9 Empathy Exercises that Help Product Teams Improve CX

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).

  1. 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.

  1. 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.

  1. 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.  

Feedback categorized by theme with sentiment breakdown
Example of auto-categorized NPS comments with sentiment assigned in a dashboard. Source: Wootric

  1. 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.

  1. 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.

Empathy Map Source

  • 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.

  1. 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.

  1. 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.

  1. 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.

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.”

Be the customer experience champion at your company. Sign up today for free Net Promoter Score, CSAT or Customer Effort Score feedback with InMoment.

5 Sneaky Biases That May Affect Your Customer Insight Analysis

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.

“The greatest obstacle to discovery is not ignorance – it is the illusion of knowledge.” – Daniel J. Boorstin

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.

Get immediate insight from comments using text and sentiment analytics.
Learn about Wootric CXInsight™

“Choice Overload”: Is Your Customer Onboarding Overwhelming? (Find Out Fast)

Starting the post-sale relationship off right is key to customer retention.

While this can’t be emphasized enough, with regards to onboarding, be careful not to mistake giving customers more choices and information for a better process.

Too Many Options May Overwhelm Customers

Chances are that your sales team pulled out all the stops to attract the attention of your current customers. Customers are drawn to large numbers of features and options when making the initial purchase decision.

You would think that after they’ve closed the deal, it’s your job to explain all the details and nuances about everything they were promised in the sales pitch. It’s time to exceed all of their expectations. But when it comes to onboarding it is very easy to overwhelm them with all of those same options that originally attracted them to your product.

What is Choice Overload?

One of the basic phenomena in behavioral economics is choice overload, which occurs when you present someone with too many choices. It is associated with unhappiness, decision fatigue, going with the default option, and choice deferral.

By showing your customers all of the customizable settings, features, and options right at the beginning of your relationship, before they are familiar with you or your product, you run the risk of overwhelming them into inaction and dissatisfaction.

There are four key factors that contribute to choice overload:

  • choice set complexity
  • decision task difficulty
  • preference uncertainty
  • and decision goal

With a more complex choice set, a more difficult decision task, more preference uncertainty, or a more prominent and effort-minimizing business goal, there is a greater chance of choice overload. Poor onboarding is one of the leading causes of churn, so overwhelming your customers during onboarding can be one of the most impactful mistakes regarding your overall customer experience.

How User Interface May Contribute to Overload

Even if you channel your eager energy into the best onboarding process on the face of this planet, that may not be enough to prevent a different kind of overload, cognitive overload. According to experts at Fluid UI, if the user interface your customers are dealing with is too stimulating or even not stimulating enough, customers may “overlook the finer details of a product or service, lose focus or ignore an important learning moment. [Humans] still draw conclusions about the suitability of the product [they] are learning about.”

Read more about avoiding cognitive overload using customer-centric design for successful onboarding and retention.

Cyberpsychology and UX 3: Preventing Cognitive Overload

Your step-by-step instructions, videos, and one-on-one walkthrough conversations for onboarding will be negatively impacted if the web pages customers interact with are cluttered. If the instructional video/ help documentation is buried in an obscure corner of your homepage, you’ll find that your Success team spends too much time acting as Support.

Customer Effort Score: One Question to Combat Overload

The easiest way to learn if you have a problem is to gather feedback from customers who have completed onboarding. Both types of overload are easily identified when you ask customers “How easy was it for you to complete onboarding?”, the Customer Effort Score question. Asking for a score and comments will bring to light the most prominent issues at this journey point.

By gathering feedback immediately after onboarding, customers who struggled with the user interface will be able to provide you specific examples from their experience that need adjustment. For example, you may hear from a customer who is colorblind that the color theme for a specific chart was tough to distinguish for them, or that finding a specific button took more time than they had anticipated.

Read more about how Customer Effort Score can improve your onboarding process.

Use Choice to Enhance Customer Experience

Driving adoption and customer loyalty is a tough job, but flooding new customers with information is not the way to go.

Gathering feedback throughout your relationship about effort, satisfaction, and loyalty will help you improve the entire customer experience and prevent you from overwhelming your customers at any point in their journey with you.

To provide an excellent customer experience, all you need to do is provide convenient information that helps your customers achieve the next milestone, or inspires them to develop a new milestone. By listening and taking action, you build trust and loyalty with your customers that will help you win customers for life.

Measure and improve customer experience. Sign up today for free Net Promoter Score, CSAT or Customer Effort Score feedback with InMoment.

The Easiest Way to Improve Customer Onboarding: Measure Customer Effort

Putting off changes to your onboarding process is too tempting. Where do you find the time to overhaul the entire process and where do you even begin? It feels like a monumental task that will take ages to do properly.

While it may seem daunting, there is a simple, quick step you can take to prioritize incremental improvements immediately: start gathering Customer Effort Score feedback after onboarding completion.

What does “Onboarded” mean?

Onboarding is a term often used in SaaS businesses and there are many ways people will define what “onboarded” means. It can vary depending on the type and level of complexity of your product or service.

As an event in the Customer Success journey, “onboarded” can mean the first point where customers start to achieve their goals. In terms of product training, it is the point where hand-holding is no longer necessary and customers are confident enough to navigate on their own.

Once you have a better idea of what “onboarded” means to your company, you can reverse engineer the process to get there.

The Easiest Way to Improve Onboarding

There are plenty of specific actions you can take when it comes to improving the onboarding process, but how do you know which actions to prioritize? You could write up a playbook for all of your CSMs, but how do you know what methods are the most successful, or what the most frequently asked questions during onboarding are?

Gathering Customer Effort Score feedback after your customers soon after they finish the onboarding process helps you prioritize initiatives when it comes to improving the onboarding customer experience.  Address the most frequent, most fundamental complaints first to incrementally change your onboarding process instead of trying to do a complete overhaul in one go.

What is Customer Effort Score (CES)?

The Customer Effort Score survey asks customers on a scale from 1 to 7 how easy it is to deal with a companies products and services. The CES survey is a transactional survey, gauging the experiences customers have after a specific touch point in the customer journey.

Why Customer Effort Score (CES)?

Why focus on effort here? Why is it a better choice than, say, NPS? Because effortless onboarding correlates with retention. According to the Harvard Business Review, “companies create loyal customers primarily by helping them solve their problems quickly and easily.”

When you ask your customers directly – “How hard was it for you to get started with us?” you will quickly identify whatever obstacles your customers faced during onboarding. Address them immediately for quick wins with big impact.

