B2B Advertising: Joint Dynamic Scoring of Account and Users

TL;DR

This paper introduces a neural network-based method for dynamically scoring individual and account-level decisions in B2B advertising, enabling targeted influence over a long sales cycle.

Contribution

It presents novel neural network architectures for joint dynamic scoring of individuals and accounts in B2B sales, addressing long-term heterogeneity and decision variability.

Findings

Strong model performance demonstrated through multiple evaluations

Effective aggregation of individual activities for group decision prediction

Dynamic scoring improves targeting over long sales cycles

Abstract

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.