Large Language Models for Retail Customer Feedback Analysis

microsoft · Tech · 2025 · source Agentic: no · promotional bias: low · source-backed · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Retail customer feedback analysis

A leading retailer needed to convert tens of thousands of daily shopper feedback comments into actionable themes, sentiment, and competitor comparisons. Manual review was too slow, and traditional NLP struggled with large review comments and inconsistent theme labels.

Constraints:

the retailer receives tens of thousands of feedback items every day
reviews may include noisy one-word or meaningless comments such as Nil, Null, or No comments
themes need to be consistent enough for analytics and trend reporting
outputs must be structured as JSON for downstream reporting
store managers and business leaders need rollups across store, district, state, regional, and national levels
sensitive data must be masked before sending reviews to LLMs
LLM outputs need evaluation before production rollout

Architecture

Microsoft ISE describes a feedback-insights pipeline using Azure OpenAI GPT models. Customer feedback is ingested from a data source, cleansed, enriched with context fields such as selected category, schema-validated, and then passed to a Themes Extraction and Sentiment Generator module. The prompt is iteratively refined: starting from free-form theme extraction, then moving to predefined retail themes selected with subject-matter expert review, then adding sentiment labels and competitor comparisons. LLM JSON outputs feed reports with sentiment graphs, trend reports, drill-down to source comments, and aggregation across the store hierarchy.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  Feedback_data_source["Feedback data source"] --> Feedback_data_pipeline["Feedback data pipeline"]
  Feedback_data_pipeline --> Schema_validation["Schema validation"]
  Schema_validation --> Responsible_AI_masking["Responsible AI masking"]
  Responsible_AI_masking --> Themes_sentiment_generator["Themes sentiment generator"]
  Prompt_template["Prompt template"] --> Themes_sentiment_generator
  Predefined_theme_vocabulary["Predefined theme vocabulary"] --> Prompt_template
  Subject_matter_expert_review["Subject matter expert review"] --> Predefined_theme_vocabulary
  Themes_sentiment_generator --> Azure_OpenAI_GPT_model["Azure OpenAI GPT model"]
  Azure_OpenAI_GPT_model --> Structured_JSON_outputs["Structured JSON outputs"]
  Structured_JSON_outputs --> Reporting_layer["Reporting layer"]
  Reporting_layer --> Store_managers["Store managers"]
  Reporting_layer --> Business_managers["Business managers"]

Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component	Role	Interacts with
`Feedback data source`	Stores customer review comments and optional category metadata.	`Feedback data pipeline`
`Feedback data pipeline`	Ingests, cleanses, enriches, validates, and prepares feedback for LLM calls.	`Feedback data source`, `Schema validation`, `Themes sentiment generator`
`Schema validation`	Validates refined feedback before LLM calls to avoid exceptions and reduce hallucinations.	`Feedback data pipeline`, `Responsible AI masking`
`Responsible AI masking`	Masks sensitive data before feedback is sent to the LLM.	`Schema validation`, `Azure OpenAI GPT model`, `Themes sentiment generator`
`Prompt template`	Instruction set that defines review format, predefined themes, sentiment labels, competitor extraction, and JSON output.	`Themes sentiment generator`, `Predefined theme vocabulary`
`Predefined theme vocabulary`	Retail-specific controlled list such as checkout experience, staff, availability, pricing, quality, variety, shopping experience, cleanliness, and layout.	`Prompt template`, `Subject matter expert review`
`Subject matter expert review`	Human review that groups similar generated themes and chooses canonical retail theme names.	`Predefined theme vocabulary`
`Themes sentiment generator`	Module that calls Azure OpenAI to extract themes, sentiment, and competitor comparisons from each comment.	`Feedback data pipeline`, `Prompt template`, `Azure OpenAI GPT model`, `Structured JSON outputs`
`Azure OpenAI GPT model`	LLM used to analyze feedback comments and produce structured insight outputs.	`Themes sentiment generator`, `Structured JSON outputs`
`Structured JSON outputs`	Machine-readable outputs containing themes, sentiment, and competitor mentions.	`Reporting layer`
`Reporting layer`	Graphs sentiment, highlights themes, supports drill-down, and aggregates insights across store hierarchy.	`Structured JSON outputs`, `Store managers`, `Business managers`
`Store managers`	Local users who use reports and comment drill-downs to improve customer satisfaction.	`Reporting layer`
`Business managers`	District, state, regional, or national managers consuming aggregated reports.	`Reporting layer`

