What is reverse ETL and how it activates your data warehouse?
The rise of data platforms
Individuals and businesses generate 2.5 quintillion bytes of data every day. Top software, information technology, and financial businesses rely on data warehouses to store, manage, and act on this vast quantity of data. Such data can be incredibly useful when integrated into business applications.
However, the problem with using data warehouses is that it can be hard to extract the data you need to make smart business decisions. The data warehouses that businesses invest in to avoid siloing data, unfortunately, become silos themselves.
That’s where reverse ETL comes in.
Below, you’ll learn more about reverse ETL and some use cases for this technology. Then, we’ll show you how you can use a customer data platform (CDP) like Lytics Decision Engine in conjunction with your data warehouse and reverse ETL.
What is reverse ETL?
To define reverse ETL, we should first clarify what ETL means.
ETL is a type of data integration process that involves copying data from multiple sources into a single destination system, such as a data warehouse. It stands for extract, transform, and load, which are the three steps this process consists of:
- Extract: Grabbing the data from its various sources
- Transform: Cleaning the data and changing it into the appropriate storage format and structure
- Load: Inserting the data into the new database
For example, you might have a CRM, file-sharing software, and an accounting program. ETL would involve extracting the various types of data from each of these sources and transferring it to a cloud data warehouse such as Google BigQuery or Snowflake.
Reverse ETL is (you got it!) the reverse. It involves querying data from your database and sending it to business intelligence and marketing tools to make your data actionable.
Thanks to reverse ETL, you can complete your data stack. You’ll have a warehouse to store data, a reverse ETL to query and retrieve data, and a customer data platform as a real-time personalization smart hub to model and activate data in various business platforms.
Why move data out of your data warehouse with reverse ETL?
A data warehouse can be a great tool to securely store the large amount of data your business has.
However, first-party data isn’t very useful if you can’t easily extract it and send it to your email, CRM, and ad tools to optimize your target marketing.
Reverse ETL can do just that by feeding the right data directly into the tools your marketing teams use every day to do their work. In doing so, you’ll save time by avoiding duplicating your data and moving it from your warehouse where it is secure.
We’ll dig into some examples of that in the next section.
Reverse ETL use cases
Now that we’ve gone over what reverse ETL is and how it works, you might be wondering what kinds of things you can do with it.
Here are some of the top use cases for reverse ETL.
One of the most prominent use cases for reverse ETL is operational analytics. Operational analytics is essentially taking the information that various teams need and integrating it naturally into their workflows.
In doing so, they can save time and make better decisions with real-time data from their customer data platform.
This makes things easier for team members because you no longer have to teach them how to read and understand business intelligence reports. Instead, the data is presented to them in a natural and intuitive manner alongside the rest of their digital workspace.
Few organizations are strangers to smaller, miscellaneous requests for data. In many cases, submitting and fulfilling these requests for data involves a lot of manual work, and you often end up with plenty of random CSV files.
Here are just a few examples of data that various teams in an organization might request:
- Sales teams asking for lead magnet download information so they can add these leads to the CRM
- Accounting teams wanting to add customer attributes to your accounting software
- Marketing asking for customers’ historic purchase data to personalize marketing messages
. . . and many more.
Naturally, organizations want to automate as much of this work as possible. That way, they can refocus on the things that move the needle.
That’s what reverse ETL can help with.
Infrastructure: Customer personalization
Personalization in marketing is everything these days. Customers don’t appreciate generic marketing messages—they want the entire experience tailored to who they are.
Following from above, reverse ETL offers a great way to enhance customer personalization.
Here’s an example using Lytics Cloud Connect. Let’s say an ecommerce site wants to target customers who made a bicycle purchase last year during a Black Friday sale. The marketing team would like to create an email campaign that targets the same customers. The historic purchase data is stored in the data warehouse.
To email an exclusive offer to this user group, the marketing team needs to create a segment using the data stored in the data warehouse. Data engineers can use reverse ETL to pull that information into Lytics Decision Engine, where a Cloud Audience is created and enhanced with behavioral data such as users’ engagement scores and interests.
Infrastructure: Accessing disparate data sources for one party
A deep level of personalization combined with the ever-growing importance of digital commerce means that there are more types of customer data stored digitally than ever before. In some cases, you might have to pull several kinds of data from multiple areas at once and deliver it somewhere else.
Reverse ETL can help with that.
For example, if you have a web or mobile application, you can use reverse ETL to deliver data customers might want to see—such as the amount of credit they have with your company—seamlessly. It could pull in other information too, such as past purchase history, or, if you’re a SaaS company, the customer’s chosen subscription tier.
Customer data platform vs. reverse ETL for marketers
A customer data platform like Lytics Decision Engine sits on top of your data warehouse to activate your customer data to your ad, email, and CRM tools. Before covering how, let’s quickly review what a CDP is.
What is a CDP?
A CDP is a software solution that streamlines your marketing by unifying data across disparate online and offline sources. Using machine learning and artificial intelligence, it interprets this data to create an accurate customer profile.
Marketers generally manage the CDP since it requires less technical skill than a data warehouse. As a result, they also benefit from it most directly, although CDPs can help other teams across an organization.
CDPs are similar to data warehouses in that they ingest data from various sources.
Unlike a data warehouse, however, CDPs are their own systems of action. This means they aren’t just a passive location to store customer records—you can use them to carry out tasks or to set up automations to perform those tasks.
Additionally, CDPs feed data into a model that is based on a customer identity, rather than simply storing the data for future use.
Customer data platforms and reverse ETL: Working together
A data warehouse such as Google BigQuery or Snowflake serves as your single source of truth for your customer data. Having a reverse ETL solution makes that data actionable to downstream marketing and business intelligence tools.
Data engineers can support their marketing teams by giving them segments from the data warehouse to the platforms used to achieve business goals. For example, you can create granular, sequel-based segments in BigQuery then sync those segments to an ad tool like TradeDesk.
Lytics’ approach to reverse ETL is having Cloud Connect sit on top of your data warehouse to query data then use the data in Decision Engine to create an AI-enriched audience. Cloud Connect makes it easy for marketing and data teams to get real business value from their data warehouse. Here are just a few of the marketing use cases that Cloud Connect can execute.
Cloud Connect use cases
- Account-based marketing. Financial services businesses can create more targeted campaigns for users associated with accounts at certain stages, and revenue levels.
- Time window. Target users where their activity includes a specific time window. For example, you can send a campaign to all users who did not log in last month or users who made a purchase during December last year.
- Joins. Gain more context on users, purchases, and events. Find all users associated with accounts who have not used a specific feature or you can join receipt records with the product details.
- Lifetime value (LTV) or rollup. Calculate the SUM of orders for the previous year and target customers in a campaign. You can also segment users who have a premium subscription and have purchased at least two products.
Once your real-time audience is created in Decision Engine, you can export it, sending the audience to 80+ apps and integrations.
Unlock the value inside your data warehouse and make your customer data actionable. Learn more about our newest product, Cloud Connect.