☕ Building a Search-Driven Customer Insight Solution with Azure AI Search

🖊️ by Meet Ved | 📅 13 June 2025

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🔍 Overview

As a Data Analyst passionate about unlocking value from raw data, I recently explored Azure AI Search by completing a hands-on knowledge mining lab for a fictional company called Fourth Coffee.

The goal? To extract actionable insights from customer reviews using Azure’s powerful AI tools — a process that mirrors real-world needs in industries like retail, e-commerce, and customer service.

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🔧 What I Built

In this project, I created a knowledge mining solution that enables smart search over unstructured customer feedback.

🔹 The pipeline included:

• ✅ Creating Azure AI Search and Azure AI Services

• ✅ Setting up Azure Blob Storage

• ✅ Uploading customer review documents

• ✅ Using Cognitive Skills:

  • OCR

  • Sentiment detection

  • Key phrase extraction

  • Image analysis

• ✅ Building a Search Index and Knowledge Store

• ✅ Querying enriched data for deep insights

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🧠 Key Learnings

✅ 1. Connecting Data with AI

I enriched plain-text reviews with AI to extract:

• 🔸 Sentiment (positive/negative)

• 🔸 Key Phrases (e.g., "slow service", "great ambiance")

• 🔸 Locations mentioned in the text

• 🔸 Image tags and captions

➡️ These skills turned raw data into searchable insights.

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✅ 2. Creating a Knowledge Store

The Knowledge Store acted like a structured database for AI-enriched fields:

• 🔗 Linked tables using entity keys

• 📊 Enabled visual analysis and dashboarding

• 🧠 Persisted all AI outputs for deeper exploration

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✅ 3. Search Indexing with AI

Using Azure’s Import Data Wizard, I created a pipeline that:

• Indexed review documents from blob storage

• Ran AI skillsets automatically

• Enabled filtering by location, sentiment, and more

🔍 Example JSON Query:

{
  "search": "sentiment:'negative'",
  "count": true
}

✅ 4.📦 Customer Review Data Overview

The input data consisted of 9 Microsoft Word (.docx) files, each containing real or simulated customer reviews for a fictional coffee chain, Fourth Coffee. These documents varied in structure but typically included:

📝 Input Data (Raw Customer Reviews)

4. Each Word document contained:

   • Multiple paragraphs of free-text reviews

   • Informal language, including typos or casual expressions

   • Mentions of locations (e.g., "Chicago", "Toronto")

   • Implicit sentiment (positive, neutral, negative)

   • Occasional embedded images (coffee, café scenes)

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🔠 Sample Raw Review (from input)

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🧠 AI-Enriched Output (Sample)

After processing with Azure AI Search + Cognitive Skills, the documents were enriched with structured metadata like this:

✅ Sample JSON Output from Azure Search Index:

    jsonCopyEdit{
      "metadata_storage_path": "https://<storage>.blob.core.windows.net/coffee-reviews/review3.docx",
      "merged_content": "I absolutely loved the coffee in Toronto... Not recommended at all.",
      "locations": ["Toronto", "Chicago"],
      "sentiment": "mixed",
      "keyphrases": ["slow service", "great mocha", "terrible experience"],
      "imageCaption": "a coffee cup on a table",
      "imageTags": ["coffee", "cup", "table", "cafe"]
    }

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🗃️ Stored in Knowledge Store as:

• Tables for:

  • KeyPhrases

  • Locations

  • Entities

  • Image Projections

• Blob Containers for:

  • Original and enriched documents

  • Captions and tags from images

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This structure allows you to:

• Search by sentiment, location, or key phrase

• Build dashboards to visualize customer satisfaction

• Query for trends across locations or time

🌍 Real-World Applications

This isn’t just an academic exercise — here’s how businesses can use it:

💬 Customer Service Teams

• Detect common complaints (via key phrases)

• Identify patterns by location/sentiment

• Prioritize areas for quality improvement

🛒 Retail & E-Commerce

• Find product-level feedback

• Segment reviews by keywords or geography

• Automate review summaries for decision-making

🧾 Legal & Document Workflows

• Extract entities (names, dates, clauses)

• Index thousands of legal contracts

• Make legal archives searchable and reportable

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🛠️ Skills & Tools Used

• Azure AI Search

• Azure Cognitive Services

• Azure Blob Storage

• Search Explorer (JSON Queries)

• Knowledge Store (Tables + Image Projections)

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🔚 Final Thoughts

This lab showed me how to bridge unstructured feedback with structured insight using the power of Microsoft Azure.

🔧 Whether it’s enhancing search, supporting sentiment dashboards, or building smarter applications — Azure AI Search is a valuable tool for data-driven teams.