/// Listen Up, Computers: You Still Can’t Beat the Human Touch
Background image copyright iunewind Almost 30 years ago, Electronic Arts famously ran an advertisement that asked, “Can a Computer Make You Cry?” It was a thought-provoking ad, but as far as I’m concerned, the answer is no. Computers can’t elicit the same type of emotional responses that another living, breathing person can, but they might get you partway there. In the center of the latest “computers versus humans” debate is the use of algorithms for recommendation engines — especially among Web-based businesses, products and services that we use everyday. Algorithms help us do everything from choosing our next read to finding a new job, but they often fall short; they just can’t replace pure peer-to-peer recommendations, emotional connections or personal experience. As more and more businesses are discovering, the beauty is in the balance of using an algorithm to collect, collate, even filter (somewhat), and then add in that secret ingredient — the human touch — to create a recipe for success. The Internet, Your Personal Shopper First, let’s address how algorithms can work effectively on their own. Take e-commerce giant Amazon, for example. It doesn’t need human curation; its collaborative, filtering-based algorithm recommends toothpaste when you add toothbrushes to your cart and suggests “Lord of the Rings” if you’re looking at the “Game of Thrones” DVD. That’s enough for Amazon because its business is built on the fact that you can get ANYTHING and EVERYTHING there. Amazon is the Web’s superstore, and as e-commerce matures as an industry, other online retailers are realizing that “there can be only one.” If they want to stand out and differentiate, they’ll need the human touch. As a recent example, celebrity stylist Rachel Zoe just inked a deal with fashion site Piperlime (owned by Gap, Inc.) to hand-pick her favorite shoes, bags and clothing — recommendations that will surely be laid over the top of Piperlime’s engine to further tailor results to shoppers’ exact needs. Other cases abound where stylists’ selections based on body type, favorite colors or brands augment algorithm-recommended products. You + Me = Us More than 10 years ago, online matchmaking sites started using basic algorithms with feature-based recommendations to pair people up. For example, if a woman listed her hometown as San Francisco and her profile mentioned dogs and wine, this basic algorithm would spit out hundreds of men with matching characteristics.
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