Summary: In this multi-part series we walk through the full landscape of Recommenders. In this article we cover business considerations as well as issues for Recommenders as a group. In the next articles we’ll discuss the details of the five major types of recommenders, improving their performance, and finally the coming impact of deep learning on Recommenders.
For all types of ecommerce it’s likely that recommenders are the single most important analytic tool you can deploy. Although hard data is difficult to come by, many informed sources estimate that for the major ecommerce platforms like Amazon and Netflix that recommenders may be responsible for as much as 10% to 25% of incremental revenue.
With that much at stake financially, not only do you need to optimize their performance but you can also justify putting significant investment behind getting the resources necessary to do so. Recommenders is just one of the many fast evolving specialties within predictive analytics and you’ll want to find experienced data scientists to help you.
If you’re new to data science or if you’ve never had to deal with optimizing recommenders before you may be confused as where to start. Pretty much every major analytic platform and cloud service offers some form of an out-of-the-box recommender you could implement. But that doesn’t answer the questions:
What Are Recommenders? Simple and Not So Simple.
Recommenders are simple at least in their objective. They are the applications which personalize your customer’s shopping experience by recommending next best options in light of their recent buying or browsing activity.
However, the question of exactly what alternatives or recommendations to present requires you to also evaluate the following issues:
Recommenders can answer all these issues but how you select and implement them will be different.
What can be a little confusing is that many applications of predictive analytics seem to be doing the same thing. That’s true. The many scoring models we use for outreach and in-bound marketing to target up sell, cross sell, and even first sell offers are in a sense recommenders. But for a clearer understanding we’ll adopt the more parochial definition that recommenders are meant to personalize web interactions to encourage your customer to consume more or linger longer.
In Very Different Environments
One of the considerations that you should keep in mind as you read on is the really tremendous differences between seemingly similar ecommerce environments. Consider and compare:
We’ll use recommenders to maximize sales in all of these environments but their implementation will clearly be very different.
Just to be clear, while recommenders obviously apply to ecommerce apps where your customer is buying something from you, they’re equally applicable to applications where you’re trying to maximize your user’s time on your site for the purpose of increasing advertising exposures. Good examples are news sites like Yahoo that personalize your feed so that you spend more time on the site.
Also, while we’re going to talk about ecommerce applications, be on the lookout for real-world applications of these same techniques. Some of these recommender categories like Association and Market Basket are just as useful for outreach and in-bound marketing as they are on line.
Recommenders Are Not All the Same
Recommenders are not all the same. If you are implementing a recommender for the first time it’s very likely you’ll be starting with an out-of-the-box solution from a vendor though you may also elect to write one from scratch using R or Python. In addition to the tactical issues described above about what exactly is it that you need to help your customer accomplish, you will want to focus on optimizing overall sales. That means looking deeply into the different kinds of recommenders and understanding the strengths and weaknesses of each.
5 Types of Recommenders
In the second article in this series we’ll talk in detail about the five main types of recommenders, how they work, and their relative strengths and weaknesses. Those five are:
Advances in basic recommender analytics has not progressed as rapidly as some other areas of data science. The most complex of these options, Collaborative Filtering has been around for five or ten years. From a business perspective the good news is that you can feel comfortable building a portfolio from existing technology. Most of the advances in the data science around recommenders have been focused on incrementally improving their performance in the specific domains in which they’ve been deployed. That will undoubtedly be your focus also.
Other Articles in this Series
Article 2: “5 Types of Recommenders” details the most dominant styles of Recommenders.
Article 3: “Recommenders: Packaged Solutions or Home Grown” how to acquire different types of recommenders and how those sources differ.
Article 4: “Deep Learning and Recommenders” looks to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.
About the author: Bill Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist and commercial predictive modeler since 2001. He can be reached at: