Customer Experience Management

Customer Experience Management (CEM or CXM) is the systematic discipline of designing, orchestrating, measuring, and continuously improving every interaction a customer has with an organisation across the full lifecycle — from initial brand awareness through purchase, onboarding, ongoing service, and eventual advocacy or churn. It encompasses the tools, processes, data infrastructure, and organisational culture required to engineer experience quality at scale, relying on journey mapping to document touchpoint sequences, voice-of-customer programmes to capture perception signals, omnichannel platforms to unify channel delivery, and analytics engines to translate interaction data into actionable improvement priorities.

Semantic Classification

Content

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Capability Relationships

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About

Customer Experience Management (CEM) is the holistic organisational and technological discipline that governs how a brand designs, delivers, measures, and continuously improves every moment of contact a customer has with it. Whereas CRM systems are record-keeping infrastructures — storing who the customer is, what they have bought, and what support cases they have raised — CEM concerns itself with the perceived quality of the interaction itself across all channels and at every stage of the customer lifecycle. The distinction is between data about the relationship and the lived experience of it. CEM formalises the intuition that operational excellence in products and services is no longer sufficient for competitive differentiation in markets where product parity is high; the manner in which goods and services are delivered, supported, and personalised to individual need is now a primary value driver.

The intellectual roots of CEM lie in service quality research (Parasuraman, Zeithaml, and Berry’s SERVQUAL model, 1988), the experience economy thesis of Pine and Gilmore (1998), and Bernd Schmitt’s marketing scholarship on experiential branding (1999). These academic foundations established that customer perceptions are multidimensional, comprising not only the functional outcome of a transaction but also the emotional, sensory, and social dimensions of the interaction. Operationalising these insights required measurement instruments — satisfaction surveys (CSAT), Net Promoter Score (Reichheld, 2003), and Customer Effort Score (Dixon et al., 2010) — and design tools, principally Journey Mapping to visualise the sequence of touchpoints from the customer’s perspective and Service Blueprint to model the corresponding organisational processes behind them.

In the 2010s, the proliferation of digital channels — mobile apps, social media, chat, e-commerce — multiplied the number of touchpoints and raised the complexity of managing consistent experience across them. The Omnichannel imperative — that customers expect context and state to transfer seamlessly as they move across channels — became the central architectural challenge of CEM. Meeting it required a data infrastructure layer built around Customer Data Platform technology that could ingest events from all channels, resolve them to a Unified Customer Profile through Identity Resolution, and expose that profile in real time to orchestration engines that could determine Next Best Action. By 2020, CEM had become a formal technology category, with Gartner recognising dedicated CEM platform vendors distinct from CRM and marketing automation providers.

The period 2022-2026 has seen Artificial Intelligence fundamentally reshape what CEM can achieve. Large Language Models have made it possible to deploy sophisticated Conversational AI agents that handle complex, multi-turn support interactions at scale and maintain contextual coherence across sessions. Predictive Personalization models infer customer intent and emotional state from interaction signals in near real time, enabling dynamic personalisation of every digital surface. Behavioural Analytics platforms ingest billions of interaction events daily, training models that surface which customers are at risk of churn, which are ready for cross-sell, and which segments respond to which intervention types. Sentiment Analysis applied to contact centre transcripts, social listening feeds, and survey verbatims provides a continuous voice-of-customer signal that feeds back into journey redesign. Simultaneously, data regulation — UK GDPR, the EU AI Act, and third-party cookie deprecation — is forcing a fundamental shift toward First-Party Data strategies and Consent Management frameworks that make CEM data collection sustainable and compliant over the long term.

Components / Architecture

A mature CEM architecture comprises seven functional layers, each dependent on the layers beneath:

• Data Foundation: First-Party Data collection from owned touchpoints (web, app, in-store, contact centre), ingested and normalised by a Customer Data Platform. Identity Resolution links device IDs, email addresses, loyalty numbers, and session cookies into a persistent Unified Customer Profile.

• Voice of Customer Capture: Survey platforms (Qualtrics XM, Medallia), social listening tools, contact centre transcript mining, and in-app feedback widgets continuously populate perception scores (Net Promoter Score, CSAT, Customer Effort Score) and qualitative verbatim data for each journey stage.

