Anyone over 30 no doubt remembers watching “Siskel and Ebert at the Movies” growing up. A “two thumbs up” review from the two movie reviewers could easily drive millions of dollars at the box office for a lucky movie.

And despite Siskel’s death, Ebert still continues the show, now with Richard Roeper as his cohort. There’s no doubt, however, that the importance of a “two thumbs up” rating these days has diminished, and it’s not because of Siskel’s death.

Indeed, the importance of all types of reviewers, be it movie reviews, restaurant critics, or even consumer reports, seems to be growing less and less by the day. No doubt the Internet is a major driver of this trend.

To date, the Internet has largely impacted reviewers by simply giving consumers access to more choices. You no longer need to wait until Sunday night to watch Ebert review a movie – you can instead go on to RottenTomatoes.com, or a movie blog, or a chat board, and get dozens of opinions about the latest blockbuster.

The same is true for getting information about autos, electronics, books, hotels, and so forth – a quick search on Google will give you hundreds of opinions about any area you want.

The downside of the explosion of information is that it becomes difficult to wade through the data and find the stuff that is actually relevant to you. Unfiltered information is useless and actually makes you want to go back to an expert like Ebert that you trust.

Most Internet review sites today, however, have solved this problem, usually by asking reviewers to give a star-rating (sometimes on multiple factors) to whatever they are analyzing. You can then see an overall summary of the collective wisdom without having to read 500 reviews.

But the best is clearly yet to come. As any loyal reader of this blog knows, I love collaborative filtering – the Amazon.com idea of “people who bought this item also bought this other item.” So far, the online review sites have not utilized this principle. For example, if you go to TripAdvisor.com, you can always find dozens of reviews about a hotel, but it is difficult to know whether a particular reviewer shares your criteria for a good hotel.

With collaborative filtering, TripAdvisor could start to match you up with people who have similar taste in hotels. So if I really liked the Motel6 in San Antonio but hated the Ritz in Half Moon Bay, and you also had the exact same opinion on these hotels, the next time I was thinking about going to Dallas, I could review your picks for Dallas and likely find a hotel that really works for me.

Collaborative filtering actually works on a much greater scale: if there are 50,000 people reviewing hotels across the world, the odds are pretty great that whatever city I am visiting that someone with my particular taste will have already reviewed a hotel in that city.

There is a new web site called Flickster.com that is doing this with movies. I’m assuming that it will work along this model – the more reviews I submit to the site, the more it understands my preferences and matches me with other reviewers. Over time, I should be able to type in any movie and get a pretty good idea of whether I will like it.

This is a lot more accurate than listening to Roger Ebert because it is a lot more personal. I’m sure that if I sat down with Roger and told him about movies that I liked and disliked, he could come up with some good suggestions (for the record, I have actually talked to Roger Ebert several times, as I used to review movies when I was in college in Chicago). But even personal recommendations from Roger would not be as powerful as the collective knowledge of thousands of moviegoers.

There’s no doubt that this model will soon be applied to movies, restaurants, hotels, and perhaps even doctors, lawyers and other service providers. But I actually think that the most interesting application of collaborative filtering will come with respect to eTail – online shopping.

Right now, every comparison shopping engine uses a star-review system to enable customers to provide feedback on their shopping experience. eBay and Amazon have a slightly less accurate system, basically asking the customer if the experience was positive, negative, or neutral. And Google’s “Quality Score” measures the frequency that users click on a Google ad, go to a Web site, and immediately return back to Google (click the back button), a measure of the lack of relevancy for a particular site.

All of these systems, however, are still the ‘old school’ wide swath of information. From general information, you get a general feeling for whether a merchant is good or bad, but you don’t really know if that merchant is right for you in particular.

For example, let’s say you don’t care whether the merchant has 24 hour customer service, but it is vital that the merchant offers Saturday delivery. You can’t easily glean this information from 500 user reviews.

But with collaborative filtering, this becomes very possible. As you review stores, the collaborative filtering system starts to understand what is important to you. It can then match you with users who have reviewed other stores. After a while, the system’s accuracy cannot be doubted. You can do a search for “digital camera” and the system can not only recommend cameras that you are likely a good fit for you, but also recommend merchants that will provide the best customer experience for your needs.

Most consumers would gladly pay a few dollars more for a product if they knew that they were going to have an awesome shopping experience. I think that that is actually the promise of comparison shopping, a promised that is definitely not fulfilled by a vague star-rating.

The end result of such a system would be tremendously valuable to consumers. After all, a company that consistently provided horrible service simply would never be matched with new consumers – there would be no way to hide from your business quality.

It would also likely spell the end of an organization like Consumer Reports, which in most ways is no different than Roger Ebert.

The Internet is making today’s experts obsolete and creating new experts who’s status is determined by statistics, not the mass media. While this will put people like Roger Ebert out of a job, the end result is a great victory for consumers – product and business transparency that will make shopping easier and more personal and hold businesses accountable for bad service or products.

1 Comment

  1. Adam Khan July 19th, 2007

    Me, I don’t care for my tastes being predictable. Because someone likes _Life is Sweet_, can they trust a computer to tell them to rent _Johnny Stecchino_?

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David Rodnitzky
David Rodnitzky is founder and CEO of 3Q Digital (formerly PPC Associates), a position he has held since the Company's inception in 2008. Prior to 3Q Digital, he held senior marketing roles at several Internet companies, including Rentals.com (2000-2001), FindLaw (2001-2004), Adteractive (2004-2006), and Mercantila (2007-2008). David currently serves on advisory boards for several companies, including Marin Software, MediaBoost, Mediacause, and a stealth travel start-up. David is a regular speaker at major digital marketing conferences and has contributed to numerous influential publications, including Venture Capital Journal, CNN Radio, Newsweek, Advertising Age, and NPR's Marketplace. David has a B.A. with honors from the University of Chicago and a J.D. with honors from the University of Iowa. In his spare time, David enjoys salmon fishing, hiking, spending time with his family, and watching the Iowa Hawkeyes, not necessarily in that order.