Category: Featured
A Personalization Manifesto – Why Bother? Part III of III
By Dan Darnell in Featured, Personalization on May 23, 2013
Personalization means delivering relevant content to a shopper based on what is known about them. Relevance is defined as that content that is most appropriate to the user’s needs. We know the goal of personalization is to deliver content which best meets the user’s needs. But what about the business needs? Ecommerce companies must also focus on maximizing key metrics like engagement, revenue, conversion rate and average order value. ... Read More »
A Personalization Manifesto – Personalize Based On User Type Part II of III
By Dan Darnell in Featured, Personalization on May 21, 2013
Before any real personalization can happen, a retailer needs to first ask, how well do I know this shopper? What kind of user type is my customer? In most cases, the shopper will be an unknown or anonymous visitor. These are users that you have never seen before or that you do not recognize. Using in the moment context and intent is the best approach in this situation.
A Personalization Manifesto – Personalize for the “Now” Part I of III
By Dan Darnell in Featured, Personalization on May 16, 2013
It is a common fallacy that e-commerce personalization must and should treat everyone as completely unique individuals. Yes, I know we all think that we are completely unique people. But the reality of it is that we are more alike in our choices and preferences than we think. What is different about each of us though, are the patterns of how we behave – or the context in each ... Read More »
Video Recommendations at Face Value
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.
Algorithms, Merchandising and Psychology – Part 2
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
Algorithms, Merchandising and Psychology – Part 1
By Robin Morris in A Word from the Engineers, Featured on May 7, 2013
I was going to write this blog about feature hashing, a massively useful trick when building classifiers and predictive models. It saves the time and complexity of building a dictionary and allows the hashed feature vector to be smaller than the number of possible features. It is smaller because the number of actual features in a particular data set is often much smaller than the number of possible features ... Read More »
Content is King – Finding the Content You Are Looking For
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 and digested in the time available).” Translation? When you can’t find ... Read More »
April Personalization Round Up
By Dan Darnell in Featured, Personalization on April 26, 2013
This month, we’ve seen a lot of coverage around the topic of personalization and the important role it plays in increasing customer engagement, loyalty, average order value (AOV) and sales for online retailers. Below, are this month’s top articles discussing the drive toward and benefits of personalization. We hope that they will inspire conversations, questions and feedback.
Collaborative Filtering and Its Importance to Personalized Recommendations in eCommerce
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 originally promoted a simplistic form of collaborative filtering where a ... Read More »
Personalization for the Unknown, Known and Named User
By Dan Darnell in Featured, Personalization on April 9, 2013
Anonymity on the internet is substantially less than it was 15 years ago. Yet retailers are still challenged when trying to provide the ideal experience to customers. On an e-commerce site, it is helpful to think of users in three categories: unknown or new users about whom you know nothing, known users about whom you know something and named users about whom you know the most. How you tailor ... Read More »