In this post, we highlight some key differences between a Customer Data Platform (CDP) and other tools generally used in a marketing tech stack. We also tackle the all-important question on many companies’ minds: “should I build or buy a CDP?.”
Introduction
At first glance, many tools in the marketing tech stack appear to be the same. These include the Customer Data Platforms (CDPs), Data Management Platforms (DMPs), Data Warehouses, Customer Relationship Management (CRM) systems, Marketing Automation tools, Analytics tools, and other systems for managing customer data.
The similarity often comes from significant overlap in features. For example, many of the above tools give you the ability to build audiences and review performance reporting.
Because of those overlapping features, lack of clarity in the market, and the fact that separate teams often choose different tools, product managers and marketers often inadvertently create Frankenstein tech stacks that do one or two things well, but make centralizing customer data a big mess.
Having a clear understanding of what a CDP is and making a clear demarcation between a CDP and other marketing technologies goes a long way in building a successful data strategy for marketing.
So, What Exactly is a Customer Data Platform?
According to the Customer Data Platform (CDP) Institute, a CDP is defined as a “packaged software that creates a persistent, unified customer database that is accessible to other systems”.
It’s worth dissecting this definition to understand the detail of what a CDP is and what it does:
- Creates a persistent, unified customer database - A CDP collects the event data and the associated attributes from a variety of data sources such as websites, mobile apps, etc. It then processes this data to unify customer identities and create a single view of your customers.
- Accessible to other systems - A CDP seamlessly integrates with third-party tools and platforms to power your downstream use-cases. These include driving effective marketing campaigns, fuelling cutting-edge analytics, enriching your data warehouse, and many more.
It is also important to note that a CDP does all this while adhering to the required data quality and governance standards, and mitigating any data privacy or security issues, all of which are significant undertakings from an engineering standpoint.
Now that we’ve established what a CDP is and what it does, let’s differentiate CDP from the other marketing technologies and systems that manage customer data.
CDP Vs Data Management Platform (DMP)
The major differences between CDPs and DMPs lie in the type of data, as well as how that data is collected.
CDPs allow businesses to collect first and second-party data in real-time through APIs, SDKs, and other sources, then allow users to process and normalize it for downstream use-cases. DMPs, on the other hand, collect mostly third-party data for creating audiences in large-scale marketing campaigns.
Another key difference between the two types of platforms is that the data collected by the CDPs contains PII (Personally Identifiable Information). This is because CDPs are mainly used by downstream tools to drive personalization (recommendations, marketing campaigns, etc.). In contrast, DMPs collect completely anonymized data (i.e. the PII is stripped off the data), so it cannot be associated with individuals.
CDP Vs CRM Platform
Unlike a CDP, a CRM specializes in helping you manage relationships with your customers. CRM allows you to efficiently manage first-party data and view, update, and report on your customer and account records.
There are some key differences between CRMs and CDPs, primarily in how they collect, process, and use the data. For starters, a CRM captures and stores data generated mostly through customer engagements. It also records transactional and sales data.
A CRM is not built to unify data coming from other systems. However, a CDP can supply data to a CRM by restructuring it and supplementing it with the required indexes for the CRM to use it effectively.
Another key difference between CRM and CDP is that while a CDP has integrations with various third-party tools for analytics, marketing, and other downstream use-cases, a CRM has its own tools for managing interactions with the customers or integrations with marketing automation tools. Thus, CRMs often have very limited integration with third-party tools for customer interaction and engagement and reporting on various types of other customer data.
CDP vs Data Warehouse
A data warehouse is a centralized repository of data gathered from multiple disparate sources. Businesses use data warehouses as a core component of their business intelligence infrastructure, using them as systems for reporting and data analysis.
While it’s true that most CDPs and data warehouses use the same technologies for data, there are very few similarities between the two otherwise. Here are some key differences between a CDP and a data warehouse:
Building Your Own CDP vs Buying One: An Important Consideration
Once you have a clear understanding of what a CDP is - and what it isn't - you can then ask the next logical question: should you buy or build your CDP?
Most CDPs have the same building blocks - they are built using common technologies that you already own or can buy easily. However, proprietary CDPs also come with connectors for the common data sources, built-in data preparation workflows, and set processes for identity resolution.
To build an in-house CDP, you will either have to implement all of these systems from scratch or find the relevant components that do these things and assemble them. Here is what a complete customer data stack looks like. Both these cases are likely to cause technical complexities and take up your IT department's significant time. There is also a risk of failure and efforts required to build and maintain the CDP.
On the other hand, it makes a lot of sense to build your CDP if it offers significant market differentiation from the proprietary ones. This differentiation includes the ability to personalize customer engagements at the required cadence, driving customer LTV, and, ultimately, increased revenue. Even then, though, most companies would be better served by spending resources on taking action on the data, instead of having internal teams manage complex pipelines.