  E-commerce businesses employ recommender models to assist in identifying a
personalized set of products for each visitor. To accurately assess the
recommendations' influence on customer clicks and buys, three target areas --
customer behavior, data collection, user-interface -- will be explored for
possible sources of erroneous data. Varied customer behavior misrepresents the
recommendations' true influence on a customer due to the presence of B2B
interactions and outlier customers. Non-parametric statistical procedures for
outlier removal are delineated and other strategies are investigated to account
for the effect of a large percentage of new customers or high bounce rates.
Subsequently, in data collection we identify probable misleading interactions
in the raw data, propose a robust method of tracking unique visitors, and
accurately attributing the buy influence for combo products. Lastly,
user-interface issues discuss the possible problems caused due to the
recommendation widget's positioning on the e-commerce website and the stringent
conditions that should be imposed when utilizing data from the product listing
page. This collective methodology results in an exact and valid estimation of
the customer's interactions influenced by the recommendation model in the
context of standard industry metrics, such as Click-through rates, Buy-through
rates, and Conversion revenue.
