Customer Data Platforms (CDPs) and their close cousin Data Management Platforms (DMPs) have been a trending topic in MarTech circles since the mid-2010s, and for good reason. Customer data is the key to delivering a great customer experience (CX) at scale.

With more and more companies reshaping their business models to better serve online and cross-channel customers in the ever-evolving post-COVID-19 “norm,” the promise of a single, unified customer view is more appealing than it’s ever been.

Attend any MarTech conference and you’ll be surrounded by evidence that major players in every imaginable industry consider accurate, real-time data and actionable insights critically important to maintaining their competitive advantage. You might even conclude that there’s consensus on the matter. According to research, the CDP market is expected to grow from $3.5 billion in 2021 to $15.3 billion by 2026, at a compound annual growth rate (CAGR) of 34.6% during that period.

Given that obvious importance, and the urgency to adopt data-driven decision-making practices by way of “digital transformation,” you might be surprised to learn that nearly 50% of digital transformation initiatives fail to achieve their goal. Of the roughly half that do make it, only 10% exceed expectations.

The reasons reported for failure are diverse, and they’ll be familiar to anyone who’s managed or executed a major technology project: lack of support from senior management; insufficient budget; infrequent or nonexistent enhancements; poor organizational cooperation; skill gaps; implementation issues; mismanaged customer databases; inaccurate data collection and uncurated metrics; and weak integration of artificial intelligence (AI) and machine learning (ML).

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Innumerable books and articles have been written on how to mitigate, solve, or avoid these issues entirely, and we won’t try to outdo them here. What we will do is highlight a few major pitfalls that seem to keep coming up for our clients as they navigate the CDP world, help you recognize them, and share some use cases and strategies for overcoming them:

Good Tools Are Not Enough: Here’s Why 

You can pick the “best” CDP – the one with the most bells and whistles, the most wonderful user interface, the MIT-graduated founders and development team, or the biggest market share — and still fail. The tools you use are important, but they’re not the most important factor in CDP success.

How to Navigate the Perilous Path from Insight to Execution

You’ve got your toolbox stocked and organized, you have the right people for the job, you have buy-in from the C-suite — things are looking great. But don’t be deceived; there are plenty of hazards along the way to your destination.

How to Avoid Pitfalls in Personalization

The stack is implemented, testing looks good, and you have a wealth of data at your fingertips just waiting to be activated and used to deliver personalized, relevant experiences to your customers. Congratulations! Now you’re in a position to really mess things up.

Measure Twice, Cut Once: How to Make Data-Driven Decisions

“Actionable insights.” More than a buzzy phrase, it’s a primary purpose of data analytics and marketing technology. It’s critical to know when and how to be confident in your data, how to use it to drive decision-making and provide an omnichannel experience, and what balance to strike between automation and the human element.