Why our motto is "Goodbye uncertainty, hello clarity"

Can AI predict the future?

A little yes, a little no.

AI correlates huge amounts of data and makes predictions based on that data. While “the future” may have a few too many unattainable data points, your customer data is another story. 

involve.ai’s machine learning models are expertly trained to look for these correlations and make predictions based on your data and industry trends. The models are equipped for various use cases and are over 90% accurate in predicting customer churn and over 70% in identifying expansion opportunities.

We apply a custom 9F and 7F model to each customer account (learn more about our machine learning models). The neural network involve.ai built makes correlations far beyond that of rule-based algorithms. Customers and their data do not always behave linearly, so having a model for both linear and non-linear trends is the ultimate asset for customer prediction.

As the model ingests more and more data, it will allocate a unique weight to each of the components that matter the most for customer health scores. Organizations can accurately identify what’s contributing to customer growth or customer churn. Here are some examples of what those components may be:

1. Product usage

2. Support ticket number and severity

3. Interaction frequency

4. Renewal sentiment

5. Customer pulse 

Say goodbye to the guesswork

Imagine you’re a B2B company with 10,000 customers globally and have manually built out customer health scores. You know that an accurate customer health score requires frequent maintenance from your teams. So, what do you do if there’s a change in your data and you begin to see churn and a decrease in renewal sentiment? 

You could hypothesize...

1. Was it a change in your onboarding process?

2. Is there something that is not reflected in your CSAT scores? 

3. Has a new feature release made it harder for users to access what they need? 

You are unsure, and your customer churns. 

Or, you could apply involve.ai to know...

1. Our sentiment analysis in emails might show that even though your onboarding was quick, customers still had unanswered questions.

2. Customers were asking your support teams for the functionality of different report sets because the current reports did not meet their needs. 

3. The billing departments of both companies were fighting over the types of invoices being sent and the communications were not visible to your CSMs or AMs. 

And you could proactively flag and address these issues through...

1. Complete visibility of all customer data in one place via a clean dashboard. 

2. Automated, up-to-date customer health scores that identify red flags.

3. Empowering your teams with the right information for proactive outreach. 

You don’t need to predict the future by yourself; when it comes to Customer Intelligence, the future is already here.