Live website intelligence
Perfume wiki and fragrance recommendations - Wikiparfum
Wikiparfum is a fragrance recommendation engine based on olfactory preferences, where fragrances are proposed according to the tastes of perfume lovers.
Last refresh
Updated 56d ago
Analyst read
Professional
Detected stack
Quick read
How to read wikiparfum.com quickly
wikiparfum.com looks like style & fashion. Traffic estimates are limited, so use the trust and structure modules first. Current AI trust scoring is 15/100.
What to do next
- The stack appears to include React.
- Open the Traffic tab if you need audience scale and geography before outreach.
- Open the Business tab if trust, monetization, or positioning is your first decision filter.
Provider Completeness
34/56 fields populated (61%)
Providers with missing fields
View field-level status
visual: 4/4
All expected fields present
meta: 3/3
All expected fields present
seo: 5/5
All expected fields present
dns: 4/4
All expected fields present
ads: 0/5
Missing: isAdvertiser, advertiserIds, advertiserNames, resultCount, transparencySignals
publisher: 3/5
Missing: directCount, resellerCount
files: 2/3
Missing: robotsSitemapUrls
traffic: 0/10
Missing: monthlyVisits, globalRank, countryRank, bounceRate, avgVisitDuration, pagesPerVisit, topCountry, topRegions, topKeywords, trafficSources
whois: 6/6
All expected fields present
radar: 0/4
Missing: globalRank, rankBucket, categories, sourceTimestamp
ai: 7/7
All expected fields present
Why this module matters
Business signals help answer “is this a real opportunity?”
Use the business tab to understand trust, monetization, audience fit, and brand posture before you spend time on outreach, partnerships, or competitive teardown work.
- Trust score and sentiment are your first risk screen.
- Business summary and audience notes speed up qualification.
- Ads and monetization patterns reveal how the site captures value.
Business Intelligence
Business Profile
Wikiparfum is a fragrance recommendation platform that uses an algorithmic engine to suggest perfumes based on users' olfactory preferences and browsing behavior, combining wiki-style fragrance information with personalized discovery tools.
Classification
Trust & Risk
Trust Assessment
Publisher Monetization
Monetization Signals
AI Visual Analysis
IAB Taxonomy
Business Insights
Business Model
E-commerce / Content & Recommendation Platform (freemium with potential affiliate/retail partnerships) model detected
Trust Level
Low trust with 15/100 score
Audience
Perfume enthusiasts, fragrance collectors, consumers seeking personalized scent recommendations, and beauty shoppers researching fragrances
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