Prepare for the Era of Intelligent Experiences — With a Sound Foundation of Customer Data and Context-Driven Insights

Sudhir Rajagopal, Research Director, Future of Customer Experience
Ornella Urso, Research Manager, IDC Retail Insight

InfoBrief, sponsored by Publicis Sapient
November 2023

Evolving digital business models and customer expectations are raising the imperative for value-based customer experience outcomes powered by customer data.

The Need to Change

1. Cyclical business disruption is giving way to continuous uncertainty. Enterprises’ technology and business strategies will continue to feel the impact from inflation, recession, and labor market risks, which in turn diminish customer spending power. The top three risks impacting strategy are higher vendor pricing beyond budget expectations, recession’s impact on business revenue, and currency fluctuations affecting local purchasing power.
2. The future value exchange between customers and businesses will be anchored in data. Digital-first organizations see insights from customer and operational data as the primary fuel for unlocking new business value. 28% of enterprises globally report that the expanding volume of customer and market data will have the most impact on their future customer experience (CX) strategies.
3. The evolving regulatory landscape will directly impact insights-driven experiences. Enterprises must navigate a global patchwork of policies around customer data use, privacy, and consent, which have direct implications on customer perceptions of brand trust. Policies such as the EU’s AI Act will impact next-generation customer experiences powered by generative AI (GenAI). Trusted data sharing will be paramount with the rise of the CX ecosystem, helping to avoid risks such as data breaches and privacy violations, which erode customer trust. Privacy legislation and customer expectations raise the stakes for enterprises, discouraging reliance on third-party cookies.
4. Experience commoditization and the transformative force of generative AI. 26% of CX executives report that experience commoditization will have the most impact on future CX strategies. Customer loyalty is a top priority for enterprises globally, but digital transformation efforts thus far have not significantly moved the needle toward CX outcomes. Improvements have remained utilitarian at best, focusing on speed, ease, and efficiency. Intelligent experiences powered by AI and GenAI will fundamentally require enterprises to anchor on customer context and value-based customer outcomes.
5. From digital-first to becoming a digital business in the reality of AI everywhere. IDC finds that, on average, about three-quarters of C-suite executives see technology and digital experiences as important to improving customer engagement. However, it is no longer sufficient just to offer digital experiences. The acceleration of tech/digital capabilities such as GenAI and automation, ML analytics, and data clean rooms has given rise to newer business models and contextualized real-time personalization, enabling enterprises’ CX offering to shift from being data rich to being data driven.

However, enterprises struggle due to capability gaps in managing customer data and harnessing value from insights — aspects that are amplified due to broader experience transformation challenges.

Firms will have to grapple with the growing data volume, which IDC expects to double by 2026 (currently over 100,000EB). Further, 28% of enterprises globally report that expanding data volumes will have the most impact on their future CX strategies. Capability gaps also extend to:

• Consistent usage of CX insights: Only a small percentage of enterprises apply insights to improve their business — 30% to optimize workforce, 27% to improve business processes, and 28% to drive real-time choice of channel, content, and recommendations.
• Harnessing value from insights: Only 16% of organizations report that they find value from more than 75% of all data collected across all sources (i.e., customer, applications, and operations).

Moreover, sharing data and making insights easily accessible in a trusted manner across the organization remain critical obstacles to delivering whole-journey experience outcomes in real time, based on a unified and complete view of the customer.

The Three Most Difficult Data Sharing Challenges:

• Organizational & People:
  • Limited staffing
  • Organizational reporting structure
  • Lack of shared metrics
• Data Management & Governance:
  • Data quality
  • Compliance needs and regulatory concerns
  • Siloed data not shared or no central data lake/warehouse
• Process & Automation:
  • Security
  • Maintaining data quality
  • No alignment across business processes

Current transformation efforts have amplified sub-optimal outcomes. This is primarily driven by organizations’ struggle to act as customer-centric organizations, since CX efforts have traditionally been executed independently, in separate functions (e.g., marketing, customer service, and contact center).

