Converse, advise, or just read the news
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
Hello CI.ty! I’m one of the involve.ai co-founders and our Chief Product Officer. I've had such a great response to my recent webinar on Gut Instinct vs Data that I've been working with our Marketing team to develop a daily series on the topic. I'll be sharing learnings on what drives net revenue retention, what to look for, and the risks of using intuition over being data-driven.Post in the comments here if there are myths you want to see us address or that your own team has busted with data! I know many of you have uncovered surprises. In the meantime, here’s... Myth #1: NPS is all you need.Net Promoter Score is broken. Don’t you agree?! “NPS can never be the only driver of churn; it should always exist in combination with other data elements.” Myth #2: Support cases = bad.Leaders should LOVE high support tickets. It’s true! We should! Support tickets may mean users are engaged and eager to succeed with your product. When support tickets indicate a problem is when the support
The involve.ai team is excited to share a new set of resources: Involved in a Minute, our video series walking users through common daily involve.ai use cases in five minutes or less. Before you yell at us, we know they aren’t all exactly one minute, but the title was too catchy to pass up!You can:Use these videos before go-live to get familiar with the involve.ai platform Watch them as you’re trying to accomplish a specific task (e.g. preparing for a value review, managing at-risk customers, or capturing changes in customer health) Learn new ways of using involve.ai!Find the ever-growing series here and comment on this post to tell us what use cases you’d like to see added to our training library.
Learn how to input a change in customer health into the involve.ai platform.
Learn how to use involve.ai to help prepare for renewal discussions with your customers.
Learn how to use involve.ai to get ready for a Quarterly Business Review or Value Review meeting.
Hi Customer Intelligence Community, I'm Eric Grunden, the CCO for MatrixCare, part of ResMed. A few weeks ago, I participated in a panel with involve.ai's Mary Poppen and WalkMe's Wayne McColluch, and while our conversation centered around the role of CS in shifting markets, we veered into some very human topics, near and dear to my heart. I wanted to share some of those thoughts here - the way CS, data, and empathy interact - and the story behind my perspective.We often, and rightfully, focus on Customer Intelligence as a means of revenue retention. But we risk forgetting that it also runs deeper than that. Good CI gives us, as leaders and organizations, a path to be more human. It allows our teams to be heroes and to build meaningful relationships with customers most in need of support. “We do not lose a customer during this time”My company experienced this firsthand during the last economic downturn, in the early days of the pandemic. As an Electronic Health Records software for pos
Watch this quick overview to understand how you can regularly use involve.ai to identify and take action on customers who are at risk of churn.
A common question we get is whether high support tickets always impacts the health score negatively. Some organizations consider a high number of support tickets a positive because it shows interaction and engagement, how does involve.ai handle that?
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