• Sentiment Analysis and NLP: Natural Language Processing models analyse free-text feedback, chat transcripts, and social mentions to extract sentiment polarity, topic clusters, and emerging complaint themes. Large Language Models generate structured summaries of agent call recordings in seconds.

• Journey Mapping and Analytics: Behavioural Analytics platforms reconstruct actual customer paths through digital properties, identifying drop-off points, friction moments, and conversion funnel leakage. Path analysis compares designed journeys against observed journeys.

• Real-Time Decisioning and Orchestration: Decision engines read the live Unified Customer Profile and apply Machine Learning models to determine Next Best Action — whether to offer a discount, escalate to human support, send a proactive notification, or trigger a satisfaction survey. Omnichannel Routing ensures the action reaches the customer through the optimal channel.

• Personalisation and Content Delivery: Predictive Personalization models drive dynamic content selection, offer targeting, and product recommendation. Hyper personalisation extends this to individual-level content generation using Large Language Models that incorporate customer history and stated preferences.

• Governance and Compliance: Consent Management platforms enforce GDPR consent choices at the data collection and activation layer. Data Governance frameworks establish data quality, lineage, and retention policies. Data Privacy impact assessments gate new data uses.

  Use Cases / Major Families

  CEM use cases cluster around five major commercial contexts:

• Retail and E-Commerce: Omnichannel experience across physical store, website, and app, including unified basket, BOPIS (buy online, pick up in-store), personalised product recommendations, post-purchase follow-up sequences, and proactive delivery exception management. E-Commerce personalisation powered by Predictive Personalization drives significant uplift in average order value.

• Financial Services: Regulated onboarding journeys with Identity Resolution and KYC compliance, personalised advisory interactions across branch, app, and telephone, proactive fraud alert communications, and churn risk modelling for current account holders. CEM in banking is constrained by FCA conduct requirements and UK GDPR data minimisation obligations.

• Telecommunications: High-volume contact centre operations where Customer Service Automation and Conversational AI handle the majority of routine queries (bill enquiry, plan changes, technical triage), with seamless escalation to human agents when emotional distress or complexity thresholds are exceeded. CEM metrics directly influence Ofcom-reported customer satisfaction and voluntary churn rates.

• Healthcare and Public Services: Patient experience management across NHS and private providers, measuring wait time experience, clinical communication quality, and discharge process satisfaction via Voice of Customer surveys. UK NHS bodies publish CEM-derived Friends and Family Test results publicly, creating accountability pressure.

• Travel and Hospitality: Pre-trip personalisation, in-trip real-time support through Conversational AI, post-trip review harvesting feeding back into property and service improvement cycles. Digital Twin of the Customer approaches model traveller preferences at individual level to enable proactive room personalisation and loyalty benefit optimisation.

  Formal Analysis

  CEM can be formally characterised as a multi-objective optimisation problem over a set of discrete and continuous design variables — touchpoint design choices, channel routing policies, personalisation model parameters, and intervention trigger thresholds — subject to constraints arising from resource availability, regulatory compliance, and customer privacy preferences. The objective function is typically a weighted combination of customer satisfaction scores (Net Promoter Score, CSAT, Customer Effort Score), revenue metrics (Customer Lifetime Value, churn rate, share of wallet), and operational efficiency metrics (cost to serve, first contact resolution rate).

  The formal touchpoint model follows Lemon and Verhoef (2016): let T = {t₁, t₂, …, tₙ} be the ordered sequence of touchpoints in a customer journey, where each tᵢ is characterised by its channel type cᵢ, ownership category oᵢ ∈ {brand-owned, partner-owned, customer-owned}, and experience quality vector qᵢ = (cognitive, emotional, behavioural, sensory, social). The overall customer experience is an accumulation function CX = f(q₁, q₂, …, qₙ, w) where w is a vector of touchpoint salience weights — higher for moments of truth (complaint resolution, onboarding, billing disputes) and lower for routine transactional interactions. The weight vector w is estimated from retrospective analysis correlating touchpoint ratings with overall Net Promoter Score outcomes.

