Major retailers like Williams Sonoma use UpStream Software for marketing analytics, including revenue attribution, targeting, and optimization. In the video below Tess Nesbitt (senior statistician at UpStream) describes how she uses Revolution R Enterprise and Hadoop to figure out the impact on various marketing channels (for example direct mail, email offers, and catalogs) on consumer retail sales.
(The slides for Tess's presentation, The Impact of Big Data on Marketing Analytics, are available for download.) Because Tess needs to massive amounts of consumer behaviour data to tease out the effects of the different marketing channels, she switched from SAS/WPS and now uses the big-data capabilities of Revolution R Enterprise to fit a survival model to more than 36 million records in less than 4 minutes. (You can find some of the details of the model and the R code used to fit it in Tess's recent presentation to the Bay Area R User Group, Statistical Marketing Analytics with Big Data (PPT).) By reducing the time it takes to fit the model from an overnight wait to the time it takes to make a cup of coffee, Tess has more opportunity to refine and improve the model, as she describes in a recent Channel Reseller News article:
“Revolution R Enterprise 6.2 enables UpStream to build highly efficient statistical models on extremely large data. Because their parallelized algorithms are so efficient, it enables us to take multiple passes at the data, build iterative models, and it provides everything we need to glean as much information and build the best models we can for our customers.”
These more powerful models in turn mean that retailers can finally understand what marketing activities are most likely to lead consumers to make purchases, as Mohan Namboodiri (VP Customer Analytics, Williams Sonoma) describes in this GigaOM Structure 2013 panel discussion.
To learn more about how UpStream uses Revolution R Enterprise, take a look at this Revolution Analytics case study.