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Humanize your data with quantified empathy.

November 24, 2021 By: Tom Arundel

As a recovering digital analyst, I’ve always understood the complexity of data-driven decision making. 

I recently encountered a frustrating experience on a major company’s website, leading me to this question: even if this company had all the numbers, charts and graphs in the world, can they empathize with my frustration?

Or did they become too focused on trying to fix the wrong things, because that’s what a few outspoken customers complained about in survey feedback?

The truth is, many companies still have siloed approaches to quantitative vs. qualitative data and decisions. But they haven’t tied the two together.

And there are dangers to these siloed approaches—some clear and some less so. But “quantified empathy” finds the sweet spot that connects emotion to business impact. 

Detached quantification.

The shift to data-driven approaches have changed how product and agile teams launch the backlog prioritization phases.

During these phases, product teams make judgment calls on what’s most important, what to implement first, and what remains in the backlog. These decisions have downstream ramifications on resource allocation, budgeting, go-to-market strategies, market competitiveness, and more.

Using traditional analytics, these teams analyzed quantitative metrics separately from qualitative data (such as voice of customer and session replay). Quant metrics included only numbers, tables, charts and graphs. This type of data helped product teams identify and quantify what was happening to users – conversion, time spent and funnel abandonment metrics.

But traditional quantitative metrics had one major shortcoming. While they’re good at answering the “what” about user behavior, they often fail to answer “why” users behave the way they do. 

When we look at quantitative data without any direct connection to the customer experience (either visually or by listening to customer feedback), we fail to understand why customers behave a certain way. This is what we refer to as “detached quantification.”

“Detached quantification is when analytics fail to connect to the human point of view in the customer experience (either visually or by listening to customer feedback).
These human connections help understand why customers behave the way they do.”

Emotional speculation.

Qualitative data helped teams address this gap by better connecting to customers emotionally, understanding them as individual human beings. New technology captured digital customer feedback (voice of customer) and allowed enterprises to observe actual browser journeys (session replay). These qualitative methods introduced critical new ingredients to the analytical process: the ability to empathize with the customer experience.

But the reality is, it can be difficult to contextualize this type of individual user feedback without understanding how other users are being impacted. 

Analysts can spend hours combing through customer verbatims and session replays. Once they find the needle in the haystack, they are left to speculate about customer intent, especially if the data is based on a single customer’s point of view. 

The inability to quantify the impact of an isolated issue to the business is a type of qualitative reasoning we refer to as “emotional speculation.”

“Emotional speculation occurs when qualitative reasoning fails to assess the business impact of a single, isolated customer experience.”

Finding the sweet spot with quantified empathy.

My dad used to love talking about “connecting the dots.” Innovation, he argued, was about discovering unexplored connections to radically different concepts.

For years, web and mobile analytics, VoC, and session replay were considered distinctly different approaches to understanding customers and their behavior. So they remained separate technologies, owned by separate teams across the enterprise.

Then a new wave of experience analytics arrived, and people started connecting the dots between quantitative user data (like conversion rates and revenue) and qualitative user data (like session replay and user feedback)

That’s how we arrived at “quantified empathy.” This is how analytics gets a heart.

Quantified empathy is a way to connect how a customer feels about your product to what it’s costing your business. It’s the process of humanizing otherwise impersonal data. It’s the ability to visualize the customer experience and assess the magnitude of that experience at scale. In other words, show me a struggling user and I will tell you how many others are experiencing that same issue – and what it’s costing your business.

Quantified Empathy is analytics with a heart. It’s a way to connect how a customer feels to what it’s costing your business. It’s the ability to visualize the customer experience and assess the magnitude of that experience at scale.

Imagine the product analyst who refuses to watch session replay, because it would take them all day to solve one individual user’s problem (detached quantification). On the other end of the spectrum, imagine the session replay or user feedback analyst who is so emotionally invested in solving an individual customer’s problem after watching a replay (or reading a verbatim), they forget to ask how many other people might have the same problem (emotional speculation).

Quantified empathy solves this dilemma by scaling an individual user’s experience from one to many. A session replay showing a single customer’s struggle can be quantified and show actual revenue opportunity.


As digital becomes a bigger part of our everyday lives, it’s ever more critical we connect the dots between what we see and hear from customers, and how their behaviors are impacting the business.

For too long, organizational and data silos have prevented these two worlds (qualitative and quantitative) from collaborating. Continuous Product Design is a methodology to address these silos, by aligning disparate cross-functional teams around a single customer-defined truth.

Even worse than misaligned product teams (at least they have data) are the teams who have neither qualitative nor quantitative data to back up their decisions. This type of decision making is what we call simply “careless guesswork.” 

Digital innovation is moving at light speed, and companies who fail to capture any customer signals will get left behind.

The power of quantified empathy is its ability to proactively address all of the disparate customer signals in real time – the ability to connect the dots, see what customers are seeing, listen to what they’re saying, and quantify all of it at speed and scale.

Join our discussion about putting the human at the heart of digital at our annual virtual conference Quantum LEAP on Feb 8-9, 2022.

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