Features and architecture
Features of Smart Store Analytics
The features of Smart Store Analytics are:
AiFi Connector - Smart Store Analytics seamlessly integrates with AiFi data, providing retailers with a centralized source for comprehensive retail data.
Analytics
Key performance indicators (KPIs) - Monitor crucial metrics such as orders, shopper count, basket size, and checkout time.
Sales growth analysis - Compare sales growth against market share and shelf placement.
Visualizations - Use heat maps and customer journey analysis for a deeper analysis of customer behavior.
Insights
- Data science/machine learning-backed product recommendations - Enhanced with advanced algorithms for personalized and effective product catalogs.
Architecture of Smart Store Analytics
Standardized metadata and self-describing data in Common Data Model facilitates metadata discovery and interoperability between data producers and apps, such as:
Microsoft Power BI
Microsoft Azure Data Factory
Microsoft Azure Databricks
Microsoft Azure Machine Learning packages
As shown in the preceding diagram, the facilitation of metadata discovery and interoperability in Smart Store Analytics works as follows:
Smart Store Analytics brings frictionless checkout data from AiFi through the AiFi Connector. The app transforms data into standard retail data models from Microsoft Azure Synapse database templates.
Customers can select Microsoft Dataverse-managed data lake or Azure Data Lake Gen2 (use your own data lake). Smart Store Analytics supports continuous replication of in-store data from the smart store provider to Microsoft Azure Data Lake Storage. Power Apps admin users can also access the managed Azure Data Lake directly for more extensibility and customization.
Smart Store Analytics runs analytics, aggregations, and machine learning.
The generated results and analytics are visualized in Power BI embedded in Power Apps.
For more information, see the Smart Store Analytics architecture documentation.