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How to Level-Up the CX Program at Your Growth Stage Business

You’ve been using Net Promoter Score in all the right ways, and now you’re looking to advance your CX program. Fear not, you’ve come to the right place!

The next level of CX for Growth Stage companies focuses on a few key things:

Taking a more holistic view of the entire customer journey
Leveraging technology to listen to hundreds and thousands of customer comments
Employing robust analytics

We’ve previously explained how to quickly build your first customer feedback program with a single survey like Net Promoter Score in a single channel. Now we’ll combine surveys with behaviors and concrete numbers to see how CX impacts metrics like product use, retention, and sales.

Yes, it’s time to level up your CX program!

Get the ebook, CX FOR EVERY STAGE: How to scale your Voice of Customer program from startup to enterprise. Learn how to improve user experience for product led growth and loyalty.

We’re sticking with the 3-step Listen, Learn, and Act model but upgrading each step’s activities from Early Stage to Growth Stage CX programs.

Step 1: Listen

In this step, you’ll gather information across the customer journey. Many people at the Growth Stage have already identified critical touchpoints in the customer journey that drive success, including:

Achieving first value
Support interactions
Using a new product or service

The Listen step focuses on asking the right questions at these touchpoints to help you optimize your CX. During the Early Stage, you offered up the Net Promoter Score survey. Now it’s time to move on to two other important CX metrics.

Customer Satisfaction Score (CSAT)

The CSAT asks customers how satisfied they were with a recent interaction, like a support call. CSAT is the most popular CX metric for transactional interactions, and you use it to gauge how well these interactions are being handled.

How might you use CSAT? If you’re with an e-commerce company, you likely use it to get post-delivery feedback on a purchase. At SaaS companies, product teams use a CSAT variation called a Product Satisfaction survey (PSAT). It’s often triggered in-app to get feedback that helps product teams optimize the user experience.

Customer Effort Score (CES)

The CES survey asks, “How easy was it to _________?” CES is used to improve systems that may frustrate customers. It allows you to capture early feedback and discover ways to make sure the path to the all-important first value is smooth.

Use CES surveys to measure how customers feel about their onboarding, which is the critical first step of the customer journey. It’s much easier to retain a customer who has had an excellent first experience with your product than win over a customer reeling from poor onboarding that missed the mark.

When you combine the information gathered from NPS, CSAT/PSAT, and CES, you can uncover previously hidden areas of the customer journey and understand how those affect overall CX.

Step 2: Learn

You now have a plethora of customer feedback from your three surveys, and it’s time to extract actionable insights. The important thing to realize here is you’re collecting feedback from thousands of surveys. That amount of data quickly becomes hard to address at scale, and text-match tags won’t capture the wealth of information available. You’re going to need more advanced tools than what you used at the Early Stage.

Customer insights through machine learning

The most insightful input from customers comes in the free-text portion of your surveys. To mine that rich data, you’ll need a tool that uses Natural Language Processing (NLP), a form of AI for real-time text categorization and sentiment analysis.

For many businesses, the wealth of customer experience data has become overwhelming. Artificial intelligence gives us the means to retake the initiative.

– Jessica Pfeifer CCO, Wootric

Advanced tools for the Growth Stage Learn step share two essential elements:

Categorization in real-time

One of the reasons you’re not manually analyzing the text is you don’t want to wait weeks for insights, and your customers certainly don’t want to wait that long for action. Natural Language Processing allows computers to auto-tag, interpret, and analyze text data as new topics arise, highlighting any issues immediately so you can take timely actions.

Sentiment analysis

Simply put, sentiment analysis tells you why your customers do or don’t love you. NLP performs sentiment analysis on your customer and user feedback, taking you way beyond the traditional text-match tagging. Not only is every topic tracked over time, but NLP also tracks the positive and negative tone and tenor of the customer voice. In a quick review, you can instantly gauge whether a specific product touchpoint performs well for your customers. This lets you see how your business initiatives are affecting your users in real-time.

Auto-categorizing feedback is a powerful step in VoC, taken with the help of new technologies like Natural Language Processing.

Link CX metrics to business outcomes

You can tie CX directly to business outcomes by linking customer survey data to business-focused metrics like purchases, conversions, churn, and sales.

Say you want to tie CX to churn through your mobile app. No problem!

Look at your post-survey 90-day churn metrics and see at what scores you begin to lose customers rapidly.
Compare that with your NPS. Let’s say your NPS shows you can tolerate some Passives and maybe even some Detractors scoring you at a 5 or 6. Cool, you can let those ride a bit if you’re resource light.
Detractors scoring you at a 0 or 4, however, could be at serious risk of leaving if they don’t receive the support they need to succeed. Put your resources there ASAP!

How else can you monitor the risk of churn? Well, if you’re a B2B business, you’re probably already looking at your Customer Health Score. Factor NPS and other CX metrics into your Customer Health Score and get even more insight with a system that takes into account behavioral metrics like the number of support tickets per user, usage of product features, and other engagement metrics.

Step 3: Act

You’ve listened, you’ve learned a lot, and now you’re ready to make an impact. But here’s the thing: CX is built by and affected by more than just one team at a company. So there are two critical parts to the Act step.

Get CX data into everyone’s workflow

A customer-centric organization relies on everyone having access to VoC data, so no individual or team at your company should ever have to search for it. All functions can drive better customer experiences and benefit from having CX data and analytics at their fingertips.

