B2B Advertising: Joint Dynamic Scoring of Account and Users
This addresses the challenge of optimizing advertising and engagement strategies in B2B sales, where decisions involve group dynamics over extended periods, but it is incremental as it applies existing neural network methods to a specific domain problem.
The paper tackles the problem of predicting B2B purchase decisions by jointly scoring both the account (buying business) and its individual members dynamically over long sales cycles, using neural networks to aggregate individual activity logs, and reports strong model performance.
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.