How it Works
Data should work for you. Incremental automates the work of data collection, analysis, and applied machine learning to provide clear and easy-to-action recommendations so your team can focus on higher-value actions.
Enterprise Data Hub
We’ve built an end-to-end data platform to reduce the effort and time it takes to apply our neutral and trusted machine learning models. With direct integrations, automated data collection, and bring-your-own data options, we normalize, clean and enhance data into a machine-learning-ready dataset. The end result is trusted measurement, prediction and simulation that get to your teams fast – in time for them to make a difference!
Data sources are integrated in minutes with just a few clicks. Through direct integrations with our ecosystem of partners or through bringing your own data (BYOD), mission critical data is seamlessly and easily integrated.
Normalization, Cleansing, Categorizing
Differences in reporting across channels are automatically normalized to provide a seamless view. Key metrics that have different channel-specific names like fulfillable units (Amazon), on-hand (Target) and available to sell (Walmart) are reconciled, time differences synchronized and geographics standardized. Customized brand specific product and category taxonomies can be overlaid to align reporting to your own business lens.
Machine learning combined with designed & synthetic experiments produce accurate and real-world grounded predictions that you can rely on with confidence. Iterative feedback loops continually improve these predictions. Automation ensures the predictions are delivered in time to inform your decisions.
Data Lake Integration
Direct integration via API’s and feeds allows you to ingest the normalized, cleansed and enriched data into your internal data lakes and business intelligence tools, allowing the insights to be more easily disseminated across your organization.
Uncover Incremental Growth
Let’s talk about your brand and the benefits of predictive, neutral measurement.