- Data for the past twelve months ... using the scoring algorithm from the past twelve months to score prior years as well.
- Frequency: Orders in past year.
- Items per Order: Total annual items (20) divided by total annual orders (4) = 5.00.
- Price per Item: Total annual demand ($800) divided by total annual items (20) = $40.00.
- 1/0 Indicator: Did customer buy using telephone channel in past year? 1 = yes, 0 = no.
- 1/0 Indicator: Did customer buy using all other online channels in past year?
- 1/0 Indicator: Did customer buy using last-click attribution to e-mail in past year?
- 1/0 Indicator: Did customer buy using last-click attribution to search in past year?
- 1/0 Indicator: Did customer buy using last-click attribution to social media in past year?
- 1/0 Indicator: Did customer buy using last-click attribution to mobile in past year?
WARNING: The rest of this post gets really "geeky" ... so if you don't like math, move along, there's nothing to see here!
Here are descriptive statistics for our variables:
The means and standard deviations are used later, when I want to create each of four factors.
Next, we run a factor analysis / principal components analysis, extracting four factors. Here is the rotated component matrix:
In this analysis, we're looking for metrics with an absolute value greater than 0.20 ... this helps us identify the variables that contribute to each factor.
- Factor #1 = Frequency, Mobile, and Social. This factor likes loyal customers who have migrated to mobile and social channels.
- Factor #2 = Telephone, Not Online. In other words, this factor favors old-school shoppers who call the contact center to place an order.
- Factor #3 = Many Items per Order, Low Price per Item: These customers like cheap items, and they buy lots of cheap items!
- Factor #4 = E-Mail + Search: Customers who buy via e-mail and search, not necessarily other online channels, fall into this factor. Kinda makes one wonder if e-mail causes search to happen, doesn't it?