What is reverse ETL? A complete guide

When surveying executives and IT leaders across 12 countries, Seagate and IDC came across a surprising statistic: most enterprises were only using one-third of their data. 

The rest was often locked inside a data lake or warehouse, with non-technical teams having limited access and visibility. In a world obsessed with being data-driven and having cutting-edge insights, it seems egregious to have two-thirds of your data remain virtually inaccessible. After all, data can be the pathway to boosting revenue, winning back customers, and streamlining operations. You just have to know how to use it. 

To take advantage of the data organizations have collected and stored, they need to make it available in the tools and systems their teams use everyday  – which is where Reverse ETL comes in. 

What is Reverse ETL?

Reverse ETL is the process of sending data that’s stored in a central repository like a data warehouse to downstream tools and business applications – like a CRM, marketing automation software, or analytics dashboard – for activation. 

Reverse ETL helps ensure data is synchronized across all the tools and applications a business uses in its day to day – or in other words, making sure data remains consistent and up to date wherever it’s stored. 

What is the difference between ETL and Reverse ETL? 

You may recognize the acronym ETL, which stands for “Extract, Transform, and Load.” ETL is the process of collecting data from various sources, cleaning and structuring it to match its target destination (i.e., transformation), and then loading it into a repository like a data warehouse. (There’s also the option to load data into a repository like a data lake before transformation takes place, in a process called “ELT.”) 

A simple way to think of the difference between ETL and Reverse ETL is that they represent two sides of the street, with traffic moving in different directions. ETL is focused on moving data into the warehouse for consolidation and enrichment. Reverse ETL is concerned with taking that cleaned, enriched data out of the warehouse and moving it into downstream tools for team-wide use. 

Understanding the mechanics of Reverse ETL

Now that we’ve given an overview of Reverse ETL, let’s get into the mechanics of how it works.

1. Step One: Extraction. This involves querying your data warehouse (e.g., via SQL) to extract the specific data that you need.  

2. Step Two: Transformation. The data that you extract will be in a specific format (data warehouses typically store structured data). So, you may need to transform data so that it matches its target destination. This is where data mapping is pivotal, to trace the movement of data between storage systems and tools into specific fields. 

3. Step Three: Loading. This is when data is loaded into its target destinations. This can be done via an API integration, manual upload, batch processing, or in real time.  

4. Step Four: Activation. Once data is loaded into downstream tools and applications, it can be leveraged by internal teams and even trigger specific actions (e.g., launching a personalized customer interaction based on their recent online and offline behavior). 

5. Step 5: Ongoing Monitoring. As with any process, it’s important to continuously check for quality. Many Reverse ETL tools are able to automatically flag failed syncs or errors to help investigate issues. 

When we talk about Reverse ETL, we tend to focus on four components: 

1. Source: This is where data originated from, which could be a website, cloud application, mobile SDK, etc. 

2. Models: This refers to SQL queries that define and specify which data sets you want to synchronize with downstream tools. 

3. Destinations: These are the tools and applications you want to deliver data to from the data warehouse. 

4. Mapping: This is when you map data from your warehouse to specific fields in your target destinations. 

5 challenges to implementing Reverse ETL (and how to overcome them)

Before going ahead with Reverse ETL there are a few things to consider, from team bandwidth to data volume and then the pricing structure of tools. 

1. Data volume 

The World Economic Forum estimates that by 2025, 463 exabytes of data will be generated daily across the globe. As data volume continues to increase at an exponential rate, businesses need to consider the amount of data they’ll need to extract from their warehouse to send to downstream tools, and how regularly they’ll need to do this to ensure synchronicity. On top of that, pricing for Reverse ETL tools can be tied to data volume, so it’s important to consider this as well. 

2. Data integration complexity

Implementing Reverse ETL can be a complex process, for a multitude of reasons. When trying to determine just how complex this will be, consider the following: 

• Your different data sources, how data is formatted, and how you’ll make this data compatible with your Reverse ETL tool and its target destinations. 

• How you’ll handle and resolve data inconsistencies to ensure quality at scale. 

• If the Reverse ETL has pre-built integrations with the tools in your tech stack (to help streamline setup). 

