What is Empathy?

Empathy is the ability to understand and share the feelings of another. For Product Managers looking to improve customer experience (CX), that definition translates to doing more than understanding the user’s pain points, but also looking at the emotional landscape of what it’s like to use the product – when it is working, and when it isn’t working.

Empathetic Product Managers ask themselves:

• How does using the product make the customer feel?
• How does the customer want to feel when using your product? What would be the best possible emotional outcome for them?
• How do I ensure the product developers understand and take the customers’ needs into consideration in their process?

The answers to those questions affect every facet of business, from acquisition to retention. It’s how, through CX, you can generate rapid growth through word-of-mouth recommendations, and sustain your success with customers who never want to leave.

Tying Empathy into CX

Empathy is a soft skill, and while those are typically difficult to measure, the effects of empathetic product development can be seen in every CX metric: Customer satisfaction (CSAT), Customer Effort Score (CES) and Net Promoter Score (NPS).

Sinead Cochrane, Senior Product Researcher at Intercom, wrote “For product teams, empathy building activities such as observing research or doing customer support is often not considered ‘real work’. However, product teams that consistently keep customer needs in mind are able to maintain and evolve their products in ways that won’t negatively impact the user experience.”

For her, empathy for product departments means “When a customer tells you something is broken, you are able to imagine the impact it’s having on the job they’re trying to get done,” and, “you realize the emotional impact the problem is having on that person.”

But I think we can go further than just recognizing the emotional impact of problems. That’s scratching the surface of what having empathy for customers can mean for producing superior customer experience.

Because empathy shouldn’t be reduced to realizing customers feel bad when a product isn’t working for them. A whole new world opens up when you also consider how you can design your product, updates, and expansions to enhance positive emotions as well.

Here are the questions that lie at the heart of empathetic product management:

• How can you get more emotionally in sync with your customers?
• Which are the most important negative emotional outcomes to manage?
• Which emotions should you seek to heighten (and how?)

To answer these questions, try these empathy building exercises.

Empathy Building Exercises for Product Managers and their Teams

1. Listen actively to discover underlying needs and emotional motivations

Often relegated to customer service and customer success departments, ‘active listening’ to find out why your customers use your product and what they really want to achieve is very important. You can’t get the depth and honesty of answers by just sending out a survey – this works much better if you do phone, Zoom or in-person interviews. In fact, Roman Pichler recommends product managers meet real users on a regular basis. You may find that your assumptions of why customers use your product aren’t accurate, or don’t tell nearly enough of the story.

“At first, our assumption was that they wanted to make more money. That often was true, but frequently we heard something different. Many simply wanted to maintain the business but run it more efficiently so they could have more free time (we heard about golfing on Fridays more than once). Others wanted to build a sustainable business they could pass on to their son or daughter.” – Jim Semick, Founder & Chief Strategist at ProductPlan

To get down to customers’ real motivations, ask open-ended questions beginning with “why” and “how.” Then make sure to record their answers in their own words (you can hand those assets to your copywriters for later use).

2. Use your own product

Empathy is often described as ‘putting yourself in someone else’s shoes’ – and there’s no better way to do this for a product manager than to actually use the product, just like any user would. You’ll empathize with users’ frustrations as you experience your own product’s shortcomings and hopefully find moments where it’s possible to create more delight.

But always keep in mind – you are not the average user. You’ll still need to listen to your users to get a complete picture of how they feel, and what problems they perceive as being severely aggravating.

3. Share verbatim comments

Someone, somewhere, is tracking customer experience metrics, sending out surveys, and collecting the answers. That someone might even be you. When reading users’ written responses, don’t just look for problems to solve and ignore the positive comments. Read them for emotion and see what conclusions you can draw about what people are feeling, and want to feel.

Pick a few relevant verbatim comments to bring to the rest of the product team. Reading these comments often helps engineers and designers feel the same joy or frustration as their users. This new emotional understanding will help you evangelize CX as a priority with everyone.

4. Mine your qualitative data and quantify customer sentiment

Those open-ended response answers are a goldmine for user research that can alert you to problems – and give you hints into the customer’s emotional state of mind. However, once you are getting more than a hundred comments a month, seeing the forest for the trees can be a difficult exercise. Qualitative feedback is notoriously tough to quantify, but it is now possible and easy to quantify sentiment with the help of machine learning.

