Earlier this summer, I wrote about personalized coupons. After thinking about the topic some more, I realized that personalized Internet coupons are not so simple, since customers can easily compare and share deals on various internet coupons, leading to perceived–if not actual–inequity. One way to be fair while utilizing data mining is to offer generic coupons with personalized recommendations, thus making the coupon seem more appealing.
I can’t help but notice personalized recommendations everywhere. Many online retailers take advantage of this by mining my purchases and even items that I have viewed. Online retailers have a wealth of data to mine. Even catalog companies seem to do a decent job of sending personalized recommendations with very little data. Retailers such as LL Bean or Lands End will send a semi-personalized mini-catalog to customers who receive gifts, whom they presumably know nothing about except for the gift that was sent–such as a catalog filled with only children’s clothing when a gift for a child is sent.
Before I proceed, I have a question for you: What was your first purchase with Amazon?
The first purchase I had from Amazon more than a decade ago was a Rage Against the Machines album. Over the years, Amazon has made many Rage Against the Machine recommendations to me, despite other purchases and frequent browsing. Amazon has a complex set of rules for recommending products that are clearly useful: I have added many things to my wish list based on recommendations. Amazon’s approach doesn’t seem to work that well for me, seeing as I rarely make purchases there, but I suspect that my infrequent shopping is more related to the lack of Amazon coupons (rather than their recommendations) as well as my extreme frugality. As a result, I have a love-hate relationship with Amazon’s recommendations.
I was impressed, however, with Amazon’s MP3 recommendations for me, when I recently visited their MP3 store for the first time. This really shocked me, since I was expecting a bunch of Rage Against the Machine recommendations. Most of the songs that Amazon recommended were not from bands/artists whose music I purchased or even browsed. I listened to many of the songs, and I really liked them. It got me thinking about how valuable personalized recommendations are, particularly for things like music, movies, books, and other items that we consume frequently. The perfect vacuum recommendation algorithm is not as valuable, since I plan to purchase few vacuums over the course of my lifetime. But I’m always on the lookout for better music, movies, and books (and so is everyone else, as evident by Netflix’s domination and the interest in their contests).
This week, I read two articles that touch on the science behind these recommendations (at least what could be reported in a news article).
- Wired article about Hunch, collects information from users via survey questions to make better recommendations.
- WSJ article about [x+1], Inc., secretly collects a small amount of information from users to make better recommendations.
Both companies essentially try to figure out which stereotypes might apply to consumers in order to make more tailored offers or recommendations. For example, the output of the [x+1] algorithm could be used to determine which credit card offers to make to a first-time web visitor on a bank site. No matter how little is known about someone, personalized recommendations could be made. That surprised me, since it seems like I often provide a lot of information (through browsing at Amazon or rating movies at Netflix) to get personalized recommendations. It was also a little Big-Brotheresque–I couldn’t help but wish that the [x+1] algorithm was much, much worse.
Personalized recommendations used to be so time-consuming back in the day. I’d get book, music, and movie recommendations from friends when growing up. And, let’s be honest, they were often terrible! I was a voracious reader as a teen, and I would outpace my friend’s recommendations. And none of my friends really read fantasy or SF novels, so I was totally lacking recommendations in those genres. In the end, I would pick a bunch of books from the library until I found a series that was addictive. But I had to read a lot of bad books along the way. It’s so easy to find good books now.
How did you get recommendations before the Internet?
Where do you get your best personalized recommendations now? I like Pandora, Netflix, and Amazon MP3. And google, if you consider search engine results to be the ultimate personalized recommendation.
- a few thoughts on coupons and discounts
- more on coupons
- a combinatorial coupon challenge
- coupon collecting and decollecting