Customer Experience Transformation Challenges (Worldwide):

• 35%: Organization focused on other IT/digital initiatives to improve operational efficiencies
• 28%: Proliferation of tools used for CX
• 26%: Legacy infrastructure preventing the support of a digital enterprise
• 29%: Organization claims to be customer centric, but struggles to activate it purposively
• 27%: Limited strategic vision from senior leadership to support CX initiatives

Customer data and contextual insights are the fuel for moving beyond the current limits of personalization and avoiding the trap of commoditized experiences.

The Failure of Personalization: Elevating Personalization with Real-Time Context-Based Awareness

Current personalization efforts that flood customers with predetermined choices have resulted in utilitarian and commoditized experiences. To differentiate, enterprises must elevate personalization and engage in value-driven and outcome-based two-way conversations — for example, dynamic outcome-based targeting, whereby firms use analytics (AI/ML) to distinguish between and act on customers’ desired outcomes (implicit) rather than on customers’ tasks or actions (explicit).

Challenges to Data-Led Customer Engagement

The primary reasons for customer engagement personalization failures include a lack of:

• A shared understanding of customers’ desired outcomes. IDC research found that two-thirds of customers only engage with a company based on engagement that is contextual and relevant.
• A unified view of the customer through customer data from across all functions of the organization (beyond marketing or customer service), as well as from the broader ecosystem of partners and communities with whom the customer engages.

Experience Underpinned by Data

Enterprises see contextual customer insights, analytics, and experience automation as important for data-driven CX. IDC research found that global enterprises want to implement the following capabilities:

• 74%: To achieve real-time decision making as it relates to customer engagement, financial operations, and/or operational activities
• 76%: To attain contextualized data for customer engagements, financial operations, and/or operational activities
• 72%: To holistically manage customer engagement processes, financial processes, and/or operational activities

Future market differentiation hinges on being able to successfully navigate the coming era of intelligent experiences to deliver value-based customer outcomes.

The release of ChatGPT has essentially democratized an intelligence-first business mindset. Combined with the evolving digital economy and a market landscape with a prevalence of as-a-service business models, businesses will have to navigate shorter customer decision cycles along with increased customer expectations. Winning and sustaining customers then requires prioritizing relational aspects of the experience lifecycle — factors such as improving customer trust, attaining more contextual hyper-personalized engagement with customers, delivering customer desired outcomes, proving customer value, and mitigating churn risk.

Organizations are actively investing in GenAI technologies and see customer engagement among the top three business areas in which GenAI will have an impact in the next 18 months:

• 49% of enterprises worldwide are doing some initial exploration of potential use cases
• 29% are investing significantly in GenAI technologies in 2023
• 23% of organizations believe GenAI will have an impact in key business areas like customer engagement

But fundamental barriers to GenAI must be resolved for enterprises to realize any measure of success:

• Security is the number one concern for enterprises about using GenAI, with 44% of organizations worldwide reporting as such
• Privacy concerns follow closely, at number two, with 37% of enterprises reporting as such

A winning combination of intelligent customer context harnessed from unified data in a trusted manner can help enterprises successfully achieve value-based customer outcomes.

Scaling intelligent experiences requires a disciplined enterprise-wide imperative to orchestrate customer experiences, beginning at the data layer and moving upward to transform experiences based on intelligent context-driven real-time insights in a trusted customer-consented manner.

Scaling Intelligent Customer Experiences

• Design fit-for-purpose intelligent experiences to deliver value-based customer outcomes:
  • Tap into the continuous flow of experiential value streams across the enterprise.
  • Prioritize both business value (revenue, profit, etc.) and customer value (customer desired outcomes, CX KPIs, etc.) to achieve value parity with customers.
• Build a foundation of unified customer data.
• Apply intelligent context to improve customer-centered experience outcomes:
  • Focus on customer context over personalization.
  • Industrialize unique insights and continually connect insights from engagement through AI.
• Embrace a security- and privacy-first mindset to achieve empathetic customer outcomes:
  • Balance the dichotomy between contextualization and privacy for empathetic outcomes.
  • Enable trusted and permissible sharing of customer insights.
  • Scale empathetic customer outcomes in a profitable manner through zero- and first-party data.
• Adopt experience analytics solutions to scale intelligent digital experience outcomes.
• Establish customer-centric governance to sustain and expand customer experience transformation throughout the enterprise.

