As the global leader in omnichannel personalization, we at RichRelevance take pride in providing the most relevant and innovative customer experience to end users. Every day we deliver over half a billion placement views to more than 50 million unique online shoppers worldwide. A shopper journey in our customers site is recorded as a sequence of Views, Clicks and Purchases which we call a Visit. All this user generated data is recorded in our front-end data centers and saved as Avro logs in HDFS on our backend Hadoop cluster. This valuable user data is used across teams to power our products and services.
Although we may not be flying around in rocket ships quite yet, retail today is about as futuristic as anyone could have imagined. It’s hard to envision a world without the 24/7 on-demand access to the endless aisles of ecommerce retail.
…and it is your customer data.
Nine out of ten purchases still take place in the store. That means that with each swipe of the credit card, customers send a strong signal of preference by means purchase. But today this signal is lost into one of these: DMP, CRM, or another three-letter acronym for your data sinks.
Les big datas sont partout… Mais comment les utilise-t-on ? Cas concret avec l’exemple de la Fnac qui a exposé sa méthode lors du Salon des Data à Paris. Une solution parmi d’autres à l’heure où les objets connectés compliquent la reconnaissance du client.
L’analyse prédictive, plus perspicace que le flair d’un commerçant? La plupart des grands distributeurs leur font en tout cas confiance pour tirer de leurs “grandes données” de quoi augmenter leurs ventes.
Analytics has always been the sexy bit of data management. That’s where the nuggets of insight are teased to the surfaced and millions made by understanding why diapers sell beer or who is newly pregnant or how to route a jet so it burns 25% less fuel. But, behind that, there has always been the grunt work of extracting data from multiple, disparate sources, cleansing it of partial or bogus records, transforming it into a consistent and usable format, and loading it into the target analytics engine.
RichRelevance Inc. faces one of the prototypical big data challenges: lots of data, and not a lot of time to analyze it. For example, the marketing analytics services provider runs an online recommendation engine for Target, Sears, Neiman Marcus, Kohl’s and other retailers. Its predictive models, running on a Hadoop cluster, must be able to deliver product recommendations to shoppers in 40 to 60 milliseconds — not a simple task for a company that has two petabytes of customer and product data in its systems, a total that grows as retailers update and expand their online product catalogs.