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Instinct vs Artificial Intelligence: What to Trust?

Updated: Jul 5, 2022

With the economy signaling a downturn, keeping customers is mission-critical.

And the key insight to churn prevention and revenue retention is understanding the correlations between your most successful customers and their interactions.

By learning these signals, CS teams can confidently identify who is at risk and/or who is ready to expand and reach out to them.

And the question is, how to build a Customer Health Scoring (CHS) system that could accurately predict these signals and combat the economic downturn.

We have invited Saumya Bhatnagar, CPO and co-founder of, to speak at our next webinar and discuss:

  • What AI can tell us when markets are in flux?

  • Steps to reveal customers' true sentiments through data analytics

  • How does an AI model that identifies risks and upsell opportunities work?

  • Customer retention with data visibility, prediction, and scalability

Join the conversation and explore how leveraging data can be your strongest bid against the bear market.


About Our Speakers

Saumya Bhatnagar, CPO and Co-founder, is a Forbes 30 under 30 alum, winner of the Stevie Gold Entrepreneur of the Year award, and recognized as the 50 Most Powerful Women in Tech by the National Diversity Council. She is an experienced Software Leader with a demonstrated history of positive project outcomes. Saumya holds a Master's in Computer Science from the University of California, Santa Cruz with a focus on Natural Language Processing.

Shashi Bellamkonda is the VP of Marketing at He is known by many as the “social media swami”, and is a marketing leader who has worked with many entrepreneurs and local businesses and taught Digital Marketing Strategy and Marketing Analytics at Georgetown University as an adjunct professor. Shashi has been called the most customer-obsessed marketing leader and continues to educate himself on technology.


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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

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