Consumers Want You to Know Exactly What They're Thinking — JPMC Head of Marketing Data and Analytics

(US & Canada) Dr. Tiffany Perkins-Munn, Managing Director, Head of Marketing Data and Analytics at JPMC, speaks with David Arturi, Head of Financial Services at Lydonia, in a video interview, about best practices for data privacy and security, and AI initiatives for achieving personalization at scale.

Sharing best practices around data privacy and security, Perkins-Munn mentions the following:

1. Robust data governance framework: The importance of strictly following financial regulations is well-recognized in the financial services sector. This includes adhering to GDPR, CCPA, and various industry-specific laws. Establishing a strong data governance framework is essential as the initial step to ensure compliance across all AI initiatives. It is important to understand how these regulations impact an organization, industry, or business.

2. Data minimization: Data minimization involves collecting and using only the data essential for specific purposes, accompanied by routine audits and the removal of unnecessary data. Data can become outdated or stale, especially in an environment where information updates rapidly and is readily accessible.

3. Encryption and security: To prioritize the customer's interests, it is essential to consider encryption and security for data both at rest and in transit. Additionally, stringent access controls must be in place to define who is authorized to access the data, the permitted actions they can take with it, and the methods of authentication to ensure the data's authenticity and reliability as the source of the required information.

4. Consent management: Consent management is essential, involving clear and informed consent for data collection and usage to safeguard privacy. Providing consumers with straightforward opt-out mechanisms is crucial for maintaining trust and compliance.

5. Data anonymization: Techniques such as tokenization and differential privacy are essential for data anonymization. Ensuring the protection of individual entities while still enabling valuable insights is key. This approach strengthens the partnership with consumers by demonstrating that their data is both confidential and anonymous. No personal information is linked to the data when used or shared publicly.

Speaking about leveraging AI to enable personal personalization at scale to drive a stronger customer experience, Perkins-Munn notes that consumers desire brands to anticipate their needs and offer relevant recommendations. However, there is a reluctance among customers to share their data, highlighting the need for a more collaborative relationship between consumers and companies.

She stresses the need for such relationships to go beyond simple opt-out options and move towards transparent communication strategies that explain how consumer data is used and the benefits it provides.

Perkins-Munn suggests that such transparency could foster trust and facilitate more effective recommendations. She points out that as an industry, there has been insufficient development of a mutual dialogue with consumers regarding data privacy, security, and its advantageous use. Additionally, she mentions that once data is adequately prepared, leveraging the following AI-driven initiatives can streamline processes and enhance personalization at scale:

1. Recommendation systems: A prevalent example is recommendation systems, which analyze customer behavior, purchase history, and preferences to suggest relevant products or content. This approach effectively enhances engagement and boosts conversion rates.

2. Dynamic pricing: Dynamic pricing is another key area. AI can optimize prices in real time by analyzing demand, customer segments, and various factors, while also tailoring offers for individual customers. This capability holds significant importance, particularly from a pricing standpoint.

3. Chatbots and virtual assistants: Advanced natural language processing (NLP) can be utilized to deliver personalized customer support by addressing user queries effectively. This involves identifying the most frequently asked questions during customer calls and guiding users through a chatbot for seamless navigation in the sales process.

4. Predictive analytics: Predictive analytics enables the anticipation of customer needs, playing a crucial role in personalization. It helps identify potential churn risks and highlights proactive measures that can be taken to enhance customer satisfaction.

5. Segmentation: Segmentation is crucial as different customer segments often experience varying levels of satisfaction based on their specific needs. Traditional segmentation methods can be overly simplistic and binary, failing to fully capture the nuances within each group. However, AI has the potential to generate more granular and dynamic customer segments, enabling businesses to develop highly targeted marketing and communication strategies. This allows for a more personalized approach, tailoring website content, email marketing, and app interfaces to better meet the unique preferences of each customer segment.

CDO Magazine appreciates Dr. Tiffany Perkins-Munn for sharing her insights with our global community.