Charmee Patel leads Product Innovation activities at Syntasa. She has extensive experience synthesizing customer, visitor, and prospect data across multiple channels and scaling emerging big data and AI systems to handle the most demanding workloads. This experience guides her work helping clients deploy innovative ways to apply AI and Machine Learning to their marketing data and developing the next generation Marketing AI Platform.
October 15, 2019 05:00 PM PT
The state of the art in productionizing machine Learning models today primarily addresses building RESTful APIs. In the Digital Ecosystem, RESTful APIs are a necessary, but not sufficient, part of the complete solution for productionizing ML models. And according to recent research by the McKinsey Global Institute, applying AI in marketing and sales has the most potential value.
In the digital ecosystem, productionizing ML models at an accelerated pace becomes easy with:
The use case for the model is retargeting advertising; it analyzes the behavior of website visitors and builds customized audiences of the visitors that are most likely to purchase 9 different products. Using the model, this manufacturer was able to maintain the same level of purchases with half of the retargeting media spend -increasing the efficiency of their marketing spend by 100%.