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Frontiers: ChatGPT Referrals to E-Commerce Websites: How Do LLMs Compare Against Traditional Channels?

Maximilian Kaiser & Christian Schulze

Marketing Science2026https://doi.org/10.1287/mksc.2025.0489article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

What the paper says

This is a descriptive study reporting financial and engagement metrics for 973 e-commerce websites, comparing organic large language model traffic (oLLM) with traditional digital channels.

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Cite this paper

https://doi.org/https://doi.org/10.1287/mksc.2025.0489

Or copy a formatted citation

@article{maximilian2026,
  title        = {{Frontiers: ChatGPT Referrals to E-Commerce Websites: How Do LLMs Compare Against Traditional Channels?}},
  author       = {Maximilian Kaiser & Christian Schulze},
  journal      = {Marketing Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/mksc.2025.0489},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.