(US & Canada) VIDEO | Customers Now Expect Businesses to Understand Them Better — JPMorgan Chase & Co. Marketing D&A Head

(US and Canada) Tiffany Perkins Munn, Managing Director and Head of Marketing Data and Analytics at JPMorgan Chase and Co., speaks with Jonathan Shiery, Partner at Guidehouse, in a video interview about her educational and professional journey, the importance of psychology in data science and AI to promote hyper-personalization, the value of access and technology, and development of AI algorithms to drive business value.

Munn has an interdisciplinary concentration in advanced quantitative methods as a result of majoring in psychology during undergrad with a Spanish minor. To continue her work in psychology, she pursued research rather than therapy. She reckons that a psychology degree is quantitative, wherein psychology majors end up taking a lot of statistical classes, including probability and different types of advanced statistics. Munn's psychology degree enabled her to acquire the necessary knowledge in research methodology for her dissertation.

Further, she believes that the analysis has helped her understand the core of what is going on and how she should be considering every output to see if she truly believes the results and how to interpret the results. Munn also believes she was destined to be in the fields of psychology, statistics, and measuring behavior.

Highlighting her professional journey, she mentions having an extensive background in financial services, having worked in consumer banking, retail, investment banking, global markets, sales and trading, as well as a hedge fund. As a result, she has had the opportunity to observe the behaviors of market participants, from traders to analysts to portfolio managers. Through all the lenses of the different firms she has worked with, Munn has investigated and analyzed many different players in the financial services landscape.

Delving further, Munn states that psychology is essential for understanding how people behave and why. It helps figure out how to communicate with them and comprehend which methods can be used to internalize, absorb, and process information.

When asked about her blogs, she shares that the objective of her LinkedIn blog is to break down complicated ideas regarding data, such as machine learning, AI, and data literacy, into simpler terms that people can comprehend easily. It offers ways for people to apply this knowledge through examples in their personal and professional lives.

Moving forward, Munn states that in 2012, Thomas Davenport initiated the concept of data science, making it "sexy." Consequently, people have become more strategic in their consideration of data and understanding of the role of AI in the landscape of decision-making and marketing.

She adds that now that data is abundant, businesses are recognizing the importance of hyper-personalization for customer satisfaction. Banks, for example, may leverage customer data and AI algorithms to understand preferences, behaviors, and transaction history, using this to offer tailored product recommendations, targeted offers, and specific campaigns. Due to the advances in technology, customers have come to expect that their assigned institutions will understand who they are and interact accordingly to engage with topics that are important to them.

Highlighting real-time analytics and decision-making, Munn elucidates how artificial intelligence-powered analytic platforms enable banks and other firms to process and analyze large volumes of data in real time. This leads to more accurate decisions that enable the companies to quickly respond to current market trends, customer needs, and emerging risks.

Furthermore, Munn states that both access and technology are effective for many reasons. Currently, everyone has access to a multitude of information and can simply type it into a phone app, such as Google, and ChatGPT to obtain the answer. On the other hand, with the development of AI algorithms, people now have the ability to segment customers more effectively with a combination of demographic, psychographic, behavioral, and preference criteria. This helps deliver personalized experiences to specific customer segments that were traditionally not possible.

Additionally, AI-powered voice assistance and natural language processing technologies have also become prevalent in banking interactions, allowing customers to inquire, perform transactions, and receive personalized assistance.

In continuation, Munn states that customer segmentation enables companies to tailor their messaging and campaigns to specific customer groups which boosts targeting and personalization. Moreover, machine learning models predict customer behavior and outcomes and identify consumers for specific actions to optimize marketing budgets and make data-driven decisions, she notes.

In conclusion, Munn reckons that machine learning techniques effectively analyze customer sentiment, enabling marketers to understand customer preferences and trends to fine-tune their strategies and address customers' issues promptly.

CDO Magazine appreciates Tiffany Perkins Munn for sharing invaluable insights with our global community.