By Susan Briggs in eCommerce, eCommerce Merchandising, Featured, Personalization on July 9, 2014
We all like to know what we are getting before we buy, right? Well the same is true for e-commerce merchandisers, marketers and executives. Who doesn’t want to know how something will look or behave on their website before pushing it live? (I know I do.)
Retail organizations are becoming more and more sophisticated, and we aren’t just talking blazers or stilettos. Under Armour reported that their first quarter net income increased 73% due in part to their global expansion (see our recent infographic on global eCommerce).
By Dan Darnell in eCommerce, Featured, Recommendations on November 21, 2013
Most people know about Amazon product recommendations for their homepage experience where the recommendations are personalized based on my browsing history and past purchases. We all know that this is a little strange when they continue to show you stuff that you have already purchased or are no longer interested in.
By Susan Briggs in Featured, Recommendations on November 12, 2013
This week, shoe retailer, Toms, took their one to one donation model to the next level by launching Tom’s Marketplace. This online store is designed to help other businesses who have a mission of improving peoples’ lives. It features over 200 products from 30 different companies and charities.
By Jen Burns in Featured, Recommendations on May 14, 2013
Our good friends over at Invodo know that 57% of consumers rely on product videos to confidently complete a purchase. Video content exists as a medium for many things, but for retailers or other information providers, it helps consumers with a purchase, an experience or both. Video recommendations are similar to product recommendations, but are more interactive and ... Read More »
By Robin Morris in A Word from the Engineers, Featured on May 9, 2013
In my last post, we discussed the recommendation algorithm, the merchandizing layer and the presentation layer as well as the fact that I am working on a KPI-optimizing algorithm that differs from a collaborative filtering algorithm. In running A/B tests on these differing approaches, we’ve found that the sites divide into two types. Type 1: ... Read More »
By Jen Burns in Engagement & Conversion, Featured on April 30, 2013
“Content is King.” You have probably heard this line a million times before. After the Internet exploded and just about anyone who wasn’t “anonymous” became considered a reliable source online, data creation skyrocketed. “Information overload” is defined as “stress induced by reception of more information than is necessary to make a decision (or that can be understood ... Read More »
By Dan Darnell in Featured, Technology and Science on April 18, 2013
How does a retailer make sense of a large amount of customer purchase data? Collaborative filtering is one technique that mathematically segments data in to like or “collaborative” groupings. Examples for online retail recommendations include filters such as “customer who bought this, also bought that” or “customers who looked at this item, also looked at these other items.” Amazon ... Read More »
By Marti Tedesco in Customer Experience, eCommerce, Recommendations on October 4, 2012
Last month, London held its annual Fashion Week. While many in Silicon Valley may have let this pass without notice, a few details on Mashable caught my eye. As you know, Baynote personalizes all kinds of content recommendations as well as product recommendations on anyone’s site. So, when I read a quote from Burberry Chief Creative Officer Christopher Bailey that Burberry is “now as much a ... Read More »
By Scott Brave in Featured, Personalization on July 18, 2012
In my new whitepaper: “The Human Need for Personalization: Psychology, Technology and Science”. The paper defines the psychology, technology and science that underlie a shopper’s desire for a personalized ecommerce experience. In the document, we look at the definition of personalization including the psychology of needs; the technological constructs that make automated personalization systems possible and the science behind the man-machine interface that brings ... Read More »