# Deep Neural Net with Attention for Multi-channel Multi-touch Attribution

arXiv: https://arxiv.org/abs/1809.02230 · HTML: https://mlsanity.com/paper/1809.02230/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 12 of 12)

- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)
- [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [LiDDA: Data Driven Attribution at LinkedIn](https://mlsanity.com/paper/2505.09861/) (2025)
- [A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution](https://mlsanity.com/paper/2302.06075/) (2023)
- [DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution](https://mlsanity.com/paper/2401.08875/) (2024)
- [Improving the Capabilities of Large Language Model Based Marketing Analytics Copilots With Semantic Search And Fine-Tuning](https://mlsanity.com/paper/2404.13077/) (2024)
- [MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms](https://mlsanity.com/paper/2603.02184/) (2026)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)
- [Attributed, But Not Incremental: Cannibalization-Corrected Attribution for Large-Scale Advertising](https://mlsanity.com/paper/2606.26690/) (2026)
- [Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?](https://mlsanity.com/paper/2608.28649/) (2026)

## Abstract (arXiv)

Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka. conversion). The marketers track all the customer journey data and try to measure the effectiveness of each advertising channel. The inference about the influence of each channel plays an important role in budget allocation and inventory pricing decisions. Several simplistic rule-based strategies and data-driven algorithmic strategies have been widely used in marketing field, but they do not address the issues, such as channel interaction, time dependency, user characteristics. In this paper, we propose a novel attribution algorithm based on deep learning to assess the impact of each advertising channel. We present Deep Neural Net With Attention multi-touch attribution model (DNAMTA) model in a supervised learning fashion of predicting if a series of events leads to conversion, and it leads us to have a deep understanding of the dynamic interaction effects between media channels. DNAMTA also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects. We used computational experiment of large real world marketing dataset to demonstrate that our proposed model is superior to existing methods in both conversion prediction and media channel influence evaluation.