The need for brands to deliver personalized experiences is not a new reality, it is just vastly different from what’s come before. At the start of this series, we depicted personalization using the analogy of the friendly corner store proprietor, whose chief concern is providing each and every customer with value. While such an experience might seem as archaic as a door-to-door encyclopedia salesman, it’s not as outdated as one might think. One Redpoint customer famously did not install its first telephone in any of its stores until 1992, just four years before it launched its website.
What is different is the complexity involved with duplicating the corner store experience at scale across a mix of digital and physical channels that encompass the modern customer journey. It’s about trying to personalize the customer experience to an almost unlimited number of customers, and doing so at precisely the right time and in the right context as those customers engage with the brand across a growing number of channels.
Early Attempts at Personalization at Scale
As consumers and brands transitioned to digital-first experiences, technology such as a CRM system or a Data Management Platform (DMP) became early attempts to gain insights about customers through data. A CRM is a valuable technology for handling human-sourced data, creating a record-keeping destination so that every time a contact is made at any point via any channel, an agent or salesperson can effectively track every interaction. And a DMP held early promise as a marketing engagement tool because of its powerful capabilities for look-alike modeling, onboarding and audience buying, which all hold value for select business use cases – particularly when a need arises to build or buy a particular audience. A DMP also can handle first, second and third-party anonymous data as well as interface with demand-side and supply-side platforms. Because of this, there was general agreement that a DMP could do anything from a data perspective.
But as those technologies matured, and as customer journeys became more complex, it became clear that both had limitations in terms of orchestrating omnichannel personalization. Neither was designed nor intended to meet elevated customer expectations for consistent personalization across a mix of physical and digital channels. A CRM, for instance, is not intended to orchestrate next best actions across channels in the context of a customer journey. Nor is it designed to clean or match data beyond simple account level details. The same is true for a DMP; despite its early promise, it never caught on as providing a single source of truth for the customer. As personalization became a competitive differentiator, its lack of first-party data (PII in particular) and lack of reporting and measurement capabilities were also limiting factors. And, as with a CRM, its main purpose (in its case finding a lookalike audience) did not require the depth of data ingestion, standardization and matching that are necessary to deliver the level of personalization that today’s always-on customers have come to expect.
Customer Data Platforms (CDP’s) are a promising technology for pulling customer data together for marketers, but they too have shortcomings. The early promise of CDPs was to unify customer profiles for marketers’ use, solving for the issues of data warehouses not having the right level of data in them, or requiring IT to compile data in some form for Marketers. As the CDP market evolved, most of the vendors in this space made the assumption that ‘someone else’ resolves the inherent quality issues in customer data, a poor assumption to make. Most CDP’s in turn have failed to deliver sufficient ROI for organizations, having failed to wrestle with the underlying data complexity and messiness. In other instances, CDPs have created value for simple use cases like onboarding audiences for paid media, but fail to scale to the rich uses cases that drive value, like multi-stage, omnichannel and/or real-time customer journeys.
Data, Nuance & A Deep Customer Understanding
Over time, as companies began to amass more and more first-party data from a wealth of new sources such as IoT and social media, it became apparent that a data aggregation tool and surface level personalization would not meet the standard for omnichannel personalization. Organizations may have been data rich, but they were still insight poor.
The need for a deeper understanding of the customer has become more pressing, particularly to meet the demand for real-time personalization and, of late, the need to incorporate AI tools. The increasing complexity of customer journeys has outpaced the ability of basic personalization technology to satisfy customer expectations for a seamless, omnichannel CX.
A typical customer or member journey – whether in retail, travel and hospitality, financial services, healthcare, etc. – is nonlinear. What was once a traditional buying journey of awareness, evaluation, and decision may now involve a dozen or more channels, with no easily predictable sequence. For instance, consider how many different reasons there might be for a customer to abandon a shopping cart. The customer may have moved on (perhaps due to a poor experience), may be using the cart as a staging area while they comparison shop, or perhaps is simply trying to trigger an automated offer – knowing that every abandoned cart usually results in a juicy offer.
The increasing complexity of customer journeys has outpaced the ability of basic personalization technology to satisfy customer expectations for a seamless, omnichannel CX.
How a brand decides to respond is often the difference between a good customer experience and a bad one. The former is based on a detailed understanding of a customer and the customer’s intentions, and it is delivered at the optimal time (up to and including real time) and on the optimal channel. A next-best action considers the entirety of a customer’s interactions with the brand – of which the abandoned shopping cart is one factor. Recency, social sentiment, and other behaviors are all factors, along with any other customer signal that tailors the response to the individual customer. Optimizing the experience for the customer in the context of the journey helps guide the customer to the desired outcome, benefiting both the brand and the customer. It also strengthens the relationship with the customer, building trust that leads to higher satisfaction, greater loyalty and a higher lifetime value. This type of response is fundamentally different than a static one-size-fits-all, kneejerk reaction that all too often is counterproductive, introducing friction into the customer journey.
A deeply nuanced customer understanding is likewise crucial for delivering a personalized CX when it comes to interaction with conversational AI and other GenAI tools. When a member of a health plan engages with a chatbot, for instance, it will ideally know the reason for the engagement and be prepared to help guide the member to a resolution.
Similar examples that demonstrate a need for a deep customer understanding abound, particularly as customers now associate real time and AI with their definition of a modern personalized CX. Consider a healthcare organization that wants to provide the optimal care path for someone with a chronic condition. When a patient visits the website, a good experience depends on how quickly the organization can recognize the visitor. This can even be when the patient is on an anonymous-to-known journey, such as a browsing session using a new device. A healthcare organization will ideally have the right customer data technology in place to be able to determine in as close to real time as possible whether a visitor to the website is a patient, or perhaps a family member, and be able to optimize the patient journey.
Technology, Mindset, Data: Recipe for the Right CX
The evolution of customer journeys – from the simplicity of the corner store experience to today’s omnichannel complexity – has made one thing clear: delivering a truly personalized experience at scale requires the right combination of technology, mindset, and data. It’s not just about responding to customer signals but doing so in real time, across channels, and with a deep, nuanced understanding of each individual.
Brands that succeed in modern personalization embrace purpose-built technologies that enable them to orchestrate next-best actions, leverage AI to enhance decision-making, and, most critically, put data at the center of their strategy. Real-time, high-quality data is no longer a “nice to have” – it is the fuel for AI, the key to understanding customers, and the foundation for seamless, contextual, and trust-building experiences.
Getting personalization right doesn’t just drive outcomes; it creates competitive advantage, fostering stronger relationships, greater customer loyalty, and long-term value. In our next part of this series, we’ll take a closer look at what customer data technology works, and how it prepares brands for this future, transforming raw data into readiness for the modern customer experience.
