CDOs and the Future of CX: Leveraging Integrated Data and AI to Meet Evolving Customer Expectations

When business gurus B. Joseph Pine II and James H. Gilmore coined the term “experience economy” in their 1988 HBR article, they made no mention of data. While they hardly mentioned anything digital, they did state that a customer’s “experiences are inherently personal, existing only in the mind of an individual who has been engaged on an emotional, physical, intellectual, or even spiritual level.

Thus, no two people can have the same experience, because each experience derives from the interaction between the staged event (like a theatrical play) and the individual’s state of mind.”

This nugget of advice from three and a half decades ago still holds true for all businesses and should probably be lesson one in the book of data-driven customer experience (CX). Companies that excel in CX can differentiate themselves, foster customer loyalty, and drive sustained growth. However, despite significant investments, many organizations still struggle to meet these heightened expectations.

The challenge is further compounded by the fluid nature of customer expectations and the increasingly fragmented data landscape. As customers interact with brands across multiple touchpoints, the ability to capture, integrate, analyze, and trust this data in real time becomes crucial.

Yet, many organizations grapple with siloed data systems, outdated technologies, and a lack of unified customer insights, hindering their ability to deliver a truly personalized experience. This misalignment not only impacts customer satisfaction, but also affects the bottom line as businesses lose potential revenue opportunities and risk customer churn.

Tiffany Perkins Munn, Managing Director and Head of Marketing Data and Analytics at JPMorgan Chase and Co., prominently stated in a CDO Magazine interview that customers today expect businesses to understand who they are, know how to interact with them, and engage them around the topics that matter to them.

Munn also explained that this is now possible because of artificial intelligence-powered analytic platforms that enable businesses to process and analyze data in real time and at scale. This facilitates interaction consistency, accurate decision-making and allows organizations to respond promptly to customer needs, market trends, and emerging risks.

In this context, the role of data, particularly AI-enhanced and trusted data, is pivotal. Modern data strategies offer the potential to create the AI-ready data that transforms CX by providing deeper insights into customer behaviors, preferences, and needs - and how to best respond at an individual level.

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The New Age of Data-Driven Customer Experience

As CX needs continue to evolve and companies push the bar ever-higher, high-trust data has become the lifeblood of effective AI-driven strategies. For businesses to harness the full potential of AI, they need accurate, timely, consistent, and complete data.

American retail giant Kroger serves roughly 23 million households initiating about 500 billion “start my cart” recommendations every year. Todd James, Chief Data and Technology Officer at 84.51˚ (Kroger’s retail data science, insights, and media company), said in a CDO Magazine interview that data acts as the foundation, which is managed through automation and relevancy sciences to offer relevant and personalized experiences in customer journeys.

Kroger is heavily focused on enhancing personalization, data use, and customer experiences to deliver value and simplicity, from the moment customers engage with touchpoints like promotional emails to when they begin shopping.

James further added that Kroger is incorporating semantic search capabilities to help customers find relevant products or suitable substitutes if something is out of stock. He further mentions leveraging relevancy sciences for aspects like “Did you forget something?” prompts as customers near the end of their purchase journey.

In a similar vein, a single, consistent view of data is crucial in providing a unified customer experience and driving business value, allowing organizations to understand and anticipate customer needs with unprecedented precision. The customer 360 approach, for example, involves aggregating data from various touchpoints and enterprise systems to create a comprehensive view of each customer that’s shared across departments and functions.

In an article exclusively penned for CDO Magazine, Senguttuvan Thangaraju, McKesson Senior Director of Enterprise Data Governance, mentioned that a 360-degree data view facilitates a holistic understanding of critical business entities such as customers, products, suppliers, patients, and more.

By leveraging actual and inferred customer attributes such as household composition or life stage, businesses can further refine their engagement strategies. AI-enabled processes, such as predictive analytics and automated customer interactions, rely on high-quality data to deliver timely and relevant experiences, thus driving consumer delight and fostering stronger customer relationships.

“The development of a Data 360 view is not merely an isolated initiative but an ongoing process of continuous data integration and insights generation, fostering a foundation for more informed decision-making and strategic planning,” writes Thangaraju.

A recent master data management (MDM) modernization report, produced by Nucleus Research on behalf of Informatica, emphasized the importance of data reconciliation when adopting an MDM SaaS solution. This process plays a pivotal role in maintaining data consistency and accuracy across diverse enterprise systems, a critical factor in achieving a unified, comprehensive view of the data. The report highlighted that successful reconciliation requires cross-departmental collaboration to integrate multiple data sources, addressing the longstanding challenge of fragmented customer insights.

Despite the promising capabilities of these data-driven approaches, achieving a true 360-degree view of the customer remains a challenge for many organizations. According to Gartner, only 14% of companies have successfully implemented a comprehensive 360-degree view of the customer. Many organizations struggle with departmental views that lack integration and reconciliation across the enterprise, leading to fragmented, inconsistent, conflicting, and incomplete customer insights.