Leading enterprises are prioritizing externally focused value-based customer outcomes that deliver value.

The foundation of a superior customer experience relies on mutual trust between the enterprise and the customer, in which data is a foundational element. As customers are aware of the negotiable power of their data, enterprises must ensure that the program or solution delivers value for the customer. Broadly, value for the customer refers to specific actions and outcomes wherein customers’ lives or businesses are intrinsically linked and improved because of products and services that enterprises offer. Value-based outcomes are the foundation for enterprises to earn customer trust by delivering on the customer’s reason for engaging the brand. In return, the customer is more willing to exchange unique personal information for meaningful outcomes, leading to greater customer loyalty and lifetime value for the brand.

• 45% of customers are willing to share data with companies but expect to receive high value in return.
• 25% of enterprises expect to create data ethics requirements in the next 1–3 years.

To successfully deliver customer empathy at scale, experience transformation programs must achieve parity in the value exchange with their customers — in other words, balancing enterprise growth and business outcomes with delivering value for the customer and ensuring customer desired outcomes are met. Generative AI has the potential to offer contextualized and hyper-personalized CX through highly customized insights.

Value for the CUSTOMER:

• Outcomes
• Experiences
• Community/Social
• Financial

Value for the ORGANIZATION:

• Business Growth
• Operational Efficiencies
• Customer Lifetime Value
• Market Innovation Competition

At the center: Equitable Value

Unified customer data is the lifeblood of a customer-centric digital business and is essential for enterprises, as it will enable them to participate in future value exchange with customers.

Enterprises must design CX solutions to begin at the data layer and move upward, breaking through departmental silos and working up to the customer engagement layer, instead of the other way around.

In 18 months, about twice as many enterprises plan to implement and use unified customer data across all organization functions as those that plan to use a centralized enterprise data service.

Priorities for enterprises include:

• A unified customer view: Over the next 12–18 months, enterprise investments in customer data and related infrastructure to enable CX capabilities are at the top of the list of investment priorities, with 40% of enterprises globally reporting as such.
• A centralized data store: Over 50% of organizations globally plan to increase investment in customer data platforms (CDPs) to gain a better understanding of customer context and personalize experiences.
• Embed customer data transparency: Leading enterprises embed transparency regarding their use of customer data within their engagement processes and provide consent options to customers. As a result, these organizations see higher levels of mutual trust with their customer bases.

Leading enterprises are also focusing on data culture as a strategic investment to drive value.

The Importance of Customer Data Attribute Measurements for Enterprises:

• 19%: Data value (i.e., attributes that drive the most business outcomes)
• 13%: Utilization (i.e., attributes used most/least)
• 18%: Data accuracy
• 11%: Data completeness
• 14%: Data sources (i.e., which attributes come from zero, first, and second parties)

Intelligent context is the foundation for value creation for both customers and businesses.

Two-thirds of customers perceive an organization as being empathetic if engagement leads to contextual customer-desired outcomes. This requires enterprises to contextualize the customer experience in real-time, leveraging AI-driven intelligent customer data.