Sales needs CX metrics at the account level in Salesforce to prepare for an upsell conversation.
Customer Success uses Gainsight or other platforms for regular communications with customers.
Customer Support is in Intercom or ZenDesk.
Product may want data in their analytics platform like Tableau.
Analysts will want to pull CX data into their relational database.

By connecting your CX program to the applications and software used by other teams, you can destroy silos and create powerful interactions that delight your customers. Look for CX platforms with native integrations and open APIs to make these connections seamless.

Optimize your product with CX

It’s all good and well to gather and distribute essential data and insights, but a CX program’s real power comes from making your product and services better.

Use your CX data to rank and address the things that matter most to your customers and thus to your business’ success. We recommend creating a dual-axis plan of attack to prioritize what you optimize.

Look at the number of impacted customers and their average score for each issue.
Combine that number with a qualitative measure of the engineering and operational effort required to address the issue.

Close the loop at scale

Once you’ve taken actions to improve CX, don’t forget to communicate back to your customers who gave you the feedback to make those changes. Let them know you appreciate their input and that it made an impact.

You now have hundreds and thousands of customers giving your feedback, and you won’t have enough resources to call each one personally. Thus you’ll need a hybrid model to close the customer feedback loop.

High touch. A customer success agent or account manager can reach out to their customers when they respond, even if just to say “Thanks!” This connection lets customers know you’re listening and appreciate their feedback. For a B2B business, this is the way to go if you have the resources.
Medium touch. Segment the list by survey scores. Sync with a platform like Intercom to trigger automated messages or schedule a weekly email campaign to each group.
1. Thank Promoters and possibly offer them an incentive to be brand advocates, perhaps by sharing their positive feedback on social media.
2. Route Detractors to Customer Success or Customer Support. That team can devote time to understanding why the customer’s not happy — especially those who didn’t leave feedback — and make the CX and relationship better.
3. Automate a reply to Passives who didn’t leave feedback, spurring a “What would make you LOVE us?” conversation.
Low touch. Respond with information-sharing and transparency, such as a blog post or newsletter at the end of the month, summarizing the feedback you’ve received and stating your plans to address issues customers have raised.

A growing company needs to grow its CX program. By expanding your view to the full customer journey, expanding the feedback you’re requesting, and then using more advanced tools to pull insights from the feedback, you’ll be ready to optimize the customer experience you provide and enjoy the success it brings.

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Quickly Build Your First Customer Feedback Program for Powerful Results

While so many companies are pondering how to grow their customer experience (CX) programs, there are plenty of CX champions looking to start a CX program. We talk with plenty of companies that are just starting up, or as we prefer to call it: Early Stage.

Contrary to popular belief, it’s not at all hard to get your Early Stage CX program started. With a little guidance, you can quickly build and implement a quality program that helps you:

Listen to your customers.
Learn from your data.
Act to optimize the customer experience.

As your business grows, you can expand your CX program with it. For now, however, you’re primed for the Early Stage option. So listen up; we’re going to get you started!

CX program fundamentals

CX programs center on Voice of the Customer (VoC) data — your customers’ feedback about their experiences and expectations for your products or services.

The key to a successful program lies in how you gather that feedback, how you process and learn from it, and then act on it.

This 3-step CX model is easy to understand, simple to get started and offers quick time to value.

Step 1: Listen

Listening starts with strategic thought:

CX metric. What are you trying to learn?
Survey process. How will you learn it?

Start by defining the goal of your CX program. Maybe your priority is to optimize your software product or to improve the support experience. Knowing what you want to learn will inform your listening strategy.

Have your goal set? Onward!

Get the ebook, CX FOR EVERY STAGE: How to scale your Voice of Customer program from startup to enterprise. Learn how to improve user experience for product led growth and loyalty.

Begin With Net Promoter Score (NPS)

It’s time to ask your customers some essential questions. We’ve bid good riddance to long, multi-question surveys. Because they’re tedious, their completion rates are dismal.

To get customers to give you actionable feedback, you’ll want to use micro surveys. These single-question surveys:

Give you a score (aka metrics!) on customer loyalty or satisfaction.
Give you deep insight by inviting the customer to explain their score in their own words.

Because micro surveys are short, sweet, and to the point, more customers will answer them, meaning your response rates will soar.

There are three core CX surveys you should have in your toolbox:

Net Promoter Score (NPS)
Customer Effort Score (CES)
Customer Satisfaction Score (CSAT)

For your first customer survey, we recommend you begin with NPS. Net Promoter Score is the gold standard for measuring customer loyalty and will give you immediate insight into your customers’ stories.

Once you and your CX program have grown, you’ll likely need other survey types. But Early Stage programs can find out what they need to know with these three.

Choose Your First Survey Channel

Alright, your question is at the ready. Now you need to decide how you will survey your customers. Each segment of your customer base probably has a preferred method of communication. Common options include:

email surveys
in-app surveys inside a web or mobile product
SMS

If you’re unsure where to start, ask yourself this: Where here are our most important customers interacting with us?

If you’re still not sure, dive into this article about how to choose the best channel for your feedback survey. Here are some general trends we see with our customers:

SaaS business or mobile app: in-product survey
E-commerce business: transactional approach like sending an email survey a few days after delivery
Airline or utility (or other business already using texts or phone calls to communicate on customer mobile devices): SMS

Next comes the question of when to survey. Keep it simple. Ask NPS 30 days after a customer onboards, or whenever they will have had enough time to form an opinion about the experience you offer. The surveys are something you can “set and forget” and then just let the feedback roll in. A CX platform will survey a few users every day, so you have constant feedback coming in. Some platforms (like Wootric) offer a free plan for early stage businesses.