Implementing a CES micro-survey means receiving open-ended feedback from customers that speak to the issues that are at the top of their minds. You’ll hear what customers struggle with specifically so that you can prioritize the changes and additions that will have the greatest impact on customer experience.

In the long term, CES can get you answers to the questions that shape your overall process such as:

  • What do customers need to be able to do or know to accomplish their goal?
  • What information do they need in order to do this?
  • Which content formats are best to convey different information?
  • What do your customers perceive to be the first value delivered point/ first value achieved point?
  • How long does it take customers to get to the first value delivered point/ first value achieved point and what can be changed to reduce that amount of time?
  • Is there information being lost in the handoff from Sales to Success? If so, what is being lost?

Focusing on minimizing friction and achieving your customers’ desired outcome encourages them to form a longer, deeper relationship with your company. This means more opportunities for upsell and cross-sell. It also means higher advocacy, which is instrumental to growth.

Iterate & Adjust as Your Company Evolves

By gathering CES feedback after onboarding completion, your company can improve retention and build a loyal customer base. You’ll demonstrate customer-centricity from the very beginning of your relationships.

Creating a customer-centric onboarding process kicks off a customer experience that will make you stand out among your competition. Even as your company and product evolve to accommodate new needs, customer feedback will guide you to the most effective onboarding process, helping you win customers for life.

Start getting in-app CES feedback for free with Wootric.

Our Machine Learning Journey: From Zero to Customer Value in 12 months


At Wootric we collect hundreds of thousands of NPS, CSAT and CES  survey responses every week. We do this across different industries and product categories. Our customers then use our various integrations such as Salesforce, HubSpot, and Slack to route this feedback to relevant teams. Some of these customers like DocuSign and Grubhub have huge user base. This means even with a conservative sampling strategy they get hundreds of pieces of feedback daily.

The Challenge of Analyzing Qualitative Feedback

The quantitative aspects of feedback — NPS, CSAT scores, for example — are relatively easy to aggregate and analyze. It is the qualitative comments that provide rich insight into customer experience, but analysis of unstructured feedback is hard. Someone could read each piece of feedback one by one, but having a human read each comment obviously does not scale. If you can’t or don’t review what customers are telling you, then why have CX program to begin with? This is where machine learning saves the day.

Here is a concrete example of problem we needed to solve:
CXI-classification of customer feedback using ML

We set out to solve this multi-label classification and  topic sentiment analysis problem for our customers. We knew that doing it at scale would require some sort of automation using machine learning (ML) and natural language processing (NLP).  Machine learning requires lots of training data — in our case it meant we needed thousands of survey responses manually labeled into different categories. Luckily our customers have had the ability to manually categorize survey responses in our dashboard.  In the last 3+ years, they have categorized thousands and thousands of comments. Fortuitous, but sometimes we would like to think we saw it coming. 😉 At this point, we have significant volume of training data, now we just needed to find the right machine learning algorithms to take a stab at the problem.

Sentiment Analysis

To get our feet wet in machine learning ecosystem, we thought we would start with sentiment analysis because there has been tons of research on this problem over last few years and there are several open source solutions claiming to solve this. We started with Stanford NLP library because it was and is still actively worked on and lots of great research papers have come out of this group. It took us about a week to get our heads around concepts of Tokenization, Lemmatization, Named Entity Recognition (NER), PoS tagging, Dependency parsing, Coreference Resolution, word embeddings and finally to embed the library into a REST API framework. The results were okay but not great for our use case.  We **think** it’s because feedback comments belong to different domain — these are responses to questions as opposed to news article, blogs and tweets. Most of feedback is short, ranging from a couple of words to couple of sentences. This does not give enough “context” for algorithms to find the sentiment. An alternative solution would have been to train our own model using Stanford NLP library but we did not have engineering resources and bandwidth at that point in time. But we had something working. It was a baby step, but a step in the right direction.

Categorizing Feedback Comments

For feedback categorization, we also first looked into a hosted service and open source libraries. The most compelling, trainable and easy to get started solution at that point in time (mid 2016) was Google Cloud Prediction API where you upload a CSV of training data and in few minutes you get a model and REST API to make a prediction. This sounded like a tailor-made solution for us. After all, we had lots of training data that our customers had manually labeled. We were able to quickly format our responses and training labels in order to meet the Prediction API requirements, and saw some initial results.

Results were better for some sets of feedback but horrible for other sets. Dissecting further we realized that the customer feedback set where prediction was more accurate belonged to DevOps, Data Analytics and similar developer centric and data analytics.  There was less success with feedback from e-commerce or other consumer-centric SaaS products. This made sense because most of our customers who spent their time manually labeling feedback had offerings catered to developers — such as New Relic, Docker etc. The bigger downside of Prediction API was that it was a black box so we did not have any ability to tune algorithms. Ironically, Google decided to deprecate Prediction API in favor of their Cloud ML Engine, driving us to improve our internal, customizable prediction methods instead.

Our experiments with Stanford NLP libraries and Google Prediction API gave us a good understanding of the complexity of problem we were tackling, provided more awareness of the ML ecosystem — people and research labs to follow, research papers to read — and finally helped us better understand the nuances of building machine learning models. It’s not as simple as having some training data, copy pasting some code from open source libraries and voila you have a ML solution. There are lots of hype and noise around what ML can do and how to go about doing it right.

At this point, we concluded that there was no shortcut and that we had to invest time focusing on high quality training data, going through various research papers, trying the algorithms in research papers using our training data and have nobs ( i.e. hyperparameters) tuned for our use case.

Delivering insights to customers

In April 2018, after six months in beta, we launched v1 of our product CXInsight™. The platform enables our customers to import and analyze customer feedback from any source. To date, we have analyzed 200,000+ comments pertaining to a wide spectrum of product categories and types of feedback — PaaS, SaaS, E-commerce, Mobile App reviews, employee reviews, social media, etc.

Of note, most of the data we’ve analyzed so far was originally collected using our competitors’ survey platforms. This is sweet validation of our goal: Regardless of how and where you collect customer feedback, Wootric gives you the best analysis.   

In our next series of blog articles, we will talk about how we have used and are using:

Our goal has never been to build a generic text categorization tool. Rather, our focus is to build the best customer feedback analysis platform. We are also aware that our system is never going to be 100% correct so we have made it easy for our customers and our own team to be human in the loop.

