In my opinion, there are few people that understand web analytics better than Avinash Kaushik – he’s brilliant. And there are few people that are more passionate about web analytics. If you haven’t read his blog, I highly recommend it. That said, there are plenty of times I disagree with him–in fact, I believe we have very different perspectives on many aspects of web analytics–but I never question his uber-mastery of the subject.

One perspective I don’t agree with is his well-known “10-90 Rule”. Put simply, Avinash says that if a company has $100 to spend on analytics, they should spend $10 on the tools and $90 on the people to make sense of the data the tool spits out. The reason for this is that analytics tools (e.g., Google Analytics, Ominture, Core Metrics, etc) puke out overwhelming amounts of data — far more than the average person can (or would want to) make sense of. Thus, the solution (according to the 10-90 rule) is to spend more money on more people to make sense of the data your tool (which you’re already paying for) is generating. You can read the full post here.

Although I agree that most web analytics tools generate way too much data, I don’t believe hiring more people to make sense of the data is the answer. In fact, I’d say it’s an indictment.

If you’re already paying for a tool (and if you’re using Ominiture, Core Metrics, etc., you’re paying A LOT), why should you pay even more to figure out what the tools are saying? Come on!

We’ve spoken with lots of people in the e-commerce space, including some of the most sophisticated users of advanced analytics systems, and do you know what they and/or their analytics gurus do? They spend tens or hundreds of thousands of dollars on gigantic analytics systems, then export all the data into …. wait for it …. a series of Excel spreadsheets to try and connect the dots and find some meaning in the mountain of data.

That’s right — they’re spending thousands every month on their analytics services and then using a $50 office program to analyze massive amounts of anonymized aggregated data to try and find what’s driving conversions.

Now, let’s imagine you run a business and you’re looking to get more customers and grow sales (i.e., conversions). What are you looking for in all that data? What’s most important to you – the browsers used by visitors or information relating to conversions? I sure hope your an

swer was information relating to conversions. 

The folks over at Trada have good blog post titled, “Impressions Aren’t Cool. You Know What’s Cool? Conversions.” We couldn’t agree more.

In my opinion, most web analytics tools have it all backwards. They seem to believe that more data = better. Frankly, I believe that the right data = better.

This is especially true with e-commerce. It’s not about measuring as much as possible – it’s about measuring the things that matter most in the most appropriate, direct, and understandable way….and then connecting all the dots so that you (the business person and/or marketer) know what works, what doesn’t, and what to do next to grow your business.

Personally, I’m less interested in data than I am answers. At Spring Metrics, we work hard every day to develop a system that helps you find answers without the need to spend hundreds of thousands of dollars on massive analytics tools and analysts to interpet the data.

But if you think wading through millions of confusing data points to find the needle in the haystack is cool…..well….I guess we’re just not that cool.