Eve recommended an apple to Adam, and they shared the first buyer’s remorse.
Water, Water Everywhere
Recommender systems select products for customers based on past experience. Sometimes the product choices are called “items” or “content”, and other nouns are substituted for the customer. The most familiar case is that of a retail businesses – many customers with many products. Amazon, Netflix and Pandora famously bet their businesses on this technology. When you see guidance to other products, a recommender is at work.
For newly minted data scientists, the subject is covered in basic training. For marketers, these systems are a holy grail, a pearl without price, the silver bullet of digital engagement and personalization. Customers are expressing themselves with choices. On some websites, the customers contribute reviews, provide ratings and share advice. The recommender system is a mediator in a very personal action.
How did a subject so important get so complicated?
Forest for the Trees
The theory and algorithms for recommenders are well established. In 2015, the grandmotherly Association for Computing Machinery (ACM) is holding it’s 9th conference on recommender systems (recsys) in Vienna, Austria. At the University of Minnesota, GroupLens continues to make news, having pioneered recommending internet news to readers. Enter social media, networking, elections and dating services - recommenders have a rich new world of application.
But, theory advances with complexity and new terminology. The subject can seem daunting, so there is a benefit to return to the basics.
All three of these paths can be taken together. Most recommenders become hybrids, combining several methods and enhancing over time. The tools to construct the systems return to a familiar few basics in statistics:
With all this power, why do I get promotions for skin cream?
My Kingdom for a Horse
Recommenders are suffering from the curses of our emerging Data Sciences. The early adopters have an advantage, so solutions that are “out of the box” are popular. Like other tools, recommenders can be purchased as hosted personalization services. Their use, too, is often in the digital channel. The internet is cheap and forgiving when these systems start. But, to quote H. L. Mencken,
“For every complex problem there is an answer that is clear, simple, and wrong.”
The subjects below are emerging in both recommender statistics and marketing. Consider researching these for building or improving your recommender (using the movie recommender as a poster child):
Tired advice remains. Using every method, applying every tool and addressing every issue provides a recipe for disaster. Consider the risk and opportunity of customer interaction, start with basic methods and make a roadmap of learning forward.
Good luck and stay tuned – this subject will challenge us all.