Digital Transformation Beyond 2025 — The New Operational Paradigm (Part 2)
In this series, I am exploring what the near-future of digital transformation is going to look like.
Major shifts in business are on the way, largely thanks to AI and the tech underpinning it, and while transformation is already a space that is notorious for getting a regular refresh, we are in the midst of a major evolution with extensive implications.
In Part 1, I looked at the rise of sentience and its impact on how businesses operate – in short, sentience and the tech which will drive it must be factored into changes to infrastructure, or there’s a serious risk of being lapped by the competition.
In this blog post, we will discover the advent of hyper-personalisation and how business must gear up for surprising and delighting clients through emotionally responsive experiences.
Let’s get into it.
Emotionally-Aware Customer Experiences
Customer engagement is about to undergo a profound change. The era of static, demographic-based personalisation is giving way to sophisticated, emotionally and behaviourally aware interactions. Through to 2026 and beyond, digital transformation is being redefined by a human-first, behavioural approach that aims to understand and respond to a customer's emotional state in real time.
This shift requires a new set of tools, a new class of metrics and, most importantly, a new commitment to building trust through ethical and inclusive design.
Decoding Customer Intent
Agencies and consultancies which specialise in customer behaviour analysis have been a mainstay of the marketing space for some time. Now, data driven insights which manifest as tailored client experiences are now integral across the entire business landscape.
Leading companies in this space are deploying sophisticated systems to map a customer's journey at a much deeper level, focusing on cognitive load and emotional state. This is enabled by two key advancements: real-time analytics and emotional state mapping. CX dashboards are evolving to include "emotion graphs" that visualise moments of tension, sharp drops in attention or interruptions in a user's flow, providing a much richer picture of the customer experience than traditional analytics ever could.
This is powering the next generation of personalisation techniques:
Behavioural Segmentation: Engines can perform behavioural segmentation, classifying users based on their actions in real time. For example, a system can differentiate between "hesitators", "fast deciders", "post-purchase validators" and "risk-averse comparers". This allows for strategic, tailored interventions that match the user's decision-making style.
Friction Mapping: Systems that detect subtle behavioural indicators of "cognitive friction" – this is when a user hesitates for an extended period, repeatedly re-reads a section or toggles back and forth between two pages… the system flags this as a moment of doubt.
Contextual Understanding: Hyper-personalisation requires a holistic view of the customer. Companies are now integrating data from across multiple touch points, including social media interactions, website behaviour, mobile app usage and customer service interactions. This works to build a comprehensive, contextual understanding of a customer's needs, preferences and sentiment.
This shift in what is being measured necessitates a corresponding shift in how success is defined. Orgs are now tracking metrics such as Decision Completion Rates (how many users successfully achieve their goal), Effort Score per Journey Stage (how difficult each step of a process is) and Emotional Net Promoter Score, which captures sentiment rather than just satisfaction. This represents a move from tracking "Clicks to Conversion" to analysing the "Behavioural Abandonment Window," or from counting "Page Views" to identifying "Cognitive Conflict Markers".
Creating Trust
As personalisation capabilities become exponentially more powerful, trust emerges as the ultimate currency. The ability to collect and analyse granular behavioural and emotional data is a double-edged sword. While it can be used to surprise and delight, it can also feel invasive if not handled with care. Research shows that 76% of consumers state they would not buy from a company they do not trust to manage their data effectively. Strategic data management is key.
Critical to establishing trust is inclusive design. Experiences must be accessible and usable by everyone, including neurodivergent individuals. This means stress-testing digital tools and interfaces for a wider range of use cases, ensuring there are multiple paths to achieve the same outcome – such as providing ADHD-friendly navigation with simplified visual patterns or embedding dynamic onboarding or purchase flows. Ideal systems are designed to adapt to user behaviour – for instance: defaulting to a simple interface for users who appear overwhelmed or confused.
Examples of Hyper-Personalisation
The theoretical benefits of hyper-personalisation are compelling, but its true value is demonstrated through real-world application and quantifiable results. See below for a showcase of how leading brands are achieving tangible ROI.
Netflix
Strategy: Personalised recommendation engine based on viewing habits and real-time behaviour.
Tech: AI, Machine Learning, Deep Learning, Behavioural Analytics.
Outcome: 80% of viewer engagement is driven by the recommendation engine.
Amazon
Strategy: Predictive AI engine for product recommendations based on shopping behaviour.
Tech: AI, Machine Learning, Predictive Analytics.
Outcome: Generates 35% of total revenue; increases conversion rates.
Starbucks
Strategy: Mobile app uses purchase history, location, and time of day for real-time, gamified offers.
Tech: Real-time data, Geolocation, AI, Gamification.
Outcome: App drives 31% of U.S. sales; increased repeat visits and spend per visit.
EasyJet
Strategy: 20th-anniversary email campaign using customer data to create personalised "travel stories."
Tech: Customer Data Analytics, Personalised Storytelling.
Outcome: 2x open rates, 25% higher click-throughs, 7.5% of recipients booked a flight within 30 days.
Cleveland Clinic
Strategy: AI-driven operational efficiency to offset rising costs by optimising patient advice and resource allocation.
Tech: AI, Predictive Analytics.
Outcome: Achieved a 0.4% operating margin, improving from a $200M loss in 2022.
And, finally, in a local example:
Freedom Furniture
Strategy: Deployed an AI-driven intelligent search and personalisation solution to enhance the online shopping experience and support an "omnichannel first" vision.
Tech: AI-driven search and personalisation platform, SAP Commerce
Outcome: Increased interaction with search by 15%, leading to a 5.5% increase in Average Order Value (AOV).
Connected Experiences as Non-negotiable
We are finally entering a space where digital and physical experiences can be brought into alignment. Customers will soon demand this wholesale; a 2026 Salesforce report indicates that 63% of customers now expect the personalisation they encounter online to be coherent with the conversations they have with staff in a physical store. Delivering this experience is impossible without a unified, real-time data platform that provides a single, real-time view of the customer across all touchpoints. Fragmented systems are a primary barrier to building trust, as they inevitably lead to disjointed and contradictory customer experiences.
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