Cloud Journey — Part 7 | Customer Data Platform (CDP)

Piece of art by “Gizem Vural”​

Cloud Journey Series:

A customer data platform (CDP) is a technology that unifies your company’s customer data from marketing and other channels to enable customer modeling and to optimize the timing and targeting of messages and offers however there is a misperception that CDPs eliminate friction with customer data hence that is why I am addressing CDP as part of cloud journey. Also there are many occasions that better to and design customer journeys that use customer-directed engagement models to earn (not collect) first-party data for empowering more effective personalization that enhances experience.

I strongly believe the real driver for cloud transformation must be adding value to customers by improving the experience and removing frictions. The prerequisite of such goal is a strong cloud foundation, micro-services, api gateway, APIOps, DevSecOps and customer data platform.

I believe in order to have a successful CDP within your organization first you will need to establish a strong micro-service and cloud foundations (refer to Cloud Journey — Part 6) and an enterprise architecture approach that is best-of-breed driven as such you will be empowered to establish a CDP that is an orchestrator. It is crucial then to integrate your customer identity and access management (CIAM) system with CDP.

Customer data enables continuity across channels, allows to offer personalization, and provides context to analytics and reporting. Supporting growing business demands, especially across an enterprise application landscape littered with customer data silos, is a complex and difficult task. Customer data management is a combination of politics, strategy and technology. As organizations continue to develop their customer data governance strategies, a growing burden is falling on architects to provide a solution that can capture, protect, enable and mine customer data for various requirements. Next, you need categorize the type of customer data initiative in scope. The following figure shows the three categories with some examples of each dimension.

The three categories of customer data management initiatives are:

Practically speaking, you will have to support multiple integration types to enable the full set of customer data capabilities. Figure below shows an example of the range of integration services required to support each component of your customer data architecture, as well as the ideal integration approach for each component. One of the greatest tools to achieve this is MuleSoft (as your API Gateway) and its powerful tool DataWeave and for events kafka/sns/msk/etc, a simple illustration of your architecture is shown in below:

Event-driven integrations require event broker middleware to enable its pub-sub approach to data flows. API integrations allow for real-time integration calls as well and can flow through an integration middleware, such as an iPaaS, to promote reusability. Bulk ETL data feeds should be leveraged for customer data integrations that do not have a real-time requirement. For example, a nightly update of a data lake or logical data warehouse within your analytical architecture can be done via bulk ETL transfers.

Ideally a CDP should be fed customer data from a diverse set of sources to enhance the customer identity and segmentation outputs. Figure below is an example of the integrated flow through a CDP and highlights the four common functional areas of a CDP.

CDPs can perform customer identity resolution based on a deterministic (that is, rule-based) approach, and some CDPs offer advanced probabilistic-style matching (that is, analytics-based) as well. Some CDPs may even allow users to combine the two approaches. Furthermore, some CDPs have advanced functionality to support machine learning (ML) models, either vendor- or customer-provided, for use in custom data configurations. Other areas of advanced or additional functionality that can be found in CDP vendor solutions are:

CDPs present an array of exciting customer data capabilities. However, a poorly integrated CDP will devolve into just another data silo that provides minimal value to the business.

Following figure shows an example of how CDPs can provide customer journey orchestration options, based on an analysis of customer data. Customer journeys represent the sum of all the interactions and experiences your customers have with your organization, across all your customer-facing channels. CDPs offer customer journey reports driven by ML-enhanced analytics. By using ML algorithms to analyze datasets, these reports offer faster and more comprehensive insights into customer journeys. Marketers and Product Managers can understand which journeys provide better customer experiences and better outcomes, and which journeys are causing customer dissatisfaction. Additionally, recommendations from a CDP customer journey optimization engine can be configured by a marketer and Product Managers to be sent to an integrated activation service, such as a multichannel marketing hub.

The four CDP types (as defined by Gartner) are listed below.



Scaling Up, Growth and Digital Transformation guy.

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