/product/version (only if supplied):
Original rating and rating scale:
Original language and review text:
Working translation (if applicable):
Theme(s):
Sentiment by theme:
Confidence / uncertainty:
Evidence excerpt (label paraphrase or translation):
Operational hypothesis (not an established cause):
Investigation / action:
Owner:
Due date:
Follow-up measure:
Status and outcome:

SYNTHETIC EXAMPLE - NOT A REAL CUSTOMER RECORD
Text: Good food, but we waited a long time for the bill.
Themes: food (positive); payment waiting (negative).
Hypothesis: possible friction in payment handover; not yet established.
Next step: manager to observe payment requests in comparable services.
Follow-up: record observations and relevant later theme counts.

CALCULATION EXAMPLE - HYPOTHETICAL
12 unique text reviews mentioning waiting / 80 included text reviews = 15%.
Count a review once per theme. Multiple themes can sum above 100%.
This is not the percentage of all customers who experienced waiting.