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CDP Definition & How it's Different from a Data Warehouse

Sandy Mangat
March 23, 2022
CDP Definition & How it's Different from a Data Warehouse

There are so many tools available to the modern go-to-market team that it can be overwhelming to figure out what you need in your stack.

One of the common questions we hear from our community is about the various data tools you need to enable a Product-Led Sales motion. More specifically, we've been hearing many questions like: "what role does the CDP play in the GTM stack, and how does it differ from other tools like CRMs, Data Warehouses, and even Product-Led Sales Platforms?"

This blog will cover the basics of CDPs and how they compare to other tools in the modern GTM team's tech stack.

What is a CDP?

CDP stands for customer data platform. CDPs collect data from multiple sources and tools to create one centralized customer profile. CDPs capture event-based data from various sources - like your product database, marketing website, and advertising platforms - to create one profile of a user/customer. CDPs then make it easy to share that profile back with your marketing automation platform or advertising tools in a repeatable fashion.

CDPs are typically used by operations & marketing teams to push data through the pipeline to other apps that the GTM team might consume.

CDPs are comprised of three primary components:

  1. A way to ingest data (usually a script that your engineering team deploys so Segment or Rudderstack can track events)ย 
  2. A way to match IDs across tools (i.e., how do I know Sally in the Facebook ads platform is the same Sally who signed up for a trial)
  3. A way to create audiences or cohorts (enabling marketing teams to build groups of users without SQL)ย 

What is Customer Data?

Customer data is all behavioral, personal, and demographic data about your users or customers.ย 

Customer data can break it down into three categories:

1. Customer fit data - Demographic and firmographic data about your users/customers

2. Product usage data - How users interact with your product

3. Intent data - How users interact with your marketing campaigns and web properties

Customer data is crucial for most businesses but especially PLG SaaS companies. In traditional sales, customer data can be captured through conversations with salespeople. But, in PLG, a salesperson might not talk to a user/customer until the end of the user's journey... so access to customer data becomes even more critical.

But, this data isn't easy to capture. In PLG, user journeys are complex. Multiple touch-points are happening across your various marketing campaigns before a user signs up and then an entire journey within the product itself, all before a user ever speaks to a human.

How Does a Customer Data Platform Work?

CDPs are great for capturing events, such as new user sign-ups, that are essential for your go-to-market strategy. CDPs take the data from your marketing website, product, apps, purchase points and then direct it to its proper destination.

Data destinations are any third-party systems used for data storage, organization, analysis, and action. These data destinations are where your organization accesses data collected by the CDP. These destinations can be tools where you action data like customer.io and Hubspot for email marketing, or they can be places where you store data like the data warehouse.ย 

Rather than writing custom code to pull event data and push it to destinations, many companies prefer to use CDP vendors like Segment or Rudderstack and instead deploy their scripts to manage data pipelines.

The Benefits of a CDP

As mentioned above, CDPs can make it easier for your technical team to build data pipelines between various tools by applying the same standard scripts from CDP vendors so that events are tracked uniformly.ย 

A few other benefits of Customer Data Platforms include

  • Event-based model: Capture data from all touch-points in the user journey in real-time
  • Standardize on events: Instead of deploying custom code for each tool, use scripts provided by CDP vendors to capture data uniformly across tools.ย 
  • Operationalize data: Push event data to destinations like Hubspot to trigger marketing automation sequences.

Where a CDP Fall Short

CDPs are a great quick fix for marketing teams who need this data ASAP. Still, it is almost a stopgap for ultimately implementing a data warehouse as your system of record for customer data (which is a better solution but a bigger lift).

Based on conversations in the PLS community, there are some limitations to using a CDP for tracking everything:

"Generally loved it, but I found there were many discrepancies between each system is connected with (which is not something they can fully control but was still frustrating as an end-user)."ย 

Where CDPs fall short:

  • Not a single source of truth for all data: CDPs are optimized for event-based data, which may limit the amount of information you can access
  • Dirty or missing data: CDP events can get missed if your users use ad-blockersย 
  • Not the data teams' source of truth: Engineering and technical teams are not using the CDP as the source of truth, which would mean marketing and GTM teams work off of a different set of data
  • Expensive at scale: Eventually, CDPs will cost you as you add more sources and destinations for dataย 
  • Rigid data model: CDPs like Segment have audience builders that are constrained by rigid data hierarchies like users and accounts

What is not a CDP

There are arguably many solutions with similar functions or have been grouped with a CDP that is not the same.

Here are just a few of the tools that could be confused for a CDP:

  • digital event distribution platform
  • data management platform
  • digital personalization engine
  • master data management platform
  • multichannel marketing hub
  • customer relationship management tool
  • product-led sales platform
  • data warehouse

Let's look at how CDPs compare to a few other tools that ingest customer data.

