Your data warehouse is indispensable for business intelligence and analytics. It’s also stuffed with information that could supercharge your marketing, sales, customer service, and even finance teams. But data warehouses are built for analytics, not operations, and they’re optimized for processing power, not speed. So valuable data is often out-of-date by the time operational teams get it — if they can get it at all.
Reverse ETL solves that problem. It extracts useful data from the warehouse, in nearly real time, and loads it into the tools operations teams already use to do their work, from marketing automation programs (MAPs) to accounting software. Read on to explore examples and learn how reverse ETL tools could benefit your brand.
What we’ll cover:
To understand how reverse ETL tools work, you need to know how the data gets into the warehouse in the first place.
The warehouse contains data from CRMs, cloud and on-prem data storage, sales applications, analytics tools, and more. Any tool in your martech stack that collects data could send it to your warehouse. Since every source formats data differently, it must all be transformed (cleaned, standardized, deduplicated, etc.) before it can be used. The two models for that process are ETL (extract, transform, load) and ELT (extract, load, transform).
Reverse ETL (extract, transform, load), in contrast, extracts data from the warehouse, transforms it for the target operational tool, and loads it into that platform — essentially reversing the ETL/ELT process. You get the data you need inside the tools you already use daily.
Here’s a visual representation of how reverse ETL sits within an ecosystem to transform and activate data for use by your teams.
Customer data platforms copy customer data from sources like CRMs, MAPs, and web analytics to build comprehensive customer profiles. Then they deliver that profile to marketing, sales, and customer service tools — and many use reverse ETL for that process. In fact, reverse ETL is a feature of most major customer data platforms on the market.
Customer data platforms enable you to build precise segments for marketing campaigns, to customize automated messaging, and to personalize customer service. Customer profiles can even help you craft better customer personas, enhancing offer and product development.
Reverse ETL and customer data platforms can’t really be compared; the former is a process, and the latter is a product that may use that process. If you aim to leverage the advantages of reverse ETL, you might want to evaluate customer data platforms based on whether they come with this functionality.
Reverse ETL works by extracting (copying) data from your data warehouse, transforming it into the format your business applications need, and then loading it into those tools. There’s no need to learn how to use a new platform; the data simply arrives and teams can take action on it immediately. And while there are many stand-alone reverse ETL tools you could incorporate into your tech stack, other tools you already use may include this as a feature.
The mechanics of how reverse ETL connections are created varies. Many reverse ETL tools offer low-code or no-code options, making it easy to get the data you need without engineering resources. And, of course, you could choose to build your own reverse ETL solution.
Whether you build or buy a reverse ETL tool, the promise is simple: the data you need, where you need it, and when you need it. Here are the main benefits it offers.
Because reverse ETL tools deliver the data you need into the tools you already use, the possible use cases are virtually unlimited. You can leverage your unique insights about your customers and your product or services to optimize every part of your business. Here are three common reverse ETL use cases to consider.
Let’s say you’re a fintech brand with a freemium business model, and you’re trying to boost conversions to paid plans. You could use reverse ETL to bring product usage data from the warehouse into your CRM to identify the subscribers who have logged in at least three times a week for 30 days, and then send them personalized offers that tempt them to convert to paid plans. You could even flag customers who haven’t used the app in the last two weeks and reengage them to prevent churn.
Imagine you’re ready to begin the beta rollout of a new premium feature: an advanced robo-advisor for personal financial planning. Marketing needs to create a segment and a campaign that targets customers most likely to benefit from the feature: people who have automated two or more savings goals and log in twice or more every week. Login events come to the MAP directly, but data about savings goals is stored in the data warehouse. Reverse ETL can bridge the gap, providing all the data needed for campaigns in real time.
The same fintech app could use product usage and subscription level data to enhance customer success. For instance, help requests from high-value customers (like premium subscribers) and people who need extra nurturing to be retained (like those who upgraded to a paid plan within the last week) could be automatically prioritized. And support tickets from premium subscribers whose product usage has recently dropped significantly (indicating a high-value customer at risk of churn) could go to the top of the list to prevent cancellations.
While reverse ETL has incredible benefits and wide utility across your business functions, like any technology, it requires thoughtful implementation — whether you’re going to build or buy reverse ETL. If purchasing a tool makes sense for you, it’s important to perform some due diligence. Here are a few key considerations when evaluating reverse ETL tools:
Ultimately, reverse ETL breaks down silos to deliver the right data, in the right tool, at the right time. It can take you from using data to truly operationalizing it. And it doesn’t just supercharge marketing, sales, and customer support; you can use it to increase the effectiveness of all kinds of business operations. Finance teams can get precise info about payment and customer activity to manage accounts. Product teams can learn what features people are using and where friction points show up. Data teams can deliver more strategic intel to inform business decisions.
Bottom line: if you’re a data-driven business (or aim to become one!), reverse ETL could be the key to activating the insights locked in your warehouse.