Datasets:

getreviews-space
/

review-insights-benchmarks

id int64 1 20	business stringlengths 8 18	platform stringclasses 10 values	rating_score int64 84 90	sentiment_score int64 80 86	authenticity_score int64 82 88	volume_score int64 76 84	topic_coverage_score int64 86 92	reputation_trend_score int64 80 86	overall_insights_index int64 81 88	priority_action stringclasses 1 value	google_score int64 84 90	trustpilot_score int64 80 86	yelp_score int64 76 84	app_stores_score int64 80 86	current_rating float64 3.6 4.4	review_count int64 62 580	target_rating float64 4 4.7	reviews_needed int64 24 90	industry stringlengths 5 16	notes stringlengths 18 35
1	Restaurant Chain	google	88	84	86	80	90	84	85	Volume	88	84	80	84	4.1	240	4.5	62	Food & Beverage	Strong rating with growth potential
2	E-commerce Store	trustpilot	86	82	84	78	88	82	83	Volume	86	82	78	82	3.9	180	4.3	44	Retail	Trustpilot reputation build
3	SaaS Platform	g2	90	86	88	82	92	86	87	Volume	90	86	82	86	4.2	320	4.6	72	Software	B2B review growth plan
4	Hotel Chain	tripadvisor	84	80	82	76	86	80	81	Volume	84	80	76	80	3.8	150	4.2	47	Hospitality	Rating improvement needed
5	Healthcare Clinic	google	88	84	86	80	90	84	85	Volume	88	84	80	84	4.3	200	4.7	58	Healthcare	Patient review strategy
6	Law Firm	google	86	82	84	78	88	82	83	Volume	86	82	78	82	4	95	4.4	52	Legal	Professional trust building
7	Mobile App	app-store	90	86	88	84	92	86	88	Volume	90	86	84	86	4.1	580	4.5	90	Technology	App store rating push
8	Retail Store	google	84	80	82	76	86	80	81	Volume	84	80	76	80	3.7	120	4.1	48	Retail	Local reputation recovery
9	Fitness Studio	yelp	86	82	84	78	88	82	83	Volume	86	82	78	82	4.2	88	4.6	42	Health & Fitness	Yelp review growth
10	Financial Advisor	google	88	84	86	80	90	84	85	Volume	88	84	80	84	4.4	65	4.7	28	Finance	High trust rating target
11	B2B Software	capterra	90	86	88	82	92	86	87	Volume	90	86	82	86	4	210	4.4	62	Software	Capterra rating strategy
12	Beauty Brand	amazon	86	82	84	78	88	82	83	Volume	86	82	78	82	3.9	440	4.3	86	Beauty	Amazon review volume push
13	Education Platform	trustpilot	88	84	86	80	90	84	85	Volume	88	84	80	84	4.1	175	4.5	54	EdTech	Learning platform trust
14	Real Estate Agency	google	84	80	82	76	86	80	81	Volume	84	80	76	80	4.2	110	4.6	48	Real Estate	Agent review profile
15	Tech Startup	producthunt	86	82	84	78	88	82	83	Volume	86	82	78	82	3.8	62	4.2	36	Technology	Early stage review build
16	Insurance Broker	google	88	84	86	80	90	84	85	Volume	88	84	80	84	4	145	4.4	58	Insurance	Trust review strategy
17	Travel Agency	tripadvisor	90	86	88	82	92	86	87	Volume	90	86	82	86	4.3	290	4.7	66	Travel	Travel review excellence
18	Dental Practice	google	86	82	84	78	88	82	83	Volume	86	82	78	82	4.1	180	4.5	54	Healthcare	Dental trust profile
19	Car Dealership	google	84	80	82	76	86	80	81	Volume	84	80	76	80	3.6	320	4	84	Automotive	Dealership recovery plan
20	Digital Agency	clutch	88	84	86	80	90	84	85	Volume	88	84	80	84	4.4	78	4.7	24	Marketing	Agency portfolio trust

Review Insights Tool Benchmarks

Benchmark dataset of 20 review insight cases with individual scores for rating, sentiment, authenticity, volume, topic coverage, and reputation trend — plus rating improvement calculator data.

Built by GetReviews.Space.

Dataset Description

This dataset contains benchmark data for a review insights tool that analyzes online customer reviews and turns unstructured feedback into useful business insights — including a rating improvement calculator.

Columns

Column	Type	Description
id	integer	Case ID
business	string	Business name
platform	string	Primary review platform
rating_score	integer	Rating health score (0-100)
sentiment_score	integer	Sentiment balance score (0-100)
authenticity_score	integer	Authenticity signal score (0-100)
volume_score	integer	Review volume score (0-100)
topic_coverage_score	integer	Topic coverage score (0-100)
reputation_trend_score	integer	Reputation trend score (0-100)
overall_insights_index	integer	Overall insights index (0-100)
priority_action	string	Lowest scoring signal to act on first
google_score	integer	Google channel score
trustpilot_score	integer	Trustpilot channel score
yelp_score	integer	Yelp channel score
app_stores_score	integer	App stores channel score
current_rating	float	Current average rating
review_count	integer	Current total review count
target_rating	float	Target rating to achieve
reviews_needed	integer	5-star reviews needed to reach target
industry	string	Industry category
notes	string	Case notes

Rating Improvement Calculator

from review_insights import calculate_rating_improvement
result = calculate_rating_improvement(3.8, 120, 4.2)
print(result["message"])
# Need 47 more 5-star reviews → projected rating: 4.21

Score Interpretation

Score	Status	Action
0-30	Critical	Immediate review strategy intervention required
31-60	At Risk	Significant review improvements needed
61-80	Healthy	Monitor and optimise review profile
81-100	Excellent	Strong review health — scale strategy

Usage

import pandas as pd
df = pd.read_csv("review_insights_benchmarks.csv")
print(df.head())

Citation

GetReviews.Space. (2026). Review Insights Tool. Zenodo. https://doi.org/10.5281/zenodo.21984115

Links

Platform	URL
Website	https://getreviews.space
GitHub	https://github.com/getreviews-space/review-insights-tool
NPM	https://npmjs.com/package/@getreviews-space/review-insights-tool
PyPI	https://pypi.org/project/review-insights-tool
Quora	https://fr.quora.com/profile/Get-Reviews-Space
SlideShare	https://www.slideshare.net/slideshow/boost-your-business-with-getreviews-space-mastering-5-star-review-growth/289290110
Pinterest	https://www.pinterest.com/getreviewsspace/
Medium	https://medium.com/@getreviews_space
Zenodo	https://doi.org/10.5281/zenodo.21984115

License

MIT — GetReviews.Space

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8.78 kB