Web 3.0 Use It. Or Lose It?

Web 3.0

Revolutionary technology that Amazon uses.

You can use it too, and increase ratings. Or ignore it. And lose listeners.

Why does Amazon know more about your listeners than you do? Why are some Internet radio stations programming music better than you?

Give me 2 minutes and I’ll tell you why. Give me 1 minute more and I’ll tell you how to fix it.

Remember Web 2.0? So yesterday. Web 3.0 is today. It is the harnessing of patterns found in user databases to create collaboration and optimized products.

They call it Collective Intelligence.

One of the best examples of a Web 3.0 company is Amazon.  If you’re like me, you will often have found their recommendations for other books incredibly insightful.  I know I often leave the site having bought a book or two more than I intended!  In fact, their ‘if you like this then you may also like that’ approach may feel like a recommendation from a friend who knows you very well, but it’s actually created using cutting edge technology that does far more than simply suggest their best-sellers.  It analyses the most popular titles among people who like the same kind of books that you do, so that the recommendations are much more likely to ‘fit’ you.

How we compare to Amazon:

Amazon will send you a notice based on your preferences in the context of other people.

“We noticed you bought Sahara by Clive Cussler. Others who have bought that book have also enjoyed Earthquake by Jack DeBrul”.

How do they know that?

They know it because they built a collaborative intelligence engine based on purchases of books.

What you need to succeed with your listeners is a collaborative intelligence engine based on opinions about songs.

They noticed that people who like Clive Cussler also like Jack DeBrul.

You need to notice that people who like Elton John also like Phil Collins.

An AMT doesn’t become smart because of how you gather the data. It’s about how you analyze it.

Dumb AMTs are like the back page of a trade magazine. Score and rank only. No understanding of common tastes. Primitive. Loser.

You can do better. Much better. And you must.

Two minutes are up. Here’s how to fix it.

I provide leading edge analysis, collectively called MusicVISTA 3.0, that uses the same Web 3.0 technologies applied to the music tastes of radio listeners.

Music fit analysis. Pure Core format optimization, Foundation Cluster segmentation. Design based on 35 years of experience. Stations in more than 40 countries win with these tools, provided by Steve Casey Research.

With an AMT, we create, through our music test sample, our own database of “customers”. In the past, we tabulated. Today, we mine the data for meaning. Rather than purchases, we have song preferences.

With an AMT, we present our own list of “books” and determine which they “buy”. And we do something even more powerful than Amazon does. We find out which ones they won’t “buy”. It is as though Amazon sent out free books for everybody to evaluate. Our techniques are actually superior to those of Amazon.

Amazon sells books. What does that have to do with programming music?

We sell an enjoyable experience that you ‘pay for’ by listening to the commercials. The songs we play most often, the sounds we keep coming back to and the artists we play out of stopsets are all “relevant recommendations” to our listeners that make the cost of listening worth it. We say, “If you like this music most, we are the right station for you”.

So we know not to recommend Martha Stewart books to Clive Cussler fans. Or Metallica to Madonna fans.

We can find a valid center of gravity. Are we about Clive Cussler or are we about Martha Stewart?  Or both? The patterns in the data (whether from the Web or from an AMT) can guide us.

Can we play both? If they both satisfy members of the same “tribe”. By using collaborative intelligence we can accomplish the two things that we must:

1. We know we’re going off on a tangent.

2. We program our station in such a way as to get back to a comfort zone quickly, so people don’t wait very long for what they came to our station to hear.

We have to be cohesive enough to be trustworthy.

If Amazon recommends books that don’t make sense for you, or Match.com sets you up with lousy dating partners, they will quickly lose your trust. Our listeners have to know that we give them the type of music they like without making them wait too long. Or we lose their trust. We must be consistent enough to be credible and worthy of their attention.

To do that for the most people possible,  we use some very powerful  tools that work just like the collaboration tools deployed on the Web 3.0. We look at who is most excited about the music. Where is the greatest agreement? What is the center of the format? These tools work every time. But they work because they build on the following:

We are a recommendation engine for people who like: ____________________.

And we need to learn what gets written in the blank.

It really is that simple. Looking at the patterns of agreement among user databases is part of the most innovative businesses, from  Google with its PageRank system that you use to search the Internet, Amazon’s recommendation engine, Match.com’s matchmaking algorithms, music recommendation engines, Last.fm and more.

In fact, radio stations are more about affinity groups than these other businesses. We need to be more focused than Amazon.

I have created powerful tools, proven and refined over a decade around the world, that will help you understand these patterns and use that information to greatly improve the quality of your programming and the size of your audience. Rush to catch up with other leading businesses. Call me at 406.209.1541 or write me at scasey@UpYourRatings.com. I’ll explain, we’ll plan, you’ll succeed.