At a simplistic level, a factor analysis takes a bunch of variables that are highly correlated, and transforms them into variables that are not correlated.
For instance, your "best" customers all have common attributes.
- They all purchased recently.
- They purchase all the time.
- They tend to buy from multiple channels.
- They tend to buy from multiple merchandise divisions.
- They tend to buy across seasons.
- They tend to buy full price and sale priced merchandise.
- They tend to pay for shipping and take advantage of free shipping.
When I run a factor analysis for my clients, I tend to observe the following trends.
- All multi behavior ends up being combined in the first factor.
- Unique channel activities end up being separated out in subsequent factors. You'll see a factor that focuses on online customers who only buy when a free shipping promotion exists as a unique factor, for instance.
- Unique merchandise activity ends up being separated out in a subsequent factor. You'll see customers who purchase iPods, and they are fundamentally different from the customer who purchases a Blu-Ray DVD player.
I like to take the top 2-3 factors, and segment customers into eight or nine combinations based on the values of each factor. Then, I watch how customers migrate between the segments, over time. You'll notice trends like catalog ---> e-mail ---> e-commerce ---> retail happen when you evaluate migration across factor segments.
So sit down with your analytics expert, and run a factor analysis with all of those highly correlated variables in your database, looking to identify the factors that truly drive your business.