Step 2: Learn

Here’s where things get exciting because your customer feedback is coming in!

The great thing about Early Stage is you can read and respond to every survey response. This will help you stay closer to the customer and develop a holistic view of your customer experience.

There are an art and a science to the Learn step that will allow you to take hundreds of pieces of feedback and make it actionable. To do that, you need to get busy.

Segment Your CX Data

Even if your company provides only one service or product, your customers are not all the same. Categories of users have different needs and are bound to experience your company in slightly different ways.

You’ve given the same NPS survey to all your customers. Your overall NPS score will let you know how you’re doing across every customer. However, your customers aren’t just one block of users. Our marketplace customers like GrubHub and Deliveroo have both consumers and restaurant owners using their app, and those two groups have different needs. By segmenting NPS, you’ll receive more actionable insights to optimize your product for the various user groups.

Take our customer Homebase. They have two user groups for their SaaS product that streamlines employee scheduling: those who create schedules and those who receive schedules. Per CEO John Waldmann, “NPS has allowed us to segment out the feedback and look at how happy restaurant managers are with the product after the recent changes versus how happy the wait staff is. Are we skewing too heavily toward one side or the other? Do we need to spend some more product cycles to improve the employee experience?”

You can take a constant pulse of your CX program by reviewing the performance of your overall business and customer segment NPS scores over time. Tracking and metricizing customer sentiment over time is very helpful when you’re looking to make improvements. The bonus is you never miss a trend.

Identify Themes in Customer Comments

While it’s interesting to read and respond to individual feedback, at some point, you will get more qualitative feedback than you can easily digest. Lots of feedback is a good thing — it means you’re growing!

Now’s the time to filter your text responses to understand the “why” behind the numerical scores. You’ll filter these responses for specific topics by using tags. Tags are associated with particular keywords you want to monitor, and they allow you to easily track the Share of Voice (SoV) of a topic. How much are people talking about price, performance, delivery, or a new feature?

Setting up this categorization does a lot of things:

It helps you follow long-term trends.
It gives you insight into a topic’s trajectory.
It lets you know if you’re addressing your customer’s concerns effectively — or you still need to do more.

Step 3: Act

You’ve listened, you’ve learned, and now it’s time to make a difference by acting externally and internally!

Close the loop with customers

Every piece of feedback is valuable. While you’re hoping for promoters telling you what a great job you’re doing, it’s the detractors who care enough to let you know what needs improvement that can help you make the most significant business gains through your CX program.

Close the loop, especially with detractors!

Reach out via email or phone and address their concerns promptly. Passing your CX data to the system, you use to communicate with customers — like Intercom or Hubspot — can make this easy. Customers will appreciate that you took the time to listen and respond. You may even turn a detractor into a happy customer.

Activate your brand promoters. When someone gives you praise in a survey response, ask them to write a review or give you a quote. These testimonials can be great ways to distinguish your brand from the competition.

If you don’t have the resources to respond individually, write a blog post that summarizes what you’ve heard and the actions you’re taking and share it with your customers.

Loop closing in practice

You may be thinking, “this sounds great in theory, but that’s a lot to expect from a new program.” Understood. Many people in the Early Stage of CX programs are also in their company’s early stage, with too much work and too few people. Our customer Albacross, a lead-gen software startup, automated closing the loop with its customers, which achieved program goals without taxing their resources.

Here’s what they do based on the individual NPS score:

Detractors (who rate their app low with a 0-6): They send two messages via Intercom asking for additional feedback. The goal here is to start a conversation and better understand why the customer is frustrated.

They send an email:

They send an in-app message that appears immediately after the user completes the survey:

Passives (who rate 7-8) receive an in-app email of gratitude, letting them know they appreciate the feedback.

Promoters (who rate 9-10) receive an email from the CEO offering gratitude and asking them to please review the company on a 3rd party review site:

Evangelize CX data

You are trying to build a customer-first culture at your company. To do that, you need to communicate, communicate, communicate.

Make sure everyone has easy access to CX information! From Customer Success and Customer Support to Product to Marketing and beyond, every person in your company has a part in creating your customer experience. Create a CX Slack channel and encourage the entire company to join. Put up wall-mounted dashboards that put CX metrics front and center with the newest feedback and the latest scores — report it right next to other critical business metrics at the next company-wide meeting.

A single survey on a single channel offers significant customer insights. Like any new program, you want to start simply, optimize, and then expand. Once you have mastered the Early Stage program, it’s easy to move on to the Growth Stage and Expert Stage.

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B2B Now Feels B2C. Here’s How to Consumerize Your Enterprise Product

Customer expectations drive the value of CX. Continuously meet or exceed expectations, and delighted customers will return and even become vocal advocates for your product and brand.

Don’t let the terms B2C (Business to Consumer) and B2B (Business to Business) confuse you. Your end-user is a human who spends a lot of time on Amazon. They’ve come to expect consumer-level digital experiences at work and play.

Welcome to the consumerization of B2B; your customers have been expecting you.

Whether your company’s releasing an app as part of its digital transformation or it’s a digital-native SaaS company, CX pros need to understand the evolution of the B2B customer experience and make sure their products meet their B2B consumers’ expectations.

The B2B focus: from customer to end-user

B2B encompasses many types of different companies with varying levels of digital experiences. They’re all still dealing with some legacy ideas of B2B as they innovate and strive to keep one foot ahead of customer expectations.

