First, let me explain what the acronym really means:

“A” stands for “Analyze”; First “M”, stands for “Monetize”; Second “M”, stands for “Mobilize”; “O”, stands for “Optimize” .

See, I managed to find four words all end with “ze”.

“Analyze” is straight forward. That’s what we, as web analysts do every day, so no need to explain, right?

One thing I want to caution is though, we need make sure that the data we are looking at is correct. If the raw data is not even correct, then that’s “garbage in, garbage out”, regardless how sound your analytical framework is. The analysis results might be misleading and hurting the business, rather than helping the business.

In the web analytics world, we usually are dealing with two sets of data. The first set of data is either the overall metrics at the site level or the most granular data at page level. For this type of data, it will be hard to make mistake. And if you ever made a mistake, even your HIPPO will quickly point it out for you!

The second set of data is at “segment” level. At this point, all web analysts should know that “segmentation” is at the core of any web analytics project. If you don’t know how to segment your visitors or traffic and analyze visitor behavior accordingly, then you should really go back to the training camp.

But the risk is also with “segmentation”. Anybody has experience with Omniture Discover on Demand or Omniture Insights (previously Discover on Premise and prior to that Visual Sciences) or similar tools, probably understand what I mean. There are two major factors could screw the data. First, the exact way to build the segments: in Discover or Insights, you have many different ways to build a segment and not all of them give you the same data. Second, the sampling issue: due to huge amount of data, a lot of web analytics applications can only give you a sample rather than the complete data set, depends on how much data you are asking for.

With this understanding in mind, it is imperative for any web analyst to have a healthy amount of suspicion of the data accuracy. Not only the data pulled by somebody else, but also the data we pull. Yes, we make mistakes too!

There are ways to mitigate the issue of course. (we have hope-it’s not the end of world!). What I usually do is to make sure that I can get the data from two different data sources and compare them side by side. If you are using Omniture, you should always try to compare the data you get from Discover vs. the data from SiteCatalyst. For example, if you are building a segment for a group of pages, you should at least compare the total visits for that group of pages from Discover with the sum of all visits of those pages. These two numbers will never match but they shouldn’t be way off. If I see a huge difference between them, that’s a red flag to me. [Bonus tip: if I have to bet $10 on either one, I will definitely bet on SiteCatalyst and go back to review the data I got from Discover]

Ok, that’s all I want to say about “Analyze”, assuming you’ve got the rest. In my next few posts, I’ll touch upon the other letters one by one and round up with a final summary. Stay tuned!

The “journey” message came along again with the first keynote from Kim Johnson, VP of Global Sales and Marketing Operations, Symantec Corporation. It is an amazing to see how she is able to manage all marketing segments in a centralized and integrated way, being able to balance the needs of the brand, the product lines and the sales organizations across the globe. For many large corporation, that’s simple a dream, probably never able to achieve though.

eMetrics Passport draw was the next on the agenda. Knowing that I won’t win anything, if the history is truly an indication of the future, I still went to each booth, having my passport stamped and prayed that miracle will happen. Of course it doesn’t. Tom-Tom GPS, $500 gas cards, 42” LCD TV all goes to our lucky winner, but not me. (crying aloud)

The next two sessions I attended both are focused on data mining, behavior segmentation and predicative analysis. Neil Mason talked about how to apply data mining and predictive analytical techniquest to get to grips with issues like visitor segmentation and understanding customers’ propensity to purchase. Gary Angel from Semphonic, continues to walk us through the process of doing behavior segmentations integrated with VOC data.

We talked about segmentation a lot. Avinash even goes to say the clickstream data and behavior analysis is useless without segmentation. But in most cases, segmentation we talked about are more “micro-segmentation”, just another name of different ways of slicing and dicing data. For example, don’t look at the aggregate site level conversion rate, but look at new visitors and repeating visitors. What Neil and Gary talked about are more “Macro-segmentation”: to look at all visitors, find variables that differentiate them from one group to the other, profiling them with demographic or voice of customer information and use the results segments to drive online marketing campaigns. This is the traditional definition of “segmentation” and it is the offline segmentation many companies have been doing for centuries. Neil and Gary are now taking them to the online world.

It’s hard to believe the summit has finally come to the end and the last 30 minutes belong to Mr. Jim Sterne, the godfather of web analytics. He highlighted the challenges ahead of us but also encouraged us to leverage the knowledge we have learned and the network we have built to create value for the organizations we serve and help the new comers in the community. So moved by his words and heart, I asked to take a picture with him like many others did.

My trip to the eMetrics Summit has come to close, now I need think about how to convince my boss to send me back to summit next year. The only way to convince him is to deliver higher value to my company. And I am confident that I can, because I have learned so much in the last three days, and I have got many friends and mentors that can help guide me along the way.

See you next year.