Retail POS Throughput Estimation Solution powered by Azure ML & cognitive services: 4-week PoC

Affine’s Retail POS Throughput Estimation Solution is a powerful and intuitive tool that enables small-scale retailers to gain valuable insights into their sales and inventory data, even without a robust transaction processing mechanism. By digitizing point-of-sale receipts and using advanced Azure machine learning and natural language processing services, the solution provides accurate and up-to-date estimates of sell-through rates and inventory on hand.  The solution can be easily adapted to work with various forms of receipts used by retailers, making it a versatile and efficient approach to collecting sales data from independent stores.

Solution Approach:

• Retail POS Throughput Estimation Solution uses OCR techniques to digitize point-of-sale receipts and extract structured data from them. This data is then processed using NLP models to identify key information such as product names, quantities, and prices.
• The solution uses Azure ML to analyze the extracted data and generate accurate estimates of sell-through rates and inventory on hand.
• The solution can be integrated with ETL processes to process and store the data, allowing it to be used for downstream analytics.
• The solution also includes a product performance monitoring dashboard that reduces the effort required for manual EDA processes to generate insights into product performance at various levels.
• The dashboard can be integrated with multiple data sources and processes to handle access requests without any performance issues.
• Retail POS Throughput Estimation Solution leverages Azure services such as Azure ML, Azure Form Recognizer, Azure Cognitive Services etc.

Benefits:

• Accurate and up-to-date estimates of sell-through rates and inventory on hand, even without a robust transaction processing mechanism
• Reduced time and effort spent on manual data entry and analysis
• Improved understanding of customer purchase behavior to drive incremental revenue via cross-sell and up-sell strategies
• Product performance monitoring dashboard reduces effort required for manual EDA processes
• Insights into customer preferences and product affinities to improve marketing strategies and drive higher sales and ROI

Agenda – 4 weeks:  

• Week 1: Initial consultation and requirements gathering. Setting up the Azure ML and Cognitive Services platforms.
• Week 2: Collecting a sample of receipts from the client's business for testing purposes. Using OCR technology to digitize the receipts and convert them into structured data.
• Week 3: Training an NLP model on the structured receipt data. Using the NLP model to extract relevant information from the receipts.
• Week 4: Visualizing the sell-through rates and inventory on hand using a dashboard or other interface.

Outcome:

At the end of this POC, the client will have a working system for digitizing and structuring their customers' receipts, using Azure ML and Cognitive Services technologies. They will be able to use this system to extract valuable insights into their sales and inventory levels and to make informed decisions about their operations.

Why Affine

Enabling business-focused data science, AI, and BI development with deep domain expertise. Affine believes in faster design to faster deployment through key differentiators- Experimentation Focus and Speed to Value.