3. Privacy & Security

Any time you move potentially sensitive data, whether within an ETL system or Reverse ETL, you introduce security risks. The most apparent risk is data exposure. Data in transit and at rest must be encrypted to protect against unauthorized access, interception, and potential breaches. This encryption should be implemented end-to-end, ensuring that data remains secure throughout its entire journey from the source system to the data warehouse and downstream applications in Reverse ETL.

Data masking might be required for compliance as well as encryption. Depending on your industry and location, you may need to adhere to regulations like GDPR, CCPA, or HIPAA, and thus protecting sensitive information like personally identifiable information (PII) may be a legal requirement.

Data governance, ensuring that only the right people have access to specific data sets, becomes more complex as data is distributed. Role-based access controls (RBAC) to manage who can access what data and audit trails to track data movement and access can help manage these risks.

4. Latency

Often, Reverse ETL data needs to be processed in real-time or near real-time. Slow data updates to the business tools receiving the data can hinder workflows that depend on current information. If data isn't updated quickly enough, it could lead to outdated insights and poor decisions or result in inconsistent or irrelevant customer interactions.

To address latency concerns, you might need to consider implementing change data capture (CDC) to only sync updated data, reducing overall sync times, or optimizing your data models and queries to improve extraction speed.

5. Scalability

As your business grows, so does your data. Ensuring your Reverse ETL process can scale alongside your organization is crucial:

• Data volume growth: As you collect more data, your Reverse ETL solution must efficiently handle larger datasets.

• Increased sync frequency: More data often means more frequent syncs are necessary to keep systems up-to-date.

• Expanding tool ecosystem: As you add more tools and applications, your Reverse ETL process must accommodate new destinations.

To build a scalable Reverse ETL process, choose a Reverse ETL tool that can handle large data volumes and high-frequency syncs. Cloud-based solutions are ideal here as they automatically scale resources based on demand. But scalability goes beyond just the tools. You also have to think critically about proper data modeling and partitioning strategies in your warehouse to optimize for scale. You should regularly review and optimize your data sync processes to ensure efficiency as you scale.

The benefits of Reverse ETL

Reverse ETL allows businesses to access and act upon enriched data for better decision-making, customer personalization, and cross-functional collaboration.

Operational efficiency 

“Operationalization” doesn’t just mean using something. In scientific research, it means making an abstract concept concretely measurable.

In the same way, data that sits in a warehouse has a vague potential to contribute value to your business. But when you use it in business apps or tools, you turn it into a central component of marketing campaigns, product development, or business planning.

All departments in a company can operationalize data. For example, finance can create a custom payment plan for B2B customers and send automated follow-up emails using an invoice and accounting software. Or customer support can automatically prioritize incoming requests or tickets based on someone’s payment tier (e.g., premium users) or lifetime value. 

Unified view of data 

Reverse ETL prevents data from being locked inside its repository. It helps empower internal teams with holistic data sets, rather than limiting them to the data that they can access in their owned tools (e.g., product teams are given a list of high-value customers and give them beta access to a new feature in their SaaS app). 

As a result, Reverse ETL helps break down data silos and make enriched data sets more accessible across an organization (rather than relying solely on engineers or analysts to manually pull data). 

One application of Reverse ETL is the creation of profiles, or a single, comprehensive view of each customer. By joining Segment's profile data with existing object data from your warehouse, you can create a unified customer view that combines information from various sources. This allows for personalized experiences across all channels, such as matching sales executives with specific customers.

Reverse ETL also enables a deeper understanding of the customer journey. Connecting anonymous IDs, User IDs, and emails allows you to trace a customer's interactions with your business over time, even before they become identified users. This historical, comprehensive view allows you to answer critical questions about customer behavior, such as how often a user has made purchases, whether they've subscribed to services, or what products they've been viewing while browsing anonymously. This enriched data set becomes a powerful tool for building sophisticated attribution models, predicting customer lifetime value, and identifying potential churn risks, ultimately driving more informed business decisions across all departments.

Marketing and sales enablement 

Reverse ETL provides marketing and sales teams with enriched, up-to-date data directly in their preferred tools. This enables more targeted campaigns, personalized outreach, and data-driven decision-making. Key applications include:

• Syncing your Snowflake customer table to Salesforce: Easily import business and reseller customers into your data warehouse, ensuring your sales team has the most current and comprehensive customer information for effective outreach and relationship management.