In the not-so-distant past, B2B tech was big. Big machines, big decision-makers, big purchase prices, big buying time, and big onboarding. Tech salespeople wined and dined the VP and C-level buyers through the months-long decision process. Procurement got involved in making sure the tech worked at least “close enough” for the largest number of people at the company. Once the systems were in, they were in for the long-term, and user experience be damned.

Today, the tech is in the cloud, and it’s the little things that are important. Tech choices are researched and made by individuals and teams. Procurement may still get involved, but they come in to find so many people already happily using the software they just need to negotiate contracts vs. deciding “are we going to use Slack or not?” It’s one tech-savvy end-user after another replacing the big buying teams, and the app just needs to let that one person do their specific job faster and better. The sales process is digital, set up is easy, and customer service is a click away.

The newer SaaS companies practicing Product Led Growth (PLG) have grown up in this digital age where everyone’s a consumer. If you are a workhorse platform, how can your digital experiences compete with these disruptors?

12 ways to “consumerize” B2B customer experience

Think of your week’s worth of online consumer experience, and there are some overarching elements of a good CX. From Netflix to Uber, there are elements you need to pull into the B2B experience.

Freemium or trial access. Appcues found 90% of users want to try a product for free. Can you design a lite but still valuable-to-the-user version of your product? If so, this can be a great lead generation channel. Give marketing adequate development resources and let them run it.
Fully digital. There’s no need for interaction with a live person to access and use the product. Users expect the app to understand their goals and take them step-by-step through the process to meet their goals.
Intuitive setup and use. No user wants to have to read an essay on how to get started; it should be one step clicks without confusion. There’s no need to look for what they need, no need to be trained by an admin — it’s right there.
Quick time to value. The user wants to do their job faster, easier, and better — make that happen quickly before they find another option.
Easy connections to existing tech. While you’re focused on the individual end-user, don’t forget your app needs to fit into the organization’s workflow, speeding time to value and allowing your power user to advocate for your app. Also, realize that while one user may need everything your app does, others just need to know the results. Make sure your app links to the app others work in the most. If others need the data from your app to work in Slack, then offer a single-click integration to send the data from your app to Slack.
Simple vs. feature-laden. Keep the interface as decluttered as possible, so it’s easy for users to find what they need. Visual clarity poses a challenge for feature-ladened platforms, so design with a bias toward simplicity. And remember, there will be light users who don’t want to have to figure out, once again, how to run a report. You still need all the features for those power-user admins, but you must also prioritize the light user’s experience.
Mobile. The more people who adopt your app, the more mobile users you’ll have. While you don’t have to design to be mobile-first, you can offer a simplified version of your desktop platform, keeping all the features someone on the go will need when they’re away from their office.
Easy to share. People work on teams for the most part, and shared apps help complete shared work. Make it easy for your power user to evangelize and share your product within the organization with a one-click invitation.
Real-time issue resolution. Digital is fast, and users expect to solve any problems encountered in real-time. Offer help right in the product through a chat function. No one minds AI if it resolves their issue — they just want it handled now.
Easy to review. Purchase decisions today reflect the power of the end-user. Decisions are made based on word of mouth and 3rd party review sites, so make it easy for your promoters to evangelize for you. Use in-app surveys to identify your happiest customers and then automate the review to link to AppStore or G2 once the survey is completed.
Seamless upgrade to paid. The freemium or trial offer will demonstrate value. When more functionality is required, make it an easy click to the premium product version.
Customer feedback. Build improvement and growth into your product by asking for feedback right in the platform and mobile app. Your buyers and end-users expect you to be optimizing your product, so make it easy for end-users to give feedback in the moment through simple NPS, CSAT, and CES micro-surveys.

Designing your digital CX for the modern B2B customer means taking a B2C mindset. Your product isn’t serving a faceless company; it’s serving the individual people who use it. Where are your people? What are their expectations? How can you connect them to value most easily? By designing to help end-users meet their goals, you create a partnership with individuals whose success is based on your product. They will tell you how to improve your product and evangelize its use to develop new customers. Once in, they want to help you make your product better, and their feedback will light the way for your company’s success.

Wootric is CX management for modern B2B companies. Book a consultative demo today.

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5 Industries Taking Advantage of Text Analytics

Text analytics, also called text mining, has countless applications. Businesses are taking advantage of text analytics to update their service offerings, improve compliance, get ahead of PR disasters, and more.

Here are 5 examples of the industries taking advantage of text analytics in 2021.

1. Hospitality

Hotels live and die by their reviews. Reviews are not only crucial to whether someone books a stay, but they also give valuable insight into what a business is doing well – or not. And while the hospitality industry has been decimated over the COVID-19 pandemic, the quickening vaccine deployment holds great promise for 2021 and beyond for the industry. Hotels use text analytics to get a deep understanding of where they excel and where they can improve, as well as what others are doing. Say some reviews mention poor wi-fi. A hotel can analyze these reviews deeper to nail down whether the wi-fi problem is a hotel-wide approach or just in some rooms. Once they’ve figured it out, they can make the fix, thank the reviewer for their feedback, and be on their way to improved reviews in the future.

2. Financial Services

The financial services sector is hugely complex. There’s an enormous amount of interaction, documentation, risk analysis, and compliance involved. Financial services firms are using text analytics to analyze customer feedback, evaluate customer interactions, assess claims, and to identify compliance risks. Take compliance. Staff can use an NLP-based text analytics solution to quickly and easily search internal legal documents for phrases relating to finance or fraud. This can save an enormous amount of time compared with doing so manually.

