What is reverse ETL and how can it improve customer success?

Sylvain Giuliani

Sylvain Giuliani

18 January 20220 min read

To be at the cutting-edge, customer success teams must work with real-time or recent data. That’s where reverse ETL comes to the rescue.

Over the past couple of decades, customer expectations have changed dramatically. For example, it used to be totally acceptable to receive a package you ordered online several weeks later (often without order updates along the way). Now, we want it delivered in a matter of days, and want to see its progress at every step.

When you combine that with rapidly evolving cultural trends and business realities, it’s now more important than ever before to optimize customer success strategies to exceed expectations, enhance customer experiences (CX), and foster customer loyalty across the board. If not, you risk losing those relationships altogether.

Although customer success measures differ based on functions (for example, support, help desk servers, etc.), they all need accurate data to ensure success. But to be at the cutting-edge, customer success (CS) teams must work with real-time or recent data. That’s where reverse ETL, combined with tools like Front, comes to the rescue.

Before we get into how well reverse ETL tools like Census and Front work together to close the customer feedback loop, there are two main concepts to understand: What is reverse ETL, and what is operational analytics?

What is reverse ETL?

Reverse ETL is an operational analytics tool that helps CS teams transform and move customer data out of warehouses and into their favorite tools (including CRM, MAP, and SaaS apps), effectively flipping the role of traditional ETL tools.

We’ll refer to the pioneers of reverse ETL, Census, for the official definition:

​​Reverse ETL syncs data from a system of records like a warehouse to a system of actions like CRM, MAP, and other SaaS apps to operationalize data.

While it may sound like just another data pipeline, there’s much more to it. Reverse ETL can help us overcome data silos inherent in centralized data warehouses and make high-quality, fresh data available to and consistent between every customer-facing team that needs it.

This isn’t just about sending your data from A to B, it’s about taking a step toward operational analytics, a world where data isn’t just locked away in your dashboards but is used to drive real-time operational decisions every day. When every customer-facing team can self-serve fresh, high-quality data (without piling up a thousand tickets on the data team’s task list), you can align your organization and applications around one source of data truth.

For CS teams, this means having access to a complete, relatively-real-time picture of customers, including how they use the product, where they’re running into issues, and how other teams have interacted with them in the past. Effectively, reverse ETL makes it easier to listen to and have a conversation with each user you serve.

What is operational analytics?

If we think of traditional analytics as decision-making based on data in dashboards and reports, we can see operational analytics as the next step in data-driven business, where the action itself is driven by data daily.

Creating a real-time flow of data from your data warehouse to your CS team can empower your team to start making daily data-driven decisions. For example, operational analytics makes it possible for CS teams to automatically prioritize support tickets based on product usage, segment audiences for marketing email drip campaigns, and plug early churn indicators from your go-to customer relationship management (CRM) platform.

This means customer service professionals can jump right into action without juggling multiple dashboards, emails, reports, or even spreadsheets. If there are technical issues affecting CX, you can share them with an engineer who can quickly fix a bug in the system.

It’s all about putting your company’s data to work and empowering everyone within the organization to make smart business decisions. As such, operational analytics by way of reverse ETL tooling enables data-driven decision-making at scale.

Operational analytics closes the customer feedback loop

CS teams who already use Front can snap on a reverse ETL tool and close the customer feedback loop.

Before reverse ETL, customer-experience-focused CS teams often used traditional analytics protocols to measure performance and plan ahead with business intelligence tools, letting them understand the average time it takes to resolve a support ticket.

However, this metric alone isn’t enough to support individual CS representatives. It can help measure and compare past and current performance but fails to give CS teams the data they need to take action and optimize performance, as well as resolve more support tickets.

The next level of data-driven CS requires support ticket prioritization, and only reverse ETL can make this possible at scale. In this scenario, operationalizing data with reverse ETL allows teams to pull customer data into each support ticket and automatically prioritize them before they pop up on the representative’s screen. Prioritization depends on chosen key characteristics like customers at risk of churn, customers primed for an upsell, and so on.

As the list of support tickets is ranked by importance, CS reps won’t have to skim through them to figure out which one they should tackle first. Operational analytics saves time by choosing it for them and allowing them to go right into action mode.

As your CS teams get used to having some extra lines on their tickets and resolving them one by one, they will enhance CX, reduce churn, and contribute to higher retention. Plus, since they’re tackling the most important tickets and accounts first, this reverse ETL use case has a direct impact on your bottom line (happy customers and a happy revenue team?! A dream come true!).

There’s no guessing with reverse ETL and operational analytics, and nothing is left to chance. Instead, it fuels CS teams to consistently follow data-driven best practices enabling product-led growth in an era when CX is more important than ever.

Speaking of tickets, Front fans already know that they won’t have to deal with traditional service tickets or have the training to understand them. As Front and leading reverse ETL tools like Census are highly intuitive, you can get members of other teams to work with you to support or fix a technical issue. The level of access you provide them is all up to you.

CRM + reverse ETL = action

Data is pretty much meaningless unless you can add intelligence to it. CRM systems provide valuable insights we can use to enhance customer relationships. However, companies often run into problems because these insights stay locked within the CRM platform.

When you build on the capabilities of a well-designed CRM (especially one that also offers the simplicity of email) with reverse ETL, you can provide truly tailored service at scale.

No one team should be the captain of your CRM. Sales can use it to better target ideal customers, CS teams can use it to provide holistic care to customers at each touchpoint, and account management teams can use it to share reports with business development teams. Reverse ETL ensures that each of these teams that rely on and tap into a well-designed CRM all work off the same, well modeled, and accurate data to drive action.

Census + Front

When all teams work from one central understanding of “what is a customer,” it sets teams up for success in all roles (including retention, cross-selling, upselling, etc.).

When CS teams use Census and Front together, they gain CX insights across multiple channels and make sure every team is as efficient in their strategies as possible. This approach helps CS teams leverage automation and deliver personalized customer experiences at scale. In this scenario, Front ensures that you’re always working with omnichannel data, while Census provides information that is always fresh and actionable.

For example, this information might trigger digital transformation initiatives to ensure that customers get more with fewer interactions. In contrast, if you’re taking a DIY approach and doing reverse ETL manually (AKA copy and pasting data or putting in tons of ad hoc requests to your data team), you risk making mistakes, and your data might not even be relevant once you finally finish. Furthermore, you may also have to deal with different tools coming into conflict.

Front and Census together isn’t just good for your customers. It’s good for your sanity and day-to-day, too. After all, no one got promoted for tedious integration work.

How to connect Census + Front

To get the most out of this section, go ahead and grab a demo of Census here. You can check out their full docs, including their Front integration, here.

The best part? Census comes with a Front integration out of the box, and you don’t have to ask any engineers for favors.

1. Connect Front by going to Census and navigating to Connections. Click “Add Service” and choose Front in the dropdown list. Then make sure that you have the necessary credentials to access your data source and connect Front and follow the OAuth flow.

2. Connect your data warehouse (BigQuery, Postgres, Redshift, or Snowflake).

3. Once connected, you can sync customer data right from your data warehouse to Front. You don’t need to type a single line of code or upload a script—just SQL.

4. After connecting your data warehouse, you’ll be ready to create your first data model and your first sync.

When CS teams use Census and Front together, they can solve the most demanding customer success challenges easily. To learn more about how operational analytics can close the customer feedback loop for your team, schedule a Census demo.

Written by Sylvain Giuliani

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