One is analyzing all the customers in the population 'at a given time in their lifecycle'
Other is analyzing all the customers in the population 'as-is'
The WFR Problem India vs. China
We ran into an interesting problem last week. The web failure rate or WFR (Refer for definition & meaning: WFR Meaning) of India was 3x that of China. Immediately the first thought is 'Is China support site better than India support site'?
Other hypotheses that come top of mind - 'China has local language support in their support site', 'Chinese customers are probably more net savvy'........
Or maybe 'we need to get more data :) and drill down to an LOB level'
Why we would have never found an answer
This was a classic example where no amount of analysis/drill-downs would get us to a solution. What we were looking at was essentially was an analysis 'as-is' level - at an 'aggregated' level.
Please note: all numbers are dummy and not real
Getting everybody to the same point
Analyzed the customers into 2 buckets:
Once that had a problem: 'Contacted Customers' or 'Contacted ASUs'
The full bucket: 'All Customers' or 'All ASUs'
The clue was that web failures/Contacted Customers was same in India and China.
whereas the web failures/all active customers was comparable
This pointed to higher proportion of installed base in India contacting
Looked at the systems by their age and found the culprit - India has a higher proportion of newer systems than China. This results in Indian customers having a higher WFR because newer systems tend to have a larger # of problems in the first half of their lifecycle
An excellent example of why it is important to look at horizontal analyses getting everyone to the same point
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