Many marketing organisations struggle to translate insight into action. SAS 360 Marketing AI addresses this with purpose-built guided workflows and customisable recipe templates for common marketing uses that help marketing teams move faster from data to decision, without relying on overstretched data science teams.
‘For the longest time, ownership of analytical intelligence has lived outside of marketing,’ said Roger Beharry Lall, Research Director at IDC. ‘As such, this predictive intelligence was hard to procure, often being costly, complex, and time consuming. Offerings like SAS 360 Marketing AI solution help democratise the skills, knowledge, and trust needed to put predictive AI in the hands of marketers.’
Flexible Deployment And Growth Path
SAS 360 Marketing AI can be deployed as a standalone modelling and scoring engine or as part of the broader SAS Customer Intelligence 360 ecosystem, where it enhances journey orchestration, decisioning and personalisation.
Organisations can start with specific use cases and expand their adoption as their analytics maturity grows.
‘Marketers don’t lack data, they lack the ability to act on it at speed,’ said Mike Blanchard, Head of Customer Intelligence Solutions at SAS. ‘SAS 360 Marketing AI removes the barriers between insight and execution, giving teams the tools to operationalise AI where it matters most: real customer decisions.’
Why It Matters To Marketers
– Turn insight into action faster: prepare and assess data quality through automated workflows; train machine learning models with guided, explainable steps; generate and activate scores directly in customer journeys; monitor performance and retrain models automatically.
– Reduce time and complexity: automates data preparation, feature engineering and model training; dramatically shortens time to deployment.
– Focus on high-impact use cases: identify customers most likely to convert; detect and prevent churn; expand into next-best offer, cross-sell, CLTV and segmentation; customisable recipe templates for common marketing uses.
– Work with your data, not against it: train models using data where it already resides; reduce costly data movement and prep.
– Build trust with transparent, governed AI: full visibility into data inputs and outcomes; built-in bias detection and mitigation; automated monitoring and governance.
