The Evolution of CDPs and Where They Are Headed

There is a lot of hype and confusion regarding CDPs. Most organizations aren’t sure what CDPs are, and how they are supposed to help marketers.

Customer data platforms (CDPs) have been around for quite some time, but it is only in the past few years that they have garnered a lot of attention from marketing industry professionals. There is a lot of hype and confusion regarding CDPs. Most organizations aren’t sure what CDPs are, and how they are supposed to help marketers. This article aims to eliminate the confusion around CDPs by explaining what they are, where they came from, and where they are headed.

What is a Customer Data Platform?

A customer data platform (CDP) is a marketing system that ingests and unifies customer data from multiple sources to enable four main marketing capabilities: data collection, profile unification, segmentation, and activation. According to Gartner, prediction modeling and decision management are optional features for CDPs.

Gartner presents a good foundation for the marketing capabilities that should be expected at the core of a CDP. However, many marketers are already looking for features that go beyond the basics. Soon, prediction and decisioning will be required features for CDPs. As the needs of marketers evolve, so too will the defining characteristics of a CDP.

CDPs Evolved to Include Self-Service Tools

One of the biggest reasons CDPs came to be was to democratize customer data. Marketers wanted separate marketing systems to be integrated and data to be aggregated and unified. Early CDPs featured automated data processes such as collection, aggregation, and cleaning. Later, business users with limited technical skills wanted self-service tools that allowed them to gain valuable insights from customer data. And marketers wanted self-service tools to build marketing campaigns. CDPs evolved to include self-service analytics. Self-service analytics tools are limited because data preparation is automated based on known use cases and predefined business scenarios. These tools are not capable of advanced analytics and offer users a limited set of options that include basic dashboards and reports.

Many Organizations Say They Already Have a CDP, But Do They?

Some organizations consider a customer relationship management (CRM) platform the same as a customer data platform. While CRMs and CDPs are both repositories of customer data, they are different tools. Some organizations consider a cloud service a CDP because they are using it for customer data collection and analytics. Cloud services tend to be general-purpose and can be used for a wide range of business use cases. CDPs are like cloud services in that they ingest data from multiple sources, and many CDPs provide a library of machine learning (ML) tools. However, cloud services are not CDPs, although some cloud services are moving towards including some CDP features. The reality is that many organizations think they have a CDP but don’t.

Where CDPs are Headed

Today, CDPs are defined by the industry based on a specific set of core features. In the future, what defines a CDP will be different than what we have today. I’ve outlined below where I think CDPs are headed in the future.

Self-Learning AI Will Become a Key Feature

Self-learning AI is an umbrella term for technologies that enable a system to become more intelligent over time. CDPs that leverage self-learning AI allow organizations to achieve much while requiring little user input. For example, marketers could define the target audience of a campaign and set a goal for segmentation such as customers who are most likely to buy a category of products. A CDP powered by self-learning AI would analyze all the available customer data and run tests to find the segmentation that best suits that goal. Self-learning AI also enables automated ML model building. CDPs will eventually include self-learning AI as a key feature.

Automation Will Play a Crucial Role

Many of the repetitive processes involved in leveraging data for analytics and models can be automated using machine learning and self-learning AI. For example, data prep, enrichment, and segmentation could be almost entirely automated. Also, CDP automation will allow organizations to handle customer data so that privacy regulations and the privacy expectations of customers are met. Some CDPs already include automated features, and automation will play a crucial role when it comes to CDPs in the future.

Customer Insights Will Be Contextual and in Real-Time

Marketers want to gain critical insights about customers, but many CDPs today are lacking when it comes to providing insights from customer data. The most critical piece of a CDP is the ability to enable automated analytical or data science capabilities such as hyper-segmentation, data exploration, and predictions. CDPs will evolve to feature automated data science capabilities which will enable the CDP to provide recommendations and personalization based on the context and intent of each customer at the precise moment.

The CDP Itself Will Make Many Critical Decisions

In the future, CDPs will be automated so that the platform itself will make many critical decisions. For example, CDPs will determine automatically which data to ingest, which segments will bring the most value, and which customers to target and on what channels. CDPs powered by self-learning AI will be capable of automated decision making.

CDPs Will Evolve to Include Use Cases Beyond Marketing

CDPs were initially designed as tools for marketing. In the future, however, automation and self-learning AI will enable the next generation of CDPs. The next generation of CDPs will include many advanced capabilities and cover a wide range of use cases – from omnichannel marketing campaigns and next best prediction for sales teams to real-time customer outreach and streamlined revenue operations for B2B. When it comes to the future capabilities of CDPs, the possibilities are endless.

Author: Abhi Yadav.  

This article originally appeared in Business2Community.

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