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Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement — arXiv:2608.18543

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18543
License (as archived): CC0-1.0
Archived: 2026-10-18T13:44:31.899Z
SHA-256 of the archived text: c7339f359a9012a63071da4f75e6b7336f6905cfc3ddaa083d990e88241f6a71
Stable URL of this page: https://metraipolis.com/documento/dc_bb46fb0990fe

How to cite

Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement — arXiv:2608.18543 — arXiv (en), https://arxiv.org/abs/2608.18543 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb46fb0990fe (Metraipolis archive dc_bb46fb0990fe, sha256 c7339f359a9012a63071da4f75e6b7336f6905cfc3ddaa083d990e88241f6a71)

BibTeX

@misc{dcbb46fb0990fe,
  title = {Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement — arXiv:2608.18543},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18543},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb46fb0990fe; sha256 c7339f359a9012a63071da4f75e6b7336f6905cfc3ddaa083d990e88241f6a71},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18543. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement. This is particularly challenging for exploratory intents such as best smartphones or latest 5G phones, where users may leave the platform for external research before purchasing. We present a scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents. The system identifies users with exploratory purchase intent and low engagement, conducts grounded multi-agent product research using behavioral signals, external knowledge, and enterprise catalog data, and delivers personalized recommendations through WhatsApp. We evaluate the framework in a 23-day production deployment involving approximately 15K WhatsApp notifications for mobile product discovery. The campaign achieved substantial CTR improvements over traditional WhatsApp recommendation campaigns, with evidence of secondary engagement through message forwarding and sharing. The deployment also generated downstream purchases and GMV impact, demonstrating the practical effectiveness of AI Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.18543 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:41:17.867Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).