Real-time
Customer Data Platform (CDP) Explained

Better CXs demand greater use of customer data and deployment of advanced software that may be susceptible to misuse. Given this predicament, the discussion around CDP advancement must go hand-in-hand with data compliance assurances.

Altruistic CDP vendors are walking the talk in this respect. Not only that, they are harnessing this challenge and transforming it into unique opportunities to implement trust-building best practices. CDPs are helping enterprises and brands in the EU to integrate mechanisms that help in ensuring compliance with the stringent General Data Protection Regulation (GDPR).

CDP, customer relationship management (CRM) systems, and data management platforms (DMP) are marketing arsenal, albeit with different roles and capabilities.

As discussed earlier, a CDP’s defining features are multi-source data gathering, collation, analyses, and use of it for hyper-personalized customer interactions across channels.

Data in a CDP system is collected from all types of sources – first, second, and third-party sources, as well as from different activation methods such as social campaigns, one-on-one interactions, in-device communication such as SMS and push notifications, and so on. CDP data is stored in systems forever unless a CTO or CISO deletes it.

A DMP also carries out similar functions of gathering, collating, and classifying data. However, it does not analyze it; it is merely a repository of data. DMPs are primarily marketing data centers; their data safe is not as broad-based as that of a CDP. Also, data is stored for short terms – typically, for the lifespans of individual campaigns or marketing activities.

Lastly, DMPs store mostly third-party data and data emanating from anonymous sources like cookies and device IDs. This last attribute accounts for a DMP’s main shortcoming – that of not being able to manage identifiable data. Hence, left to themselves, DMPs are redundant in this age of hyper-personalized customer experiences.

CRMs collect data mainly from direct interactions between customers and brands’ touchpoints. Good examples are one-on-one communications such as a sales call, an email campaign, or assisted chat. Such data is typified by personal information, contact details, survey responses, and more.

As such, CRMs are primarily the go-to data resource for customer-facing service executives, field sales staff, call centers, and the like. However, CRMs seem like dwarfs when juxtaposed with the cutting-edge capabilities of CDPs.

As for data warehouses, they are largely systems of data records, while real-time CDPs are live systems of full-funnel data processing and activation of segments for campaign orchestrations. This implies that the former are used mostly by IT teams, while the latter, by marketers.

The above differences and similarities make it clear that neither DMPs or CRMs, nor data warehouses are as fully armed as CDPs, to handle the volatile dynamics of data-driven marketplaces. Rather, their roles are confined to fulfilling niche or narrow requirements.

Hyper-personalization and targeted messaging are the obvious top use cases of a CDP, making the platform the favorite among B2C companies. Within this space, retail, eCommerce, and the services sector are at the forefront of CDP deployment, since unified customer snapshots and personalization are most relevant to them, and CDPs deliver just that.

Not surprisingly, enterprise spending on CX has increased substantially worldwide – from $471 billion in 2018 to a predicted $641 billion in 2022, registering over 36% of topline growth.

In a survey, 46% of responding companies said CX is their top business priority. With favorable CXs, companies with $1 billion in earnings are likely to earn an additional $7 million annually. From the consumer context, 73% say a good experience is key in influencing their brand loyalties.

What’s more, taking the right messaging one step further, CDPs help marketers deploy them at the right time, making them more contextual. This is the ideal “right-sizing” of CXs, and companies just can’t have enough of it.

CDP use cases are also spread across these domains:

• A unified view of customer profiles: CDPs offer a holistic view of the customer, amalgamating data from numerous channels and touchpoints of a buying journey.
• Predictive analytics: Advanced CDPs enable marketers to stay ahead of ever-changing customer behaviors.
• Retargeting: Consistent data flows from multiple sources help in retargeting prospects that missed earlier interactions.
• Measurable outcomes: Due to the accuracy, speed, and diversity of CDP data flows, outcomes are measurable.
• Real-time personalization at POS: Enhancing relationships with offerings such as personalized coupons based on customers’ purchase history/intent, past behavior, and other data.
• Arrest churn: Pre-empting churn with interventions based on churn prediction modeling.
• Customer acquisition: Using models such as lookalike, a CDP extrapolates data from historical snapshots and stitches lookalike profiles to apply to prospective customers and convert them.
• Long-term relationships: The abovementioned “right-sizing” and “right-timing” of interactions help build sustainable relationships. This is a significant plus in a marketplace characterized by a lack of brand commitment.

As a retail business, should you learn the recipe of the CDP delicacy and cook one, or treat yourself to it? Ponder over these pros and cons, and make a choice.

Let’s glance into the dominant near-term CDP trends to cover not only the ‘here and now’ of the technology but also the ‘what now?’ of it.

Customer identity and access management (CIAM) will get top-of-mind consideration, both by the vendors and the industry. This is an obvious first step to realizing greater outcomes from all CDP classes.