We’ve recently witnessed an incredible change in customer behaviors. Buyers today are typically digital-first; they begin their buying process by reaching out to websites, apps and social media to begin initial research and compare options, and when they start interacting with brands, they expect consistent, interconnected interactions across all channels, whether social media, chatbots, call center, or in person, and 76% of consumers, according to a recent McKinsey & Company report, prefer receiving personalized communications across these challenges and is a key factor in their consideration when making purchasing choices.
“Customers are changing the way they engage with businesses. And how businesses engage with them, how we understand them, is changing,” says Tim Crawford, CIO strategic advisor at AVOA. This shift is part of a broader focus trend of customer experience (CX), which Crawford notes is “rooted in data.” It requires companies to understand customers across different touchpoints and departments.
That all starts, of course, with data: Behavioral data, transactional data, demographic data, preference data, data used with tools that analyze web traffic, and more. Just having the data isn’t enough. It must be put to good use to optimize customer experience.
Digital Twins and Optimizing Customer Experience
Alexandre Wentzo, CEO of process intelligence and process management provider iGrafx, explains how digital twin technology is ideal for modeling customer behavior. “Digital twins are often used to analyze and improve the customer journey through modeling. And it’s critical to do so because how people buy now is very different than just a few years ago,” Wentzo explains. For example, digital twins can be used to test and simulate how process changes might impact customers, ensuring the smooth interactions customers seek. “The technology enables organizations to model and test different scenarios to understand and optimize customer experiences before implementing actual changes,” he says.
Digital Twins can also enable hyper-personalized communications by creating dynamic, real-time virtual models of customers through synthesizing data from their historical interactions—website clicks, app usage, purchase history, social media activity, CRM, IoT and transaction logs into a single model that updates continuously. For example, a retail digital twin might detect a customer browsing winter coats on mobile, then instantly adapt email content, push notifications, and in-app offers to highlight complementary accessories or adjust in real-time based on search changes. By mirroring behavior across channels in ways that previous predictive analytics could not, including multi-dimensional simulations, hyper-personalization, and predictive maintenance, digital twins allow brands to deliver contextually relevant messaging at scale. This granularity ensures communications feel tailored to individual circumstances rather than generic campaigns.
Leverage AI-Fueled Analytics
Beyond real-time adaptation, digital twins predict future needs using AI-driven analytics. They analyze historical patterns to forecast actions—like anticipating a subscriber’s likelihood to cancel a streaming service based on their declining engagement, and this would automatically trigger personalized retention offers on the communication channels the customer has most engaged in the past.
For instance, automotive companies use digital twins to predict when a driver might need maintenance, sending proactive reminders via their preferred channel, app, email, or in-car alerts. Rather than being based on raw mileage numbers, as in the past, the digital twin will use actual car usage patterns and system data. This predictive capability reduces reliance on reactive strategies, intending to foster instead anticipatory customer engagement that boosts their loyalty.
The Deloitte Insights: Retail Digital Twins, retailers using digital twin modeling see up to 25% higher conversion rates from enhanced personalization, while operations boost efficiencies by reducing stockouts, improving customer loyalty and decreasing customer churn.
Modernize Legacy IT Systems to Optimize Data Management
Updating legacy systems is no longer a choice but a strategic imperative for businesses aiming to deliver cutting-edge customer experiences in an increasingly digital-first business world. Outdated infrastructure—often siloed, rigid, and incompatible with cloud-native tools—creates bottlenecks that cripple real-time personalization, hinder omnichannel coordination, and limit scalability.
Steve Tukavkin, vice president of IT and digital at JFK International Air Terminal and operator of Terminal 4 at JFK Airport, highlighted data integration issues that challenge their digital transformation journey. In our story, JFK Airport’s Data Transformation Strategy Takes Flight, Tukavkin explained how one of the terminal’s most significant challenges was around sourcing, getting access, automating access to the correct data, and legacy systems were one of the reasons why.
For instance, aging mainframes struggle to process the terabytes of behavioral data required for AI-driven recommendations. At the same time, monolithic architectures can’t support the API integrations needed to synchronize chatbots, CRM platforms and IoT devices.
These limitations force companies into reactive, one-size-fits-all engagement strategies, eroding customer loyalty as competitors leverage agile systems to anticipate needs. Many studies find that consumers abandon brands with inconsistent cross-channel experiences—a direct byproduct of fragmented legacy tech stacks. Conversely, organizations adopting modernized, modular systems such as microservices or systems often report faster deployment of their customer service innovations, from hyper-personalized loyalty programs to AI-powered service bots. The stakes are clear: Without dismantling legacy technical debt, businesses risk losing relevance in an era where seamless, predictive, and instant interactions define competitive advantage.
Security Can’t be a Customer Experience Inhibitor
All too often, security safeguards meant to protect data, systems, and people create friction, but security efforts mustn’t get in the way of great customer experiences. “The security experience is as important as the customer experience, and security measures need to be implemented in ways that don’t frustrate users,” says Jonathan Feldman, CIO at Wake County.
Technology and security professionals should collaborate to create secure and ideal customer experiences. That requires breaking through the silos that typically exist in these enterprise domains. But as many experts emphasize that the customer experience should be the starting point for business strategy rather than an afterthought, departmental silos in other areas of the business need to be shattered as well.
Betzi Aviv, global head of fintech solutions and technology services provider Amdocs, emphasizes that these are lessons to be learned from companies like Amazon that excel because of their frictionless customer experience and technology governance models that align with customer-focused strategies.
“When someone buys a house, we tend to say that the mortgage is not a product. The mortgage is an enabler of a product. The product is the house. A person doesn’t look for a mortgage because they need the mortgage. They want to buy a house,” he explains. “When you look at it as an experience, and you understand that experience is buying a house, then you have the perspective you need to fit the elements supporting that specific experience. To succeed here, you must break the silos and have product, security, technology, and sales teams collaborating to create the best experiences,” he says.
