Choosing a CDP: 5 Keys to a Best Practices Framework

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Most companies are in various stages of digital transformation. One aspect of this larger enterprise-wide transformation is often a focus on the customer, particularly on improving the customer experience through personalizing their experience with the company, regardless of channel and the type of interaction.

A customer data platform (CDP) can be a foundational technology to enable and accelerate this larger transformation. Done right, the CDP can be the hub of a company’s entire marketing technology stack, helping orchestrate customer data and optimizing the customer experience. 

The CDP market is growing rapidly but is also complicated and can be confusing for buyers. Far too many vendors focus on what they can sell instead of what their customers really need. The result has been confusion and costly disappointments for enterprises that have made this investment. Having worked with a number of companies that got it right and some that did not, we have identified a framework that can help lead to success.

What Do Businesses Really Want From a CDP?

The answer is specific to each company, based on business objectives, the overall digital transformation and customer strategy, the current state of their customer data infrastructure and marketing technology, and the sophistication of their customer engagement and personalization efforts. Taking the time up front to frame the appropriate criteria and then methodically using this framework to evaluate vendors is essential to laying the foundation for a successful partnership. It is also important that all stakeholders be involved and have input into the decision-making process, even if the budget is held by the primary stakeholder, which is typically marketing. 

A Best Practices Framework 

We have seen successful enterprises use most elements of this framework to not only choose the right company to partner with, but also to ensure buy-in from various constituents and to inform and provide the road map for successful implementation and user acceptance beyond the purchase decision. Inclusive, cross-functional involvement is a key marker of success. Executive sponsorship and commitment from top leadership of the enterprise also help ensure success. This is easier if the CDP is helping drive the overall strategic objectives and direction of the enterprise. 

It is important to have a realistic expectation of time and budget needed for a successful implementation, rollout, and ongoing support, beyond just license costs.  

There are five major categories for evaluation in this framework:

1. Product

2. Use cases

3. Deployment timeline

4. Platform architecture

5. Vendor corporate profile

1. Product

This is the most important category and is divided into six major subcategories for evaluation. The relevance and weighting of each element is, of course, specific to each company’s situation.

A. Strength and robustness

Key indicators:

  • The ability to offer the solution in the environment of choice, i.e. on-premise,  public/private cloud, hybrid options, vendor owned/controlled vs. enterprise owned/controlled
  • The capacity to handle high volumes of data sources and complexity of data from multiple vendors
  • The maturity of the offering, with enterprise capability/sophistication
  • Feature-rich, strong out-of-the-box capabilities
  • The ability to support future growth and broader use
  • Security, governance, and self-service features, including monitoring, tracking, logs, and role-based permissions
  • Response times meet/exceed requirements 

B. Core CDP functions

  • Data management: Ingestion, management, processing, compute, storage, transformation
  • Data model and architecture: The ability to handle online and offline, structured and unstructured
  • Matching/identity resolution
  • 360-degree customer profile/view
  • Audience discovery
  • Segment management, creation, evaluation, and activation
  • Ability to integrate with first-, second-, and third-party data
  • Built-in channel orchestration/activation
  • The ability to extract/export/feed external systems/vendors
  • The ease and speed of adding new data sources

C. Extended CDP functions

  • Decisioning/next best action capabilities
  • Built-in machine learning/AI
  • Integration with existing (big) data environments
  • Native integration with reporting platforms

D. Activation and partnerships 

  • Robust integrations and partnerships with ESPs, CRM, marketing cloud, DSP/DMP (cookie-based platforms)
  • Activation capabilities, both to internal systems and external platforms

E. Ease of use

  • Logic language basis
  • Quick turnaround/ease of configuration
  • Flexibility of match/identity logic
  • The ability to transform data, while tracking changes
  • The ability to create, simulate, and deploy logic changes to production
  • Ease of onboarding a new data source or data vendor

F. Analytics and reporting 

  • Inventory of out-of-the-box reports and dashboards
  • The ability to define/update specific reports and dashboards (without SQL coding)
  • The ability to conduct analysis on customers/events/interactions
  • Support for advanced modeling, integrations with analytics, machine learning and data science tools 

2. Use Cases

Use cases may extend beyond marketing to customer strategy, experience, and service functions. Business stakeholders lead this aspect of the evaluation. Beyond a unified customer view, a well-defined and identified list of use cases helps realize better business outcomes. Examples include:

  • Identifying, defining, visualizing, and continuous updating of customer journeys
  • The ability to identify high-value customers prior to acquisition, i.e., the ability to model look-alikes
  • Creation and management of attributes
  • Segmentation
  • Attribution
  • Better and smarter targeting, e.g., rapid testing and learning across channels
  • The ability to support owned, paid, and earned channels
  • Handling complex attributions and modeling
  • The ability to support customer look-ups, if relevant

3. Deployment Timeline

Implementation speed is a key deciding factor. Generally speaking, requirements include quick deployment with initial, high-value/high-priority use cases and subsequent use cases on the journey toward an enterprise CDP. The project management office (PMO) should be responsible for this.  

4. Platform Architecture

Look at the overall fit of the platform with its evolving architecture, business, and IT needs—including on-premises vs. cloud, possible complex configurations, and customization requirements. Particular emphasis should be placed on the following:

  • Data security
  • Governance
  • Scalability

The enterprise architecture team leads this.

5. Vendor Corporate Profile

Consider the health of vendors in terms of reputation, analyst ratings, size and scale, proven industry experience, talent pool available, and unique patents/value-adding intellectual properties. Procurement takes the lead here.  

Rigorous but Worth It

This sort of rigorous process, while time-consuming, has consistently proven its value, helping sort out claims from reality and overall capabilities from the ones that matter most. The effort is well worth it to realize the CDP promise of bringing all customer information, from multiple channels, brands, and geographies, into a single system. It’s an opportunity to gain a clear, 360-degree view of customers and drive exceptional customer engagement with a flexible, data-driven approach—which can be priceless.

For some enterprises, the right CDP can be at the core of their martech stack; clearly defining capabilities like this can save significant investment as well as drive revenue growth. 

The field of CDPs is growing increasingly complex as providers improve solutions and businesses define their needs and expectations. By taking control of the process, businesses not only drive results faster, they help evolve the CDP space into solutions that match customer needs. 

Naras Eechambadi is the founder and CEO of Quaero, a world-class data management and analytics platform empowering enterprises to integrate, discover and democratize their customer data.

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