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To compete with the likes of Amazon and Google, brands need a true platform approach to customer data, one that connects a patchwork of siloed martech solutions by creating a single, comprehensive set of data. However, data unification is only the first step. That platform must also empower everyone who interacts with that data, from marketers and marketing analysts to data scientists. This is the niche customer data platforms (CDP) were designed to fill.

Previously, in the absence of such a platform, enterprise brands invested heavily in unifying and connecting data from siloed systems within a generic data store — a huge undertaking which sometimes took up to a year using traditional technologies. Yet when the project was complete, the marketing organization still faced two enormous hurdles. First, the data store had already grown obsolete as technologies, channels, datasets and organizational structures continued to evolve. Second, every new customer data-related initiative required more IT and/or other technical interventions, from creating new customer attributes to measuring cross-channel ROI.

Let’s consider five gaps in this traditional approach — and how a CDP can bridge them.

1. All the Data, All the Time

To wrangle the complexity of today’s martech stacks, a CDP requires both strong data governance and extreme flexibility — a combination that enterprise warehouses, data lakes, and even some CDPs are not equipped to deliver. These traditional solutions require massive upfront IT work to model data — and yet even more resources every time data models evolve.

By contrast, the purpose-built data store of an enterprise-grade CDP connects and unifies billions of transactions and customer events without upfront data modeling, and makes it available to business users right away.

Related Article: Customer Data Platforms: The Truth Beyond the Hype

2. Marketing Autonomy

Pristine, comprehensive data alone can't support data-driven marketing. And traditional solutions force marketers to rely on tech experts to access data, pull lists, create customer attributes, design tests, etc.

By contrast, an enterprise-grade CDP provides a user interface — built on top of a purpose-built data store — that “thinks” like marketers, enabling them to create and manage entire campaigns on their own. They can define customer attributes, use them to build highly targeted audience segments, ensure cross-channel orchestration, and implement testing and measurement — all without the intervention of tech experts.

Related Article: Understanding the CDP Landscape: Which One Is Right for You?

3. Actionable AI and Machine Learning

When exporting data from a generic data store, data scientists must spend precious time validating the quality of that data and even more time readying the data for analysis. And yet, the results of their hard work often yield few real-world results, because they lack practical ways to upload their findings into actionable systems.

CDPs should ideally ensure data scientists are working with the right data and that they can begin modeling immediately, with minimal data prep. A CDP should also support seamless ingestion of customer scores and other AI/ML model findings, so marketers can start leveraging them right away.

4. Seamless Activation and Cross-Channel Orchestration

Most enterprise brands are juggling dozens of channel execution vendors, limiting orchestration to a single channel or within a single marketing cloud suite. Multichannel marketing hubs can make some difference, but lack the scalability to handle billions of rows of historic data.

With the right CDP, marketers can creatively build segments to meet specific business objectives, instantly validate segment size, and automatically select optimal channels based on customer's historic behavior. An enterprise-grade CDP can leverage that same data to drive “personalization at the edge” — i.e. automated personalization based on individual's purchase data, browsing behavior, etc.

Related Article: Customer Data Platforms Bring Omnichannel Choreography Within Reach

5. Holistic, Statistically Valid Testing and Measurement

At most large enterprises, reporting is as siloed as activation and orchestration. Measuring cross-channel effectiveness requires massive data crunching. In the end, marketers end up relying on last-touch attribution. An enterprise-grade CDP enables marketers to quickly create control groups that scale across every channel, so marketers can scientifically test ideas, measure lift and ROI, and gain a holistic picture of a campaign’s performance, enabling true refinement and optimization of their marketing strategies.

From Patchwork MarTech to 1 to 1 Personalization

By connecting disparate data and systems, empowering marketers, and streamlining the work of data scientists, an enterprise-grade CDP can drive next-gen, data-driven marketing strategies. Here are just few examples of what you can do:  

  • Driving the all-important second purchase: Use AI to calculate the window of opportunity in which new customers are most likely to make a second purchase; draw on historical data and look-alike models to identify highly personalized second-purchase offers; execute intelligence-driven campaigns at speed accordingly; and keep refining messages with scientific testing and measurement.
  • Driving higher customer lifetime value: Build look-alike models on a wide range of demographic, purchase and behavioral characteristics; automate processes to reach new customers; identify the best offers; create highly personalized messages; deliver them at the right time and via the right channel(s).
  • Deploying effective retention strategies: Create or customize AI models that discover signals of churn risk specific to your business; learn how these signals change over time; quickly launch personalized and channel-optimized campaigns; and measure impact to support evermore refined messaging.