3. Medical Affairs and Pharma

Medical affairs specialists help move pharmaceutical products from R&D to commercialization. This involves an encyclopedic knowledge of drug body and government regulations, as well as drug compendia. Medical affairs specialists are using text analytics to parse each of these and automatically report back on changes. The specialists can then course correct depending on what these changes mean for the drug they’re developing. Using text analytics rather than human effort reduces the time spent on tracking these changes, and is more accurate and far-reaching as well. Download AI for Medical Affairs Whitepaper

4. PR and Advertising

Text analytics is brilliant at sentiment analysis – something that PR is all about monitoring. Text analytics can run in real-time to track the sentiment in mentions about a particular company, alerting them to potential brand reputation emergencies. In advertising, text analytics can help monitor the reach of a campaign and how it’s being received. For example, a leading provider of Media Monitoring and Social Influencing used Lexalytics’, an InMoment company, API to create custom dashboards to analyze its customers’ media relations programs in terms of sentiment, engagement, perception, and performance.

5. Retail

In retail, the customer is always right. E-eCommerce retailers in particular need to make sure that the customer experience is as positive as possible, and with the boon in online buying during the pandemic, this is more important than ever. A poor experience means a customer is unlikely to return – even more so than in physical stores that people frequent due to their proximity. Many e-tailers are turning to text analytics to curate, collate and analyze feedback that helps identify points of friction when using an ecommerce website or dealing with customer support.

Would you like to know how text analytics can help your business or industry? Get in touch

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How to Calculate Net Promoter Score (NPS Calculation)

You hear the term tossed around in most any meeting focused on customers: “What’s the NPS? How many Promoters do we have? How many Detractors?” You may be asking yourself “What is NPS and what should we be doing with it?”

Net Promoter Score (NPS) is a simple, powerful measure of customer loyalty. By asking customers to rate their likelihood to recommend a product or service on a 1-10 scale, you can gain actionable insights to guide decisions across your business.

Let’s break down NPS calculation and see how it works.

The NPS survey

Essentially, an NPS survey asks your customers this simple question:

The survey then logs the response and gives the responder a chance to explain their answer in an open-text format.

That’s it! Because the survey is short, sweet, and to the point, customers are more likely to respond. And you’ve just gained valuable information ready to be turned into insights and used to improve your offerings.

Many NPS surveys offer this text box at the bottom of the questionnaire asking for reasoning behind their responses. This is also a valuable tool to gain better insight into your customer’s specific experiences.

Collect NPS Data with a Survey

Make sure that during the process of NPS calculation you are determining what specific information you are looking for from your audience. Make sure you know what you need feedback on, where you have the bandwidth to improve, and how you want to segment your customers in order to get the most specific results.

InMoment can help you get instant NPS analytics when you download the NPS software. Want to try it out? Get a free 30-day trial here.

How to Calculate NPS: The NPS Calculation Process

Once you have the customer feedback (step one), the fun part begins with NPS calculation.

Respondents are classified into three groups based on their answers:

Promoters: Rating 9 or 10. Loyal customers who are a great source of referrals.
Passives: Rating 7 or 8. Customers who are satisfied with the service but are susceptible to competitors.
Detractors: Rating 0 – 6. Unhappy customers who can damage your brand.

What is the NPS Formula, and How Does it Work NPS Calculations?

NPS Calculation gives you a clear indication from one moment to the next of how happy your customers are. Real-time tracking can alert you to threats to your business, allowing you to take quick action. Tracked over time, it gives you insight into which of the company’s actions have resulted in the most customer value. Step three is to find the percentage of promoters and detractors. Lastly, step four is to calculate the NPS score using the information you have acquired so far.

To do the actual NPS calculation, subtract the % of respondents who are Detractors from the % of respondents who are Promoters.

NPS = ((# of Promoters – # of Detractors)/Total Survey Participants) x 100

Interpreting Your NPS Score

Now that you’ve calculated your net promoter score, of course you want to know what the number you ended up with actually means. Net promoter scores are expressed as a number ranging from -100 to +100. Any score above 50 is typically a good NPS. This would be because at least 50% of your company is a promoter, while less than 50% would fall under detractor. The most important thing you can do with your net promoter score is acknowledge it, and try to improve it.

Utilizing Customer Feedback

The answer to the open-ended NPS follow-up question tells you the “why” behind the rating. Mining this text for insights is what makes NPS calculation so powerful – because it gives you rich information on the customer experience you’re providing. Analyze the text answers and use them to guide the actions you take.

Don’t forget to follow up with the customer and close the feedback loop. Imagine immediately responding to a Detractor’s complaint, targeting your Passives with an information campaign, or asking a Promoter to review your product online.

Creating Additional Questions for Your NPS Survey

When you create an NPS survey, you typically do so with the sole purpose of measuring NPS. However, sometimes you need to measure NPS and acquire additional information that can help you to improve after you’ve learned your NPS score. This is where you need some key driver analysis. While it’s usually used for Customer Acquisition, key driver analysis can help you identify what your strengths and weaknesses are specifically and how you should address them in the future.

Ongoing Voice of the Customer

Repeat the NPS survey at regular intervals. Segment your NPS by types of customers to understand the “why” behind your score and how your decisions impact customer loyalty.

Once you have NPS calculation down, you’ll be ready to add in additional metrics over time at key customer journey touchpoints. When you combine the feedback from your NPS survey with feedback from CSAT (customer satisfaction) and CES (customer effort) surveys, these 3 core CX metrics give you a great foundation for making business decisions based on the authentic voice of the customer feedback.

