This company offers a machine-learning platform that dynamically personalizes website and application content to optimize conversions and engagement. Their platform automates website personalization, including layouts, menus, and content, without requiring in-house development.
Funding
Funding not disclosed
Founders
Product
Problem
Businesses struggle to effectively leverage consumer data to personalize experiences and optimize conversions due to the complexities of data wrangling, feature engineering, and model selection. Building and maintaining machine learning pipelines for real-time decision-making requires significant time and resources, often hindering agility and responsiveness to changing consumer behavior.
Solution
mParticle Predictions (formerly Vidora) provides a machine learning platform that automates the creation and deployment of dynamic personalization strategies. The platform streamlines the machine learning pipeline by automating data wrangling, feature engineering, and model selection, enabling businesses to quickly transform raw consumer data into actionable insights. By integrating with existing data lakes, warehouses, and CDPs, mParticle Predictions facilitates the ingestion of diverse data sources and the delivery of real-time machine learning results across various business functions. This allows businesses to make data-driven decisions, optimize consumer experiences, and increase conversions across multiple channels.
Target Audience
The primary target audience includes enterprises focused on consumer data, particularly those in e-commerce, subscription services, and media, seeking to improve personalization and drive conversions.
Features
- Automated data wrangling tools for aggregating raw behavioral and attribute data at scale
- Automated feature engineering that searches across multiple time windows and nonlinear transformations
- Automated model selection and hyper-parameter optimization for faster integration and value creation
- Real-time decisioning capabilities that combine batch and real-time data for machine learning decisioning
- Integration with data lakes, data warehouses, CDPs, CRMs, ESPs, and DMPs
- Prescriptive uplift modeling and real-time behavioral modeling techniques