Price optimization for tiered and seasonal products
You should be able to use linear regression to find correlation between the factors which cause your products to sell better (or worse).

There are many correlations you can test against in this data set. Some examples are:


If a product has been marketed aggressively, does it sell more quickly?
If a low tier item is available, do fewer high-tier items sell?
If multiple high-tier items are available, are fewer sold of each item?


Keep in mind that correlation does not necessarily imply causation. Always think about other factors which may cause sales to go up and down. For example, you may sell more high tier items in a season one year than another year. But, this could be due to changes in the overall economy, rather than changes in your pricing.

The second thing you can do is perform A/B tests on your product sales pages. This gives you clear feedback right away. Some example tests could be:


Show the user one high-tier product and one low-tier product (A). Show the user two high-tier products and no low-tier products(B). Which page generates more revenue?
Send out marketing emails for a seasonal sale 5 days in advance to one group of users (A). Send the same email to a different set of users 1 day in advance (B).


There are many possibilities. Use your intuition and think about previous knowledge you have about your products.