The article suggests ways to improve data science life cycles, such as building infrastructure to manage engineered features for specific ML models and seeking tools that eliminate the need for feature engineering. It also emphasizes the need for solutions that automate the end-to-end modeling cycle and reduce resource needs. The article concludes by stating that reducing inefficiencies in the data science lifecycle can lead to more accurate predictions and better user experiences that drive ROI.
Key takeaways:
- Predictive analytics is crucial for B2C brands, with applications in personalizing customer experiences, optimizing customer acquisition and retention, and forecasting demand.
- Current machine learning models used for predictive analytics can be time-consuming, costly, and risky, requiring constant iteration and new feature experimentation as the business environment changes.
- Brands can improve their data science life cycles by building infrastructure that helps them more easily create, manage and select their engineered features for specific machine learning models, reducing costs and complexity.
- Software solutions that automate the end-to-end modeling cycle and reduce the resource need in terms of both people and infrastructure can help brands make more accurate predictions faster, driving better user experiences and ROI.