I would be remiss not to thank Stanford NLP, Richard Socher, Lukas Biewald, Sebastian Ruder , Google ML research whose research papers, blogs, tutorials, videos and guidance have directly or indirectly helped us build CXInsight product.

Please stay tuned to our Engineering Blog for next series of articles on this topic.  The Hacker News discussion is here.

Build vs Buy Customer Feedback Software: Making the Best Decision for a Survey Tool

Should enterprises build their own customer feedback software? After all, they’ve got the engineering talent and resources to take it on.

If you’ve got the resources to do it, creating such tools can be tempting, but more often than not, these solutions are trouble to build and maintain.

Why Companies Choose to DIY

Forget “to be or not to be”.

For businesses facing software decisions, it comes down to “build vs buy”. It’s always a balance between finding immediate solutions to problems and considering long-term growth.

Here are a couple of the tempting advantages of building software solutions for yourself:

  • “Anyway you want it, that’s the way you need it” – Journey

When you build something for yourself, it will solve all of your problems in exactly the way you wish. The dashboard will look exactly how you want it to look. The functions will pull from exactly the data you want it to pull from. If your business has specialized needs, a custom solution is functionally ideal.

  • Guaranteed compatibility with everything you already use

Your company has a suite of software that it’s already using. When the data in one software can’t be read by your system of record, people end up typing notes in manually, or other time-consuming methods to get important information recorded for everyone else in the organization. Building software for yourself means you can guarantee compatibility with everything you already use, and if you think ahead enough, compatibility with software you intend to acquire.

Unfortunately, these benefits will only bring value if you can spread out the significant cost of building custom software (time, energy, and resources) over a large number of clients and your engineers’ time isn’t better spent on other projects. Let’s face it, a customer feedback solution for Customer Success/ Customer Support is unlikely to be a priority for your product team.

Building your own software is expensive and getting a high enough ROI on this kind of project is difficult. Add in the ongoing costs associated with maintaining what you’ve built and buying a solution becomes very appealing.

Why You Should Buy Customer Feedback Software

Besides being incredibly labor and resource intensive, trying to build a solution requires months and months of brainstorming, planning, and coding. If the in-house solution isn’t properly and thoroughly planned out, with input from multiple functional teams, this can actually create more headaches and manual processes in the long term. Even worse, if the tool does not add value to the employees that it was built for, it could go unused.

When it comes to surveying your customer base, experts have already thought out a vast number of details, building standard settings and customizable options based off of best practices. There is a reason why customer feedback is a whole industry, and that is because rigorous methodology is paramount to actionable insight.

Get the ebook, The Net Promoter Score Software Buyer’s Guide.

8 essential questions to find the perfect technology for your organization

Customer feedback software creators like Wootric have developed and iterated a variety of features to make starting and running a robust feedback program convenient and valuable. These tools automate gathering feedback and surfacing insight, which can be sent out for action. Buying customer feedback software gets both immediate and long-term value out of a customer feedback program:

Automated Sampling

If you’ve ever gone through the trouble of listing out, segmenting, and randomly sampling your users/customers, you know how tedious this task can be.

Multi-channel survey solutions – that reach your customers via email, in web products, and via text – help you automatically survey the appropriate random sample to capture different segments of your customer base. You can get feedback from both decision makers who do not log-in to the platform very often via email surveys, and feedback from daily end-users via in-app surveys.

Wootric’s standard settings allow you to survey your customers with two different methods. You can keep the flow of feedback constant and random, avoiding various biases that may sneak in if you are not aware of them. This method gives you a daily pulse of feedback, usually Net Promoter Score (NPS), which provides a good sense of user sentiment on any given day, and can show you trends over time.

You can also send surveys based on completion of different events. For example, you may want to send out a Customer Satisfaction (CSAT) survey after a support ticket is closed, or trigger a Customer Effort Score (CES) survey after a customer completes onboarding. Implementing these three micro-surveys at various customer journey touchpoints will get you a holistic “Trifecta view” of your accounts.

With both of these methods, you have the power to change the time frame dictating eligibility to take a survey and what percentage of users/visitors we sample, including the option to survey less than 1% of the customer base, if necessary.

Automated Safe Guard: Intelligent Throttle

It’s always important to have safety features. Customers are already inundated with information every day. You don’t want to add to that annoyance by sending the same survey to them over and over again in a short period of time.

Avoiding survey fatigue requires having separate controls for a slew of different situations that enterprise feedback software companies have thought out and prepared for. These include control over how often any individual will see a survey and how often individuals can respond to the same question.

Sampling Page

For example, after one of your customers takes an in-app survey, that customer will not be shown another survey for another 90 days. You can change the number of days between surveys to suit your needs. You also have control over the number of days between surveys for people who decline your surveys.

All of these settings can be manipulated for each of the survey delivery channels that Wootric provides, as well as for each type of survey (i.e. NPS, CSAT, or CES) you choose to send. For Voice of the Customer programs using multiple delivery channels, Wootric has cross-channel safety features so customers don’t feel overwhelmed by your surveys popping up everywhere they turn.

If you decide to base your surveys off a triggering event, our survey throttle prevents customers from being bombarded with the same satisfaction survey in a short amount of time. While it is standard to have this throttle on, this can be overridden if you want every single triggering incident to fire off a new survey.

Auto-tagging and Segmentation for Insight

A Voice of the Customer feedback program doesn’t stop at just gathering feedback. The key to success is in the insight and action that happens after you’ve gathered customer feedback. If your engineers build a way to gather feedback but that data ends up sitting in a silo, unorganized, then you will never realize any value.

Tagging and segmentation features in enterprise customer feedback solutions aim to make sorting and analyzing survey responses easy and insightful.

insight with tagging & segmentation

Different customer segments will have different needs and therefore different feedback. The segmentation feature in software platforms like Wootric enables you to analyze CX metrics like Net Promoter Score by customer properties. You can pass various properties, like geographic region, or persona, to drill down to specific segments and understand what’s important to your different types of customers.

Tagging is an incredibly powerful tool when it comes to dealing with qualitative feedback. Frequency analysis lifts trending topics out of customer comments, and various teams can find relevant feedback with a single click. 

For example, a product team can view all comments under a feature request tag and prioritize the most frequently requested ones from the highest value customers.

Tagging can be done manually for companies receiving smaller quantities of responses. For companies overwhelmed with feedback, expertly built tools like Wootric can save you time and effort.

Check out our guide to auto-tagging for more benefits and ideas on how to start.