Common hurdles in customer data management further exacerbate this challenge. Data silos, hybrid cloud environments, resistance to change, and the proliferation of personally identifiable information (PII) create complexities in managing and protecting sensitive data. Issues such as the lack of a single version of the truth, data quality concerns, and privacy regulations also hinder the effectiveness of data strategies tied to customer experience initiatives and exacerbate lack of confidence in data.

Thangaraju lists “resistance to data-centric culture and siloed governance” as a vital challenge. “Fostering a unified data-centric culture is essential for the success of the Data 360 initiative, demanding proactive change management efforts. It is imperative to address challenges related to not engaging in organization-wide collaborative data governance, ensuring a cohesive approach, and breaking down silos for the initiative's effectiveness.”

Moreover, the impact of bad data extends beyond external customer interactions; it also influences internal operations. Enabling sales, service, and marketing teams with comprehensive and accurate customer data enhances their efficiency and effectiveness. Organizations across industries are actively democratizing AI and data capabilities to enhance the workforce performance.

The Toronto Dominion Bank Group (TD) recently announced a generative AI (GenAI) virtual assistant to help contact center workers answer customer inquiries faster. Similarly, Best Buy will be rolling out AI tools to help care agents assess customer conversations in real time, providing them with relevant recommendations. The tools will summarize conversations, detect sentiment and utilize data from the call to reduce the likelihood of a similar issue recurring in the future.

Otis, the world’s largest elevator and escalator manufacturing, installation, and service business, gathers data from its units spread all over the world and leverages AI/ML capabilities to predict changes in traffic patterns and provide seamless experiences to passengers.

Pooja Dewan, then CDAO at Otis Elevator Company, mentioned in a CDO Magazine interview how the company’s mechanics are internal customers of the data organization. Giving mechanics the data-driven insights into operational performance helps speed repairs and improve uptime for elevator passengers. “When we empower our mechanics, they provide better service to our customers.”

Compliance and Governance in the Light of CX

However, as organizations increasingly rely on data to enhance customer experience (CX), the importance of data ethics, compliance, and governance cannot be overstated. Meera Dugar, JPMorganChase VP, Consumer and Community Banking (CCB), shares in a CDO Magazine interview that providing customers with a great experience across touchpoints such as a branch, data on a website, or the prompts offered by a chatbot, solidifies loyalty and enhances their journeys. She highlighted that this experience reassures customers that their data is safe and inaccessible to anyone outside the designated environment.

Dugar also mentioned leaders putting themselves in customers' shoes in terms of desiring great experiences while also ensuring adherence to the rules and policies that they advocate.

Compliance with stringent data governance laws is essential for ensuring ethical data handling. Regulations such as Controller and Processor Binding Corporate Rules (BCRs) mandate strict guidelines for how data is collected, processed, and protected. With the concept of responsible AI becoming increasingly critical, organizations that deploy AI technologies that leverage customer data need to ensure that these systems operate ethically and transparently. Responsible AI involves not only complying with data protection laws but also addressing potential biases, ensuring fairness, and maintaining transparency in AI decision-making processes.

Speaking about managing the regulatory risk around the application and adoption of AI models, Dugar stresses the importance of ensuring that data is securely containerized to prevent any leakage. She points out that when providing investment recommendations, it is crucial to maintain transparency and trust with customers.

For instance, if a customer is advised to invest in a particular stock with a high return rate, it is essential to ensure they are not misled into fraudulent schemes like pump-and-dump strategies. This transparency, according to Dugar, is central to building strong customer partnerships and trust in AI-driven systems. “We have to be very cognizant of the customer, making sure we're doing the right things. And at the same time, providing the services and the value behind AI.”

Additionally, Dugar stresses starting with basic models and gradually progressing to the more sophisticated ones. This involves refining workflows with better filters, prompts, and responses, ultimately creating smarter models that offer unprecedented value. By doing so, organizations can achieve significant breakthroughs, or "aha moments," enhancing both customer satisfaction and the perceived value of technologies like generative AI.

Creating a Customer-Centric Data Strategy for Connected and Consistent Enterprise Engagement

At the heart of every effective CX data strategy is the recognition for centralized and unified customer data across the enterprise. A need for data management that transcends departmental silos and CRM boundaries. The 2024 Informatica CDO Survey reveals a significant trend — while 58% of organizations plan to invest in data management capabilities, many are expected to use five or more tools and manage over 1,000 data sources — 79% anticipate the number of data sources to increase in the coming year. This fragmented approach to data management underscores the necessity of an integrated solution.

Speaking at a recent CDO Magazine event around AI-readiness, Deepak Jose, Mars Wrigley's Global Head of One Demand Data and Analytics Solutions, shared that traditionally, CPG companies have relied on structured internal data sources and third-party data. However, the past decade has introduced new types of data. For example: first-party data from direct consumer interactions, such as visiting the M&M's website or store; second-party data obtained from retail partners like Amazon or Walmart, and zero-party data — consented information provided by consumers.

