Operationalizing data means using the data from repositories like data warehouses to add business value - and two popular solutions enable it — reverse ETL tools and customer data platforms (CDP).
Reverse ETL is a data integration process that pulls data from data warehouses or lakes into business applications. Meanwhile, a customer data platform (CDP) is a platform that merges customer data from various sources.
As products, Reverse ETL tools and CDPs are not mutually exclusive. So, reverse ETL vs. CDP — which one should you choose? Or is it prudent to employ both solutions?
Here’s a comprehensive customer data platform (CDP) vs. reverse ETL comparison to help you evaluate their applications, benefits, and differences.
What does reverse ETL mean?
Reverse ETL is a data pipeline that moves data from centralized data repositories to third-party tools such as CRMs, BI tools, and SaaS tools in real-time.
Reverse ETL involves:
- Extracting data from a source — a data warehouse, data lake, or data lakehouse
- Transforming it so that it can sync with the third-party tools
- Loading it directly into business tools — Salesforce, Helpdesk, Marketo, HubSpot, Adobe Analytics — for informed decision-making
To set up reverse ETL, you can build API connectors syncing warehouse data with BI tools in real-time, provided you have the required engineering strength. Otherwise, you can use reverse ETL solutions like Census or Hightouch.
What are the benefits of reverse ETL?
The core advantage of reverse ETL is operationalizing data and enabling data consistency across teams. Let’s explore each application.
Reverse ETL and data operationalization
Data operationalization means integrating analytics into business workflows. Here’s an example.
If your core offering is a product, then your users don't take insightful actions on your website. This takes place inside the product.
The product usage data is in your warehouse, whereas your marketing team runs its campaigns using its business stack — CRMs, marketing and email automation, and more.
Operationalizing the product usage data to run personalized campaigns requires syncing it with your business systems, and that’s where you need a reverse ETL pipeline.
Reverse ETL and data consistency
According to Gartner, data consistency is the foundation of many business use cases. However, maintaining distributed data repositories can lead to inconsistencies in data.
Here’s how reverse ETL can help, according to Astasia Myers, who wrote the Reverse ETL primer:
“Sending data in real time to SaaS systems can be helpful for making sure there is a consistent view of the customer across all systems. Data consistency helps create a continuity across the business since functional teams — sales, GTM, analytics — are working off the same data, even if using different SaaS products.”
Besides operationalizing data and enabling consistency, the other main benefits of reverse ETL include:
Enabling access to actionable data: Customer data is usually spread across multiple systems, and gaining a 360-degree view of the customer requires building integrations for each source.
With reverse ETL, data teams can automate access to analysis-ready data by syncing it to operational tools in real-time.
For example, your data team could build algorithms to segment customer data with key identifiers regarding their interests, pain points, needs, and more. Reverse ETL can sync the segmented data with business systems in real-time so that business teams can access it whenever they need it.
Building personalized customer experiences: Timely access to a customer's interactions is crucial for business teams to improve customer experiences. Reverse ETL provides business teams access to the latest customer data sourced from the various touchpoints and made analytics-ready in warehouses.
Retool, a no-code app development platform uses reverse ETL to sync product usage data with its CRM to send personalized emails, leading to a 32% increase in the email response rate.
Boosting the productivity of business teams: Business teams are more agile and productive when they depend less on engineering or IT to access the right data. Reverse ETL allows them to benefit from real-time data using platforms they already use in their routine workflows.
For example, design workspace company Zeplin uses reverse ETL to identify Product Qualified Leads (PQLs), boosting sales productivity and increasing lead conversions.
Now let’s look at a CDP (Customer Data Platform).
What is a CDP (Customer Data Platform)?
CDP (Customer Data Platform) consolidates customer information from multiple sources — customer touchpoints. Here’s how Gartner defines the concept:
“A customer data platform is a marketing technology that unifies a company’s customer data from marketing and other channels to enable customer modeling and to optimize the timing and targeting of messages and offers.”
The main goal is to help organizations streamline the real-time data they track using various platforms and build unified customer profiles for better marketing. This involves scrubbing and cleaning data to drive action — marketing campaigns, advertisements, and other programs to engage customers with personalized experiences.
- Provide a 360-degree view of the customer
- Gather data from multiple sources into one platform — first-party, second-party, and third-party data from online and offline sources
- Unify customer profiles across systems
- Connect with other systems (marketing automation, warehouses, CRMs) to allow marketers to execute campaigns
- Improve targeting for marketing campaigns
What data goes into a CDP?
As customers interact with multiple businesses, they leave behind a trail of their interaction details — online (websites or blogs) and offline (in-store). These details are called customer data, and CDPs help organizations merge all that data in one place.
Customer data platforms (CDPs) usually deal with four different kinds of customer data:
- Identity data: Personally identifiable information such as name, address, contact information, and email.
- Descriptive data: Information such as family background, career, hobbies, and more that provides more context about the customer.