Integrations & Webhooks: Break Down Data Silos & Trigger Workflows

With native integrations and webhooks, you can achieve some of the same benefits of building your own software, i.e. automated workflows among platforms and a consolidated overview of important account information.

Switching back and forth between platforms disrupts workflow. With that in mind, Wootric has built a host of native integrations such as Slack, Salesforce, Gainsight, and Hubspot, to get customer feedback into the hands of those who can act on it like Customer Success, Product, Marketing & Sales. For other apps, Wootric can connect via incoming and outbound webhooks or Zapier.

This means you can push Wootric’s data out onto the platform of your choosing, and Wootric can listen for instructions to fire a survey based on events from whatever app you choose. The possibilities for data exchange are endless. Best of all, this sharing happens in real-time, so your information will always be up-to-date.

Learn all about use cases for connecting platforms with webhooks here.

Spend Your Time Acting on Insight Immediately

When it comes to build versus buy, there is great peace of mind that comes with buying an enterprise feedback management platform. You’ll have experts guide you through the set-up, listening to your company’s specific needs. You can get started immediately, reaping the rewards of a stellar customer feedback program now, including higher customer retention, happiness, and company growth.

Customer Success Analyst: When to Hire Someone Dedicated to the Data

The Customer Success Analyst has evolved to be the go-to person for all the data – or as Marketo put it in their Linkedin job ad, “the primary deliverable of the Customer Success Decision Analyst is to convert our Customer Success operation at Marketo into a highly data-driven business where we can measure, analyze and optimize every aspect of our engagement with our customers.”

This includes data like:

  • Feature usage patterns
  • Maturity scores
  • NPS results
  • Voice of customer qualitative feedback
  • Customer journey mapping
  • Customer experience metrics
  • Capacity models

Among all of the hats that CSM’s wear, the number-crunching, data-heavy, quantitative analyst hat is one of the most time-consuming. But because of the data-savviness this role demands, CS analysts also hold the keys to unlocking incredible potential when your business is scaling up.

The CS analyst role isn’t *just* about collecting data for dashboards and reports (and basing recommendations on that data) though. It complements the Success Operations role, which builds new tools and processes to scale CSM’s everyday activities. As the person navigating multiple platforms for data on a day-to-day business, CS Analysts know how information flows and who needs what information.

For one of Wootric’s customers, Chorus.ai, CS Analysts also take ownership of the technical onboarding process for new or upgrading customers, ensuring “a smooth implementation, including initial and ongoing training for customers.”

It’s a prime position from which to watch for opportunities to make big impacts on the success of customers – and the success of the company. That’s the subtextual expectation: By being in charge of the data, the CS Analyst knows how to use it to find untapped value.

What does a CS Analyst need to know?

Experience working with large amounts of data (SQL, Python or R) and with survey and analysis tools (Wootric, Tableau, etc.) are must-haves, but the most important qualification is having taken that data and used it to produce actionable insights.

Analysts are data story-tellers. They work with the numbers and provide context for them, creating reports to recommend strategic options and solutions. A listing for a CS Analyst position at Salesforce described one of the responsibilities as “assist in developing and delivering presentations for senior executives”, which requires strong public speaking and presentation skills.

If a company struggles with data silos, CS Analysts must bridge the gap between teams. Not only must analysts overcome the technical issues of compatibility, but they need to possess strong internal communication skills to overcome any organizational walls that may be contributing to the data isolation.

While CS Analysts own the quantitative facet of Success, a customer-centric mindset and empathy for the humans behind the numbers distinguish a great analyst from a good one. These soft skills help analysts frame their analysis to produce long-term, customer-centric solutions that support CSMs to retain customers.

What does this role look like in real life?

For some companies, the CS Analyst position can be a foot-in-the-door to Customer Success.

Anthony Enrico, VP of Customer Success at Emailage, created the Analyst position because his “CSMs were being asked to spend enormous amounts of time compiling reports and the opportunity cost of spending time deepening relationships and loyalty with customers was too great.” As a leader within the organization, Anthony was also doing a lot of work with these reports, when his time was clearly better spent working on strategy, escalations with his CSMs, and focusing on new business opportunities with the company.

So Anthony hired Bryan Mehrmann, now a CSM at Emailage. Bryan was originally brought on as the first CS Analyst to support the CSM team. Bryan compiled and sent out daily reports on customer usage trends to identify and correct anomalies as early as possible. He took detailed revenue projection reports and distilled them for the C-suite for their weekly use. Bryan took on more responsibilities as time went on.

Working together, Anthony and Bryan shaped the role as it is today. As for how the position fits into the CS team, Analysts can be promoted to full CSMs after they’ve achieved a comprehensive understanding of the product, metric drivers, and relationships with Emailage’s customers.

On the other hand, CSMs may choose to specialize in VoC data analysis like Customer Success Analyst Tim Dressel at Qualer. For him, there’s the usual collection and analysis of customer data in spreadsheets, but also a lot of room for innovation and collaboration. If he sees a red flag in the metrics, he leads investigations into those customer issues, working with his cross-functional team (and collaborating, at times, with Qualer’s Head of Technology) to make sure customers’ needs make it into the software they develop.

How do you know if you need a Customer Success Analyst on your team?

Customer Success Managers are often being pulled in five different directions at once, and when that happens, they sacrifice time on one task for another. Not only does Customer Success provides data and insight crucial to their own day-to-day, but they are the go-to team for reports for the C-suite.

Customer Success needs data. Data is at its core. So if your Customer Success team doesn’t have time to live and breathe data, you may be at the tipping point to bring in an analyst who can parse the numbers for you. This is especially important for scaling processes when anecdotal experiences have to give way to metrics.

For some companies, bringing on an Analyst to Customer Success may happen by incorporating a company-wide business analyst into the team and transitioning them into a full-time Success Analyst. Depending on the company, Customer Success may not need an Analyst until their team is four or five CSMs.

The most common theme among companies looking to hire a CS Analyst is major growth.

For example, a Wootric customer that recently started trading publicly, DocuSign, decided to add the Customer Success Analyst position as they accelerated their growth.

Analysts (& their data) are a CSM’s best friend

For Customer Success, the best way to prove value, whether it’s to senior management or to a customer, is with numbers and context. Having a role dedicated to creating robust reports to highlight value and propose inventive, data-backed solutions is an excellent way to help your current CSMs be the best that they can be at scale.

Automatically send customer feedback to Salesforce, Gainsight and Slack for quick action. Learn about InMoment’s integrations.