It is therefore important for enterprises to leverage intelligent context-related capabilities to bridge data with experience by:

1. Ensuring deeper understanding of the customer context across multiple dimensions
   • Real-time engagement optimization should be based on current and prior customer actions, preferences, sentiment, and channel.
   • Currently, 55% of companies maintain context continuity across multiple customer journeys throughout the whole customer life cycle for most or all customers and brands.
   • Context-based insights for CX rely on synchronous and asynchronous communications and, thus, adaptive two-way conversations with customers.
   • 19% of global enterprises plan to use customer engagement analytics to gain a deeper understanding of customer context and intent and the purposes of customers’ interactions. In many instances, engagement analytics can also offer insights into customer sentiment.
2. Industrializing insights to wire a deeper understanding of the customer into the brand
   • Make the collection of insights a byproduct of customer engagement, not a separate process.
   • Incorporate customer understanding into the enterprise’s day-to-day actions; integrate customers and their insights into product and service development, business decisions, technology decisions, business process changes, and so forth.
   • Make customer intelligence and insights-driven actions a two-way street. Customer insights drive not just front-end engagement; they also power the enterprise’s operating model (organizational structure; employee roles, functions, and responsibilities; performance measures; etc.).

Amplifying innovative experience analytics solutions in a fit-for-purpose manner contributes to experience-led business resilience and growth.

Enterprises aiming to navigate new digital business models will need to do so on a sound foundation of data and technology, underpinned by an architecture oriented around trusted data sharing across the digital CX ecosystem. IDC research indicates that enterprises are investing in technologies for customer trust, ubiquitous/connected experiences, AI-driven experiences, digital customer ecosystems, and edge data.

• Pervasive AI: AI and ML analytics are critical in rapidly delivering contextualized insights to orchestrate dynamic and adaptable customer engagement across channels, customer journeys, and life-cycle stages. IDC research found that, globally, 70% of enterprises have rapidly embraced GenAI, with 38% doing some initial exploration into potential use cases. The top three application areas in which enterprises believe GenAI holds the most promise are knowledge management (customer and employee facing), marketing, and conversation. In this context, synthetic data can be applied within foundational AI models to study potential new customers’ behaviors and identify new customer segments while simultaneously preventing the misuse of personal and sensitive customer data. AI-based recommendation engines, when infused with GenAI and other sentiment analytics capabilities, not only learn in real-time from customer journeys and understand and “recognize” customer preferences; they also proactively engage customers through content (recommendations, offers, messages, etc.) created in real time based on mined AI insights, including customer emotion, sentiment, and intent.
• Data clean rooms: With the deprecation of third-party cookies, or “the Cookiepocalypse,” and a big focus on data privacy as a backdrop, data clean rooms enable enterprises to collaborate within their partners’ ecosystems, deriving greater value from the data (e.g., high-quality targeting) while preventing the loss of customer data privacy and security.
• Edge-first experiences (IoT and devices): The rise of the always-connected customer will push the imperative for analytics at the edge through end-point environments, such as smart homes, leisure and entertainment venues, and transport hubs. This will require enterprises to ensure data and insights (from analytics) can be exchanged and managed from the edge to the core to control customer experience.
• Customer data platforms (CDPs) are not dead, but: Customer data is an essential element in understanding customer needs, opinions, and sentiment across all go-to-market models. CDPs have emerged to support enterprise customer data services for all customer-facing functions. These orchestration services may be provided by other tools/solutions in the CX technology stack, such as AI, journey orchestration, and engagement analytics.

Customer data is a trusted asset; as such, the security and privacy of customer data unequivocally falls on the enterprise.

• 59% of customers are concerned about privacy when companies seem to know a lot about them.

Facilitate permissible and trusted data sharing: Communicating in a predictable manner — one that continually considers the customer context — requires enterprises to share customer data and insights across the enterprise in a permissible manner.

• Globally, 53% of enterprises agree that sharing and collaborating on insights has a significant or major impact on improving their most important business outcomes.
• A further 17% of organizations agree that sharing insights and collaborating are the most important drivers of business improvement.
• Centralized customer privacy and consent policies enable the cross-enterprise tracking of data subject access requests.
• Introduce parameters that govern data use, such as timing, expiration, priority, and consent.

Embrace a privacy-first mindset and champion customer data transparency: Leading enterprises track customer data transparency across journeys and provide consent options to customers. As a result, these organizations see higher levels of mutual trust with their customer bases.

