20 Years of Unrealised Promises (Part 1)

Marketing Automation and Customer Data Trends That Never Fully Materialised 

In the last two decades, the evolution of marketing automation and customer data management has been relentless. Promises were made, trends were hyped, and the tech landscape seemed to offer boundless potential. Yet not every prediction hit the mark. In the end, some innovations simply didn’t deliver, falling short of their anticipated impact or never achieving the traction needed for success. Let’s look back at 20 years of unrealised promises and trends that made bold assertions but didn’t quite live up to the hype. 

The Early Promise of AI in Marketing Automation 

AI has been a buzzword for as long as most of us can remember, with significant momentum building in the early 2000s. The idea of ‘intelligent’ marketing systems that could learn from customer behaviours and autonomously deliver personalised messages was, and still is, appealing. Early marketing platforms promised that AI would revolutionise every step of the customer journey, moving us beyond simple rules-based automation into a future where campaigns would run almost entirely on autopilot. 

However, the AI capabilities available then—limited largely to basic rule-based logic and clustering algorithms—were far from the advanced, dynamic AI we have today. These early AI features struggled with handling real-time data processing or adapting quickly to shifts in consumer behaviour. As a result, marketers faced clunky tools that still required significant manual oversight and optimisation. It’s only in recent years, with the rise of machine learning models and natural language processing, that the true potential of AI in marketing is beginning to be realised. 

Omnichannel Marketing as the Silver Bullet 

Omnichannel marketing was heralded as the ultimate answer to customer engagement. The idea was simple: unify every marketing channel into one seamless experience for the customer. However, the reality has been far from simple. Technology challenges, coupled with organisational silos and budget limitations, have made true omnichannel marketing difficult to achieve for most businesses. 

In the early years, platforms supporting omnichannel marketing lacked the ability to merge disparate data sources in real time, and consumer data was often scattered across systems. Integrating online and offline data to offer a genuinely unified view of the customer was a technical mountain that many marketers couldn’t climb. While today’s platforms have moved closer to this promise, omnichannel remains an ongoing struggle rather than the all-encompassing solution it was initially advertised as. 

The Rise and Fall of Predictive Analytics 

When predictive analytics became accessible to marketers, it seemed to promise clairvoyance: the ability to forecast customer needs, predict churn, and even anticipate the next best action. This trend was bolstered by the exponential growth of data sources, and the thinking was that with more data, models could accurately predict what a customer would want next. 

However, the predictive models were only as good as the data feeding them. Many early predictive analytics efforts struggled with data quality issues, inaccurate models, and lack of proper integration with other systems. It became clear that to be effective, predictive analytics required substantial investment in data hygiene, team expertise, and continuous model improvement—elements that were often overlooked in the rush to adopt predictive solutions. 

Additionally, predictive analytics faced regulatory pressures as data privacy laws tightened, limiting the types of customer data marketers could legally use. Over time, predictive analytics morphed into a tool with limited and specific applications, rather than the industry-defining breakthrough it was supposed to be. 

Big Data and the Promise of the “360-Degree View” 

The concept of a “360-degree view” of the customer promised a complete, centralised understanding of every customer interaction across all channels, touchpoints, and journeys. But if you’ve ever worked with customer data, you’ll know it’s hardly that simple. 

For years, “big data” was the answer touted as the key to unlocking this 360-degree view. But data challenges were greater than anticipated. Collecting, cleaning, and synthesising data from various sources—especially across disconnected platforms and siloed departments—turned out to be a monumental challenge. The software and technology were often unable to keep up with the vast amounts of data being generated, and while big data projects rolled out, they rarely delivered the cohesive insights promised. This left many companies sitting on mountains of data with little actionable intelligence. 

In the end, big data efforts shifted towards building actionable “segments” and “insights,” but the holy grail of a true 360-degree view of the customer is still largely aspirational. 

The “Single Customer View” Dream of CDPs 

As data fragmentation became a known challenge, the Customer Data Platform (CDP) emerged as a solution to provide the much-desired Single Customer View (SCV). CDPs were marketed as a magic bullet: the one platform that could unify customer data across every system to deliver a complete, actionable profile. 

Despite the hype, many businesses have struggled to implement CDPs effectively. In reality, even the best CDPs rely on high-quality, well-structured data—which isn’t always available. Moreover, technical issues with integrating data from legacy systems, different teams’ inconsistent data formats, and real-time processing challenges have hindered CDPs’ effectiveness. While CDPs can be powerful when properly implemented, they didn’t quite live up to their promise of a universal SCV, especially for companies that lacked the resources to maintain high data standards and system integrations. 

Read more of our posts focusing on CDP’s 

Behavioural Targeting and Personalisation at Scale 

Behavioural targeting was supposed to change the game—allowing marketers to segment and personalise messaging down to the individual level based on real-time data. However, it soon became apparent that scaling personalisation to this degree was far more complex than anticipated. Early platforms offered rigid segmentation rules and limited data points, often leading to generic or inaccurate messaging that fell short of true personalisation. 

Moreover, as privacy regulations increased, behavioural data became harder to gather. GDPR and similar regulations limited companies’ ability to track and personalise based on behaviour. Consequently, the promise of truly granular, large-scale personalisation has become more challenging than ever to achieve. 

Realising the Value of Today’s Tools 

Looking back, these trends were early attempts to harness emerging technologies in ways that weren’t yet fully achievable. While they didn’t fully materialise as promised, they served as stepping stones for the advancements we’re seeing today. Today’s AI capabilities, for instance, are more robust, data unification is more feasible with modern platforms, and personalisation is achieving new heights within the bounds of data privacy. 

As marketers, it’s crucial to remember that technology alone won’t deliver solutions—it requires careful implementation, quality data, and continuous optimisation. The past 20 years have given us a more grounded perspective, reminding us that while hype and potential are enticing, sustainable value comes from measured steps and realistic expectations. 

In my next post, I am going to focus on how to take advantage of the future opportunities, so I am not dusting this off again in 2045!