Customereye

Margaret Robinson

Apparel & Fashion • United Statesmargaret-robinson.com

Review data last updated .

Based on public reviews and response behavior.

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75 days of reviews, with a steady monthly review flow. Busiest month: 40% of reviews. Languages: en 100%.

Overall Score

40/100
Short observation window (75 days)
−10.9 vs. last month3.00 ★ average rating
40/100 from public reviews over 3 months, moderate confidence. trend not shown: window under six months.

Apparel & Fashion average: score 56.3, rating 3.64 stars, reply rate 31.5%, over 3,581 ranked companies · benchmark 9 Nov 2026

Score

40.0

Industry average 56.3

16.3 below average

Rating

3.00

Industry average 3.64

0.64 below average

Reply rate

—

Industry average 31.5%

not measured

Competitive Performance Rank

Among ranked Apparel & Fashion companies

#2850of 3581 ranked in Apparel & Fashion
Top 80% · Bottom Tierup 815 from last month
#1 (best)#3581 (worst)
#2848Peer
#2849Peer
#2850This company
#2851Peer
#2852Peer

Only companies with enough recent, scoreable reviews are ranked.

Overall Customer Sentiment

Below Average

54% of reviews positive, 46% negative

54%
positive
  • Positive54%
  • Neutral1%
  • Negative46%

Sentiment Balance

Covering October 2026, not the company's full review history.

—
positive
  • Positive—
  • Neutral—
  • Negative—

Takeaway: Positive sentiment accounts for 47% of reviews in this period, with 52% negative and 1% neutral.

Sentiment Trend Over Time

Review activity
Hidden

Based on 3 months of reviews, too short to read a trend

Theme Distribution

Share of theme mentions

Delivery Shipping
Hidden
Onboarding
Hidden
Pricing
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Loyalty
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How this analysis was produced

The figures on this page come from the most recent reviews this company received, up to a per-company limit, so the period they cover depends on how quickly this company receives reviews: a busy company's sample may span only a few weeks, a quieter company's may span years. The window actually covered is stated above with the score. The analysis identifies patterns across many reviews at once and is not intended as a judgement on any individual one. For a complete analysis covering all available feedback, using AI rather than automated rules, .

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