Data, Information & Knowledge

Data -> information -> Knowledge. Which is the supposed value chain.

A close look at these terms:

Data is a set of un-analyzed observations; It is 'record of something happened'
Examples:
Observations on visits from visitors with and without coupons to a shopping cart
Observations on calls from customers with issues
Observations on sales by record at Reliance Digital

Information is data with a 'purpose' and 'meaning' - the purpose & meaning coming from 'summarization' and a 'context'; It is 'How you know it happened & What Happened'
Examples:
Visits from email tend to buy less often than visits from affiliates
Patients with metabolic syndrome are more likely to have a heart attack than patients without

Here each of the examples is summarizing data with a purpose - and the context is important. For a web analytics person the second information above is alien as he does not understand what 'metabolic syndrome' is and for a doctor, the first statement is alien as a doctor cannot understand what 'affiliate' is :). So some knowledge is necessary to qualify information
Information cannot inform in the absence of knowledge

Knowledge is a set of recipes and contexts in which the recipes become effective - It is what is possible to do, when it is possible to do, to achieve what is it necessary to do; It is 'what to do about Information'
Example:
Because visits from email tend to buy less often, we need to focus more on affiliates
Because metabolic syndrome is more likely to result in heart attack, these patients must be given extra care

Analyzing Effectiveness of an e-support site

For an introduction to e-support sites:
http://webanalyticsnuggets.blogspot.com/2010/11/benefits-of-efficient-support-site.html

Web Failure rate
The single most important metric to analyze success of an e-support site is 'Web Failure Rate' or WFR.

It is defined as the proportion of support site visits that did not find a solution to their problem online. So if there were 100 visits to the support site and if 20 of them did not find a solution to their problem online, the web failure rate is 20%

How to calculate Web Failure Rate:
This is the tricky part.
That a visitor did not find a solution to their problem online is proven if he contacted a non-online channel to find a solution. This could be a call by the visitor to the toll free #.

In this scenario, there has to be a common field that links the visitor to the caller. It could be a 'service tag' or it could be a 'customer number'.......
In case of unique service tags or customer numbers, the visitor would enter it to find a solution on support site; and would also refer to it during his call with the customer service representative.



The shaded area above is web failures.

The key in e-support is to minimize the web failures and hence the web failure rate (WFR)

Will post on the other metrics that are used in e-support in a separate post