Reverse ETL


Brands that truly value their customers dream of a seamless data stack. Until recently, achieving this dream was a major challenge. However, the advent of cloud data warehouses has revolutionized how organizations handle their data by providing a centralized platform for efficiently storing and analyzing data.

Yet, as businesses aim to unlock the full potential of their data across teams, a new demand has emerged: the ability to seamlessly integrate data back into operational processes. This growing need gave rise to Reverse ETL, which acts as a bridge between data warehousing and real-time operational agility.

You might have come across the terms ETL and Reverse ETL in the context of data centralization. While both perform important functions in powering your martech stack, not all Reverse ETL solutions are the same.

Let’s delve into the fundamentals of these processes and explore their roles within the modern tech stack of today’s data-driven marketing world.

What is Reverse ETL?

Reverse ETL is a data-integration tool that helps you extract customer data from your data warehouse, transform it within the warehouse, and copy it into whatever SaaS program you want to use. Reverse ETL is the opposite of traditional ETL, wherein the data warehouse is the target of the data that’s being extracted from the third-party source. 

The information can be repurposed and used for marketing and sales, whether your company is B2B or B2C. Whatever core metrics you have stored, you can sync them back to your business processes. Common types of data used in reverse ETL include:

  • Churn rate
  • Recurring revenue
  • Daily active users
  • Customer acquisition costs

What is ETL?

ETL (or Extract, Transform, Load) is the process of taking data from multiple sources, transforming the format to gel alongside data formats from other tools, and streamlining it into a single data warehouse or other type of cloud data storage. The data warehouse stores all of your data in one place, which then requires Reverse ETL to extract the data so that marketing, sales, and other teams can use it.

Why use Reverse ETL? 

Typically, data warehouses cannot load data directly into a third-party source because the data needs to first be transformed to meet the formatting requirements of that source. But once it’s collected into a single warehouse, it can be difficult to conform it into usable information. That’s where reverse ETL comes in. It allows you to activate that data at scale without manually creating CSV files.

Why would you do a Reverse ETL rather than a traditional ETL?

More companies are starting to use Reverse ETL tools as marketing operations are starting to revolve more and more around the data warehouse – instead of the data warehouse being on the periphery of the process. With the data warehouse acting as the gravitational center of your marketing strategy, it makes sense for it to be the source of the data and for other SaaS tools to connect directly to it as a means of receiving the customer data.

What is the impact of Reverse ETL?

Reverse ETL operationalizes data, allowing teams across an organization to access the data they need within whatever system they use. With the decline of third-party data, Reverse ETL is a great tactic for getting first-party data into ad channels. When you combine a Customer Data Infrastructure (CDI) with a Reverse ETL, the foundational elements are there for a composable CDP.

Reverse ETL vs. CDPs

Customer data platforms (CDPs) are third-party data storage services that serve as both a mini data warehouse and data activation solution. These platforms are also created to give you identity resolution, audience management, and built-in data activation across your other tools.

 At first, it can simplify the process of activating stored data, but there are drawbacks to using a CDP:

  1. Data Privacy – CDPs store your data outside your firewall, causing concerns around GDPR and CCPA, especially when working with PII.
  2. Inflated Costs – pricing models on CDPs can get really expensive, especially at the enterprise level. Storing and activating data points across a large record of customers can become cost-prohibitive.
  3. Stale Data – Transferring data from your data warehouse like Snowflake to a CDP can take hours to ingest all the data. Having your CDP deploy this data to a SaaS solution like your ESP can take even longer. This makes the agility out of your marketing and creates a serious data lag. Flash sales and other timed events become a nightmare. Say goodbye to real-time data when using a CDP.
  4. Slow To Transition – Switching to a CDP can take months, if not a full calendar year, to phase in the new tech and get your marketing team versed in the new system. A warehouse-native solution like MessageGears cuts this time down dramatically.

Perhaps the biggest downside, you no longer own the data once you deploy a CDP. While you may still have copies in your company’s data warehouse, the CDP simultaneously hosts the data on its own server. This can cause major concerns, particularly if your data is subject to HIPAA or other privacy regulations. Other disadvantages include the inability to completely customize your data. And on top of that, the cost of using a CDP is extremely high.

How does MessageGears solve for Reverse ETL and CDP needs?

MessageGears is a cross-channel marketing platform that connects directly to your data warehouse without needing to copy, sync, or map your data like traditional SaaS tools. With powerful Reverse ETL functionality, our audience segmentation tool is purpose-built so that non-technical marketers can easily activate customer data on any channel live and in real time. This includes channels within the MessageGears platform – like email, SMS, mobile push, and in-app messaging – as well as third-party channels, like your social or Google ad campaigns, for example. You can create dynamic audiences that allow your team to send highly personalized cross-channel messaging campaigns based on anything and everything you know about your customers. See how we fit in the modern data stack.

Looking to talk with an ETL expert? Reach out, and we can help you find the right solution.