marketing_ops · ecommerce · workflow
Insights Generation from Customer Feedback Using LLMs for a Leading Retailer
Organizations with high feedback volumes found manual review too time-consuming, and traditional NLP models proved ineffective at handling large, varied review comments.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Data ingestion and enrichment
A data pipeline ingests feedback from a data store and performs data cleansing and enrichment before LLM processing.
Tools used
Azure OpenAIGPT
Outcome
(not stated)
What failed first
Traditional NLP models were found to be ineffective for handling large review comments, which prompted adoption of LLMs.
Grounding & classification
Source type: technical build writeup
16 fields verified against source quotes.
data extractiondocument classificationsentiment analysisform submissionhuman review describedtools describedvendor confirmedworkflow describedretailcustomer satisfactiontechnical build writeupback office opsmarketing opsdata sync enrichmentextract classify route