ALM

Around 2016, regulated industries and Big Tech began moving away from internet cookie tracking as recycled cookies created security and discrimination issues for consumers. ALM, which publishes content attracting major audiences in the legal industry and six other markets, needed to keep building advertising and lead-generation revenue while using customer data securely under Web 3.0 privacy expectations. The company also wanted to incorporate AI into its marketing so it could understand audiences better, preserve privacy rights, and serve more relevant content and commercial offers.

ALM worked with IBM Business Partner Sherloq to integrate Lawyerpages and build an AI-driven data fabric with IBM Watson. Sherloq and ALM collected and filtered internet data, scrubbed and secured it in a blockchain compliance ledger, and implemented a permission and cognitive fabric as middleware between business and consumer internet layers. Sherloq used IBM Watson Natural Language Understanding and Natural Language Classifier to read, process, and categorize data at scale, with models developed in IBM Watson Machine Learning Accelerator and IBM Watson Studio, deployed on IBM Cloud Kubernetes Service and integrated with Google APIs for cookie-free targeted advertising.

ALM can predict the origin source of consumer data to a 99.8% level of accuracy while addressing permission, compliance, security, and custody requirements. The Law.com Lawyerpages site saw year-over-year growth rates of at least 50%, nearly doubled total impressions to 5.29 million over six months, and achieved strong renewal rates. Using the Sherloq with IBM Watson platform compressed the Lawyerpages development engineering cycle substantially, and ALM avoided hiring about 10 additional data scientists. A law firm customer, the Law Offices of Mark E. Salomone, saw an approximately 45% increase in signed intake in 60 days versus the prior six months.

• Cookie-free audience intelligence can still support advertising and lead generation when first-party data is secured with auditable custody and AI enrichment.
• Embedding NLP and predictive models into a shared data fabric let ALM turn directory and engagement data into actionable marketing programs faster.
• Privacy, compliance, and monetization reinforced each other when blockchain custody and Watson-based categorization were built into the same middleware layer.