MAKING THE LEAP TO HYPER-PERSONALIZATION
One trend continually on the rise is shopper dissatisfaction with what once passed for personalization. More so than ever before, your customers want and even demand that you know them better, understand their individual needs, and inspire them to continue shopping. The time has come to rethink personalization by making the leap to Hyper-Personalization. With Hyper-Personalization, and the new features launched in support, we allow you to take the next evolutionary step and do what the marketing clouds and rest of the personalization industry can’t: deliver real-time personalized and shoppable experiences at the individual level.
RETHINKING PERSONALIZATION AND DELIVERING ON SIGNATURE MOMENTS
This time last week we were wrapping up our 8th annual RichRelevance Personalization Summit in EMEA. As always, we sought out a unique venue removed from the hustle and bustle of the busy streets of London where we could bring together international brands and retailers for an evening of networking and dinner, followed by a day of thought leadership, personalization success stories, and announcements of innovation on what’s to come for RichRelevance in 2019 and beyond.
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.
We’ve finally slowed down enough to reflect on how we did in 2014 and the final holiday push: pretty darn great if I may say so myself. We ended another year at 100% uptime all year with record loads and HUGE growth.
Eleven data centers enable the company to maintain uptime and performance at global scale, while increasing speed to delivery, for the world’s largest retailers
Twenty-one years ago, a year before the first web browser appeared, Walmart’s Teradata data warehouse exceeded a terabyte of data and kicked off a revolution in supply-chain analytics. Today Hadoop is doing the same for demand-chain analytics. The question is, will we just add more zeros to our storage capacity this time or will we learn from our data warehouse infrastructure mistakes?These mistakes include:
- data silos,
- organizational silos, and
- confusing velocity with response time
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We continue to think about our customers, friends and families on the east coast as they fight through yet another storm. We’re built to serve you and are here to help in any way you need. You can also make a donation to the Red Cross here.