appeal to target customers
introduce new product category
increase margins
increase average order size.
Step 2: Fortunately, BuyGoodClothes has focus-group data that suggests what apparel is most appealing to the male business-casual segment. The company also knows its revenue and margin on each product, so it has all the data it needs for a simple algorithm.
Step 3: Since its most important objective is capturing the profitable customer segment of professional men, BuyGoodClothes weights this criterion at 40 percent, while the other criteria are weighted at 20 precent each.
Step 4: Using office-suite software, BuyGoodClothes builds a model that lists all of its product SKUs (though product categories would be sufficient if the data are similar across the category). Along the four decision criteria, each product is given a score on scale of 1 to 5 -- 5 if it best fulfills the criterion. (For yes/no decisions, 5 stands for yes, 1 for no.) For example, a pair of cargo pants might get a score of 4 on "appeal to target customers" and a score of 1 for the yes/no "introduce new product category" criterion. The scores are then weighted and the result is a final ranking score for each product SKU.
Step 5: Based on the results of the algorithm, BuyGoodClothes decides to promote on the home page a picture of a man wearing flat-front khaki pants and a casual blue oxford and carrying a canvas briefcase.
Step 6: BuyGoodClothes tracks customers' click-paths and conversion rate and determines that the oxford should be replaced with a spring polo shirt. Sales continually increase as BuyGoodClothes monitors the data and continually improves its merchandising.
