Product recommendations, also known as “recs,” are a cornerstone to an effective ecommerce merchandising strategy. When fully optimized, recs typically increase retailer revenues by up to 5%.
Today, we’re happy to announce that the Relevance Cloud is out of beta and available to all with the 15.02 release. This release introduces Build (API-based personalization building blocks) and delivers enhancements to our Recommend and Discover products, helping you to personalize every step of your customers’ purchase journey and setting you up for an exciting 2015!
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.
Today, I’m super excited to announce the launch of the Relevance Cloud™– what we at RichRelevance believe to be the most comprehensive personalization solution for retail today. The Relevance Cloud is a re-imagining of all RichRelevance products with new features and more simple ways to access, use and implement each of RichRelevance’s products.
Personalization is what empowers retailers to create a 1-1 relationship with customers online. By tracking engagement and other KPIs, you can quickly take stock of how are you doing with those relationships.
Malcolm Gladwell a récemment vulgarisé le terme « outlier » (valeur aberrante) en l’utilisant pour désigner des personnes performantes. Toutefois, dans le contexte des données, les valeurs aberrantes sont des points de données très éloignés d’autres points de données, c’est-à-dire atypiques… Read more
Malcolm Gladwell recently popularized the term ‘outlier’ when referring to successful individuals. In data terms, however, outliers are data points that are far removed from other data points, or flukes.
It’s no secret that many households spend on the latest and greatest TVs when leading up to the Super Bowl. In fact, after I initially began working in retail a number of years back, I was surprised to find out that not only do people buy TVs specifically for Super Bowl parties, but some also go through the trouble of returning the TVs after their party (I know, exhausting)!