Skip Navigation

Customer 360

A deep understanding between customer interests and purchasing patterns is a critical component of any retail business intelligence operation. This solution implements a process of aggregating customer data into a “360 degree” profile and uses advanced machine learning models backed by the reliability and processing power of Azure to provide predictive insights on simulated customers.

Customer 360A deep understanding between customer interests and purchasing patterns is a critical component of any retail business intelligence operation. This solution implements a process of aggregating customer data into a “360 degree” profile, and uses advanced machine learning models backed by the reliability and processing power of Azure to provide predictive insights on simulated customers.Python Web JobEvent HubETL (Python)Machine LearningTrained modelStream AnalyticsAzure StorageBrowsing DataBatch ETL and Predictive Pipeline12SQL DWDemographics, products, purchasesMerged customer profilew/ engineered featuresEnriched customer profile with predictionsHD Insight(Spark R Server)Enriched customer profilesPower BI Dashboard3456

Disclaimer

©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the Solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licences in order to create similar datasets.

Customer 360A deep understanding between customer interests and purchasing patterns is a critical component of any retail business intelligence operation. This solution implements a process of aggregating customer data into a “360 degree” profile, and uses advanced machine learning models backed by the reliability and processing power of Azure to provide predictive insights on simulated customers.Python Web JobEvent HubETL (Python)Machine LearningTrained modelStream AnalyticsAzure StorageBrowsing DataBatch ETL and Predictive Pipeline12SQL DWDemographics, products, purchasesMerged customer profilew/ engineered featuresEnriched customer profile with predictionsHD Insight(Spark R Server)Enriched customer profilesPower BI Dashboard3456

A Data Generator pipes simulated customer events to an Event Hub

A Stream Analytics job reads from the EventHub, performs aggregations

Stream Analytics persists time-grouped data to an Azure Storage Blob

A Spark job running in HDInsight merges the latest customer browsing data with historical purchase and demographic data to build a consolidated user profile

A second Spark job scores each customer profile against a machine learning model to predict future purchasing patterns (i.e., is a given customer likely to make a purchase in the next 30 days and if so, in which product category?)

Predictions and other profile data are visualised and shared as charts and tables in Power BI Online

  1. 1 A Data Generator pipes simulated customer events to an Event Hub
  2. 2 A Stream Analytics job reads from the EventHub, performs aggregations
  3. 3 Stream Analytics persists time-grouped data to an Azure Storage Blob
  1. 4 A Spark job running in HDInsight merges the latest customer browsing data with historical purchase and demographic data to build a consolidated user profile
  2. 5 A second Spark job scores each customer profile against a machine learning model to predict future purchasing patterns (i.e., is a given customer likely to make a purchase in the next 30 days and if so, in which product category?)
  3. 6 Predictions and other profile data are visualised and shared as charts and tables in Power BI Online