MicrosoftProductionEvidence: Medium65/100

Blue Bungalow transforms online shopping with AI-powered personal assistant

Blue Bungalow, a leading Australian fashion retailer, sought to enhance its online shopping experience by introducing personalized product recommendations, accurate sizing advice, and intuitive product search. Preezie, a technology partner, designed an AI-powered shopping assistant leveraging Microsoft Azure, Azure OpenAI, Semantic Kernel and Elasticsearch. The conversational assistant enables natural language queries, product recommendations, and provides direct product comparisons, closely mimicking in-store shopping support online. Semantic Kernel plays a central role, interpreting user intent and context to deliver precise search results and tailored alternatives. The assistant helps shoppers find similar products, suggests substitutes if items are unavailable, and integrates image recognition for visually matched recommendations. Product comparison tools offer side-by-side views of features, customer reviews, and pricing, empowering shoppers to make faster, more confident purchase decisions. This implementation reduced the burden on human support agents by instantly handling routine queries and requests, unlocking more value from large product catalogs. Significant impact was observed: customers interacting with the assistant spent double the time on site, had a 40% higher add-to-cart rate, conversion rates ranging from 85% to 110% higher, and a 7% increase in average order value.

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Organization
Blue Bungalow
Industry
Retail
Location
Australia
Published
February 2025

Reported outcomes

Impact: +40%

Other quantified impact

Conversion rate: +85–110%Revenue: +7%
Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 40% increase

Microsoft DevBlogs – Semantic KernelFeb 6, 2025Customer storyInferred claimMedium evidence strength

Add-to-cart rate 40% higher among AI-engaged customers.

Normalized claim

Conversion rate: 85-110% increase

Microsoft DevBlogs – Semantic KernelFeb 6, 2025Customer storyExplicit claimMedium evidence strength

Conversion rate increased by 85%-110% among AI-engaged customers.

Correction recorded Jul 26, 2026 · Previously: % increase · Preserved the reported 85-110% conversion-rate range without collapsing it to a single value.

Normalized claim

Quantified impact: 7% increase

Microsoft DevBlogs – Semantic KernelFeb 6, 2025Customer storyInferred claimMedium evidence strength

Average order value (AOV) increased by 7% among AI-engaged customers.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
Blue Bungalow
Provider
Microsoft
Maturity
Production

Deployed an AI shopping assistant using Azure, Azure OpenAI, and Semantic Kernel for natural language search and recommendations

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

  • 1Conversational AI Shopping Assistant
  • 2Personalized Product Recommendation Engine
  • 3Real-time Product Discovery and Comparison
  • Deployed an AI shopping assistant using Azure, Azure OpenAI, and Semantic Kernel for natural language search and recommendations.
  • Implemented Elasticsearch for real-time product retrieval and similarity matching.
  • Integrated image recognition for visual product matching.
  • Enabled side-by-side product comparisons with summarised customer reviews and attributes.
  • Time spent on site doubled among AI-engaged customers.
  • Add-to-cart rate 40% higher among AI-engaged customers.
  • Conversion rate increased by 85%-110% among AI-engaged customers.
  • Average order value (AOV) increased by 7% among AI-engaged customers.
Architecture

The solution combines Semantic Kernel to interpret user intent and manage conversation flow, Azure OpenAI for advanced language understanding, Elasticsearch for vector-based product search, and .NET Core for scalable backend infrastructure, all deployed in Microsoft Azure's cloud.

Sources & evidence1
Evidence: Medium65/100Evidence strength
  • Customer explicitly identified
  • Deployment status explicitly supported
  • Primary source available
  • Quantified outcome available
  • Technical implementation details available
Type: Customer StoryPublished: Feb 6, 2025Publisher: Microsoft DevBlogs – Semantic KernelEvidence: PrimaryConfidence: High
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