  Online purchase decisions in organizations can go through a complex journey
with multiple agents involved in the decision making process. Depending on the
product being purchased, and the organizational structure, the process may
involve employees who first conduct market research, and then influence
decision makers who place the online purchase order. In such cases, the online
activity trail of a single individual in the organization may only provide
partial information for predicting purchases (conversions). To refine
conversion prediction for business-to-business (B2B) products using online
activity trails, we introduce the notion of relevant users in an organization
with respect to a given B2B advertiser, and leverage the collective activity
trails of such relevant users to predict conversions. In particular, our notion
of relevant users is tied to a seed list of relevant activities for a B2B
advertiser, and we propose a method using distributed activity representations
to build such a seed list. Experiments using data from Yahoo Gemini demonstrate
that the proposed methods can improve conversion prediction AUC by 8.8%, and
provide an interpretable advertiser specific list of activities useful for B2B
ad targeting.
