Particularly interesting is your #6 – "Massive Pace of Change on the Web" In my experience, this is where predictive algorithms combined with behavioral targeting succeed on the web. Specifically – assume you want to improve the relevancy of homepage promotions and make sure each promotion resonates with each particular user. KPI = CTR. You develop the creative offers to be included and deploy predictive behavioral targeting javascript to your homepage. As data accumulates, and as more data accumulates, the algorithms develop evolutionary models that begin to associate content with each visitor. As visitor characteristics change, so to does the content that is delivered.

Granted, massive pace and change and the ability to predict outcomes for Amazon.com and nytimes.com are radically different, but there are automated methods available that are effective in improving relevancy and CTR.

Matt

Website Optimization Manager
Amadesa.com – Website Testing, Personalization and Behavioral Targeting

Controlled studies are good because they use a well established and very carefully reviewed process for accounting for all the myriad reasons resulting in a desired response other than the one in which you are interested. The irrelevant factors are removed and only the real results of your program get measured.

Suppose you want to know, using Avinash's example above, if $15 off to the people from Florida does any good. There are a million different reasons why people buy something – you only want to know if your 15 bucks to these people made any difference. (Of course, they really aren't people and we should all be honest about that: they are unique IP addresses, which is another thing all together!) Statistically controlled studies get the real answers and it really works.

Problem is, as far as I can tell, nobody is doing studies comparing a Treated Group with a Control Group. I've never even seen the requisite infrastructure attempted. At least around here (www.marketingassociates.com), that's about to change.

David Corliss

A bit more, I´m sure that we are starting a new stage in DM power applications by the amount of web data information.

But the key is not DM or Predictive Analitycs or AI , the question is …for what? : For understand the past, for reduce dimensionality, for predict some KPI's or behavior of the future? …

How about to use consumer rating product´s with buy propensity or sales projections?

How about real time intelligence engines for sales?

How about to use – in a classical way – for inferencing?

I don´t try to view the world under topics as 'web analyst' or 'data miner' or 'crm analyst'…

I prefer understand the techniques, the business needs and limitations and choose the best way to do.

Good post. Thanks

Anonymity is an issue; here are some common examples on how web analysts work around the anonymity problem:

a) On a commercial site, web mining can help optimize campaign design/messaging/placement to increase click through rates. Multivariate testing can help optimize many alternative campaign options for completely anonymous users.
b) Tracking anonymous but unique visitors over time can help identify optimal behavior patterns. Allowing personalized online messages for targeting anonymous (but uniquely identified) visitors.
c) Authenticated online purchasers or subscribers are not anonymous. Such groups offer great cross/up sell opportunities via web data mining.

Web mining makes the need for Holistic data even more critical
For CRM, holistic data in needed. Detailed web data is an important component for understanding customer behavior across all touch points. This is easier said than done, but data integration is a tough problem even among offline sources.

I need numerical examples so that only i can understant how to use the google analytics data and how to evaluate the problems.

if there any tool to compute these data.

Please………..