  Real-Time Decisioning in CEM operates as a Markov Decision Process: at each state s (the current Unified Customer Profile snapshot including interaction history, segment membership, predicted churn score, and channel context), an action a is selected from the action set A (Next Best Action options including message delivery, offer extension, support escalation, or no action) according to a policy π(a|s) trained to maximise expected Customer Lifetime Value subject to contact frequency constraints. Modern CEM platforms implement this as a multi-armed bandit problem or a deep reinforcement learning policy, exploiting the continuous feedback loop between action outcomes and updated customer state to improve policy performance over time.

  Key Terminology

• Moment of Truth: A touchpoint where customer perception of the brand is disproportionately influenced, identified through correlation analysis between per-touchpoint satisfaction and overall Net Promoter Score. First popularised by Jan Carlzon at SAS Airlines (1987) and operationalised in CEM by Shaw and Ivens (2005).

• Customer Journey: The end-to-end sequence of touchpoints and experiences a customer undergoes from need recognition through to post-purchase evaluation. Distinct from the customer lifecycle (a relationship-level construct) in that it is episode-specific and maps a particular intent or task.

• Voice of Customer (VoC): The systematic collection and analysis of customer feedback — through structured surveys, unsolicited feedback, social listening, and contact centre mining — to surface perception data that drives Journey Mapping priorities and service redesign.

• Experience Gap: The difference between the experience a customer expects and the experience they receive at a touchpoint, directly analogous to the SERVQUAL expectation-perception gap. CEM investment is fundamentally targeted at closing experience gaps at high-salience touchpoints.

• Next Best Action (NBA): The Real-Time Decisioning output specifying what a CEM system should do next for a given customer in a given context — drawn from an action catalogue including offers, communications, service interventions, and no-action options. NBA optimises for a blend of customer satisfaction and commercial value.

• Omnichannel Orchestration: The process of coordinating actions across all channels — digital and physical — to deliver a consistent, contextually aware experience that carries customer state and history across channel transitions without loss. Requires the Customer Data Platform and Identity Resolution as foundational infrastructure.

• Touchpoint Taxonomy: The classification of all possible points of customer-brand contact into categories — brand-owned (website, app, store, contact centre), partner-owned (resellers, distributors, third-party platforms), and customer-owned (reviews, social sharing, word-of-mouth). The taxonomy structures Journey Mapping and determines the scope of CEM programme intervention.

• Churn Risk Model: A Machine Learning classification model that assigns each active customer a probability of discontinuing the relationship within a defined time horizon (typically 30, 60, or 90 days), enabling proactive retention interventions. Input features include recency, frequency, monetary value (RFM), service usage patterns, Sentiment Analysis of recent interactions, and channel engagement metrics.

  Academic Context

  CEM as a scholarly field draws from multiple theoretical traditions that span service management, marketing science, human-computer interaction, and organisational behaviour. Service quality research by Parasuraman, Zeithaml, and Berry established the SERVQUAL instrument (1988), identifying five service quality dimensions — tangibles, reliability, responsiveness, assurance, and empathy — that predict satisfaction gaps between customer expectations and perceived delivery. This expectation-gap model provides the foundational measurement logic for CEM: every CEM programme implicitly or explicitly identifies where expectations are not met and intervenes to close the gap. Schmitt (1999) introduced experiential marketing, arguing that brands should create sensory, emotional, cognitive, behavioural, and relational experiences rather than merely communicating functional benefits — a reframing that shifted the discipline from quality management toward experience design as a strategic creative activity. Pine and Gilmore (1998) provided the economic theory underpinning this shift, arguing that staged experiences commanded premium pricing and loyalty that goods and services alone could not sustain, anticipating the trajectory of competition in digital markets.

  The most cited definitional framework for CEM is Lemon and Verhoef (2016), who conceptualised customer experience as a multidimensional construct accumulating across prepurchase, purchase, and post-purchase stages, with three categories of touchpoints: brand-owned (controlled by the firm), partner-owned (intermediaries and platforms), and customer-owned (peer reviews, social sharing, community discussion). This taxonomy was significant because it acknowledged that a substantial portion of what shapes Customer Experience lies outside direct organisational control — peer reviews and social recommendations may outweigh any brand-controlled touchpoint in the prepurchase stage — and required CEM practitioners to broaden their scope from channel management to ecosystem management. Brakus, Schmitt, and Zarantonello (2009) developed a brand experience scale measuring four dimensions — sensory, affective, intellectual, and behavioural — that predict customer satisfaction and loyalty above and beyond traditional attitude measures, providing a psychometrically validated instrument for CEM measurement.