- Behavioral data: Data such as type and number of product purchases by a customer or website visits, providing details about customer interactions with your touchpoints.
- Qualitative data: CDPs collect qualitative data in the form of customer opinions about a product, motivations behind purchases, referrals, and testimonials. They also record offline interactions from stores or events.
What are the benefits of a CDP?
If you want to understand your customer’s behavior, needs and sentiment fully, you must have a single platform that paints a complete picture of the customer. That’s where a CDP helps.
Some of the top benefits of CDPs include:
- Unify customer data across channels: CDPs collect customer data from diverse online and offline sources in a single platform.
- Generate a 360-degree view of the customer: CDP collects data about every aspect of a customer's interactions across all touchpoints. This helps create targeted marketing strategies and solutions to get more conversions.
- Democratize customer-centric information: CDPs place customers at the center of an organization to allow standardized access to all business and customer-facing teams.
Here’s how Danny Newman, a contributor at Forbes, highlights the benefits of a CDP to other teams like finance or operations:
“For finance teams, real-time customer data can help make smart, quick decisions about products, services, and other company investments. For operations teams, it may help better understand purchasing trends leading to more proactive supply chain management or procurement methods. And obviously, better data means better decisions for the C-suite overall.”
A CDP might sound eerily similar to a CRM or a data warehouse. So let's explore those differences before we proceed.
How is a CDP different from a CRM?
Both customer relationship management (CRM) tools and customer data platforms (CDPs) collect and manage customer data. The differences are rooted in the kinds of data they collect.
CDP collects data on all customer interactions, right from the first touchpoint. This includes information on anonymous visitors. It can hold large volumes of data gathered from all sources — online and offline.
Meanwhile, a CRM is used to maintain data on known customers or leads. Also, it cannot pick up offline data automatically.
Danny Newman highlights the difference by saying:
“A CRM is designed to help your company engage the customer. A customer data platform is like a CRM on steroids.”
How is a CDP different from a data warehouse?
A CDP and a data warehouse are both data repositories that collect and store data from multiple sources. The main difference is “the how”.
CDPs consolidate data around an identity — the customer profile or an account. Warehouses collect and store data to answer queries informing business decisions.
Other differences include:
- CDPs collect and manage only customer data. Data warehouses deal with large data sets as they store all business data.
- CDPs provide a live view of customer data. In comparison, data warehouses load data in batches and don’t enable real-time insights. It’s more like a snapshot of data at a certain point in time.
- CDPs don’t require specific technical skills and can be operated by non-technical users. Data warehouses require engineers to perform complex transformations to merge data from multiple tables.
Now let’s understand the differences between reverse ETL vs. CDP.
Reverse ETL vs. CDP: Exploring the differences
As we mentioned earlier, reverse ETL is the process of syncing warehouse data with business applications in real-time. Meanwhile, CDPs are tools that help you build a unified view of a customer or an account by consolidating all customer-centric data from various sources.
Here’s a table summarizing the key differences in the reverse ETL vs. CDP debate:
|Definition||A data integration process that transfers data from a data warehouse to third party operational tools such as CRMs, CDPs, and more.||A third-party platform for collecting customer data from multiple sources to provide organizations with a 360-degree view of customer data.|
|What is it?||A data integration pipeline||A dashboard|
|Purpose||Operationalizing analytics for the business teams and enabling data consistency across all teams.||Building customer profiles to primarily optimize sales and marketing.|
|Ownership||Data engineering team is responsible for the deployment and management of reverse ETL pipelines.||Marketing professionals own CDPs.|
|Use cases||Reverse ETL is ideal for analytics-mature organizations with a modern data stack and numerous applications of advanced analytics.||CDPs are ideal for organizations looking to improve their digital marketing and advertising efforts by monitoring customer interactions.|
Customer data platform (CDP) vs. reverse ETL: Which one do you need?
Reverse ETL tools and CDPs offer different functionalities catering to particular use cases. You can use both tools as reverse ETL focuses on all business data, while the CDP caters only to customer data.
However, you also don’t have to use both solutions. The right answer lies in understanding your business needs, use cases, tech stack, and engineering resources.
For instance, setting up reverse ETL pipelines requires significant engineering know-how. This can be solved by investing in reverse ETL tools rather than building them in-house.
Next, not every organization needs reverse ETL. You must already have a modern data stack and several analytics use cases. If you do, you can use reverse ETL tools to sync warehouse or lake data with all business applications, including the CDP.
Once that data is available, a CDP can help your marketing and sales teams build unique customer profiles and run targeted campaigns.
So, rather than thinking reverse ETL vs. CDP, you can look for a scenario where you reap the benefits of making them work together to solve business problems and build personalized experiences across multiple channels.
Reverse ETL vs. CDP: Related reads
- What is reverse ETL and how does it enhance the modern data stack?
- Data transformation: Definition, processes, and use cases
- The future of the modern data stack in 2022
- The great data debate: Unbundling or bundling?