  The 'old world' instrument, survey, remains a tool of choice for firms to
obtain ratings of satisfaction and experience that customers realize while
interacting online with firms. While avenues for survey have evolved from
emails and links to pop-ups while browsing, the deficiencies persist. These
include - reliance on ratings of very few respondents to infer about all
customers' online interactions; failing to capture a customer's interactions
over time since the rating is a one-time snapshot; and inability to tie back
customers' ratings to specific interactions because ratings provided relate to
all interactions. To overcome these deficiencies we extract proxy ratings from
clickstream data, typically collected for every customer's online interactions,
by developing an approach based on Reinforcement Learning (RL). We introduce a
new way to interpret values generated by the value function of RL, as proxy
ratings. Our approach does not need any survey data for training. Yet, on
validation against actual survey data, proxy ratings yield reasonable
performance results. Additionally, we offer a new way to draw insights from
values of the value function, which allow associating specific interactions to
their proxy ratings. We introduce two new metrics to represent ratings - one,
customer-level and the other, aggregate-level for click actions across
customers. Both are defined around proportion of all pairwise, successive
actions that show increase in proxy ratings. This intuitive customer-level
metric enables gauging the dynamics of ratings over time and is a better
predictor of purchase than customer ratings from survey. The aggregate-level
metric allows pinpointing actions that help or hurt experience. In sum, proxy
ratings computed unobtrusively from clickstream, for every action, for each
customer, and for every session can offer interpretable and more insightful
alternative to surveys.