VIDEO: 3 Ways Machine Learning Will Transform Your VoC Strategy

Jessica Pfeifer, co-founder and Chief Customer Officer at Wootric, spoke at Totango’s Customer Success Summit on March 6, 2018 about how the Customer Experience landscape is evolving and how companies need to adapt to the rapid changes with the help of machine learning.

Her talk covers the ways customers are changing, how companies can fail to recognize these changes, and how machine learning empowers companies to adapt quickly to the new customer mindset.

Machine learning makes it easy to break down customer feedback data silos within organizations, giving Customer Experience champions a holistic view of the Voice of the Customer and a competitive advantage on companies that do not take advantage of new VoC technologies.

Learn more about getting insight from qualitative data with InMoment CXInsight™.

Customer Success Operations Manager: Does Your Team Need One?

Customer Success teams are expanding – not just in size, but in scope. New roles are emerging as CS is maturing as a specialty, specifically roles like Customer Success Operations (CS Ops).

At early-stage startups, Customer Success Managers will find themselves covering this function, but as the company grows, it can be extremely valuable to separate this function into a dedicated role within CS to help scale up.

What does a Success Operations Manager do?

Think of “Success Operations” as a product that promises to optimize processes for its customers, i.e. the Customer Success Managers.

CS Ops managers establish a baseline of productivity using metrics like net MMR churn and how difficult it is to learn about new product features. They talk to CSMs to learn what pain points they face in their day-to-day responsibilities and observe how processes currently work.

They segment the current customer base to distribute the workload effectively among CSMs. CS Ops managers look for consistent issues across the whole Success team, break the issues down into manageable components, and create solutions with measurable results.

“There is nothing so useless as doing efficiently that which should not be done at all.” – Peter F. Drucker

Using the information they’ve gathered, CS Ops managers may build tools like custom dashboards, or establish automatic workflows among software platforms to make the CSM’s job easier and help them be more productive.

A CS Ops manager will “onboard” CSMs, teaching them how to use the new tools at their disposal, and check in frequently with their “customers”. In this sense, they are CSMs to the CSMs.

In short, Customer Success Operations managers are responsible for providing tactical support to the rest of the Success team, helping them improve their KPIs and their efficiency.

What does a CS Operations Manager need to know?

Customer Success Operations Managers should be familiar with:

  • Customer Relationship Management Software (e.g. Salesforce, Gainsight, Totango)
  • In-app messaging Software (e.g. Intercom)
  • Support platforms (e.g. Zendesk, FreshService)
  • Key Performance Indicators (KPIs) for Customer Success

Each company will have a unique suite of different platforms that it uses, and CS Ops managers need to be quick to become fluent in most, if not all of them. This is crucial for the role since data silos are a major hindrance to organizational efficiency and detract from your customers’ experience.

Additionally, Success Operations Managers will need many of the same ‘soft skills’ that CSMs use. For example, CS Ops managers need to be able to actively listen to the struggles of the CSMs to come up with valuable solutions.

What does this role look like in real life?

For Feedvisor Customer Success Operations Manager Shachar Avrahami, he came into the company as the first “Professional Services team member.” As the team grew from a one-man operation to a multi-person team (and the company scaled up), Shachar’s manager asked him to create his own role – Customer Success Operations Manager, “and I became the first person to assume this new position and help define it.”

He says, “I am the owner of our team’s processes on a macro level, making sure all teams are aligned with the strategy for each part of the customer’s journey.”

How do you know if you need a Success Operations Manager?

Giving a concrete number at which you need to hire a CS Ops manager is difficult. It depends on the capacity of your current CSM team. As a rule of thumb, you will want to look into hiring a Success Operations manager after you’ve hired your fourth or fifth CSM.

For some organizations, the new role may be an internal promotion of a CSM. For other companies, it may be wise to bring in an individual with experience in a ‘project manager’-like position to help streamline Customer Success processes, aligning everyone under the common vision that is handed down from the C-suite and creating a more consistent experience for customers.

Like Robert S. Kaplan, co-creator of The Balanced Scorecard, says, “consistent alignment of capabilities and internal processes with the customer value proposition is the core of any strategy execution.”

How do you advocate for a CS Operations Manager role?

Understand that a CS Operations Manager’s responsibilities are nearly the same as those of a Sales Operations Manager. The justifications for the CS Ops role are similar.

The operations role increases the productivity of your customer-facing Success team members, who carry the weight of recurring revenue on their shoulders. Not only does this mean management can hire fewer individuals for the customer-facing roles, but each CSM’s key performance indicators will improve at rates that were impossible before this specialized role.

Having a CS Ops role also improves visibility into the Success team’s business outcomes, places for improvement, and what projects need to be prioritized for Customer Success.

For an excellent breakdown and comparison of the Sales and CS Ops positions, click here.

Operations For Smooth Scaling

There will always be growing pains as a start-up matures and finds success. Operations experts specialize in finding technical solutions for when people are stretched beyond their limits. Creating a Customer Success Operations position is an effective way to proactively combat capacity issues for the Success team and deliver a consistently positive experience for your customers.

Access Voice of the Customer insight in your system of record with InMoment’s native integrations, including Salesforce, Gainsight, & Totango.

Soft Skills are Real Skills – In CX, You Need These 10

“Soft skills” have traditionally been undervalued, and that’s slow to change. But more companies are realizing their worth. And even if the skills themselves are difficult to quantify (how much more likeable is Job Applicant A than Job Applicant B?), their effects aren’t.

The soft skills CX professionals possess directly affect metrics like:

  • Net promoter scores
  • Customer satisfaction scores
  • Customer effort scores
  • Qualitative survey feedback on customer support interactions
  • Qualitative data gleaned from online customer reviews
  • Number of referrals and recommendations

Human-to-human interactions can make or break those scores, generate referrals or cancellations, and either fuel word-of-mouth growth or silence it.

But before you break out your old copy of Dale Carnegie’s How to Win Friends and Influence People (a classic for a reason), I’d like to talk about why I’m reading more articles now on “soft skills” as they apply to customer service, customer success, and customer experience.

Because we need them more now than ever.

“So let’s uncomfortably call them real skills instead.

Real because they work, because they’re at the heart of what we need to today.

Real because even if you’ve got the vocational skills, you’re no help to us without these human skills, the things that we can’t write down, or program a computer to do.”

– Seth Godin, Let’s stop calling them ‘soft skills’, Medium

What Exactly Are Soft Skills?

