Imagine you’re a retailer and you’re trying to plan your next line of products. What information do you need to know? A useful way to look at it is by exploring attributes—the variables of the product and the customer base. Do wealthy suburban women prefer blue or green purses, and do they like them to be traditional or fashion-forward? Which purses do you already carry in blue or green, and just what the heck is meant by traditional, fashion-forward, and everything in between?
The value of attribute analysis expands across industries. An entertainment company—say an HBO or a Netflix—needs to know what current movies and TV shows its customers like so it can better decide which future movies and shows to buy or create. Do they prefer longer movies or shorter ones, happy endings or sad ones, scary or comedic or dramatic themes? If you want to know these things, it’s very useful to know the attributes of entertainment offerings.
A roadblock to use is that, in many industries, there is no widely accepted taxonomy of product attributes, and many manufacturers don’t classify their products. So companies that want to do some analytics need to create their own.
Apparel manufacturers, for example, don’t classify their products in any systematic way. So leading retailers are spending considerable time and effort classifying product attributes on their own. Zappos, an Amazon subsidiary specializing in shoes and leather goods, involves three different departments in product classification so that it can optimize customers’ searches and create the most effective offers. The classification involves product type, style, color, pattern, brand, and price. This can get complex: Customers can choose from more than 40 different material patterns—pearlized, patchwork, pebbled, pinstripes, paisley, polka dot, plaid—alone. You need to know that a customer has bought patchwork-patterned goods in the past to be comfortable recommending them in targeted offers.
In entertainment, the king of attribute analysis is Netflix. There is a decent classification system available from IMDb, but Netflix thought it could derive competitive advantage from a more detailed classification structure—with almost 80,000 categories of movie types, as well as their actors, directors, and so forth. The company uses human classifiers to do this work, and has a 36-page guideline to attribute classification.
Netflix, of course, uses the attributes for its movie recommendation engine, but it doesn’t stop there.