Build end-user loyalty. Sign up today for free in-app NPS calculation feedback with InMoment.

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Low Touch to High Touch: Customer Follow-up Strategies for Any Size Company

A good customer experience improvement program depends on two-way conversations between companies and their customers. It has been reported that nearly half (43%) of customers don’t bother complaining because they don’t believe companies care. Get ahead of that stat by demonstrating your company not only wants the feedback, you act on it. The three-tiered approach to customer follow-up (high touch, medium touch, and low touch) allows every company to effectively respond to customers, even if they can’t commit a lot of resources.

Treat customer feedback like a gift. It’s not enough to just gather Net Promoter Scores (NPS), you need to follow up with responders to let them know that 1) you appreciate their effort, and 2) their feedback has impact. Even if you can’t deliver everything customers ask for, they will remember that they were heard and appreciated. Closing the feedback loop will help you retain customers, increase response rates, and hopefully create loyal brand advocates.

Ready to respond? Good! Consider your resources and choose from three levels of engagement:

High Touch. This more resource-intensive approach has proven very effective for B2B companies. Every time a customer gives feedback, a Customer Success Manager or Account Manager contacts them. Don’t worry, it doesn’t have to be an in-depth correspondence. Sometimes a simple “thank you” is all that’s needed. Other times, you can dig deeper into their response (and deeper into the relationship) in order to make the feedback even more actionable.
Medium Touch. We get it — not every business has the headcount to personally respond to every piece of feedback they receive. Automating the feedback loop is a time and resource saver. Segment your customer responses by the rating each customer gives and you can still have a personalized impact where it counts most. Your follow up plan could look something like this:
- Promoter. Send a “thank you” and possibly offer an incentive for the customer to share your product.
- Detractor. Route the response to Customer Success or Customer Support to uncover why that responder isn’t happy, especially if they didn’t leave further feedback explaining their rating.
- Passive. Deliver a message to passive raters who didn’t leave feedback, engaging them in a “What would make you LOVE us?” conversation.
Low Touch. If you have too many users to provide individual responses, or you don’t have contact information, you can still close the loop! Develop blanket communications that offer transparency and information sharing:
- A monthly blog post or newsletter. Summarize the feedback you’ve received, and detail the actions you’ll be taking in response to issues customers have raised.
- Product updates or release notes. CX champions in product or UX can use these to communicate “We heard you! Today we <fixed X or launched Z>.”

All three levels of engagement deliver impact, so choose the one that best fits your needs. InMoment customer Albacross chose a medium touch model, which resulted in 2X the NPS scores and a 2X ratings increase on Capterra.

By closing the loop with customers, you show them that you’re not only listening to their feedback, you consider it so important that you’re using it to make their experiences better. This simple step can turn ambivalent customers into vocal fans.

InMoment is CX management for maximizing customer lifetime value. Book a consultative demo today.

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Wootric Joins InMoment to Accelerate CX Innovation and Growth

As we begin this new year, we want to share some great news.

Today we’re excited to announce that Wootric is joining InMoment, a market leader in customer and employee experience. InMoment serves many of the largest, most sophisticated global organizations from Starbucks to Ford to VMWare.

This next step in our evolution means great things for our customers and other businesses seeking a modern approach to CX improvement.

We will continue to deliver the world-class product experience you expect from us. In addition, our pace of innovation and our ability to support our customers around the globe will accelerate as we leverage the considerable resources and expertise of InMoment.

Our customers will also be able to tap into InMoment’s expertise and enterprise solutions as their CX needs evolve beyond our turnkey approach.

Read the official press release here.

Seven years ago, we founded Wootric with a mission to empower customer-centricity in every organization through modern, always-on CX improvement. We launched with a high-response in-app microsurvey and quickly disrupted a dated approach to gathering and responding to Net Promoter Score feedback.

Guided by input from our customers, we invested in omni-channel feedback collection, AI-driven customer journey analytics, and native integrations with the modern tech stack — all the while staying true to the flexible, lightweight, user-centric approach to CX improvement that businesses expect from Wootric.

Wootric now delivers the fastest ROI in the Experience Management category on G2. Over 1200 businesses worldwide, including DocuSign, Zoom, and Comcast, use Wootric software to improve customer lifetime value with insights and action from voice of the customer data.

We thank our customers, partners, investors, advisors, and above all members of our exceptional team for their support, and for choosing to be on the CX journey with us.

Together, with InMoment, we will make 2021 an amazing year for customer experience!

With gratitude,

Deepa Subramanian, CEO & Jessica Pfeifer, Chief Customer Officer

The founders would also like to extend a special thank you to Steve Gurney at Viant Group for representing Wootric through this process.

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How to Train AI to Analyze Your Customer Feedback

Customer comments are the lifeblood of any CX program, giving you the “why” behind customers’ NPS, CES, and CSAT scores. But until recently, it’s been nearly impossible to make sense of feedback from hundreds of customers at a time. Using artificial intelligence (AI) to automate text analysis gives you the consistent and fast insights you need, at scale.

That said, automated text analysis isn’t just about technology. Humans need to put in the time upfront to teach the machine, by providing an accurately tagged set of feedback for AI to work from. The quality of that training data sets up the quality of your text analytics results, or as the old saying goes “garbage in, garbage out”.

Let’s look at what you need to be successful with automated text analytics. We’ll dig into the basics of text analytics, the inconsistencies of manual tagging, and how to create good training data and models.