Often referred to as “people skills,” ‘soft skills’ don’t have a hard definition. In fact, they’re remarkably hard to pin down.

If you try to define these skills with a list of what they entail, you’ll run into trouble. Everyone has their own set.

Some argue that part of the definition of ‘soft skills’ is that they are something you’re born with. But others, including Seth Godin, say that’s “crazy because infants aren’t good at any of the soft skills. Of course, we learn them.”

(When was the last time you met a baby with a good work ethic?)

Seth Godin calls for five categories of ‘soft’ skills: Self Control, Productivity, Wisdom, Perception, and Influence.

Others cite the ability to listen, accept feedback, and communicate effectively. Or qualities like charisma, empathy, friendliness, patience, and reliability. Problem-solving skills get thrown into the mix with teamwork and attentiveness.

I like this exhaustive list from the balance which offers 6 categories of soft skills with sub-lists of specific skills under each. Their categories are:

  1. Communication skills
  2. Critical thinking
  3. Leadership
  4. Positive attitude
  5. Teamwork
  6. Work ethic

But even those don’t make it into “The Five Soft Skills Recruiters Want Most” that made it into the eponymous Fast Company article. Those were: Problem solving, adaptability, time management, organization and oral communication.

In 2013, Google tested its hiring hypothesis that prioritized top grades from elite universities in STEM subjects. They found that, in practice, the eight most important qualities of Google’s top managers were:

  1. Ability to be a good coach.
  2. Willingness to empower, rather than micromanage.
  3. Taking an interest in people’s success and well-being.
  4. Ability to be productive and results-oriented.
  5. Communication and listening skills.
  6. Willingness to help employees develop their careers.
  7. Holding a clear vision and developing a strategy for the team.
  8. Possessing key technical skills that allow the manager to advise the team.

Technical skills came in dead last. The rest were ‘soft skills.’

For our purposes, I’d like to simplify the definition of these skills and stop calling them “soft” – period. Let’s call them “people skills.”

People skills are what you need to relate to people, be understood, and be liked. Likeability is one word that encompasses myriad characteristics, including charisma, reliability, empathy, and willingness to take a stab at solving problems. Above all, we’re talking about genuinely caring about people.

If you get that one thing right – you’ve already got the core soft skills you need.

Relationships Can Make Or Break a Business

Businesses are rising and falling based on the quality of their relationships with their customers – and employees.

For subscription-based services in general, and SaaS in particular, success metrics like retention, customer lifetime value and cost-to-acquire are all correlated with how well businesses relate to, and engage with, their customers.

These are people skills.

And as artificial intelligence is taking over so many of the human-to-human interactions businesses have traditionally had with their customers, the human interactions that do happen are coming under more scrutiny.

In Top Customer Service Trends for 2018 by Kate Leggett, Vice President and Principal Analyst at Forrester, Kate points out the repercussions of increasing AI and self-service in customer service.

“With customers increasingly using self-service, there are fewer opportunities for engagement with agents who can lend a human touch.”

That means three things: Those fewer opportunities are under more pressure to produce positive results, human-to-human interactions will be reserved for bigger problems that AI can’t handle, and those complex issues will require both accurate diagnoses and empathy.

“These organizations will focus on the quality of interactions as measured by customer retention and lifetime value. Agents will need to be more highly skilled and better compensated. Old management principles that focused on efficiency must be relaxed. Ultimately, technologies such as quality monitoring should be replaced by customer feedback.”

As companies race to differentiate themselves based on customer experience, these interactions become vitally important.

“Forget about your company’s historical point of differentiation. Customer Experience reigns supreme today and you will either be rewarded or punished for how you are treating your customers.”

– Bill Carmody, founder & CEO of Trepoint, “Customer Experience is Your ONLY Differentiator. You’re About To Be Rewarded or Punished”, Inc.

With hundreds of “soft skills” listed, it might seem like a lifetime’s worth of study for anyone who isn’t confident in their natural gifts of gab. Yes, you can learn people skills. You can certainly improve them. And to really make an impact on CX, you and your customer support or customer success team may have to. So let’s concentrate on the skills that make the most impact.

The 10 People Skills You Need Most for CX

  1. A genuine willingness to help – Not only does a genuine willingness to help make customer support agents shine and customer success managers effective, this instinct to solve problems and make positive impacts bleeds into other areas as well. For example, a customer success agent who becomes aware of a problem through customer feedback can patch the issue – or the agent can investigate the problem and actively work with other teams to bridge that success gap for everyone, strengthening the product or service and the company as a whole.
  2. Empathy – Customer support professionals are often trained to “show empathy” by repeating phrases that come off as insincere at best: “I understand that this can be frustrating.” Empathy phrases can be incredible tools (this is a very good list), but only when used with discretion (so it doesn’t sound like you’re reading off of a card). But empathy is about more than the words you use. It’s the desire to really understand where someone else is coming from and what they need to thrive. That’s Customer Success 101, right there: Taking the time to learn about your customer’s business and challenges so you can understand your product from their perspective.
  3. Communication – Communication skills, the ability to listen carefully, explain clearly and treat kindly are must-haves in the People Skills toolkit, but there’s another type of communication customer service and success teams should have: Cross-communication. You’re at the nexus between your customers and your business which puts you in a unique position to gather data customer sentiment, use, and engagement that everyone else in your business needs. Make sure they get that info.
  4. Emotional Intelligence – Connected to empathy in that you’re aware of other people’s emotions, Emotional Intelligence also means you’re aware of your own. It’s self and social awareness of mood, emotional strengths and weaknesses, and potential underlying motivations behind behavior. In practice, this means knowing when to praise team members and how to constructively criticize. With customers, often it’s about understanding how your actions and responses can positively affect their moods to create memorable experiences.
  5. Integrity – Managing expectations by honestly telling customers what they can and can’t expect builds a tremendous amount of trust and sets customers up to have positive experiences when businesses don’t overpromise. Being able to set expectations also builds trust with internal teams.
  6. Problem-Solving – The best problem-solvers are the ones who jump in as soon as they see a rough patch arise and have enough confidence to figure it out if a solution doesn’t immediately present itself. Really, it’s all in the attitude. You don’t have to know the answer to everything to help. You just have to be willing to figure out the answer that’s needed.
  7. Stress Management – Dealing with people, even lovely coworkers and customers – is inherently stressful to most humans. The ability to manage that stress and not take it out on those around you is one of the best ‘People Skills’ you can cultivate. One bad day can lose a lot of clients when you think in terms of not just the client you’re speaking to, but all of the future clients they can bring in with recommendations.
  8. Listening Skills – This is one everyone in the company, from the Founder on down, needs to have, because listening to your customers effectively, focusing on their needs and desires (instead of your needs), is how great products and companies are built. More than that, though, is the willingness to listen internally as well – to people from different departments who often have valuable insights to add.
  9. Leadership – Once you uncover a good idea or customer feedback that requires action, it’s a real skill to be able to inspire others to follow your lead (especially if those others are above you). This becomes easier when you work from the mentality that your role is to make those you lead wildly successful. Everyone wants to follow a leader who gives them what they need to do their best work and get the best results.
  10. Team Building – Team building across departments brings leadership to a whole new level. Reaching out and forming relationships with people in other departments is something anyone can initiate. And when you approach your co-workers with an open willingness to help and collaborate, you won’t get turned down.