CDP
"Customer Data Platform"
Design for use by marketing and operations teams.
๐Ÿ‘‰ Designed to store only event-based data related to the customer
๐Ÿ‘‰ Smaller in scale
๐Ÿ‘‰ Creates a cross-channel data profile for customers
CRM
"Customer Relationship Management"
Used by sales teams and broader GTM teams.
๐Ÿ‘‰ Designed to track and hold data about customers
๐Ÿ‘‰ Used to analyze existing pipeline data and forecast future sales
๐Ÿ‘‰ Used for prospecting new customers
PLS
"Product Led Sales" Platform
Designed for sales teams to uncover new opportunities and take the next best action
๐Ÿ‘‰ Combine data from your data warehouse and CRMs & become the source of truth
๐Ÿ‘‰ Deliver insights to sales teams
๐Ÿ‘‰ Orchestrate events in third-party tools
DWH
"Data Warehouse"
Created for product and engineering teams to have an easily accessible record to analyze all customer data
๐Ÿ‘‰ Designed to store all product and sometimes company data
๐Ÿ‘‰ Larger in scale
๐Ÿ‘‰ Provides access to data so analysis can be done through third-party tools like analytics (product), BI (ops), or PLS (sales).

CDP vs CRM

While a CDP collects and pipes event-based customer data between your various apps, a CRM collects, organizes, and manages data on customer interactions and then pushes it to third-party tools. A CRM is a record of your relationship with a customer and is used by sales in their daily workflow. A CDP may have a "record" of that customer, but it is not a tool your sales team would use to look up information about a customer or make updates based on progress in the sales cycle.

CDP vs Product-Led Sales Platforms

A Product-Led Sales platform, like Pocus, is also very different from a CDP. A PLS platform combines customer usage data (from a CRM) and product data (from a CDP or data warehouse) to create a single plane of glass view of your customers. This source of truth can then be used to power your GTM insights, orchestration, and experimentation.

You might choose to use a PLS platform with your CDP. However, Pocus strongly recommends connecting directly to the source of truth, which is typically the data warehouse or application database.

Why PLS instead of CDP?

Our community captured it best - a CDP doesn't always have ALL the data. Due to a more narrow implementation for a specific use case or ad blockers that prevent event tracking, a CDP won't always have the best data. Data you can trust is critical to PLS success. If your sales team can't trust that they are getting the most accurate picture, they won't use the tool.

A PLS platform like Pocus, on the other hand, sits directly on top of your data warehouse or CRM or other systems of record to combine data, deliver insights, lead scoring, and orchestrate events in 3rd party tools via workflow automation.

CDP vs Data Warehouse

A data warehouse (DWH) is a huge storage bin where all of your business data and information is stored from multiple sources. DWHs are much like a large data pool that can be accessed for reporting or analysis.

CDPs are event-based tracking for customer data, whereas data warehouses are a complete historical picture of your customers and business. Data warehouses do not include the function of matching and merging data into categories as a CDP does. Data warehouses also don't standardize any of the data that is added to them.

Why We Think a Data Warehouse is a Better Source of Truth

CDPs are not the end all be all of data storage and information. There is nothing wrong with using a CDP to start a GTM campaign. Especially when you do not have much information to be stored or have hyper-specific intentions for that campaign. However, a few reasons why we think that DWHs are a better "source of truth" than a CDP are:

  • CDPs do not house all of your company's data, as we mentioned above. They only provide insight on data that is specific to the consumer
  • CDPs make cross-team collaboration difficult, especially for marketing and data teams. It disperses the data that your company needs and breaks it into sections for each team rather than having all of the information that can be used across channels.
  • CDPs do not give flexibility to their structure. They are built using unadaptable models that may not fit your data goals. This leads to you having to write SQLs into the model, which can be time-consuming.
  • CDPs provide very restricted access to the data within and cause issues when it comes to executing goals and analysis. DWHs gives you unlimited access to your data.

If you do use a CDP, we recommend piping it back into a data warehouse (system of record).

Data warehouses can hold all of the data your company needs for success without the drawbacks that a CDP can maintain. They keep your customer data and can be combined with PLS software like Pocus to sort it and give your teams the information they need to position your service better.ย 

Why Might You Need a CDP?

CDPs can be beneficial when it comes to marketing campaigns and their management. Because they help understand a specific customer, these programs help you complete marketing goals. As you build your PLG motion, you can leverage the CDP to track critical events in your app that map to "Aha!" moments and then use those events as triggers for a more personalized onboarding experience.

Know When to Add CDP to Your Stack and When to Invest in a Data Warehouse

When adding a CDP vs. a DWH in your PLG tech stack, you need to ask yourself what your goals are.

If your goals are to drive event-based messaging, a CDP is a great way to easily connect your product to your messaging tools to drive that automation. Perhaps your goal is to create more personalized paid ads; a CDP can help create a complete picture of your customer. If your plans are focused on enabling sales teams with product data, we recommend the combination of a data warehouse and PLS platform.

CDPs can offer you insight into customer data and help other marketing campaigns. Data warehouses can provide a specific product and customer information and, when combined with other software like a PLS platform, can complete the same functions with some added benefits.

CDP Definition & How it's Different from a Data Warehouse
Sandy Mangat
Head of Marketing at Pocus
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