Geeky, but useful!
I use a Factor Analysis to produce the map, using 12 month buyers and weighted historical demand percentages for each variable.
Ok, enough of the geekiness.
On the left hand side of the image, we see the average attributes for first-time buyers. Notice that the price points are low. On the bottom left, we see Christmas focused first-time buyers ... on the upper left, we see Spring/Summer first time buyers (acquired primarily via organic search and third parties).
On the right hand side of the image, we see loyal buyers. What other attributes are tied to loyal buyers?
- High Price Points.
This is a fascinating dynamic. Loyal buyers tend to buy high price points ($50+), but buy them using discounts/promotions, eliminating the gross margin benefits derived from higher price point items.
The Merchandise Forensics Map helps us observe attributes that belong together - we get to see the attributes associated with Cyber Monday, or purchases in May, or via Paid Search. This helps us understand why there are issues with the business - in the case above, this business struggles with customer loyalty - notice that new buyers buy low price point items, while existing buyers purchase high price point items at a discount. This is a huge disconnect, the business is being managed in two separate realms. That's not good.