This is an opinion article by Tejas Manohar, CEO at Hightouch.
With innovations in warehousing technology (like Snowflake) and the rise of best-in-class analytics tools that form the ‘modern data ecosystem’ (like dbt), the data warehouse has fast become the single source of truth for many organizations, capable of keeping internal teams informed and powering sophisticated data use cases.
Solutions like Snowplow enable companies to capture and manage their behavioral data, delivering rich, granular data into the data warehouse at scale. But the next question is: what do you do with that data in your warehouse? Behavioral data sitting in the data warehouse is a missed opportunity. It’s worth considering how best to surface this data and ensure it gets to the tools and teams who need it most.
That’s where Reverse ETL comes in.
What is reverse ETL?
Reverse ETL is necessary because your data warehouse — the center of your data stack that serves your organization — has ironically become a data silo in itself. Without reverse ETL, your business’s core definitions only live in the warehouse.
You are already building key definitions in SQL on top of your data warehouse. Your key definitions such as lifetime value, Product Qualified Leads (PQLs) and Marketing Qualified Leads (MQLs), propensity score, customer health, Annual/Monthly Recurring Revenue (ARR/MRR), funnel stages, and more. Sure, you can easily create reports and visualizations using this data in BI tools or SQL, but these insights are much more powerful if they drive the everyday operations of your business teams across sales, marketing, finance, and more in the tools they live in.
Use cases of Reverse ETL
Now that we’ve established reverse ETL as the process of copying data from the data warehouse into operational systems, let’s consider what can you accomplish with Reverse ETL.
There are 3 primary use cases for Reverse ETL:
- Operational Analytics — feeding insights from analytics to business teams in their usual workflows and tools so they can make data-informed decisions.
- Data Automation — Automating ad-hoc data requests from other teams. For example, when the finance team requests product usage data for invoicing.
- In-App Personalization— with a growing number of data sources, reverse ETL connects those sources to personalize customer experiences.
Reverse ETL enables Operational Analytics
Reverse ETL solutions free behavioral data from the data warehouse for business teams to use it in various tools like:
- CRM systems such as Salesforce, Hubspot, and Pipedrive to better understand customer acquisition;
- Marketing automation platforms such as Pardot, Marketo, and Omnisend to deliver personalized marketing campaigns;
- Customer Support and Success Helpdesk solutions like Zendesk, Gainsight, and Totango to pre-emptively identify and reduce churn;
- Workflow tools like Slack and Asana to automate internal ops processes.
Reverse ETL enables Data Automation
In any organization, there are a high number of manual requests for data and CSV files floating around and with any manual process, automating the process can save valuable time and resources. Here are a few common examples of everyday data requests from various teams that can be automated with Reverse ETL:
- Marketing wants to retarget site visitors and send conversion events to Facebook ads;
- Sales wants to see credit consumption on contacts and accounts in Salesforce;
- Customer Success wants product-usage data inside Gainsight;
- Product wants a Slack feed of customers who have seemingly become inactive;
- Finance wants a CSV of rolled up transaction data to use in Excel or Google Sheets.
Reverse ETL enables In-App Personalization
As users increasingly expect highly personalized, streamlined journeys, it’s incumbent on data teams to find ways to bring relevant data to the in-app experience. Reverse ETL enables you to send behavioral data to your application, enabling you to personalize the experience for your customers.
Let’s say you’re an e-commerce company. Your data science team calculates a score on top of the data warehouse or data lake describing the user’s likelihood of buying a product . Your product team wants to drive more purchases by offering discounts to users who are deemed unlikely to make a purchase. Since your data warehouse is too slow to serve user-facing experiences, your engineers can use Reverse ETL to move the propensity score to a production database to serve customers personalized in-app experiences in real time.
Or, let’s say you’re a B2B SaaS company. Your customers keep asking to see their credit allotment inside of your web app, but that data lives in Salesforce. Your developers don’t want to integrate with Salesforce and keep up with their API changes. Luckily, chances are that Salesforce data is already in your data warehouse (via an ETL tool like Fivetran). Your app can then show your customers their billing information just like any other user data.
Snowplow and Reverse ETL
Reverse ETL solutions like Hightouch, alongside tools like Snowplow to capture and manage your behavioral data, are growing in prominence as a way to close the loop on the data lifecycle and help you get more from your data asset. To learn more about how Reverse ETL and Snowplow can work together, check out the next post in our series: Why Your Data Warehouse Should Be your CDP (Customer Data Platform).
Here are some ways companies are activating their data using Reverse ETL solutions:
- Retool increased their reply rate by over 32% by sending product usage data to HubSpot and Salesforce for personalized outreach;
- Zeplin improved sales productivity by sending product usage data to Salesforce for prioritizing leads;
- GoSite increased merchant sales by 42% by sending financial lifecycle marketing emails.