What “soft skills” – or People Skills – do you see the most need for in CX?

Be the customer experience champion at your company. Sign up today for free Net Promoter Score, CSAT or Customer Effort Score feedback with InMoment.

Automatically Analyze Qualitative Customer Feedback with Auto-tagging

Customer experience professionals live in a world overflowing with data. Sitting on that wealth of information is frustrating when you know it has incredible potential.

If you are tracking CX metrics, like NPS or CSAT, the numbers help you quantify customer loyalty and satisfaction. But it’s the customer comments that come with those surveys, all of that rich qualitative data, that give you invaluable context for why customers feel the way they do.

Until now, it’s been difficult to analyze qualitative data because it is so unstructured.

This is where tagging comes in.

Using software to analyze qualitative data

Modern customer feedback software comes with the ability to tag customer comments. Tagging feedback has two functional goals: Routing and Insight.


Creating a tag for specific stakeholders, e.g. “product”, quickly sorts feedback to be routed to the correct teams for follow-up. Product teams can simply click a button to see verbatim comments regarding feature requests and support teams can be more proactive by checking for comments under a “bug” tag.


Tagging comments by relation to product, website, or customer experience helps themes emerge. For example, you may see that most of your detractors are tagged with “shipping” or “price”. This will help you prioritize and address issues in real-time.

Tagging comments manually doesn’t scale, however.

If you are receiving less than 100 comments a month, manually tagging comments can work. But customer comments can pile up just like emails in your inbox. Constant monitoring results in little else getting done. When you find yourself drowning in responses, CX feedback can feel overwhelming — just like your inbox.

This is where using software to auto-tag customer comments saves the day.

Auto-tagging gives you real-time categorization of large quantities text feedback

Auto-tagging automatically sorts qualitative comments for you using AI-powered text analysis, and it happens in real-time. This helps you surface themes and see trends that the human brain has trouble processing on its own.

For example, you may find that pricing issues are mentioned in 80% of your detractor comments in the past couple months, or a new feature is mentioned in 65% of your promoter comments since it launched.

Auto-tagging serves as a dynamic tool to quickly sort massive amounts of feedback for routing to the appropriate teams for insight and immediate follow-up.

We’ve provided the first steps and some suggestions to start auto-tagging in real-time.

Using machine learning to auto-tag

When you’re drowning in feedback, we recommend using natural language processing to auto-categorize feedback. Customer feedback software, like Wootric, can tag and surface themes in your feedback based on what’s important in your industry.

Automatic text classification is the ultimate time saver when it comes to comment feedback. While this isn’t a necessary step, for large amounts of feedback, it is an incredibly powerful tool for true automation in your tagging system.

How to set up text-match Auto-tags

The time you save by setting up an auto-tagging system can be spent taking action based on the insight lifted out of your survey feedback.

If you aren’t using machine learning software, here are the steps to take in planning your text-match auto-tagging system and some suggestions to get you started.

First, Some Questions to Ask Yourself

When you start to tag your feedback, read every comment you receive in a period of time, perhaps a week or a month, and consider the following:

  • What topics/features/issues stand out in your comments?

For example, you may see that many of your customers talk about your Support team’s response time, or the value your product/service has brought to them. These general themes will serve as jumping off points for brainstorming tags and keywords.

  • Is there industry or business specific vocabulary or jargon that you might want to track?

For SaaS companies, you may want to include terms like “dashboard”, “widget”, or “in-app” as tags or as text-match keywords. Oftentimes, these terms will be abbreviated, like UI for “user interface”. 

You can even choose to create tags for team members to alert them whenever they are mentioned by name. This might be helpful for a customer support agent who wants to see what customers are saying about their interactions.

As you read through your sample of comments, make a note of the words and phrases you spot customers using. They may be using different terms than the language you and your colleagues use as professionals in your industry.

  • Which teams will you be sending customer feedback to and what terms are relevant to them?

You want to be routing comments to the right teams. For example, a product development team will be interested in comments about user interface, integrations, or feature requests while your support or success team may be more concerned with bugs or implementation.

Nested Tags or Parent-Child Tags for Tag Hierarchy (SaaS example)

Once you’ve answered these questions, start grouping specific terms under broader terms. This is going to help you create hierarchy within your tags, also called nested tags.

Nested tags are labels associated by a hierarchy. The ‘sub-tag’ or ‘child tag’ is a tag that is more specific and can be categorized under a ‘parent tag’.

When any of the ‘child-tags’ are text-matched to a comment, feedback platforms will also tag that comment with the corresponding ‘parent tag’. Comments tagged with only the ‘parent tag’ do not include any of the words associated with any of the ‘child-tags’.

This allows you to pull comments that mention any of the specific integrations through the child-tags. At the same time, the broader “integrations” tag pulls comments that mention integrations in general, e.g. suggested integrations from our customers.

Choosing Text-Match Keywords or Keyphrases

For auto-tagging, it is important to choose the right words or phrases to match the tag to the comment. Text-match tags use an “exact match” rule for automation.

This is where having read through some of your current open-ended feedback is useful. You’ve seen the specific words that your customers tend to use when writing about different issues. It may also be helpful to use a thesaurus to come up with synonyms for the words or phrases you choose to match on.

Remember that text-match is very literal, so you will need to include variations on the words and phrases you choose. For example, an “implementation” tag should match on “implement”, “implemented”, “implementation”, and “setup”, as well as “set-up”.


We’ve compiled a list of auto-tags that are commonly used by SaaS businesses. You may be able to use some of these in other industries as well.

