  When a business sells to another business (B2B), the buying business is
represented by a group of individuals, termed account, who collectively decide
whether to buy. The seller advertises to each individual and interacts with
them, mostly by digital means. The sales cycle is long, most often over a few
months. There is heterogeneity among individuals belonging to an account in
seeking information and hence the seller needs to score the interest of each
individual over a long horizon to decide which individuals must be reached and
when. Moreover, the buy decision rests with the account and must be scored to
project the likelihood of purchase, a decision that is subject to change all
the way up to the actual decision, emblematic of group decision making. We
score decision of the account and its individuals in a dynamic manner. Dynamic
scoring allows opportunity to influence different individual members at
different time points over the long horizon. The dataset contains behavior logs
of each individual's communication activities with the seller; but, there are
no data on consultations among individuals which result in the decision. Using
neural network architecture, we propose several ways to aggregate information
from individual members' activities, to predict the group's collective
decision. Multiple evaluations find strong model performance.
