These 3 types of customer data platforms are not one and the same

A true customer data platform must integrate data from across functional and channel-specific silos into a single customer view that is accessible in real-time

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Gartner found that 89% of businesses are going to compete on the basis of customer experience in the coming years. This new reality has led to a recent surge in interest in customer data platforms (CDPs), and that’s because CDPs solve one of the great challenges companies face today when it comes to customer experience: unifying siloed customer data into an easily accessible and persistent Golden Record to know all that is knowable about customers.

Although interest has increased, there is still a lack of clarity about what capabilities the technology needs to be worthy of the name. An increasing number of technologies are starting to fall into the CDP bucket, and many CDP vendors appear very similar on the surface, but they do not all do the same thing.

Some companies are looking to morph DMPs, CRMs, email and website tools into a CDP, and go out of their way to obfuscate use cases because they need to distract from the fact that they cannot provide the level of data processing performance required by enterprise CDPs. However, if a CDP vendor is not all about supporting enterprise-wide, real-time decisions with their use cases, then it is safe to say they are playing the distraction game. Most CDP vendors simply provide data assembly, or a combination of data assembly and analytics functions, and are not built for the performance and scale required to operationalize data across the enterprise.

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With analysts calling 2018 the “year of the customer data platform RFP,” it is important that companies understand what they should be looking for when they request proposals from vendors. While most “customer data platforms” fall into one of these three buckets – point solutions, data aggregation or management technologies, and purpose-built solutions – they are far from one and the same:

Point solutions are good at collecting some data, but their limits there also limit the range of decisions they can activate

There are many point solutions already in the marketplace that can integrate and manage some portion of a customer’s data. While point solutions – like tag management software and DMPs – collect some valuable customer information, they have limitations in the breadth of data processed, their real-time scale, and/or their ability to integrate with other customer engagement technology. These technologies are often useless in processing PII as well.

Data aggregation or data management technologies cannot handle real-time decisions

Traditional data management technologies, such as data warehouses, are backwards-looking and collect only summary data. This limits marketers and other business users in their ability to engage with consumers. CDPs are different because they ingest, clean, and link data at real-time speeds.

Data lakes are repositories that house a broad variety of data, whether structured or unstructured, and serve as a wonderfully easy solution for holding a variety of customer data. However, for companies to fully capitalize on the advantages of their data lakes, they must implement automated processes to manage the data to produce accurate customer profiles for analytics and engagement. These profiles can only be created if powerful PII cleaning, matching and keying capabilities are available.

Purpose-built solutions or native enterprise CDPs can handle a persistent record across the enterprise to deliver highly personalized omnichannel experiences and drive real-time decisions

This technology integrates all data – whether batch or streaming, structured or unstructured, and first-, second-, or third-party – and is accessible across all customer journey stages. Ideally, a CDP will be deployable on any type of data technology or engine, including SQL, No-SQL or hybrid environments. This maximizes the flexibility, performance and efficiency of the CDP solution. It’s also important that CDPs have agile master data management (MDM) capabilities, as more and more enterprises want to turn their digital engagements into something intelligent and they must employ analytics to do that. Having agile MDM capabilities in the CDP ensures that the user can truly curate the data as needed for the business.

CDPs need to enable enterprise-wide, real-time decisions and since the market is increasingly becoming flooded with those that cannot, many vendors will generate a lot of confusing noise. As a result, buyers will need to perform excruciatingly detailed due diligence on the technology and bring a good understanding of digital transformation if they want to make a quality CDP selection.

When looking for the right CDP, decision-makers need to remember that not all CDPs are created equal. They all have core capabilities that allow for data collection, modeling and segmentation that can be used for targeting and personalization, but range in complexity. In most cases, companies are truly looking for a native enterprise CDP to best execute their strategies.

The core difference between a native enterprise CDP and other solutions – often masked as a CDP – lies in its ability to make complete and linked real-time customer data easily accessible to the business and ever-expanding digital requirements. This requires ultra-highspeed matching capabilities that most companies lack. A true customer data platform must integrate data from across functional and channel-specific silos into a single customer view that is accessible in real-time, enabling an organization to deliver personalized and contextually-relevant customer interactions across all enterprise touchpoints. Technology should not be considered a CDP unless it can provide this 360-degree customer view that can be updated in the cadence of the customer, whether in minutes, seconds or on demand.

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