Category Archives: customer data

176: Rajeev Nair: Causal AI and a unified measurement framework

Rajeev believes measurement only works when it’s unified or multi-modal, a stack that blends multi-touch attribution, incrementality, media mix modeling and causal AI, each used for the decision it fits. At Lifesight, that means using causal machine learning to surface hidden experiments in messy historical data and designing geo tests that reveal what actually drives lift. Attribution alone can’t tell you what changed outcomes.More

174: Joshua Kanter: A 4-time CMO on the case against data democratization

Joshua spent the earliest parts of his career buried in SQL, only to watch companies hand out dashboards and call it strategy. Teams skim charts to confirm hunches while ignoring what the data actually says. He believes access means nothing without translation. You need people who can turn vague business prompts into clear, interpretable answers.More

168: AI’s talent crunch: Marketing jobs on the brink and those set to thrive

At risk are campaign operators, generic content creators, and report-pulling analysts. Set to thrive are resident AI implementation experts who select worthy tools, data orchestrators connecting proprietary data to AI, product/customer marketers with genuine empathy, ethics guardians preventing bias issues, and localization specialists understanding cultural nuances.More

167: Moni Oloyede: The marketing ops identity paradox, attribution is a waste of time and GTM engineering is just sales ops

Your buyers can’t remember why they bought from you, our brains physically can’t store that information correctly. But we’ve built elaborate attribution systems pretending otherwise. Moni helps us understand why we need to stop crediting random touchpoints and start measuring how effectively each content piece performs its specific job in moving people through your funnel.More

152: Sarah Krasnik Bedell: A data eng turned marketer on embedded marketing analysts and batch vs webhook pipelines

What happens when a data engineer with an obsession for truth-testing crashes into marketing’s ‘best practices’? Sarah’s journey from code to growth unfolds like a trained detective story, where she picks apart marketing myths and rebuilds them with an engineer’s first principles. Her fresh take on centralyzed vs decentralyzed data team structures favors embedding an analyst deeply in marketing and growth teams.More