Balance the dichotomy between privacy and context by maximizing value gain from zero- and first-party customer data: Maximizing the value of insights from zero- and first-party customer data (i.e., from “owned” data assets) would naturally lead to profitable business outcomes. It would also mean inherently adhering to privacy and consent by design when designing future intelligent experiences.

Enterprises can improve data culture practices by introducing metadata schema to represent and measure the proportions of zero-, first-, second-, and third-party data.

Data Types Offering Enterprises the Most Value (Currently / In 18 months):

• Zero-Party: 21.7 / 21.7
• First-Party: 38.2 / 26.5
• Second-Party: 18.1 / 25.0
• Third-Party: 22.1 / 26.8

Customer-centric governance and adequate metrics/KPIs are needed to measure and sustain enterprise-wide CX transformation.

Customer-centered C-suite leaders are prioritizing CX initiatives that achieve customer and organizational value parity. Providing an empathetic experience requires the whole organization to actively own CX.

With experiences continually transforming through the lens of technology and data, the CIO has moved to the top — as a very strong and active champion — to build a foundation for customer empathy at scale, supported by other customer-centric C-suite executives, such as the CXO and CCO.

It is important to create a disciplined enterprise-level focus for CX transformation:

• Companies with enterprise CX strategies should develop processes and governance to tie CX initiatives to the CX strategy at the department/business function level, using metrics to measure the outcomes.
• Companies that do not have much experience with strategic CX transformation can start with a departmental focus as an initial step to test and learn.

Metrics and KPIs need to be aligned with value-based business outcomes to measure and improve experience innovation.

• IDC predicts that, “By 2024, at least 30% of organizations will have introduced new success metrics to track and measure the internal and external flows of customer value creation.”

Enterprises should therefore:

• Measure and improve CX across a more diverse set of metrics (CSAT, CES, retention, referrals, etc).
• Measure quantitative employee satisfaction and experience and its correlation with the performance of the whole organization.
• Use voice-of-the-customer insights to identify specific CX metrics that need to be improved.
• Closely align and link EX data and measures with CX data and measures to drive a better CX through a better EX.

For more information, visit our website.

About IDC

International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets.

With more than 1,300 analysts worldwide, IDC offers global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries. IDC’s analysis and insight help IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives.

Founded in 1964, IDC is a wholly-owned subsidiary of International Data Group (IDG, Inc.), the world’s leading tech media, data, and marketing services company.

IDC Custom Solutions

This publication was produced by IDC Custom Solutions. As a premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets, IDC’s Custom Solutions group helps clients plan, market, sell, and succeed in the global marketplace. We create actionable market intelligence and influential content marketing programs that yield measurable results.

IDC UK
5th Floor, Ealing Cross, 85 Uxbridge Road, London, W5 5TH, United Kingdom
T 44.208.987.7100

@idc @idc idc.com

Privacy Policy | CCPA

Relevant Links

• Building Intelligent Customer Experiences Through Unified Data and Context-Driven Insights
• Industry Deep Dive: Intelligent Customer Experiences in Travel & Hospitality
• El Índice de Crecimiento de la Experiencia del Cliente (CXGX) en Servicios Financieros: Una Nueva Herramienta para el Crecimiento en América Latina (LATAM)
• El Índice de Crecimiento de la Experiencia del Cliente (CXGX) en Servicios Financieros: Una Perspectiva para Ejecutivos Latinoamericanos (LATAM)
• The CX Growth Index: A New Framework for Measuring and Prioritizing Customer Experience Investments in Financial Services
• Le CX Growth Index (CXGX) : Un nouveau standard pour la croissance dans les services financiers européens (Europe)
• El Índice de Crecimiento de la Experiencia del Cliente (CXGX) en Servicios Financieros: Una Nueva Herramienta para el Crecimiento en América Latina (LATAM)
• Le CX Growth Index (CXGX) : Un Nouveau Standard pour la Croissance des Services Financiers en Europe (Europe)