  Shaw and Ivens (2005) introduced the concept of moments of truth — high-salience touchpoints that disproportionately shape overall experience perception — which became the organising concept for CEM investment prioritisation. De Keyser, Lemon, Klaus, and Keiningham (2015) introduced the Touchpoints, Context, Qualities (TCQ) nomenclature to move the field beyond the brand-centric touchpoint framing and account for context (physical, social, digital) and experience qualities (cognitive, emotional, behavioural, social, sensory) simultaneously, providing a richer analytical vocabulary for CEM practitioners.

  More recent scholarship has turned to AI-augmented CEM. Wedel and Kannan (2016) examined personalisation at scale as a marketing science challenge, connecting the big data capabilities of Customer Data Platform systems to CX constructs that matter for competitive performance. Grewal, Roggeveen, and Nordfält (2017) situated CEM within the retail ecosystem, examining how technology — Artificial Intelligence, IoT, robotics, and autonomous systems — reshapes retail experience across physical and digital environments. Kumar and Pansari (2016) linked customer engagement — an active CX construct encompassing co-creation, social advocacy, and purchase behaviour — to firm financial performance, providing the mechanism through which CEM investment generates returns. University of Manchester researchers including Tanner and Cayzer have examined CEM big data frameworks, with Maklan and Klaus (2011) at Cranfield providing early empirical work on what CEM implementation actually requires in organisational terms.

  Current Landscape (2026)

  The global CEM market was valued at approximately USD 15.84 billion in 2026 and is projected to reach USD 34.02 billion by 2032 at a CAGR of 13.6%, driven by the accelerating adoption of AI capabilities across all platform tiers (Markets and Markets, 2026). The UK CEM market generated USD 955.7 million in revenue in 2025 and is expected to grow at a CAGR of 16.1% to USD 3.1 billion by 2033 (Grand View Research, 2025).

  The platform landscape in 2026 is dominated by three competitive dynamics. First, experience management suites led by Qualtrics (now part of SAP ecosystem) and Medallia compete on the depth of their voice-of-customer analytics and the breadth of their listening post integrations — from survey platforms to social listening, call analytics, and IoT sensors. Medallia’s AI layer pinpoints missed opportunities and delivers predictive insights automatically; Qualtrics XM covers customer, employee, product, and brand experience in an integrated platform. Second, CRM-native CEM offered by Salesforce (Agentforce 2.0, Einstein AI, Data Cloud) and Microsoft (Dynamics 365 Customer Insights) compete on the depth of integration with sales and service workflows, with Agentic AI capabilities enabling autonomous agents to qualify leads, manage resolution workflows, and update records without human intervention. Third, composable CEM exemplified by Adobe’s rebranded CX Enterprise (formerly Adobe Experience Cloud), which in 2026 positions AI agents and a Coworker orchestration tier as the platform’s core abstraction, enabling brands to assemble modular best-of-breed capabilities around a shared Customer Data Platform layer.

  A defining challenge in 2026 is the governance of Agentic AI in customer-facing contexts. CEM organisations are debating how much autonomy to grant AI agents that can take actions — modifying account details, issuing refunds, scheduling appointments — on behalf of customers without human review. Unpredictability and accountability in agentic loops are top concerns (Adobe Summit, 2026). The UK’s ICO has issued guidance on automated decision-making in customer service contexts under UK GDPR Article 22, requiring human review pathways for decisions that significantly affect consumers. The EU AI Act’s classification of certain customer-facing AI systems as high-risk imposes transparency and documentation obligations on CEM deployments in European markets.

  Zendesk’s 2026 CX Trends report identifies contextual intelligence — the synthesis of AI capability, first-party data, and human judgement — as the new CX standard. KPMG’s 2025 Customer Experience Excellence research names personalisation as the strongest of six CX pillars, with a 20.3% impact on the overall experience excellence score. Over 80% of organisations are either using or planning to adopt generative AI for customer interactions (Conectys, 2025), though only 42% of consumers trust organisations to use AI responsibly in CX contexts, creating a brand risk that CEM programmes must actively manage through transparency, explainability, and human escalation design.

