1. Linear/Pearson Correlation: The most misunderstood term as far as i know. Before doing anything else, check if the 2 variables share a linear relation. Correlation values without a linear pattern is meaningless. And also be aware that in many softwares (including MS Excel), the default is pearson correlation, for which a linear relation between the two variables is a requirement.
2. Significance Test: Many many people into Analytics (?) will never ever understand this or will never try to understand this. Just because you see 2 groups doesn't mean that you can do a significance test. Know something or everything about sampling and designs before talking about significance test.
3. Lift and Cumulative Gains Charts: They are different, period. Don't confuse one with another.
Lift - Without a model, we get 30% of the responders by contacting 30% of the customers. Using a model, we get 60% of responders. The lift is 60/30 = 2 times.
Cumulative Gains - Using the model, if we contact 30% of the customers we get 60% of all responders.
4. Clustering/Segmentation and Profiling: Let's make this simple. Clustering/Segmenting will answer - Can my customer base be broken up into distinct groups based on certain attributes/characteristics? Customers within a group will be very similar to one another while customers across groups will be different.
Profiling will answer - Who are my best customers? What do they purchase? How often? What is their ethnicity, their household size and income, etc.? In many cases, profiling usually follows clustering/segmentation. Who are the customers in Group 1?
Signing off with:
"There must be some kind of way out of here,"
Said the joker to the thief
"There's too much confusion,
I can get no relief"
-- All along the watchtower by Jimi Hendrix
Posts
