AI Models Collecting Dust? A Marketers Remedy

A continued growth area for AWS has been services in the ML (Machine Learning) and AI (Artificial Intelligence) areas. Your own organization may have invested in the development of models using popular services such as AWS Sage Maker. Although these tools and the underlying models are powerful, marketers often find it challenging to use them to meet their customer engagement goals because of their lack of proximity to their marketing automation tools. Marketers may initially seek to explore working with centralized data science teams and specialists working with AWS tools, but are put off by the challenge of integrating the results in a low-latency and secure method with marketing cloud solutions (e.g. Adobe Marketing Cloud or Salesforce Marketing Cloud). 

With AWS Pinpoint, the recommendations and predictions provided by these various ML & AI services are co-located in AWS, which helps overcome major data redundancy and latency issues. Marketers can rapidly ingest the results of AI & ML models built on AWS by centralized data science resources, or specialized marketing focused data scientists; in either case, predictions and recommendations are easily made available for consumption by AWS Pinpoint’s audience management features to drive customer engagement with timely messaging.

A sample architecture that integrates AWS Pinpoint with AWS Personalize (personalized recommendations powered by AI) is available here.