As you start to receive feedback you should refine your tags to be more specific to your business needs.

Here’s a list of common tags for SaaS companies to start with:

Tag name: Matches on:
“Product” parent tag Terms specific to your product like the name, or terminology for features, e.g. “Amazon”
“Product A” child tag Name of one of your more specific products or services if you have more than 1, e.g. “Prime Music”
“Product B” child tag Name of another product or service if you have more than 2, e.g. “Prime Shipping”
“Bug” “issue, issues, crash, crashes, bug, bugs, buggy, error, errors”
“Competition” Names of your competitors
“Documentation” “docs, documentation, article, articles, help article, FAQ, FAQs”
“Feature request” “wish, add, would like”
“Implementation” “implement, implemented, implementation, setup, set-up”
“Integrations” parent tag “integration, integrate, integrates”
“Integration 1” child tag Words specific to one integration, change the tag label to the specific integration, e.g. “Slack”
“Integration 2” child tag Words specific to another integration, with the corresponding label, e.g. “Salesforce”
“Performance” “speed, slow, fast, uptime, downtime, 404”
“Price” “cheap, expensive, promo, promotion, deal, price, price tag”
“Support” “support, onboarding, on-boarding, issue, broken, assistance, service, tech support, help, helps, helping”

Human Review: Manually Tagging for Refinement

Monitor your feedback for a couple weeks after you set up your auto-tagging system. If a comment should be tagged, but isn’t, add more keywords to the text-match tag. Manually tag any comments that are difficult to text-match.

A good example would be a comment like “I tried to connect your software to my CRM but it didn’t work.” This comment is clearly related to integration, but text-matching wouldn’t catch this. After manually tagging this comment, you can then add “connect your software” as a keyphrase to the integration tag.

Human review becomes a tool for refining your existing auto-tags, instead of the main workhorse. As time passes, you’ll spend your time scanning for edge cases and new issues or topics that require a new auto-tag.

Do this check periodically to ensure your insight is accurate. Maintaining your valuable tagging system will save you time in the future.

If you are using machine learning, use manual tags to train the AI to be more accurate in the future. In case you spot an inappropriate tag, the AI also learns each time you remove a tag that it generated.

Feedback Routing & Driving Action

Surveying customers is the first phase in your transformation into a more customer-centric company, but you will plateau if you sit on the feedback. Setting up an auto-tagging system means feedback is sent to relevant teams in your organization in real-time. Trends are lifted more easily from qualitative feedback, and your customer-centric organization will be empowered to actively pursue customer happiness.

Measure and improve customer experience.

Get auto-tagging with Wootric customer feedback software. Sign up for a free trial.

Communication Tips & Tools for Customer Success Managers

In Customer Success, communication with accounts can make or break the job. Upping your skills—and having the right tools to make the back and forth efficient—can help you win customers for life.  

Wootric has gathered some tips and tools to help you communicate with your customers at scale.

In the first part of this three-part series, we gave you tips and tools to help with time management. Use the time you saved to improve your customer relationships and communication processes.

Communicating with Customers


  • Nail down specific measurable criteria/objectives in onboarding

When you start building a relationship with the client, the most important part of ensuring client success is establishing what success means to them. Oftentimes, clients come to you with large, lofty, general goals like “improve customer experience”. Create SMART goals with your customers during onboarding and establish a baseline so that you can prove to them, objectively, that your company is delivering value.

“You can focus on adoption, retention, expansion, or advocacy; or you can focus on the customers’ Desired Outcome and get all of those things.” Lincoln Murphy, co-author of Customer Success: How Innovative Companies Are Reducing Churn and Growing Recurring Revenue

  • Master telling a client “no” with grace

Nobody likes to hear “no”, not toddlers, not teenagers, and especially not adults. When you are dealing with customers, you will inevitably run into requests that you cannot and should not fulfill. It’s an unpleasant part of the job.

You can deal with this situation in a multitude of ways, and prior experience with your customer can guide you to the best method. It might be suggesting the closest alternative, or it might be providing a detailed explanation. Regardless of how you choose to tell them no, it is key to maintain your relationship with them, and maintain your position on their team, as their advocate, the whole time.

  • Listen for the “silently churning”

All too often CSMs default to listening to the clients who shout the loudest. This is a natural human response, but leaves you vulnerable to neglecting your clients who are less vocal. Just because someone isn’t complaining to you in an email or over the phone, doesn’t mean they’ll renew when the contract is up.

Maintain a pulse on your client portfolio with the help of metrics like NPS, CES, and CSAT. Surveying customers after interactions and a couple times a year will provide invaluable insight into the health of your accounts. Survey feedback and analysis helps focus on the “silently churning”, the customers who are simply disengaging instead of yelling, and helps to narrow down what actually drives their lack of enthusiasm.



Boomerang is a free email extension that lets you schedule emails to be sent, remind yourself if you don’t hear back, and take messages out of your inbox until you actually need them. Boomerang will archive your message, then bring it back to your inbox at a time you choose, marked unread, starred or at the top of your message list. You can use Boomerang as an automation tool for following up or checking in with clients, especially when you don’t hear back from them.

Text expansion apps like Text Expander:

Text expansion applications use a few basic mechanisms to make typing faster. Abbreviate blocks of text that you use often and the app will replace it with the full block of text that you assign to the abbreviation. For example, you could have the app insert “Customer Success Manager” everytime you type “csm”.


Grammarly uses AI to detect grammar, spelling, punctuation, word choice, and style mistakes in your writing, offering you alternatives in real-time. Grammarly has recently been detecting micro-aggression and intent in emails, offering alternatives to help maintain professional relationships. It can also offer vocabulary enhancement suggestions for people using English as a second or third language.

Note: if you regularly use the Google suite of software, like Documents or Slides, you’ll have to stick with their autocorrect algorithms or take the extra step to upload documents into Grammarly’s own dashboard for corrections.


Doodle is a straightforward scheduler that helps you coordinate a time for meetings. You suggest a few dates and times for your participants. Doodle then creates a polling calendar that can be sent to them for feedback. As each person selects the dates and times they are free, Doodle aggregates the responses to tell you which option works best for everyone.


Calendly is also a scheduler that helps you schedule meetings without the back-and-forth emails. It has many more integrations and features than Doodle, which means it takes more getting used to, but is much more robust. Calendly takes time zones into account for each invitee and even allows you to request payments via Paypal and Stripe.

Retain more customers. Sign up today for free Net Promoter Score feedback with InMoment.

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