  A common sales strategy involves having account executives (AEs) actively
reach out and contact potential customers. However, not all contact attempts
have a positive effect: some attempts do not change customer decisions, while
others might even interfere with the desired outcome. In this work we propose
using causal inference to estimate the effect of contacting each potential
customer and setting the contact policy accordingly. We demonstrate this
approach on data from Worthy, an online jewelry marketplace. We examined the
Worthy business process to identify relevant decisions and outcomes, and
formalized assumptions on how they were made. Using causal tools, we selected a
decision point where improving AE contact activity appeared to be promising. We
then generated a personalized policy and recommended reaching out only to
customers for whom it would be beneficial. Finally, we validated the results in
an A\B test over a 3-month period, resulting in an increase in item delivery
rate of the targeted population by 22% (p-value=0.026). This policy is now
being used on an ongoing basis.
