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Noli

Marketing Personalization

Beauty shoppers face too many undifferentiated product choices and often do not understand their own skin or hair needs well enough to identify the right products with confidence.

100-strong Alpha community
86% of users receive a “good or excellent skincare match.”
87% users report increased shopping confidence
92% of its users

Reported impact

Business outcome

Noli reports stronger consumer trust, better match quality, and higher shopping confidence through personalized, science-based beauty recommendations.

  • Customer Experience

Solution

Accenture helped London-based beauty startup Noli design and launch an AI-powered recommendation platform in four and a half months. The implementation combines deep consumer research, first-party interaction data, more than 1 million skin data points, thousands of product analyses, and L'Oréal’s proprietary scientific datasets inside Noli’s AI Beauty Matchmaker. That recommendation engine uses AI and generative AI to interpret a shopper’s skin or hair needs and return personalized skincare and haircare matches through Noli’s multi-brand digital experience. Microsoft Azure provides the cloud infrastructure underneath the platform and was architected to collect first-party data, support future scale, and accommodate AI agents as Noli expands to new markets. Accenture also shaped the end-to-end consumer experience, product, marketing, technology environment, and operating model, while continuous testing with thousands of consumers and a 100-person alpha community feeds ongoing refinement.

Data flow

Consumer inputs and ongoing first-party interaction data flow into Noli’s Azure-based platform, where the AI Beauty Matchmaker combines those signals with more than 1 million skin data points, thousands of product analyses, and L'Oréal’s proprietary scientific datasets to generate personalized skincare and haircare recommendations that are served back through Noli’s online shopping experience.

Solution architecture

2 components · 2 layers
  1. Serving
    • AI Beauty Matchmaker Provides personalized AI-driven skincare and haircare recommendations using consumer signals, product analyses, and scientific datasets.
  2. Uncategorized
    • Microsoft Azure Hosts the cloud infrastructure for Noli’s platform and supports data collection and future scalability.

Architecture clues

  • AI Beauty Matchmaker is powered by over 1 million skin data points, thousands of product analyses, and L'Oréal’s proprietary scientific datasets.
  • Cloud infrastructure is built on Microsoft Azure.
  • Noli tests new enhancements with a 100-strong Alpha community.
  • The MVP launched in four-and-a-half months.
  • The platform architecture was designed to gather first-party data and accommodate AI agents as the company grows.

Evidence from the source

"87% users report increased shopping confidence"
"93% 5-star reviews on TrustPilot"
"From the initial spark to the MVP launch, the startup brought Noli to life in just four-and-a-half months"
"Its cloud infrastructure is built on Microsoft Azure, and the platform’s architecture has been designed to gather valuable first-party data and accommodate AI agents as the company grows, expands into new markets and scales its technology operations."
"Noli isn't just another e-commerce platform; it’s your personal beauty match maker – a cutting-edge, multi-brand platform that leverages the power of AI and generative AI (gen AI) with rich consumer data and research to offer personalized, evolving skin and haircare recommendations."
"Noli’s magic lies in its AI Beauty Matchmaker—a personalized AI recommendation engine powered by over 1 million skin data points, thousands of product analyses and L'Oréal’s proprietary scientific datasets which decodes beauty and helps consumers make confident, right-for-them choices."
Transparency Evidence & methodology 96/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Noli

    Noli customer story

    First-party
    https://www.accenture.com/en/case-studies/song/noli-uses-ai-beat-beauty-jungle-find-your-perfect-match
    Source type
    Customer-published story
    Published
    May 29, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 29, 2026 · Accenture · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.