A quick primer on AI training sets

Analyzing customer feedback from unstructured text can be complex. In one sentence a customer may talk about a variety of topics, offering negative, neutral, or positive feedback (sentiment) about each of them. It’s the job of machine learning to recognize what the customer is talking about and identify how they are feeling about those topics. With text analytics, it only takes an instant to:

Tag comments / categorize themes
Assign sentiment to each of the tags and the comment overall
Aggregate results to find insights

Text analyzed for sentiment and themes

Again, machine learning is only as good and accurate as the data set you (the humans) provide to train the algorithm. So you need to do it right.

At Wootric, we have a lot of experience helping teams create training datasets. While we have sets of tags that are specific to various industry verticals, we also build custom machine learning models for many customers. Custom models are helpful for companies that are in a new vertical or a unique business.

Training model process

For the most part, companies have a good feel for what their users/customers are talking about and the topic tags they need. If they’re not sure, we can analyze their data and work with them to help them think through a set of tags to get them started. Once they have a set of agreed tags, they start creating the training data.

The process for creating this training dataset goes something like this.

Decide what tags are important to your business
Create definitions for those tags so everyone knows exactly what the tags mean
Pull 100-200 customer comments

Our customer assembles a team of at least 3-5 people who independently review each comment and determine:

If the comment sentiment is overall positive, neutral, or negative
Which tags apply to that comment

That last point is where things get interesting for a data analyst like me.

Manual tagging: an inconsistent truth

Many companies still believe having human tagging and analysis is superior to AI. We’ve seen one employee hired full-time to pour over spreadsheets, organizing data and pulling insights, which takes A LOT of time. Other companies bring in a team of people (the interns!), which introduces inconsistencies. Not only is it expensive and time-intensive, manual tagging isn’t necessarily accurate.

These same inconsistencies appear when creating training datasets because the process starts with manual tagging. Customer teams creating training data are always surprised by the level of disagreements on “defined” tags. It can take a few rounds of work to iron these out.

Tag definitions vary

Not all tags carry the same level of complexity. Some tags make it easy for people to agree upon a definition, while others may be more ill-defined. Vague tags tend to invite more disagreement between human labelers who label the same dataset independently.

Let’s look at a couple of examples from the software industry:

“MOBILE” — applied to any feedback containing references to a mobile app or website functionality. This should be straightforward for a group of human labelers to apply similarly, and would most likely only result in a few disagreements between them.
“USER EXPERIENCE” — a more complex phrase with many different definitions of what could be included in a user’s experience. When a comment mentions search functionality, is that UX? How about when they say something like “While using the search bar, I found information on…“? Or even “Great product”? Because there is so little clarity on what fits in this category, the training team will surely disagree, leading to more rounds of tagging and defining.

The good news is that at the end of the process, after a few rounds of defining the tags and applying them, the team REALLY knows what is meant by a given tag. The definition is sharper and less open to interpretation. This makes the machine learning categorization more meaningful, and more actionable for your company, which leads to an improved customer experience.

Getting to a good training model

Let’s look at a real-world example of creating a training set, and the level of label agreement between the people creating those labels.

A recent enterprise customer used 8 human labelers on the same initial set of 100 comments, and we then evaluated the labeler-agreement of each tag. During this exercise, each labeler worked independently , and we charted the agreement scores.

In the following two charts, the number in each cell represents the strength of the agreement from 0 – 1 between two labelers (F1-Score calculated from Precision and Recall values).

1.0 would be the ideal labeler agreement value.
0 means no agreement.

The lower row of the chart contains the average agreement across all cells for that labeler.

In Figure 1, it’s clear that the tag is fairly well-defined, which results in an overall average labeler agreement of ~0.83. This is on the higher end of what we typically see.

Figure 1 – agreement on a well-defined tag

In other words, even a well-defined tag doesn’t garner complete agreement between labelers. Labeler 5, our most effective labeler, only scored a 0.85 average. Labeler 1 and 8, with an F-1 score of .75, didn’t apply the tag, in the same way, a significant portion of the time. But it’s still considered successful.

Now, look at Figure 2, which shows the first-round results of the team’s effort to consistently apply a more complex tag. It resulted in an overall average labeler agreement of only ~0.43.

Figure 2 – disagreement on a vague tag

For the same group of labelers tagging at the same time, two different tags demonstrate nearly 2x difference in overall agreement — showing again that even we humans aren’t as good at manually categorizing comments as we would like to think.

Even when teams agree on 1) what tags to use and 2) the definition of each tag, they don’t necessarily wind up applying tags in the same way. It takes a few rounds for teams to come close enough to consensus to be useful for machine learning.

Ready for autocategorization

Text analytics is not a perfect science. When are the label agreement results ready for prime time? Typically, we consider a model good enough to deploy once the F-1 score is around 0.6 (give or take a bit based on other factors). Like most things in life, when you invest more time upfront — in this case boosting F-1s with additional rounds of tagging/defining — you typically end up with better results.

Makes sense of customer feedback with InMoment CXInsight text analytics.

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CX Tipping Point: 7 Signs You Need Text & Sentiment Analytics

The potential for machine learning to elevate the customer experience has everyone buzzing. AI-powered text and sentiment analysis can be an incredible solution for specific problems that CX pros face.

But how do you know when the time is right to move to the next level of CX? Are there new tools you can purchase to step your game up? How do you know they’ll be worth it?

