Recently active topics
One of the most critical moments in your involve.ai journey will be the mapping of your data, as this sets the baseline for prediction accuracy, dashboard views, and ongoing updates. Involve.ai’s data mapping feature enables organizations to integrate and map data sources entirely on their own with as-needed support from our Data Science Consulting team, and we work with you throughout the process to ensure success. There are a few essential best practices that will help you make the most of this part of your journey. Before your project. Share high level information about your tech stack with the Data Science Consulting team as early as possible - in the kick off meeting if possible! This helps the team be prepared for the types of integrations you’ll be setting up and any nuances we’ve learned from experience. At the start of your project. Establish demographic data. We suggest finding a way to populate the following types of attribute data, if possible: Customer ID/Name Active
Our AI predicts customer behavior with greater than 90% accuracy. How do we know? We test and measure the performance of our models regularly, in a variety of ways. Model training test accuracy - 94% accuracyA statistical measure, performed after any model training update.In this test, we feed our models an incomplete set of data and compare its results to the actual correct outcomes we have withheld.Machine learning statisticians consider this test the gold standard. While we understand and can accommodate the desire to review how the models perform against your own company’s customer data, there are important caveats to understand with correlational accuracy, as you’ll see below. Churn and Active accuracyA correlational measure, performed weekly for each involve.ai customer.This is the type of comparative measure you might perform yourself. We compare what the model thought would happen to the actual customer outcome, as logged in your system of record. In the involve.ai dashboard
Note: This feature is not currently available for all customers, but will be rolled out universally in the coming weeks. See the feature announcement for more information.About Health Score IngredientsSee how your organization’s KPI’s uniquely impact health scoresHealth Score Ingredients shows you how involve.ai’s machine learning models weigh each type of customer data your organization provides. The weighting you see is unique to your organization, based entirely on which variables have historically most significantly correlated with churn.In this example, the KPI most impactful to health for this particular customer is Product Usage.Understand your customers’ data-driven segmentationHealth Score Ingredients also shows how our AI segments your customers. By analyzing all possible variables simultaneously to determine which customers cluster most closely together, these segments allow normalization, the ability to determine accurately whether each metric for a specific customer is low
Already have an account? Login
Now entering the CI.ty...
No account yet? Create an account
Enter your username or e-mail address. We'll send you an e-mail with instructions to reset your password.