If you live in the US, you've probably visited a Williams Sonoma store for gourmet food or quality cookware for the kitchen. And if you've shopped at Pottery Barn or West Elm stores for furniture, those chains are part of the Williams Sonoma stable as well. All three brands have major online stores, all supported by a sophisticated marketing operation.
Well, that marketing operation just got even more sophisticated. Williams Sonoma has teamed up with UpStream Software to implement advanced marketing analytics that can better target prospective customers, and help Williams Sonoma's marketing team better understand the effectiveness of their various marketing channels:
As well as cutting costs by not sending catalogs to unresponsive customers, the new technique is helping the retailer reallocate funds to more effective online marketing channels like e-mails and display ads. “We’ve seen our ability to target with the catalog improve using these techniques on a scale that we haven’t seen with any sort of small technical improvement,” says Mohan Namboodiri, vice president of customer analytics for Williams-Sonoma. “This is a qualitative improvement in our ability to target the right type of customer with the right type of messaging, and it’s not something that we’ve had available up to now.”
The underlying algorithms, developed by statisticians at UpStream, are developed using the R language and deployed to production with Revolution R Enterprise. As described in this Revolution Analytics case study, UpStream draws data from dozens of distinct sources and uses Big Data statistical models to optimize marketing operations for Williams Sonoma and many other retail clients:
UpStream Software’s purpose-built application is a modern, high-performance big data analytics engine, scoring 50 million records per day for each of UpStream’s customers. It marries Revolution Analytics’ Big Data Analytics capabilities with Hadoop’s data management and computational power. Since no two clients are exactly alike, the statistical methods that underlie the analytical models can be customized to meet each client’s exact requirements.
From an analytics perspective, the company borrowed approaches from forward-thinking and more analytically mature industries. For example, UpStream adapted models used in the bioscience sector, where GAM (Generalized Additive Model) survival analysis techniques effectively measure differences in the outcomes in patients under different treatment regimens. However, many of the methods that UpStream wanted to use had not been designed for Big Data analytics. Using Revolution R Enterprise, which is based on the power of the R statistical platform, UpStream built a “big data analytics engine” utilizing multivariate statistics, time-to-event models and GAM survival analysis techniques.
You can see how UpStream implemented their solution with Hadoop and Revolution R Enterprise in this webinar replay, or read more at the links below.
Internet Retailer: Williams-Sonoma targets e-customers with a “treatment” approach