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13 Te exce e dcvey: ce acee T daa ce f ee yea f beva Sc a ae e a wa a d a cey ae be f e daw feece f We ed a ve , e a va acee ve e ed f aay APPENDIX METHODOLOGY e way e cey convergent data to draw con- clusions. 13 In quite a few of the analyses, we look at "views per item" as a eae f eecvee ad success. There are a few rea- sons for this. Most importantly, dividing by the item inventory count is a good way to control f deece ce a- keting size. Larger companies tend to have larger content vee, ad dc make comparisons between larger and smaller web pages. When we normalize content marketing outcomes by the item inventory, we make con- e ae e ca- rable, large and small. Further- more, it is possible to think of this as a measure of content marketing return on invest- ment: Your content inventory is your investment, and the ex- posure to your audience is the return that you get out of this investment. As such "views per item" is a good way to quantify how much you are getting out of your existing content. To keep our visuals more ac- cessible for a larger audience, we stuck to presenting aver- ages, and we chose to keep standard deviations out of our visuals. However, internally, we observed other distribution parameters such as the me- dian and the percentiles, and we checked for a reasonable a f aca - cance. We share with you the results that passed a reason- able test of skepticism.