In this op-ed, Lyndall Spooner, founder and CEO of 5D, argues that companies are spending millions on customer experience platforms and seeing little return, not because the technology fails, but because they fundamentally misunderstand what customer experience really is.
Companies are spending millions on customer experience platforms and getting nowhere. The failure isn’t about the technology. It’s about a fundamental misunderstanding of what customer experience actually is.
Most CX platforms treat customer experience as something measured through satisfaction surveys, support tickets, maybe some journey mapping. But customer experience isn’t a department, a dashboard or a score. It’s everything. Every product decision, every price change, every ad, every interaction shapes whether customers choose you or walk away. And if you’re only measuring a narrow slice of that reality, you’re missing what actually drives business outcomes.
Every product feature, every price point, every ad campaign, every service interaction requires different insights and actions. Treating CX as just “how satisfied are customers with our service” misses the mark on what matters.
At every stage, four things drive customer decisions: what you offer, what it costs, how well you deliver it, and what your brand stands for. At first glance, these might look like separate issues, but they’re interconnected. A great product loses to mediocre service. A fair price means nothing if customers don’t trust your brand. But most companies track these dimensions in separate, siloed systems. Brand research lives in one place, satisfaction scores in another, operational metrics somewhere else. Each tells part of the story, but none of them “talk” to each other.
When satisfaction drops, can you immediately see if it correlates with a competitor’s pricing move, an operational change or a shift in brand perception? When loyalty improves, can you pinpoint exactly which product improvements or service changes drove it? You can’t if you have data that’s disconnected. The power comes from integration, from seeing how everything connects to actual business outcomes.
Of course, this integration only matters if you can act on it. That requires building your own company-specific bespoke insights tools and analytics, not relying on whatever generic tools are in market. Standard correlation analysis can’t capture your specific business structure, your unique customer segments, or the time lags in how changes affect behaviour. Generic models can’t tell you how long a product improvement takes to impact loyalty in your market, where diminishing returns kick in on pricing, or how much revenue a five-point improvement in satisfaction actually generates for your business.
Companies need models trained on their data that understand the context, including models that can simulate what would have happened if different decisions had been made and models that let you test future scenarios before committing resources. They need language models that recognise the industry terminology and spot patterns specific to the company’s customer base.
Most importantly, companies need “institutional intelligence” that stays with the company. When analysts leave, their expertise walks out the door. Custom models and AI agents trained on your data become the expertise that never leaves, that stays continuously updated, that anyone in your organisation can query any time. They connect dots across datasets that humans might miss and scale knowledge without scaling headcount.
But all of this only works if it lives in one place: a smart data hub where all your customer and market intelligence connects. Not just a repository where data sits, but an active system where insights flow between different levels of your business, and in which high-level views of overall brand health sit alongside detailed views of specific products, journeys, markets and segments. Executives can see the big picture while product teams can drill into exactly what matters for their decisions.
Here’s what this looks like in practice: you notice satisfaction dropping in a customer segment. Your integrated data immediately shows it correlates with a competitor’s pricing change and a recent process change. Your custom models quantify the impact and simulate different responses. Your agents surface similar situations from the past and what worked then. Decision-makers at every level can access this information without waiting for analysts to build reports. You can act quickly, measure results and the system learns from the outcome.
That’s what a mature CX program should look like. The technology exists. What’s missing in most companies is the recognition that CX is everything your business does, and only through linking all parts of the business will data truly have the positive impact on growth it has the potential to deliver.