Data flow

Shopper feedback enters a data pipeline from a feedback data store. The pipeline performs ingestion, cleansing, enrichment, and schema validation, then sends refined review comments to Azure OpenAI GPT models. The prompt classifies each comment into predefined retail themes, assigns positive/negative/neutral sentiment, extracts named competitor comparisons or NA, and emits JSON. Reporting layers aggregate the results into sentiment graphs, weekly trend reports, and drill-down views from store level up through district, state, regional, and national hierarchies.

3 supporting quotes

“A data pipeline can be built to ingest the data from the data store for any data processing activities such as data cleansing and data enrichments”article, stated

“Themes Extraction and Sentiment Generator module does the job of calling the OpenAI to generate the themes from each of the review comments.”article, stated

“The output should be in a Json format.”article, stated

Why this architecture

The design combines data preparation, controlled prompting, structured output, and reporting because customer feedback is noisy and high-volume. Predefined themes prevent semantically identical labels from fragmenting analytics, schema validation and masking reduce production risk, and JSON outputs let the LLM results become trend reports, sentiment graphs, and store-hierarchy rollups.

3 supporting quotes

“There is always a possibility that you don’t often get consistent themes generated for each run for the same data”article, stated

“This results in inconsistent themes that can’t be used for any analytics purpose such as creating trend analysis graphs for themes.”article, stated

“One of the techniques to generate consistent themes is to identify a pre-defined set of themes.”article, stated

Models used: Azure OpenAI · GPT models · Power BI · Azure Blob Storage · Azure Machine Learning

Scale

The feedback workload is tens of thousands of comments every day.
“there are tens of thousands of feedbacks received everyday.”article, stated
Reports aggregate from store level to district, state, regional, and national levels.
“aggregated to higher levels in the store hierarchy, including district, state, regional, and national levels.”article, stated
The predefined retail theme set in the example contains nine categories.
“Checkout Experience Insufficient Staff Product Availability Product Pricing Product Quality Product Variety Shopping Experience Store Cleanliness Store Layout”article, stated

Alternatives considered (roads not taken)

Manual review of feedback

Rejected because: Manual review is time-consuming at daily feedback volumes.

“manually reviewing large volumes of feedback can be time-consuming, especially when there are tens of thousands of feedbacks received everyday.”article, stated

Traditional NLP models

Rejected because: The article says traditional NLP was ineffective for large review comments.

“Traditional NLP models are found to be ineffective especially for handling large review comments.”article, stated

Free-form theme extraction

Rejected because: Free-form themes vary across runs and days, preventing reliable analytics.

“different runs may extract different themes with the same underlying meaning.”article, stated

Send raw feedback directly to the LLM

Rejected because: The article recommends cleansing, enrichment, schema validation, and sensitive-data masking before LLM calls.

“Building a data pipeline that performs data ingestion, data cleansing, data enrichment and schema validation”article, stated

“If the reviews or feedbacks contains any sensitive data, make sure those are masked before the reviews can be send to LLM”article, stated

Patterns

`batch-offline-processing` (data-processing)

The feedback pipeline processes large volumes of review comments through cleansing, enrichment, validation, LLM extraction, and reporting.

“Building a data pipeline that performs data ingestion, data cleansing, data enrichment and schema validation”article, stated

`safety-policy-enforcement` (governance)

Schema validation, output format constraints, evaluation before production, and sensitive-data masking are treated as safeguards for LLM insight generation.

“It is always good to validate the data against the schema to avoid hallucinations and any exceptions from LLM”article, stated

“make sure those are masked before the reviews can be send to LLM to generate insights”article, stated

`eval-driven-development` (evaluation)

The article emphasizes iterative prompt refinement, consistency checks across daily/weekly data, and output evaluation before production rollout.

“Building the prompt is an iterative process. Start with a very basic prompt.”article, stated

“the importance of evaluating the output from LLM, using different evaluation techniques, before the solution can be rolled out to production.”article, stated

`human-in-the-loop-gate` (governance)

Retail subject-matter experts help consolidate generated themes into a canonical predefined set.