  UK Context

  The UK has a well-developed CEM industry and academic tradition. Cranfield School of Management, led by Professor Stan Maklan, has been a primary academic contributor to CEM theory and practice, with research spanning CEM implementation maturity, the impact of CEM programmes on firm performance, and the evolution of CEM in the age of digital transformation. Professor Maklan and colleagues published some of the earliest empirical work on what CEM implementation actually entails in practice (Maklan and Klaus, 2011; Klaus and Maklan, 2013), providing the conceptual foundations widely adopted by practitioners.

  The University of Manchester’s Alliance Manchester Business School has active research on CEM in the context of big data analytics (published as “Customer Experience Management in the Age of Big Data Analytics: A Strategic Framework”) and on metaverse retailing and customer journey, reflecting Manchester’s industrial heritage as a logistics and retail hub now intersecting with digital transformation. The Customer Experience Conference Manchester (annual, Midland Hotel) is a significant industry gathering for CX practitioners from Northern England’s financial services, retail, and manufacturing sectors.

  UK-specific CEM challenges include the FCA’s Consumer Duty regulation (effective July 2023), which imposes a positive obligation on regulated financial services firms to deliver good outcomes for retail customers — effectively mandating CEM capabilities including Voice of Customer monitoring, journey testing, and outcomes evidence. NHS patient experience management, governed by NHS England CEM frameworks and the Friends and Family Test, provides a large public-sector CEM implementation context. UK retailers including John Lewis Partnership, Marks and Spencer, and Tesco have been CEM innovators, investing in unified loyalty and Omnichannel platforms; Northern English industrial firms in manufacturing and logistics increasingly use CEM tools to manage B2B customer experience at key account level.

  TechMarketView’s 2025 UK Customer Experience Market 2024-2027 research notes that UK enterprises are investing heavily in AI-driven CEM capabilities, with cloud-based deployment dominant and data residency and UK GDPR compliance shaping vendor selection. The UK’s strong contact centre industry — concentrated in Newcastle, Leeds, Sheffield, and Manchester — is a major CEM deployment context, with AI-assisted agent tools and Conversational AI replacing routine call volumes while human agents focus on complex and emotionally sensitive interactions.

  Future Directions (2026-2030)

  Several structural trends will define CEM evolution through 2030. The agentic CEM model — where autonomous AI agents orchestrate multi-step resolution journeys, proactively intervene at churn risk signals, and negotiate service recovery without human queuing — will move from early adopter to mainstream as Agentic AI governance frameworks mature and trust in AI autonomy builds. Digital Twin of the Customer approaches, currently at pilot stage in sectors including telecoms and financial services, will provide probabilistic individual-level models that enable truly proactive CEM — simulating the impact of proposed service changes on customer perception before rollout, identifying at-risk customers weeks before observable churn signals emerge, and enabling interventions calibrated to individual preference rather than segment averages.

  Empathetic AI — systems that recognise emotional cues in customer communications and adjust their tone, pacing, and escalation logic accordingly — will move from experimental to standard in contact centre deployments. A 2024 survey found that 71% of customers believe AI can make service more empathetic, and 67% want AI that adjusts its tone to their emotional state (CX Network, 2025). The challenge is making empathetic responses genuine and culturally sensitive rather than formulaic, requiring training on diverse interaction corpora and human review mechanisms.

  Spatial Computing will create new CEM challenges and opportunities as AR interfaces, ambient computing environments, and immersive retail experiences introduce new classes of touchpoints whose experience quality needs to be designed, measured, and managed. CEM frameworks developed for screen-based digital channels will need extension to account for spatial presence, embodied interaction, and multi-sensory experience dimensions. The UK’s NICVE and similar spatial computing research centres will contribute to the academic frameworks for spatial CX design and measurement.

  Privacy-preserving CEM will become a competitive differentiator as third-party cookie deprecation, consent management complexity, and consumer privacy awareness constrain data availability. Techniques including federated learning, on-device personalisation, and differential privacy will enable CEM capabilities to be maintained on First-Party Data alone, while Consent Management UX will evolve from compliance checkbox to genuine value exchange — communicating to customers what data is collected, how it improves their experience, and how to modify or withdraw consent.

