PART 1

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:

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:

• Bag of Words with Naive Bayes and SGD Classifier (Part II)
• word2vec with PCA, Logistic Regression, SVM and SGD Classifier
• Bidirectional LSTM
• CNN
• Custom word embeddings
• Productizing — DevOps around ML
• UX and UI
• Online learning and Human in the loop

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.

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.

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.

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.

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.

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.

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.

“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?

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.

Routing:

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.

Insight:

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.

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”.

Suggestions

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.

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.