Reverse ETL for Consumer Brands & Unsiloing Data Forever


Data warehouses such as Snowflake, Redshift, and BigQuery have become fundamental to the data strategy of many consumer brands: they facilitate data automation and centralization as well as visualization and analysis. 

But while they’re excellent at creating a single source of truth around your brand’s data, extracting the data out of the warehouse and sending it to a destination is challenging. It requires manual processes (e.g., spreadsheets and headaches) because the warehouses themselves become data silos.

Enter reverse ETL, the last stretch of data infrastructure necessary to pipe data from your warehouse to destinations (such as your marketing and customer tools) so that everyone at your brand quickly gets the data they want, where they want it. 

What is reverse ETL?

Reverse ETL (Reverse Extract, Load, Transform) refers to the process of copying data from a data warehouse to another platform (usually a SaaS tool) in order to leverage that data. 

The term came about as it describes the inverse of ETL (Extract, Load, Transform), the traditional method of copying data from one or more sources to a data repository.

visualization showing ETL process

In the case of reverse ETL, the opposite process is in place: 

visualization showing reverse etl for eCommerce

With a full cycle data platform, you can constantly update your entire stack from getting the freshest data into your warehouse to updating your tools when you need them.

The problems that reverse ETL solves for brands:

  1.  It un-silos data

Data warehouses (and, by extension, analytics platforms) provide an extraordinary unlock for brands. 

They save countless hours of work, prevent spreadsheet-induced migraines, power remarkable optimizations and insights, and make analyzing vast amounts of data not only possible, but scalable and simple. 

But the warehouses/analytics platforms have become data silos themselves. 

Once the data is centralized, it’s partially stuck in that centralized location, and it requires another manual process via spreadsheets/CSVs to get it out and uploaded into your marketing tools.

edited image of a silo on a farm being crossed out and the caption "Please, say no to siloed data"

Enter reverse ETL: the last section of piping that replicates transformed data from your data warehouse back to your other eCommerce tools. 

  1. It automates the data that you wish could be automated 

With the new infrastructure in place, all that is required for dynamic segment building is the initial effort on your end:

In your analytics platform, you can build a segment (let’s say, customers with an LTV of $300+), and via reverse ETL, the segment is sent to your marketing platform(s) of choice. 

The LTV data is kept fresh in the marketing platform(s) via daily data refreshes: any new customer who passes the $300+ LTV threshold will be automatically added to the segment, and you’ll never have to worry about updating it again. 

This will save your marketing team hours every week, and potentially hundreds of hours every year, as the manual work will be eliminated. 

  1. More reverse ETL use cases for eCommerce brands
Time between orders 

You can push time between orders to your marketing platforms and automatically send customers replenishment reminder emails based on their past purchase behavior. 

For example, if one customer segment purchases approximately every 35 days and a second segment purchases every 73 days, you can send reminder-to-buy emails based on their next expected purchase time. As our merchant ECD found, this increases the likelihood of purchase and increases LTV

RFM Data

In the same way that we mentioned a dynamically updated segment based on an LTV threshold, you can do the same with RFM Data

For example, if you have email flows built for high RFM Score customers (i.e., your high value customers), you can create always-updated RFM segments in your marketing platforms. 

Discount Usage

Customers who have never used a discount may be less price sensitive vs. the customer who uses discounts frequently. By creating dynamic segments of customers who have never used a discount, your marketing team can send more expensive product offerings. 

The reverse is true, too. For customers who have used discounts, your team can send them more affordable products, or other discounts to encourage them to purchase more often. 

The other game-changer: maximizing advertising platforms’ potential 

You can also send data to your advertising platforms, like Facebook. By piping your customer data into Facebook, you’ll repower Facebook’s lookalike audience builder (a billion-dollar-brand-building but now somewhat now-depowered feature) for your marketing team.

In doing so, you will increase the likelihood of bringing more customers like the ones you have now, and empowering your marketing team to bring true lifecycle marketing to Facebook:

Visualization of retargeting customer flow in Facebook
  1. Reverse ETL automatically updates customer data, such as Discount Code Use and Product Purchased, in Facebook daily
  2. The marketing team retargets your existing and shows them personalized ads based on their actual interactions with your brand
  3. They target existing single buyers to nurture them to become Multi-Buyers and High Value Customers (HVCs)
  4. Data about your HVCs or your Churning/Lapsed HVCs is piped into Facebook to retarget them; the marketing team shows them personalized ads and drives them to repurchase
  5. Your brand data builds lookalike audiences based on your own customer data for optimized acquisition

Daasity Audiences: the only purpose-built reverse ETL tool for eCommerce brands

Daasity is an eCommerce data and analytics platform, and we have built (and continue to rapidly expand) a reverse ETL product called Audiences

Audiences allows you, and your team, to do everything we outlined in the previous section: send metrics and customer segments to your marketing and eCommerce tools, from the Daasity platform. 

With Daasity + Audiences, eCommerce brands have a complete data solution, in which they can centralize, analyze, and push their data back into their marketing and eCommerce platforms. 

Everyone has access to write their own SQL code to pull the exact data they want to sync. Non-technical teammates can also push segments and customer records through built reports.  

Data is no longer siloed, and segment updating is automated. 1-to-1 personalization in marketing becomes the norm. 

Want to send a customer segment through an SMS flow in Attentive, based on a particular survey response? You can! Want to send some other segment an email offer in Klaviyo based on their gender? You can. 

If you’d like to see all about how Daasity works, you can check out an on-demand demo.

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