empathy data
Ritika Puri

Ritika Puri

Empathy Data: What It Is, Why It Matters


Recently, the topic of empathy has been getting a lot of attention in executive leadership circles. A new study from workplace advocacy group Catalyst found that empathy has the power to drive business outcomes, influencing metrics such as engagement, innovation, and retention.

Customer Success (CS) teams, working at the front lines of a company, are in a strong position to influence empathy. After all, CS teams are responsible for listening to peoples’ needs, understanding their frustrations, paying attention to their professional milestones, and problem-solving their issues alongside them.

The problem with empathy is that it’s historically been tough to measure. Even though executives know the importance of human interest, how do you put a number to the concept? At Quala, we see the answer to this question as a mission-critical strategic advantage. In this article, we take a first crack at tackling the challenge.

What Is Empathy Data?

At Quala, our goal is to help companies strengthen their ROI (return on investment) equations through the lens of customer success. The term ‘empathy data’ is a term we’ve created to describe insights that connect CS KPIs with human interest. So what exactly does this concept mean?

Let’s start with the word empathy, which means the “ability to understand and share the feelings of another,” according to the Oxford Dictionary. There are three types of empathy, according to experts:

  • Cognitive empathy refers to understanding what another person might be thinking or feeling. It helps with communicating information in the right way.
  • Emotional empathy makes it possible to share the feelings of another individual. It is helpful for building emotional connections.
  • Compassionate empathy is about the expression of concern. It moves people to take action.

Empathy data has roots in the field of qualitative research, which is an academic phrase that describes the practice of working with non-numerical data. Unlike quantitative research, qualitative research is about capturing insights through text, images, or videos. These are insights that cannot be rolled up into accounting spreadsheets.

Historically, it’s been tough for companies to make business decisions based on qualitative data. But in recent years, technology advancements in data science, artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) have made it easier to identify and measure meaningful qualitative insights.

Empathy data points paint a picture for an overall story while pushing the deeper question “why.” 

How to Utilize Empathy Data

One of the biggest misconceptions about empathy data is that it’s “fluffy,” superfluous, or difficult to connect to ROI. In actuality, empathy data can make quantitative data more actionable and prescriptive.  The key is to strategically pair stories with numbers in your reporting. In other words, try to find the story driving the trend that you’re seeing.

Let’s say, for instance, that you notice a peak in customer churn. In addition to sharing “what” happened, executives will also be interested in “why,” in order to course correct potential problems in the future. In this case, you can poll your frontline customer success teams and ask for the story behind specific churn events.

Step 1: Start with the numbers

One of the challenges of analyzing empathy data is that information tends to be open-ended. How do you connect dots into a clear narrative? 

The first step is to establish a sense of focus through quantitative metrics. 

At Quala, we’ve written extensively about key customer success metrics and what they can tell us. Here are 8 that our team sees as being most actionable: 

  • Net revenue retention (NRR) measures CS’s role in driving steady revenue growth.
  • Health scores quantify the success of a customer relationship.
  • Customer churn rates measure the people who have unsubscribed and stopped using your business.
  • Frontline feedback for topical interest tracking, which makes it possible to identify trending areas of discussion.
  • Average revenue per user (ARPU) helps support financial planning by helping create forecasts around the value of each customer (and customer category).
  • Monthly recurring revenue (MRR) supports predictable financial planning and is the foundation for measuring SaaS business growth.
  • Customer retention cost (CRC) is a measure of loyalty and can tell you how much a company spends to retain a customer — and how to identify the right insights for keeping a customer on board.
  • Net promoter scores (NPS) gauges customer satisfaction on a 1-10 scale.

Step 2: Ask the question of “why” behind each metric

After identifying key revenue metrics, the next step is to uncover the story behind the trend (the “why” underlying the “what”). Remember that there is always a deeper story to every quantitative trend.

Before jumping into the numbers to uncover that deeper trend, it’s important to keep some statistical ground rules in mind. One guiding principle is that correlation is not causation — even when two trends seem related, it’s not necessarily the case that one resulted in the other occurring. 

For instance, you might notice that your customer health scores declined following a new product release. With further investigation into your frontline feedback, you might see negative comments about the product release.

It may be easy, given this perspective, to make the judgment call that the product release caused the decline in health scores. Dig deeper, and you may find your assumption to be incorrect:

  • After doing some media research, you may notice that there have been recent natural disasters among geographic clusters of customers
  • You may notice that health scores began to decline before the feature release
  • It may be the case that health scores increased among people posting negative comments — after all, more engaged customers may be more likely to speak up

The bottom line is that even though there’s a correlation, there isn’t a causal relationship. The repeated question “why” creates a path to the true story. 

This process will be messy — the best way to organize your “research artifacts” (text, screenshots, visuals) into a document or note-taking tool.

Step 3: Paint a picture with words, by numbers

When you were a kid, did you ever paint by numbers?

If you enjoyed this mindful activity then you’re in luck: the process for working with empathy data is similar. In step 1, you identified the metrics that are most important to your business. In step 2, you create a collection of research artifacts. Now it’s time to mash them up into a comprehensive communication tool.  Create a straightforward narrative that executives will find appealing. Here is a simple format:

What

Churn increased

Why

  • High-level trend 1
  • Trend 2
  • Trend 3

Deeper insights

[customer quote 1]

[customer quote 2]

[customer quote 3]

Proposed response plan

[step 1]

[step 2]

[step 3]

The report structure above communicates a deeper story than a simple information export. Empathy data (“why” and “deeper insights”) is what connects dots between “here’s what happened” and “here’s what we’re doing about it.”

This picture, beyond telling a story to executives, further empowers the role of customer success in driving business outcomes.

Final Thoughts

“Business as usual” has not been the case for quite a while now. Empathy is more valuable than ever before — especially given the stressors that many people are facing in their everyday lives. The more we listen, the more we can respond with compassionate leadership in the moment. Customer success leaders are leading a very important movement.

Quala’s Frontline Intelligence allows Saas leaders to fill in the “why” behind the “what”, tapping into insights from frontline teams, customer interactions, and more. Not yet using Quala? Request a demo here to learn more.


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