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Microsoft Commercial and Professional Services India

Fractal

Marketing Personalization

Today's retailers face an ever-growing set of challenges, from rapid employee turnover to meeting high expectations for personalized customer experiences.

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In a one-year period, the retailer realized an annual financial gain of $25 million across use cases including pricing and promotions, visual merchandising, and store operations.
This foresight prevented unnecessary expenditure, saving the client an estimated $1.25 million in potential costs for a system-wide signage overhaul Customers can now see a live demonstration of Trial Run next time they visit their closest Microsoft Innovation Hub (formerly known as Technology Centers, or MTCs).

Reported impact

Business outcome

"We solve problems across functional areas, helping them along all the steps of their value chain." Effective use of AI accelerates value across business areas from resource utilization to supply chain management, helping people do their jobs faster and more accurately.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Fractal built its Microsoft solution to improve customer-personalization workflows. Today's retailers face an ever-growing set of challenges, from rapid employee turnover to meeting high expectations for personalized customer experiences. Key components included Azure OpenAI Service ran model inference or heavy processing, and Azure AI Studio coordinated workflows, prompts, or deployment. User prompts and domain content are orchestrated through Azure AI Studio, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so Fractal can return context-aware answers or automation in the flow of work. The stack stayed governed and extensible on Microsoft. "We solve problems across functional areas, helping them along all the steps of their value chain." Effective use of AI accelerates value across business areas from resource utilization to supply chain management, helping people do their jobs faster and more accurately.

Data flow

User prompts and domain content are orchestrated through Azure AI Studio, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so Fractal can return context-aware answers or automation in the flow of work.

Solution architecture

2 components · 2 layers
  1. Compute
    • Azure OpenAI Service Runs large language models that generate grounded responses, summaries, and copilots.
  2. Orchestration
    • Azure AI Studio Provides the build environment for prompt orchestration, safety controls, and model tuning.

Architecture clues

  • "Azure Marketplace gives us the ability to reach millions of customers around the world in an efficient manner," Dias says, "not only to get discovered-but also to transact business." Dias appreciates the value Microsoft places on security and privacy.
  • "Those tools provide the bedrock for our solutions." Fractal's customers seek cutting-edge GenAI technology that doesn't compromise on scalability, maintenance, privacy, and security.
  • Today's retailers face an ever-growing set of challenges, from rapid employee turnover to meeting high expectations for personalized customer experiences.
  • Utilizing Azure OpenAI Service allows Fractal to address these needs effectively, capitalizing on Microsoft's robust and secure infrastructure.

Evidence from the source

"We solve problems across functional areas, helping them along all the steps of their value chain." Effective use of AI accelerates value across business areas from resource utilization to supply chain management, helping people do their jobs faster and more accurately.
"We're able to bring the latest and greatest because of our deep relationship with Microsoft." Fractal is a leading adopter of Microsoft AI offerings, including Azure OpenAI Service , Microsoft Copilot , and Microsoft Cloud for Retail .
In a one-year period, the retailer realized an annual financial gain of $25 million across use cases including pricing and promotions, visual merchandising, and store operations.
This foresight prevented unnecessary expenditure, saving the client an estimated $1.25 million in potential costs for a system-wide signage overhaul Customers can now see a live demonstration of Trial Run next time they visit their closest Microsoft Innovation Hub (formerly known as Technology Centers, or MTCs).
Today's retailers face an ever-growing set of challenges, from rapid employee turnover to meeting high expectations for personalized customer experiences.
Utilizing Azure OpenAI Service allows Fractal to address these needs effectively, capitalizing on Microsoft's robust and secure infrastructure.
Transparency Evidence & methodology 80/100 reference value · 89% 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 value80/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • 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. Microsoft

    Fractal customer story

    First-party
    https://www.microsoft.com/en/customers/story/1798075568757336532-fractal-azure-ai-studio-retailers-en-india
    Source type
    Vendor-published customer story
    Published
    Aug 5, 2024
    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 Aug 5, 2024 · Microsoft · 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.

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