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Like some trending technologies, customer data platforms (CDPs) have their believers, hype-cutters and those who cite market confusion.

Many marketers and brands are invested in CDPs, and major marketing technology providers are buying into it, or, more accurately, acquiring it. Some researchers have attempted to cut through the hype: One Forrester report said CDPs overpromise and underdeliver and “lack crucial capabilities to solve for identity resolution, data hygiene, and cross-channel orchestration.” And others want to make things less confusing: CDP Institute last year launched RealCDP, a program to "reduce the confusion plaguing the Customer Data Platform industry."  

Ultimately, it’s marketers and brands who are tasked with getting the most out of CDPs. And as we move along here in this new year (time for 2021 predictions yet?), we've uncovered some tips regarding how you can actually make the most out of a CDP. 

Beware of Data Complexities, Compliance Issues

CDP buyers need to be mindful of common mistakes enterprises make when entering into a CDP engagement. Enterprise data complexities, data governance, compliance issues, adjoining technical dependencies, ill-informed total cost of ownership and immature internal analytical capabilities make adoption and ROI slow to achieve, according to Steve Chitwood, general manager of the Global Digital Consulting Group at Mindtree, an IT and technology consultancy. 

“While CDP product offerings — ‘buy’ options — have matured and mushroomed at rapid pace over past two years, the technology offerings underpinning the ‘build’ solution options — easily deployable cloud tools and services — have matured and simplified greatly,” Chitwood said.

Related Article: Is That New CDP Truly a Customer Data Platform?

Know Your Data Landscape Readiness

Many organizations underestimate the readiness of their data landscape for CDP, build or buy, and risk delays, cost increase and limitations, according to Chitwood. “Readiness issues,” he said, “include data governance, compliance, stewardship issues, master data management (MDM), etc.”

Organizations with a high availability of anonymous or unidentified behavioral and interaction data (B2C, large media spends), have a particularly large and transformative opportunity. “But,” Chitwood added, “the solutions are complex and the risks — cost/time/speed — are high.”

Recognize Your CDP Path to ROI

For single-brands, midsize enterprises and relatively simplified data environments focused specifically on marketing activation objectives, the CDP product landscape is full of options, according to Chitwood. For multi-brands with large ecosystems, complex enterprise data systems, sophisticated advanced analytics requirements and a use-case maturity roadmap that extends beyond marketing activations, both build and buy scenarios should be evaluated, he said. “Source data volume, variety, veracity and velocity are key considerations in defining an effective approach,” Chitwood said. “The fastest path to ROI has proven to be an iterative approach based on identifying and proving a limited number of high-value use cases with limited initial data sources."

Related Article: Hyper-Personalization: Why a CDP Isn't Everything

Prioritize Your Business Use Cases

Since CDPs are technically complicated, there’s a tendency to evaluate them based on feature-filters, said Tony Byrne, founder and CEO of Real Story Group, a technology advisory firm. However, remember that these platforms are really designed to empower marketers and other customer experience engagement specialists. “So the key to vetting solutions is how well they match up against your business use cases,” Byrne said. 

Byrne said his firm evaluates CDP vendors according to nine key business scenarios. None of the three-dozen-odd major CDPs out there, he said, excel at more than two or three use cases. “So it’s really important as a customer to prioritize your business use cases going in,” Byrne said.

Try Before You Buy Your CDP

Testing out the software seems like a no-brainer. However, Byrne said he’s amazed at how many enterprises select key pieces of their stack without actually testing out the platforms. “That’s like buying a car after watching the salesperson drive it around the dealer’s lot,” Byrne said. “You need to get behind the wheel.”

Byrne added this is tricky with a CDP, but still very doable. Employ your use cases to test the vendors through any RFP/demo process, Byrne said, then arrange a hands-on bake-off and proof-of-concept. “This is not always easy and will require you to get your data house much more in order than you thought,” he said. “But guess what? You’ll have to do that anyway, so now at least you’ll have a better sense of the operational requirements you’ll need to address with any new CDP.”

Consider Your Broader Omnichannel Stack

Ideally you’re investigating CDP technology because you have omnichannel ambitions. This means you need to understand, and ideally delimit, the scope of the CDP at a time when vendors are trying to rapidly expand the remit of their platforms, Byrne said. “Just like you shouldn’t license a CDP from a Journey Orchestration Engine (JOE) platform, you should remain cautious about using your CDP as a JOE, too,” Byrne said. “For large enterprises in particular, the 2020s are going to be about separation of concerns for rapid adaptation and multi-touch customer journey coherence. Scope your CDP effort accordingly.”

Related Article: Clearing Up CDP Misconceptions

Recognizing the Analytics, Data Science Maturity

Organizations that are considering a CDP often have parallel aspirations around analytics and data science maturity, according to Chitwood. These, he said, can often be approached in tandem in a single/shared solution. Often overlooked in product considerations, he added, are the advanced analytics components: data science workbench, ability to import/test analytical models, well-governed exposure of data sets for exploration and intellectual property ownership of data science.

Understanding the Big CDP Picture

The bottom line for CDP? It has emerged as a standard in the modern marketing technology (martech) ecosystem driven by the promise of a single point of reference for customer information. The core construct is a centralized customer profile comprised of behavioral, transactional and interactional attributes and analytical insights exposed for consumption across the customer experience/martech landscape, according to Chitwood.

The primary use cases, and business owners, are typically marketing related focusing on omnichannel personalization, optimization and orchestration across segments and journeys. “Early adopters,” Chitwood added, “are now looking toward more complex use cases based on artificial intelligence (AI) and machine learning (ML)-driven analytical output along with use cases outside of the marketing domain.”