Did Holiday Traffic Unwrap your Experience Debt?

Now that the holiday rush is over, it’s time to debrief. 

How did your brand do?  Did the marketing and ecommerce components hold up?  Was the flow of traffic smooth, or jammed up like the G110 gridlock of 2010? If the latter, what experience debt did your brand pay as a result?

There’s been a lot of buzz around the impact of technical debt on your customers—whether you’re an airline with an aging reservation system, a retailer with a dated ecommerce engine, or a bank with an outdated payments infrastructure.

But have you ever considered “experience debt” that is accumulated by your brand?  It’s like technical debt but it’s felt by the consumer and ultimately paid by the brand in the form of lost loyalty and lost revenue.  Experience debt becomes highly visible and expensive in December.

What Is Experience Debt?

The debt that accumulates when outdated data, fragmented systems, and manual processes quietly compromise the customer journey, until scale or complexity makes the gaps impossible to ignore.

When thinking about your martech stack or ecosystem, there are a variety of items that can unintentionally create or even hide experience debt. And if not addressed, every new campaign that is run by your brand introduces inconsistencies, friction, or outdated messaging that can silently degrade overall CX performance.

How to Identify and Tackle Experience Debt

Outdated and disconnected data. If you are still using relational data stores, relying on standalone customer data platforms, or running martech campaigns without a strong data governance process in place – you likely have some symptoms of experience debt.  With customer engagement systems making decisions based on an inaccurate or incomplete view of the customer, personalization initiatives become timeboxed in nature (vs. holistic), and channels outside marketing may not be accounted for in the brand to consumer decision delivery process.

With channels competing instead of coordinating, experiences can feel inconsistent, even when systems are working as designed. Over time, outdated data introduces delay into the “data signal to insight to action” process. With this delay and ultimate distrust in data, martech leaders often must insert static rules and processes to compensate. Customers experience the outputs of poor data architectures in the form of repetitive message, conflicting recommendations, and lack of continuity.

Fragmented or poorly integrated systems. Customer engagement should be designed around customer intent and desires, not tool limitations within the martech stack. When solutions within a stack don’t share context, orchestration becomes disjointed and channels operate independently. Poor integration – as with any process - pushes martech teams toward brittle point-to-point connections, batch processes, and data workarounds that drop context, delay signals, and constrain future use cases.

If we think about the components of the martech stack and the outcomes of poor integration, we start to see the following:

• Decisioning becomes static versus adaptive, and channel-first rather than customer-first. Channel first systems often optimize or push decisions in isolation.
• AI models start to compete instead of collaborating to accomplish use cases and outcomes.
• Rules, whether frequency or eligibility based, contact or constraint related, start to conflict across touchpoints. Over time, even small changes become risky and slow as integration complexity increases operational fragility, discouraging experimentation and improvement and adversely impact the customer experience.
• Measurement and optimization of customer engagement initiatives also stall, with no end-to-end view of the customer journey and insights trapped inside individual tools.

The result is experience debt that customers feel as disjointed, repetitive, and inconsistent engagement—and that martech leaders feel as mounting complexity, reduced agility, and diminishing returns from their stack.

Manual processes that should be automated. As we know, speed and relevance when it comes to customer engagement are pinnacle. Nothing introduces friction, delay, and inconsistency into the process more than manual processes that are long overdue for automation.  If you have the opportunity and ability to automate a process, please do it! If your team is still relying on spreadsheets, hand-built segments, campaign-by-campaign specific rules, and human approvals for non-AI processes to compensate for system gaps, decisioning slows and real-time signals go unused. Manual processes are difficult to scale, error-prone, and highly dependent on individual knowledge, leading to inconsistent execution across channels and teams.

To manage risk and compensate, we often see teams add guardrails, exceptions, and static rules that prioritize control over customer relevance, hard-coding past assumptions into future customer experiences. Over time, iterative improvement and continual optimization stalls because testing, learning, and iteration require too much effort, and personalization remains shallow because it is too costly to advance. Customers experience this debt as repetitive, mistimed, or generic interactions, while martech leaders experience it as operational drag, reduced agility, and customer engagement strategies and programs that just cannot keep pace with business growth or rising expectations.

If this past year’s holiday surge exposed cracks in your martech foundation—that came in the form of broken journeys, outdated messages and offers, and inconsistent promotions—maybe now is the time to revisit that martech stack architecture and address lingering technical debt, so that both it and downstream experience debt can be reduced.

There’s no doubt that experience debt can be hard to diagnose and measure, but with the right processes in place, such as agile sprints to address the biggest experience debt issues, it can be reduced!

Jonathan Moran is head of MarTech solutions marketing at SAS, with a focus on customer experience and marketing technologies. Moran has more than 20 years of marketing and analytics industry experience, including roles at Earnix and the Teradata Corporation in presales, consulting, and marketing.

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