AI-powered platforms, like InMoment CXInsight™, automatically sort your customer comments into themes while simultaneously assigning positive or negative sentiment. This provides you with a big picture understanding of how customers feel about your product and why. Categories of feedback vary by business sector and business model–payment processes & delivery for e-commerce, perhaps, while UX and usability may surface for SaaS products. Quantifying the sentiment of what your customers are talking about can help you track emotional trends over time. Presenting this kind of data alongside verbatim comments connects customer emotion with real business consequence.  

5. Set empathy KPIs

What gets measured gets done, and adding empathy into your product development work is no different. The KPIs for empathy may look a little different than your typical performance indicators, but the good news is: They’re not difficult to get. You’ll find key performance indicators like NPS, CES and CSAT are a good start, and comments in the open-ended questions can give you insight into the metric. Start identifying what kinds of ratings and qualitative answers correlate to genuinely happy customers – and frustrated customers likely to churn.

6. Chart out an empathy map

You’ve done your user journey, but even though it’s part of the buyer persona building process, you may not have done an empathy map.

• What your user sees – on competitors’ websites, common visuals in their industries, maybe what they enjoy watching or reading
• What your user says – how they measure success, what they say they want, what they say about your product
• What your user hears – what their influencers are saying, not just about your product, but about their jobs and what constitutes success, what they enjoy, what they don’t like about their experiences with your competitors, etc.
• What your users think and feel – worries, aspirations, what they really want, what really annoys them

Notice how the empathy map includes business/industry-specific observations, but also branches out into the user’s personal life and larger environment. People are not their jobs – or even their ‘jobs to be done.’ For true empathy, you have to look at the whole person.

This is a great activity to get other teams involved in – consider hosting a meeting with Customer Success, Sales, Marketing and Customer Service for a wider scope of insights.

Activities involving multiple teams help to build a shared understanding of your customers’ experiences that can strengthen the whole company.

7. Add happy moments to your Customer Journey Map

You’ve probably mapped out your customer/user journey, but you probably didn’t include this: Happy moments. See if you can take your old customer journey map and mark the points where positive, fun, delightful things happen. Can’t think of any? Then you have some serious CX work to do!

And of course, also note points where you’ve observed friction, difficulties, and problems, and address those in the order of biggest impact + easiest to implement.

8. Work in Customer Support for an afternoon

Whether that means answering the live chat questions, picking up the phone, or monitoring your product’s customer Slack channel, try out being the Customer Support agent for an afternoon to and put yourself on the front lines! There’s no better way to find problems than to let customers tell you exactly – and in great detail – what they are. And they’ll likely throw in how frustrated it makes them feel too.

9. Build a prototype to test your emotional hypotheses

By now, you probably have a few ideas on how you can improve the customer experience, and it might be time to test those theories. Create a prototype for a select group of qualified users to try (and react to). And, if possible, have them test the prototype in a testing facility that allows you to observe their reactions as they use your product.

If there is a Golden Rule for empathy, it’s a simple one: Forget your assumptions and be genuinely interested and curious about what people are feeling (not just what they’re doing) while using your product. Empathy is a learned skill that needs practice so don’t forget to try out these empathy exercises on a frequent basis for enhanced customer experience.  

To quote Maya Angelou: “People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Data is the beating pulse of business, but customer data is more like DNA. Customer data, if we’re using it right, directs how we grow and what we develop. But what happens if that customer data becomes corrupted by our own bias?

We can’t grow or develop in the ways we need to.

But what is bias exactly? Where does it come from?

The most prevalent bias is, perhaps, confirmation bias – seeking out data that confirms our existing beliefs.

In an early study of confirmation bias, young children were asked what features in a sports ball are important to the quality of a player’s serve. Some said size, others said material, some dismissed color as a factor – but once they’d made up their minds, they failed to acknowledge evidence that was contrary to their theory – or explained away evidence that didn’t fit.

But what’s worse, especially for those of us using data to steer our businesses, is that confirmation bias caused them to not generate alternate theories unless someone asked them to. They missed exploring and finding other possibilities.

