Retailers, today, have access to a variety of data sources that include social media, opendata, data generated by the internet of things. The combination of their proprietary data with public and purchased data provides valuable insights into customer purchase patterns and demand.
Microsoft’s vision is to provide a platform that stresses the need for everyone, and every company, to have a data culture with technology accessible to all. The democratization of data along with the democratization of tools is driving Big Data analytics forward for many retailers.
Machine Learning capabilities enables Retailers to use this vast trove of data to program systems to make predictions and inferences based on patterns in the data. Machine learning uses historical data to create a model that can be used to predict future events.
However, machine learning is usually self-managed and on-premises, requiring complex systems, training and expertise of data scientists.
To help overcome the challenges, most businesses have, in deploying and using machine learning, Microsoft recently announced Azure Machine Learning.
If you are interested in using the Azure Machine Learning Service you will be able to sign up to be notified of availability at www.azure.com/ml
To learn more, join this webinar, on June 25: Krishna R, Regional Head, Mu Sigma, joined by guest speakers Mike Gualtieri, Principal Analyst, Forrester Research, Inc. and Roger Barga, Group Program Manager, Microsoft shed light on the rapid, seamless and cost-effective deployment of real-time predictive analytics on the cloud.
Real Time Predictive Analytics in the Cloud http://bit.ly/1qvP1wU
Some examples of Machine Learning Applications for Retail:
Customer Retention: Reducing customer churn is a big priority for retail. The patterns in large amounts of data can be used for determining churn and proactively addressing it. Retailers can determine which customers are more likely to leave or buy based on their shopping behavior. It enables retailers to target at risk customers with incentives to reduce churn and high value customers with tailored service.
Recommender Systems: Recommendation systems predict customer interest in products, based on their historical profile of previous purchases, clicks, views and other criteria. Recommender systems typically produce a list of recommendations in one of two ways – through collaborative or content-based filtering. Recommendation systems can be extremely useful for Retailers to deliver a personalized experience and drive customer loyalty.
Demand Forecasting: Having the right inventory, right assortment and right product is critical for Retail. Machine Learning can be used to recognize customer demand patterns based on various factors. For example, MAX451 is helping a large retail customer determine what products a customer is most likely to purchase next, based on ecommerce data as well as brick and mortar store data.