“A subject matter expert from the retail domain can help in coming up with the predefined set of themes for the retail Industry.”article, stated

Tradeoffs

Use predefined themes.

Chose domain-specific controlled theme list over open-ended theme generation.

Rationale: Controlled themes improve consistency and make trend analytics possible.

“One of the techniques to generate consistent themes is to identify a pre-defined set of themes.”article, stated

Require structured JSON output.

Chose JSON key-value output over free-form natural-language summaries.

Rationale: Structured output can be aggregated into reports and hierarchy-level analytics.

“To generate the LLM output in a desired format, such as key-value pair, the output format can be mentioned in the prompt.”article, stated

Build data preparation before prompt execution.

Chose ingestion, cleansing, enrichment, and schema validation pipeline over direct LLM calls on raw feedback.

Rationale: Preparation improves context, removes junk comments, and reduces hallucination/exception risk.

“prefixing the category to the comments provides a good context to the comments.”article, stated

“making the refined data available for the LLM calls is the first step towards getting best outcome from LLM.”article, stated

Expose drill-down reports.

Chose sentiment graphs with access to actual shopper comments over aggregate sentiment charts alone.

Rationale: Managers need both trends and the underlying comments to take effective action.

“provide a drill-down feature that allows users to view the actual comments from shoppers directly from the sentiment analysis graph.”article, stated

Gotchas (what bit them)

LLMs can generate inconsistent theme names that break analytics.

Root cause: The same underlying meaning can be expressed as cleanliness, neatness, or tidiness across runs.

Resolution: Use a predefined retail theme vocabulary and prompt the model to choose from it.

“Cleanliness, Neatness, Tidiness all pertain to the cleanliness of the store.”article, stated

“the prompt can be fine tuned to generate the themes from the above predefined themes.”article, stated

Noisy comments can cause hallucinations or exceptions.

Root cause: Feedback data may contain meaningless one-word comments or schema problems.

Resolution: Clean meaningless comments, enrich with context, and validate against schema before LLM calls.

“removing one-word comments or the comments that are meaningless such as Nil, Null, No comments, etc.”article, stated

“validate the data against the schema to avoid hallucinations and any exceptions from LLM during the insight generation process.”article, stated

Prompt snippets are not portable without refinement.

Root cause: Retail, banking, hospitality, and other industries have different theme taxonomies and output needs.

Resolution: Iteratively adapt prompts and predefined theme sets to each domain.

“Themes differ for industry verticals. For example, the themes in retail may be different from themes in the banking industry”article, stated

Sensitive customer feedback can violate Responsible AI principles if sent raw to the LLM.

Root cause: Feedback may contain sensitive data.

Resolution: Mask sensitive data before sending reviews to the LLM.

“If the reviews or feedbacks contains any sensitive data, make sure those are masked before the reviews can be send to LLM”article, stated

Evaluation

The article recommends evaluating LLM output with different techniques before production, checking consistency of theme generation across daily or weekly feedback data, validating input schema, and reviewing generated themes with domain experts before locking a controlled vocabulary.

Metrics: theme consistency across runs · schema validation pass/fail · hallucination avoidance · sentiment correctness · competitor mention extraction · weekly trend report usability · store hierarchy aggregation

“See the consistency of theme generation by running it many times on daily or weekly feedback data.”article, stated

“Something that is not covered in this blogs is the importance of evaluating the output from LLM”article, stated

Outcomes

The proposed system extracts themes, sentiments, and competitor comparisons from retail feedback.
Structured JSON output can feed reports, sentiment graphs, weekly trends, drill-down views, and store-hierarchy rollups.
Predefined themes address inconsistency in free-form LLM theme generation.
Schema validation, masking, and evaluation are identified as necessary before production deployment.

The source is an implementation reference based on a retailer engagement, not a measured production case. It does provide a scale driver: tens of thousands of feedback items every day, with aggregation from store level up to national management.

“we explored how Large Language Models (LLMs) can be used to extract valuable insights from customer feedback.”article, stated

“Various reports can be generated from this output. These reports may use graphs to visualize both positive and negative sentiment.”article, stated