Corpus ID: 202541664

Learning from Multi-User Activity Trails for B2B Ad Targeting

@article{Mishra2019LearningFM,
  title={Learning from Multi-User Activity Trails for B2B Ad Targeting},
  author={Shaunak Mishra and Jelena Gligorijevic and Narayan L. Bhamidipati},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.00057}
}
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… Expand

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References

SHOWING 1-10 OF 23 REFERENCES
Identifying Decision Makers from Professional Social Networks
TLDR
This paper presents LDMS, the LinkedIn Decision Maker Score, to quantify the ability of making a sales decision for each of the 400M+ LinkedIn members and discusses its online usage in multiple applications in live production systems as well as future use cases within the LinkedIn ecosystem. Expand
Understanding Consumer Journey using Attention based Recurrent Neural Networks
TLDR
An attention based recurrent neural network (RNN) which ingests a user activity trail, and predicts the user's conversion probability along with attention weights for each activity (analogous to its position in the funnel) is proposed. Expand
E-commerce in Your Inbox: Product Recommendations at Scale
TLDR
A system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users is described, which was evaluated against baselines that included showing popular products and products predicted based on cooccurrence. Expand
Ad click prediction: a view from the trenches
TLDR
The goal of this paper is to highlight the close relationship between theoretical advances and practical engineering in this industrial setting, and to show the depth of challenges that appear when applying traditional machine learning methods in a complex dynamic system. Expand
Probabilistic Modeling of a Sales Funnel to Prioritize Leads
TLDR
Two models, called DQM for direct qualification model and FFM for full funnel model, are presented that can be used to rank initial leads based on their probability of conversion to a sales opportunity, probability of successful sale, and/or expected revenue. Expand
Modelling customer online behaviours with neural networks: applications to conversion prediction and advertising retargeting
TLDR
The proposed model enables the use of largely available customer online behaviours data for advanced digital marketing analysis and uses Monte Carlo simulation to estimate the conversion rates of each potential customer in the future visiting. Expand
BIDDING ON THE BUYING FUNNEL FOR SPONSORED SEARCH AND KEYWORD ADVERTISING
In this research, we evaluate the effectiveness of the buying funnel as a model for understanding consumer interaction with keyword advertising campaigns on web search engines. We analyze data ofExpand
Deeply supervised model for click-through rate prediction in sponsored search
TLDR
A deeply supervised architecture that jointly learns the semantic embeddings of a query and an ad as well as their corresponding CTR is proposed and a novel cohort negative sampling technique for learning implicit negative signals is proposed. Expand
Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks
TLDR
This study proposes a novel approach to learn representations of mobile user actions using Deep Memory Networks and validate the proposed approach on millions of app usage sessions built from large scale feeds of mobile app events and mobile purchase receipts. Expand
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
TLDR
This study presents a novel advance match approach based on the idea of semantic embeddings of queries and ads that significantly outperforms baselines in terms of relevance, coverage and incremental revenue. Expand
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