Summary of A Deep Learning Approach For Imbalanced Tabular Data in Advertiser Prospecting: a Case Of Direct Mail Prospecting, by Sadegh Farhang et al.
A Deep Learning Approach for Imbalanced Tabular Data in Advertiser Prospecting: A Case of Direct Mail Prospecting
by Sadegh Farhang, William Hayes, Nick Murphy, Jonathan Neddenriep, Nicholas Tyris
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper bridges the gap between traditional direct mail marketing and modern machine learning techniques to improve customer acquisition. Specifically, it focuses on deploying methodologies that leverage machine learning for targeted and personalized direct mail campaigns. By combining the effectiveness of direct mail with the power of ML, businesses can enhance their prospecting efforts and acquire new customers more efficiently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Get ready to learn about how businesses can grow by acquiring new customers! This paper is all about using a old-school marketing method called direct mail in a new way – by adding some cool computer tricks. It’s like giving your sales team superpowers to find the right people and send them the perfect ads. By doing this, companies can get more new customers and be successful. |
Keywords
* Artificial intelligence * Machine learning
