Advocating a customer-first approach, Supreet Kaur, AVP at Morgan Stanley anticipates a significant shift in required skill sets within data teams to include product development capabilities.
“I use data to point me towards facts and my past experiences along with customer interactions to help me create the right solutions for the customers at the right time. I don’t think any of these things can work in silos, it’s always a combination of these that produces magical results,” says Supreet Kaur, AVP at Morgan Stanley.
She anticipates a significant shift in required skill sets within data teams to include product development capabilities.
“As technology becomes more accessible, there will be a greater push towards developing solutions that are tightly integrated with business strategies and accessible to everyone in the company,” she adds. These advanced data products will enhance customer experience and engagement by providing personalised insights and real-time solutions.
Supreet’s career has been dedicated to harnessing data and AI to drive innovation and solve businesses’ most critical problems. In 2020, she joined ZS Associates, where she developed data products and solutions to assist with their launch strategies. In both roles, she acted as an AI evangelist, educating CXOs about the impact of data and AI within the organisation.
After a successful stint in the healthcare industry, Supreet switched to Morgan Stanley. There, she manages existing AI products and develops data-driven use cases to solve customers’ pain points and reduce operational inefficiencies. A notable achievement at Morgan Stanley was developing a data-driven strategy to measure the efficacy of any AI engine, for which they recently received a patent.
Talking to CXM Today, Supreet discusses her customer-first philosophy and about the advancements in data analytics.
Excerpts from the interview:
What is your approach to developing and implementing data strategies that support customer engagement and product development?
I advocate a ‘customer-first’ philosophy, where the focus is on understanding and addressing the pain points dictated by the business, rather than enforcing product ideas to them. Once the use cases have been identified, then source the right data and collaborate with subject matter experts to understand it so that I can design effective solutions.
What data streams have you explored to gain unique insights into customer behaviour?
I have either used synthetic data generation techniques to test solutions or sourced data from external vendors, despite the higher costs. These approaches provide deeper insights into customer behaviour that enable more targeted and effective engagement strategies.
How do you navigate the balance between the art and science of data-driven decision-making?
I use data to point me towards facts and my past experiences along with customer interactions to help me create the right solutions for the customers at the right time. I don’t think any of these things can work in silos, its always a combination of these that produces magical results.
What advancements do you foresee in data analytics to help gather deeper behaviour insights?
I think data analysis will evolve, it will no more be about creating large spreadsheets or charts, it will be about creating data products with an intuitive UI, so that all business stakeholders can interact with it, and have an almost human-like interaction.
Also Read: Effective Customer Engagement Is Business Critical Amid Economic Uncertainty
How should brands address ethical considerations when implementing data science and AI solutions for customer engagement?
Working in highly regulated industries has underscored the importance of rigorous fairness and bias testing, along with robust legal consultations. These practices may slow product launches but are crucial for building trust and ensuring compliance, and safeguard both the company and its customers.
How do you envision data science and AI reshaping the customer engagement landscape?
Looking five years ahead, I anticipate a significant shift in required skill sets within data teams to include product development capabilities. As technology becomes more accessible, there will be a greater push towards developing solutions that are tightly integrated with business strategies and accessible to everyone in the company.



















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