By Dan Darnell in Big Data, Customer Experience on June 2, 2014
There are several common data mistakes that retailers make in the effort to lead consumers down the path towards making a purchase. Because consumers can now discover, research and buy products online, retailers have access to a wealth of data on their shoppers. Unfortunately, many retailers miss the mark in their attempts to leverage this data and drive conversions.
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 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 Scott Brave in Featured, Personalization, Recommendations on July 10, 2012
Misconceptions of machine learning can run fairly deep. People not only have unrealistic expectations of what a machine can automatically figure out, but truly impossible ones. I sometimes think of this as the omniscience vs. intelligence fallacy. Intelligence is the ability to generalize and learn useful patterns from data and/or experiences. This is what we humans do naturally every day. But intelligence requires data. Very often the data that the ... Read More »