How Can AI Help Your Customer Success Teams

Can AI predict the future? A little yes, a little no. AI takes huge amounts of data to predict correlations and predictions based on that data. The future may have a few too many unattainable data points, but this is not the same case for your customer data.


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

Our AI/ML uses a custom 9F and 7F model for each account. Unlike a rule-based algorithm, the neural network we built makes correlations far beyond fathomable by humans. 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 action prediction.


Graph: How involve.ai algorithm works


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

  1. Product usage

  2. Support tickets

  3. Interaction frequency

  4. Renewal sentiment

  5. Customer pulse

Now, if you’re a B2B company with 10,000 customers globally and have built out your customer health score, 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 begin to hypothesis-

  1. Was it a change in your onboarding process?

  2. Is there something that is not getting 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.

What our AI/ML model could have helped you identify-

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

  2. The 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.

These could have been flagged by-

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

  2. Presenting the red flags within your up-to-date customer health scores.

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

This is just one example of how AI can step in and unify your data and become your ultimate data interpreter. You don’t need to predict the future by yourself; when it comes to customers, the future is already here and our dashboard is waiting on your queue to launch.


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