Here’s a short list of the data elements needed to perform SQUINCH:
Item data, which includes product number, item description, category description, selling price (if you offer quantity discounts, use an average selling price), cost, units sold, dollars sold, page number featured and number of square inches used to sell the product in the space.
Costs associated with creating the catalog, namely advertising costs. Ultimately, you want to evaluate the performance of items in terms of their contribution to overhead and profit (assuming you’re breaking even on fulfillment).
A marked-up catalog where all of the items are denoted with unit and dollar sales. This is a helpful visual tool that complements your data analysis. If a product is performing exceptionally well, it’s helpful to know if a specific creative treatment is facilitating sales. If so, perhaps that treatment can be applied to other items to improve their sales as well.
If you don’t have software that calculates space measurement, choose one measuring technique and apply it to the entire book. Then use the exact same technique in the future.
Takeaway tip: Have only one person do the measuring. If too many people get involved, you’re sure to get inconsistent measurements and possibly skewed results.
The key is to pick a method of measurement, document it and adhere to it in the future so that when you do another analysis, you have apples-to-apples data.
And how should you treat non-selling space? If white space is brand-enhancing, then it should be considered so for all items in the catalog, not just those on the page where it’s used. If non-selling space is presenting a message particular to an identified group of products (e.g., a lead-in spread on laptop computers for a Hewlett-Packard business solutions catalog), that non-selling space could be allocated specifically and equally to the products in the category of interest.