Jul 14, 2022

Read Time 5 min

What makes ‘good’ data in Customer Success with ChurnZero Implementation Team Lead Lexi Quinn


As a customer learning and education specialist at ChurnZero, I spend a lot of time consulting with subject matter experts—mainly implementation managers and CSMs—to create new customer education content.

While the topic of our conversation varies, the takeaway is always the same: You will achieve the best results if your data is good.

This advice isn’t anything you haven’t heard before. Every business article since the beginning of time has echoed its sentiment.

But what actually makes “good” data, and how does it contribute to Customer Success?

To get a better understanding of the relationship between good data and Customer Success, I sat down with ChurnZero Implementation Team Lead Lexi Quinn. In this interview, Lexi shares the characteristics of data quality, why you need to conduct data cleanup early and often in onboarding, and how to use ChurnZero as a tool for quality assurance.

Anna Garber: How do you instill in customers that data is the bedrock of Customer Success at the start of implementation?

Lexi Quinn: We talk to customers about the importance of clean data well before they even sign a contract. Sales engineers and account executives build these talking points into their technical calls with prospective clients to set expectations early on. We’ve invested a tremendous amount of time and effort into creating a feedback loop to alert sales to the major implementation pitfalls that slow teams down so prospects can take the necessary actions to mitigate any risk.

At the start of implementation, our team outlines every point in the process where they will discuss data quality efforts. We also reiterate the foundational tenets of data quality such as the importance of having unique identifiers for accounts and contacts to accurately map data across systems. We share examples of how clean data keeps them from getting bogged down as they progress further in configuration.

The reason we talk so much about data quality with our customers is first and foremost to ensure the onboarding process is as smooth as possible. The best way customers can get ahead of the game and prepare for implementation is to conduct data cleaning. Time spent improving the accuracy of your data is always time well spent.

AG: Do you have any examples of how customers have applied this tenet during their implementation?

LQ: I worked with a customer who exemplified the ideal of data preparation. From the start, the customer involved stakeholders who owned systems and data that would be integrating with ChurnZero. Before the end of implementation, they created segments in ChurnZero to ensure that significant data points were populating correctly and consistently. If a data point was amiss, they quickly resolved it and created a process to continually update that data going forward. When a data point couldn’t be reconciled or was deemed unreliable, they documented the limitation so it was clear where and how it may skew associated segments and automation triggers. The customer completed the onboarding process on time due to their foresight and mitigation plans.

AG: You place a big emphasis on data cleanup as a part of implementation. How do you help customers see the positive impacts of good data early in the process?

LQ: We stress that a commitment to data quality is a key pillar of our customers’ success from day one by discussing strategies for quality assurance. Our technical implementation includes two separate calls specifically to review data quality—one in the middle of implementation after we’ve imported most of a customer’s core data into ChurnZero, and another at the end of implementation. During these calls, we review line-by-line every core data field and its impact on a customer’s ability to automate and capture information.

Because one of the biggest effects good data has in ChurnZero is the ability to trigger automation to the right accounts at the right time. The power of this functionality depends on a customer’s data and segmentation strategy.

At ChurnZero, we use our own product to help customers identify incomplete data at the start of the QA process. Our segmentation feature provides customers with a dynamic list of their accounts and contacts that are missing core fields. We review these QA segments throughout implementation to monitor progress and even benchmark our customers’ core field data.

After the QA segments are built, we advise customers to clean up the data that’s mission-critical to their daily operations before their team begins working in the platform.

So often, the configuration phase shines a spotlight on the weak spots in a customer’s data if gaps identified during QA go unresolved. Teams do themselves a grave disservice by brushing data troubles under the rug. It’s tempting to turn a blind eye or postpone needed solutions when you’ve got so much else going on. But those issues will fester and eventually come out. Only when they do, it’ll be double the work to go back to the start and undue what’s been done to fix the root of the problem.

AG: What are the consequences of a CS team not being aligned on their data priorities and strategy?

LQ: I’ve seen customers experience personnel changes during their implementation. As a result, there can be fallout due to internal misalignment of key data points. After a customer’s primary contact leaves, sometimes, the team members who help backfill this effort are left with little insight into the significance of the chosen data points and how they are calculated. Without that internal communication or education on the value of the original data that’s pushed into the platform, teams have to step back and reevaluate the data points that were originally identified as important. This can be a big undertaking. They have to backtrack quite a bit to reassess their data before continuing with configuration. And while we can provide recommendations around what other customers do and what data they track, it’s not always relevant as this information is highly specific to each customer’s business.

Getting agreement on the key data points from the stakeholders who implement ChurnZero and the stakeholders who will be using ChurnZero is absolutely critical.

AG: Why do you think it’s difficult to define what makes “good” data?

LQ: This is a hard question to answer. Many people, especially those who don’t enjoy working with data, have trouble grasping why it matters more than anything. We can demonstrate that there are data gaps, but until the impact of those gaps become clear, good data can be a nebulous concept.

This matters particularly in ChurnZero where segments are the foundation of all automation and workflows. If your segment has flawed criteria—such as outdated or missing data fields—then any automation that’s powered by that segment will inherently be flawed. Examples of affected automation could include an alert that notifies CSMs when an account field is updated or criteria for an account to enter a journey. Inaccurate segments will have a waterfall effect as many ChurnZero features connect and build on each other to create more advanced workflows. This can be hard for customers to comprehend at the beginning, which is why we do our best to demonstrate its criticality throughout the process.

Factors that make data “good” include its accessibility, accuracy, breadth, timeliness, just to name a few. Our 7-point audit checklist for CS platforms is a great resource that outlines the top data quality factors to assess as well as how to build a QA segment and which key fields to include. It also shares steps to measure the effectiveness of your CS platform once it’s up and running.

Be confident in your Customer Success data

Customer Success teams sit on a trove of customer data. But what good is that data if it can’t be trusted? Make it a practice to audit your Customer Success platform regularly to improve your team and your business’s confidence in your data. And just as importantly, make sure your Customer Success data is accessible to the wider organization. Your data can be in tip-top shape, but if other team’s can’t use it, then you have another problem on your hands.

Learn how ChurnZero’s data-sharing features help turn Customer Success data into insights for other teams in our blog “How to use ChurnZero to share Customer Success data across your organization.”

Are you a ChurnZero customer who’s ready to clean up your Customer Success data? Reach out to your CSM and implementation team to discuss strategies to get better data quality using ChurnZero.


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