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Spotting Influential Customers for Targeted Offers with Machine Learning [Upcoming Webcast]

Amber Trendell
Marketing Director

Research tells us that 65% of shoppers rate personalized offers and promotions as important to their shopping experience [Source: Retail in 4D]. But with increased awareness and skepticism around data use and privacy concerns how can retailers deliver targeted promotions to customers that don't want to share their data and are not actively engaged in a loyalty program? 

Identifying customers using social media and transaction data can help retailers better understand the network effect of trendsetters. In our research, retailers can see improvement in targeted offer efficacy by identifying trend-setting stores and allocating product based on their influence radius.

We are co-hosting a webcast with collaboration partner, Professor Georgia Perakis at MIT Sloan School of Management, on May 9th. The webcast will focus on a detailed case study exploring how to leverage multiple data sources and machine learning to drive increased revenue through optimized promotions and targeted offers. 

By attending this live webcast you will gain insights on:  

  • Current research using machine learning techniques to determine trend-setting stores and customers
  • How to identify trendsetters within transaction data to trigger pricing strategies
  • Improving demand forecasting with transaction data
  • The impact of social and transaction data on influential customer and store analyses including 'The Network Effect' and Targeted Offers   

Can't make it live? Register and you will receive the recording.

The Oracle Retail Science team and collaboration partner, Professor Georgia Perakis of MIT Sloan School of Management, are working on a joint research project to predict customer trends using sales transaction data. If you are interested in learning more about the Oracle Retail Science Customer Group & this research contact us today.

You may also be interested in: 

Innovation in Retail: Machine Learning guidebook

Retail in 4 Dimensions: Understanding Consumer Behavior in an Age of Relativity 


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