There are other types of bias too, including:

Algorithmic bias – When the data used to teach an AI machine learning system reflects the implicit values of the humans involved in collecting, selecting and using that data. You might remember the 2015 uproar around Google’s image recognition AI algorithm that auto-tagged photos of black people as gorillas? Yes, that happened. And in 2009, Nikon’s image recognition algorithms consistently asked Asian users if they were blinking.

Survivorship bias – When the data analyzed only comes from success stories.

Sample bias – When the population you collect data from doesn’t accurately reflect the population you’re trying to learn about.

Avoiding bias when gathering, analyzing and acting on data is impossible. Bias creeps in with assumptions, instincts, guesses, and ‘logical’ conclusions – and mostly, we don’t even know they exist until someone without those particular biases point them out.

But, while we can’t escape biases, we can try our best to account for them when we collect, analyze and interpret data.

How to fight bias in your data

In Forrester’s The illusion of insights recording, Forrester Vice President and Research Director Sri Sridharan makes three recommendations to reduce bias in data.

She says to “triangulate insight” by using multiple methods of arriving at an insight, and cross-validation. For example, pairing behavioral data with feedback from customer surveys to see if you arrive at the same or similar answers.

Her second piece of advice is to create a “self-correcting system of insights” that connects customer data with an effective action to create a closed loop of action, learning, and optimization. Essentially, this means testing the data by taking action and iterating based on how well you succeed in addressing the issue.

Tracking a ‘North Star’ metric like NPS or CSAT over time can be very helpful in confirming whether the changes you make are having the desired effect.

Sri’s third piece of advice is to “show your work to build trust” both internally and with customers. Your customers will be quick to correct you if your insights don’t hold true for them – and you have the bonus of showing them how hard you’re working to make sure they have what they need to succeed.

But there is also the potential for bias to happen before any of these fixes can be made – especially in Customer Discovery.

Bias in Customer Discovery, Before You’ve Even Gotten to the Data

Bias in whom you ask

Who you survey, interview or meet with can bias your results. This is called “sample bias” – but it can also turn into confirmation bias. Sample bias happens when some members of the intended population are less likely to be included than others. Think of all of the different segments of users you have – what would happen if you only surveyed one of those segments? You would get responses that don’t work equally well for all of your customers.

This can slide into confirmation bias if the population you select is more likely to give you the answers you want to hear.

And, there’s also the risk of “survivorship bias,” if the people you’re surveying are the customers who are still with you, rather than the users who have churned. Current users are much easier to collect data from, and while they can give you important insights, they can’t tell you why your churned customers left.

Bias in how you ask

How you frame questions can have a dramatic effect on the responses. In fact, by the wording you use in a survey, or even your tone of voice in a phone interview or facial expressions in an in-person interview, you can effectively steer the conversation to deliver exactly the answers you’re hoping to hear. Many of the words we use have positive or negative associations that cause people to react accordingly.

Biased question: How much do you like the color blue? (This presupposes they like the color blue at all)

Unbiased question: How does the color blue make you feel? (A much more neutral phrasing)

Or, if you aren’t specific enough about the information you want, you risk confusing your respondent and getting answers that aren’t at all helpful. Unless you have a professional market researcher on staff, you may want to stick with established questions like NPS and CSAT.

Bias in what you ask first – and last

The order of the questions you ask can also bias your results, and you’ll need to review your question order carefully to make sure the sequence doesn’t cause biased responses. Typically, you should ask general questions before specific ones, ask positive questions before negative ones, and ask questions about behavior before questions about attitude.

Bias in when you ask

Holidays and the summer months, when families often take their vacations, can be problematic for both response rates and sample bias. For example, if you send a survey during religious holidays, you’ll likely get different responses rates from different groups of people, who may or may not be taking that time off.  Be aware of your timing, including if you’re sending surveys during deadline rushes, before or after holidays, or other significant patterns that may affect who responds and how they respond. To be on the safe side, don’t send your survey at the same time every year – send a few, at different times, to get the most accurate feedback.

Objective Data Leads to More Accurate, More Valuable Insight

Sherlock Holmes famously tells Dr. Watson that he never forms a theory before he gathers all of the facts. But he’s much better at disassociating himself from the results than most of us are (and he’s fictional). Bias has a way of seeping into our results, and how we view and react to our results. But, when we put bias-countering measures in place, like gathering data from different sources, using different types of data, and checking our work through a process of action and iteration, we can get to the truth in the end.