Customer Lifecycle Management

A survey of how publishers like BuzzFeed and TIME use AI in audience data strategy: segmentation, personalisation, testing and predictive modelling, with AI treated as strategy support rather than plain automation.

Provectus built a churn prediction model for Audiobooks.com from five years of historical data — listening activity, clicks, search logs, subscription renewals across 2.5 million user profiles — using ML pipelines on Amazon SageMaker and Kubeflow. At-risk customers…

Bombora sells intent data for publishers who want to monetise audience data. Joining its Data Cooperative gives publishers insight into anonymous web visitors — which businesses they’re from, what they’re interested in — which turns generic inventory into…

The first published empirical study of churn prediction in scholarly publishing, using a 6.5-year dataset from an unnamed major academic publisher. The model predicts customer defection from resampled usage patterns — volume and frequency of content downloads —…

CyberRisk Alliance, a cybersecurity business intelligence company, combined first-party data from Hum with Bombora’s third-party intent data to sharpen B2B sales prospecting — finding high-intent prospects and marketing to them specifically.

The Society for Scholarly Publishing implemented Hum’s Customer Data Platform, pulling in data from sources including The Scholarly Kitchen to understand member interests and personalise outreach.

Karger Publishers partnered with Hum to understand reader behaviour across its journal portfolio — mapping content interests and targeting engagement campaigns using Hum’s data and AI segmentation.

Zendy built a revenue-sharing model into ZAIA, its AI research assistant. ZAIA uses Retrieval-Augmented Generation over both open-access and licensed paywalled content, and publishers get paid when their paywalled research is cited in ZAIA’s answers — a direct…