Jose explained that these diverse data sources often exist in silos within large CPG companies, creating "data islands" that hinder a unified understanding of the consumer. “Sales, marketing, and supply chain explain the same consumer in different ways, and that is the problem of these data islands.” To address this issue, Mars aims to build a connected data foundation that integrates these various data streams, enabling a cohesive understanding of the consumer.

Jose also highlighted an example of how Mars' proprietary AI tool, Brahma.ai, combined consumer trend data from the chewing gum market with internal sensory data to generate insights that led to the creation of Respawn, a chewing gum brand designed for the niche market of gamers. By pinpointing an unmet need in the market, Mars successfully launched a product that catered to this specific consumer segment.

Having worked in the airline, banking, and now energy industries, Angela Zhao, Platform Owner at New Zealand-based Genesis Energy, observes that some industries are more mature in customer experience than others. These industries are mostly driven by market conditions (how competitors differentiate themselves) and the nature of the product (digital vs physical).

Zhao explains that the retail market of the energy sector is competitive, and the players heavily rely on the price lever to compete on a largely physical and asset-intensive product. So, there are untapped opportunities in end-to-end customer experience as a differentiator.

“This experience exists online and offline, and across different touchpoints. One of the challenges of data management is bringing digital and analog data across the full customer journey together to provide true intelligence about the customer. Another challenge is knowing the true cost and profitability of customers so that high-value customers can get premium service,” Zhao adds.

Future of CX and Data — The CDO’s Role and AI

As we look to the future of CX, technologies like GenAI are set to play a transformative role in marketing and sales. By harnessing the power of AI, organizations can significantly enhance content delivery and personalization, leading to more effective marketing campaigns,dynamic interactions, and better customer experiences. The foundation of trusted data is crucial for supporting AI in these areas, as it ensures that the AI systems operate with accurate, up-to-date, and relevant information.

On the data management side, an AI-powered integrated approach to data management can accelerate processes by automating tasks such as data cleaning, reconciliation, preparation, and integration, enabling faster and more accurate decision-making. By handling large volumes of data in real-time, AI allows organizations to quickly uncover patterns and insights that drive customer engagement and personalization. This ultimately leads to enhanced customer experiences and a competitive edge.

Kroger’s 500 million annual shopping cart recommendations and Mars’ consumer insights-driven chewing gum launch both highlight the crucial role of AI and the necessity of an integrated, data-driven mindset.

“CDOs have a critical role to play in driving customer centricity. First and foremost is education and culture - help the business envision great customer experiences using data and insights. Then it's about creating products that break the data silo and tangibly show how better decisions can be made on customer experience or directly affecting it,” states Zhao.

She however positions it as an organizational challenge, which calls for changes in people, systems and processes. “The CDOs would be needed to show small value drops and progress along the way and employ good change management techniques. They will also need to make friends with Chief Technology Officers and Chief Customer Officers to make the change happen. Because, it doesn't matter how many great insights the data team produces, if the business teams don't use it or take actions from them,” Zhao elaborates.

Speaking about the impact of technologies like GenAI, Zhao sees them as “powerful tools and an evolving phenomenon that no one can afford to ignore.” She adds that many data vendors and tools already have AI embedded, from data management to governance, and from engineering to modeling. “We should seek ways to use the tools to help solve the data integration and silo-breaking challenge. On the other hand, coupled with the in-house ML and predictive analytics traditions, the LLMs can supercharge the customer personalization and service use cases.”

Executive Action — Leveraging Trusted Data and AI to Meet Evolving Customer Expectations

• Evolving Customer Expectations: The landscape of customer experience (CX) is rapidly changing, with rising demands for personalized, consistent, and connected interactions across all touchpoints.

• Unified Data Infrastructure: A unified data infrastructure at the enterprise level is essential to break down departmental silos and CRM boundaries. This enables organizations to create a 360-degree view of the customer—a challenge that few companies have successfully overcome.

• Data Integration and Insights Generation: Seamless data integration and reconciliation are critical for supporting real-time analytics and AI-driven insights. Fragmented systems hinder the ability to deliver accurate and timely customer interactions.

• Challenges of Data Governance: With increasing reliance on data, challenges related to governance, compliance, and ethics become more prominent. Organizations must ensure AI systems are transparent, compliant with regulations, and free from bias to maintain customer trust.

• Future Role of CDOs: CDOs will be at the forefront of transforming CX by dismantling data silos, fostering a data-centric culture, and collaborating with CTOs and CCOs. By embracing AI and integrated data management, organizations can unlock the full potential of personalized customer experiences.

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Informatica (NYSE: INFA), a leader in enterprise AI-powered cloud data management, brings data and AI to life by empowering businesses to realize the transformative power of their most critical assets. We have created a new category of software, the Informatica Intelligent Data Management Cloud™ (IDMC), powered by AI and an end-to-end data management platform that connects, manages and unifies data across virtually any multi-cloud, hybrid system, democratizing data and enabling enterprises to modernize their business strategies. Customers in approximately 100 countries and more than 80 of the Fortune 100 rely on Informatica to drive data-led digital transformation. Informatica. Where data and AI come to life.™