• Linking offline conversions to online ads for more ROAS visibility: Use Reverse ETL to automatically attribute offline conversions and revenue to the original Google Ads that initiated the customer journey, providing a more accurate picture of your return on ad spend (ROAS) and eliminating the need for manual data uploads.

• Asking questions such as what is the best predictor of customer churn? Leverage the unified customer data provided by Reverse ETL to analyze patterns and behaviors that precede customer churn, enabling proactive retention strategies and personalized interventions.

• Discovering the traits and behavioral patterns customers with the highest lifetime value have in common: Utilize the comprehensive customer profiles created through Reverse ETL to identify key characteristics and behaviors of your most valuable customers, informing targeted acquisition and retention strategies.

Product development

Reverse ETL helps product development teams by providing comprehensive, real-time data about user behavior, preferences, and interactions. This wealth of information enables product managers and developers to make data-driven decisions, iterate quickly, and create more user-centric products. They can then answer fundamental product questions, such as:

• How does the user onboarding experience affect customer loyalty? By syncing user onboarding data with long-term engagement metrics, product teams can analyze the correlation between initial experiences and customer retention, allowing for targeted improvements in the onboarding process to boost loyalty.

• What does the customer journey of a certain audience segment look like? Reverse ETL allows product teams to map out detailed customer journeys for specific segments, providing insights into touchpoints, pain points, and opportunities for optimization, which can inform feature prioritization and user experience enhancements.

• Does our product recommendation algorithm lead to larger basket sizes? By connecting recommendation data with purchase information, product teams can evaluate the effectiveness of their recommendation algorithms in driving more significant transactions, enabling continuous refinement of these systems to maximize customer value and revenue.

4 real-life examples of Reverse ETL

Reverse ETL has proven to be a game-changer for businesses across various industries, enabling them to leverage their data more effectively and drive tangible results. Here's how four companies have successfully implemented Reverse ETL to transform their operations.

Crossfit

By implementing Reverse ETL with Segment, CrossFit consolidated data from its three distinct business lines (Gym Affiliates, Education, and Sport), creating a unified view of its customers. This enabled them to build more targeted marketing campaigns, resulting in a 24% increase in CrossFit Open registration click rates and saving 10-15 hours per campaign through automation.

Read more: CrossFit Customer Case Study

Sanofi 

The global healthcare leader used Reverse ETL to create "golden profiles" of healthcare professionals (HCPs) by combining online and offline data sources. This allowed Sanofi to deliver personalized, omnichannel communications to HCPs, significantly improving their ability to educate doctors about new medications and treatments, ultimately leading to better patient outcomes.

Read more: How global healthcare leader Sanofi uses Twilio Segment to build golden profiles, increase omni-channel engagement, and improve patient outcomes

MongoDB

The database company leveraged Reverse ETL to provide developers with timely product information, increasing engagement and revenue. By creating comprehensive customer profiles and activating them across various channels, MongoDB achieved a 100x increase in registration rates for specific events and significantly improved their return on ad spend (ROAS).

Read more: How MongoDB gave developers timely product information and increased revenue

DAZN

The sports streaming platform utilized Reverse ETL to enhance fan engagement during live events. By syncing real-time data from their warehouse to downstream tools, DAZN was able to send personalized notifications and implement features like 'group watch'. This data-driven approach resulted in an impressive 18% interaction rate in one of their retention and cross-sell campaigns.

Read more: How DAZN achieved an 18% interaction rate in one of their retention and cross-sell campaigns using Twilio Segment

These examples demonstrate how Reverse ETL can be a powerful tool for businesses to unlock the full potential of their data, enabling more personalized customer experiences, improved operational efficiency, and ultimately, driving growth and revenue.

Reverse ETL with Twilio Segment

With Twilio Segment, businesses are able to harness Reverse ETL alongside all the capabilities of a complete customer data platform like real-time identity resolution, profile portability, and automated data governance. 

As a result, businesses can enrich their identity-resolved customer profiles in the data warehouse before syncing them with downstream tools for activation. 

A key benefit of this is that Segment provides every type of a data pipeline in one platform (e.g., event streaming, ETL, Reverse ETL) – meaning businesses don’t need to look to multiple different vendors to orchestrate and manage their data.