# B2B Advertising: Joint Dynamic Scoring of Account and Users

arXiv: https://arxiv.org/abs/2209.14250 · HTML: https://mlsanity.com/paper/2209.14250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Hierarchical Attention Network for Action Recognition in Videos](https://mlsanity.com/paper/1607.06416/) (2016)
- [Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce](https://mlsanity.com/paper/1907.00400/) (2019)
- [Predicting the outcome of team movements -- Player time series analysis using fuzzy and deep methods for representation learning](https://mlsanity.com/paper/2109.07570/) (2021)

## Abstract (arXiv)

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.