  Research & Literature

  1. Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64(1), 12–40.
  2. Pine, B. J., and Gilmore, J. H. (1998). Welcome to the Experience Economy. Harvard Business Review, 76(4), 97–105.
  3. Schmitt, B. H. (1999). Experiential Marketing: How to Get Customers to SENSE, FEEL, THINK, ACT, and RELATE to Your Company and Brands. Free Press, New York.
  4. Brakus, J. J., Schmitt, B. H., and Zarantonello, L. (2009). Brand Experience: What Is It? How Is It Measured? Does It Affect Loyalty? Journal of Marketing, 73(3), 52–68.
  5. Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review, 81(12), 46–54.
  6. Shaw, C., and Ivens, J. (2005). Building Great Customer Experiences. Palgrave Macmillan, Basingstoke.
  7. Maklan, S., and Klaus, P. (2011). Customer Experience: Are We Measuring the Right Things? International Journal of Market Research, 53(6), 771–792.
  8. Klaus, P., and Maklan, S. (2013). Towards a Better Measure of Customer Experience. International Journal of Market Research, 55(2), 227–246.
  9. Dixon, M., Freeman, K., and Toman, N. (2010). Stop Trying to Delight Your Customers. Harvard Business Review, 88(7/8), 116–122.
  10. Lemon, K. N., and Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69–96.
  11. De Keyser, A., Lemon, K. N., Klaus, P., and Keiningham, T. L. (2015). A Framework for Understanding and Managing the Customer Experience. MSI Working Paper Series No. 15-121, Marketing Science Institute, Cambridge MA.
  12. Wedel, M., and Kannan, P. K. (2016). Marketing Analytics for Data-Rich Environments. Journal of Marketing, 80(6), 97–121.
  13. Grewal, D., Roggeveen, A. L., and Nordfält, J. (2017). The Future of Retailing. Journal of Retailing, 93(1), 1–6.
  14. Kumar, V., and Pansari, A. (2016). Competitive Advantage through Engagement. Journal of Marketing Research, 53(4), 497–514.
  15. Verhoef, P. C., Kannan, P. K., and Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing. Journal of Retailing, 91(2), 174–181.
  16. Homburg, C., Jozić, D., and Kuehnl, C. (2017). Customer Experience Management: Toward Implementing an Evolving Marketing Concept. Journal of the Academy of Marketing Science, 45(3), 377–401. [University of Manchester Research Repository]
  17. Tanner, J., and Cayzer, S. (2022). Customer Experience Management in the Age of Big Data Analytics: A Strategic Framework. University of Manchester Alliance Manchester Business School working paper.
  18. Halvorsrud, R., Kvale, K., and Følstad, A. (2016). Improving Service Quality Through Customer Journey Analysis. Journal of Service Theory and Practice, 26(6), 840–867.
  19. Qualtrics (2025). XM Platform: Enterprise Experience Management. Qualtrics Corporation, Provo UT.
  20. Medallia (2025). Customer Experience Management Platform Documentation. Medallia Inc., San Francisco CA.
  21. Adobe (2026). Adobe Summit 2026: CX Enterprise and AI Agent Architecture. Adobe Inc., San Jose CA. [tapcxm.com/adobe-summit-2026-cx-field-notes/]
  22. Salesforce (2025). Agentforce 2.0: Agentic AI for Customer Experience. Salesforce Inc., San Francisco CA.
  23. Markets and Markets (2026). Customer Experience Management Market Size, Share and Industry Analysis to 2032. MarketsandMarkets Research Pvt. Ltd., Pune.
  24. Grand View Research (2025). UK Customer Experience Management Market Size and Outlook to 2033. Grand View Research Inc., San Francisco CA.
  25. Zendesk (2026). CX Trends 2026: Contextual Intelligence as the New Standard. Zendesk Inc., San Francisco CA. [zendesk.com/newsroom]
  26. KPMG (2025). Customer Experience Excellence Research 2025: The Six Pillars. KPMG International, London.
  27. TechMarketView (2025). UK Customer Experience Market 2024-2027. TechMarketView Ltd., London.
  28. Conectys (2025). Customer Experience Trends 2026: Strategic Insights for CX Leaders. Conectys Group, Brussels. [conectys.com]

Provenance