There are clear signs that your CX program is ready for, and your company could quickly benefit from, text and sentiment analysis. And we’ll delve into them here.

Before we get going, some definitions:

Text analysis takes qualitative customer comments and determines relevant themes. Software companies might see themes such as ‘feature request’, ‘bug’, or ‘pricing’. This allows you to quickly see what your customers are focusing on, and then dive in to see what they’re specifically saying about each topic.
Sentiment analysis offers micro and macro insights into how your customers are feeling about your company and products. It determines whether the text received for each text theme is positive, negative, or neutral. It also analyzes the comment as a whole, assigning sentiment to the entire verbatim text.

Let’s look at the 7 signs text and sentiment analytics will be worth the investment for your company.

1. You have a mature or quickly-maturing CX program.

Those of you considering text and sentiment analytics probably already have a few key elements in place:

A customer experience strategy and a Voice of Customer listening system
A C-suite sponsor who has been fostering a customer-centric culture across the whole company with NPS as the guiding star
A system asking for feedback through the entire customer journey

Now that you have a relatively mature CX program, you’re wondering how to extract even more value out of it.

2. You receive 500+ comments per month (or you’re headed there.)

Ideally, you want to listen to all of your customers – not just a sample or the first to respond. In reality, at a certain point the sheer volume of incoming customer feedback is more than a CX program can handle without an upgrade. You know this is the case when:

You feel excitement and dread regarding the amount of feedback you receive.
You’re anticipating a whole lot more comments soon.
You’ve even had to cap the number of comments you receive in a day to avoid being overwhelmed with the task of organizing and responding to everyone.

Overwhelming amounts of feedback is an amazing problem to have, but a problem nonetheless. Using text and sentiment analytics, you can turn unstructured qualitative feedback, like NPS comments, into organized insight in a matter of minutes.

Text and sentiment analytics allow you to analyze customer feedback using Natural Language Processing, looking something like this:

Read Google’s case study on Wootric and Natural Language Processing here.

By combining text and sentiment analytics, you can search negative comments and quickly assess, for example, that 80% of your negative comments are about pricing. Or 45% of your customers in the Northeast region are talking about slow delivery times. That summary lets you know where to focus resources, and how quickly you need to make the change relative to other company priorities.

3. You’re sitting on a goldmine of feedback, but unable to get actionable insights.

Do you have a backlog of comments waiting to be read and sorted? Or maybe you’ve skimmed a few comments to answer the urgent ones, but you keep putting off the others.

One of our clients came to us with NPS survey comments from thousands of users. But rather than mining that information, they were running focus groups to prioritize feature requests because it was easier. They were duplicating efforts to get information they already had but couldn’t access and act on.

“The two biggest mistakes [in CX] are not doing qualitative research in the first place and then not putting it to use.” –Morgan Brown, Product Manager at Facebook and coauthor of ‘Hacking Growth’

If you’re feeling this pain, it’s time to automatically mine the insight from that pile of comments you’ve been sitting on. Turn anecdotes and hunches that you’ve got about your customer experience into evidence-backed insight by using. And do it quickly with text and sentiment analytics.

Source: CXInsight™ Dashboard

Sliced and diced organized feedback is easily available with many platforms that offer text and sentiment analytics. Doing this can help you understand the root cause of trends – like the needs of different customer personas or geographic regions – more comprehensively.

4. Manual feedback organization & categorization is insightful, but painfully slow.

While some customers duplicate efforts between data gathering and focus groups to get insight, other CX pros just bite the bullet and spend hours reading customer comments, labeling them, and funneling them into an unwieldy spreadsheet. They’re understandably frustrated by how difficult it is to get actionable insight.

By using text and sentiment analytics, humans can get huge quantities of customer feedback sorted and analyzed at the push of a button. Better yet, computers don’t have bad days or lose focus.

Once organized with tags, your time is freed up to look at the themes and trends that arise from the noise, then create actionable strategies based on those insights.

Now you can jump straight into action and the interns can work on more interesting, valuable projects!

PRO TIP: To get high quality insights at the push of a button, algorithms need to be trained. Be sure your feedback management software vendor has a team that will work with your data to ensure you get valuable insight from the start. With more data and occasional human guidance, you’ll get better and faster insight over time.

5. Your CX program lacks a real-time issue detection system.

An important element to providing a good customer experience is making sure any issues are handled quickly and efficiently. If you can detect and address them before your customer has a real issue, your CX program has paid for itself.

One of the benefits of having text and sentiment analysis is that your data and insights are updated in real-time. This means you have a new issue detection system.

Source: CXInsight™ Dashboard

This works best for a more mature customer feedback program with an established baseline, or status quo. For example, you know that on any given day, in any given geographic region, about 10% of your comments are tagged with ‘out of stock’ as an issue. When you check in and see that in Texas, 25% of comments coming in are tagged ‘out of stock’, that raises a red flag. You can immediately dig into specifics, read through the verbatims, and send those comments to the right people for follow up before the issue blows out of proportion.

The CX dream of being proactive in solving issues can be achieved with the help of automated organization of qualitative feedback.

6. Your internal teams aren’t agreeing on CX priorities.

It’s a given that successful companies focus on customer needs and experiences. The question is: is everyone at your company seeing the same information in the same way? If not, you’re wasting time and costly resources with competing priorities, and it is definitely time to invest in tools to fix it.

By having your CX tech parse the text and sentiment of your 1K+ daily inputs of customer feedback, you can democratize the information and insights across every team at your company. And that will ensure team leaders can quickly align to address the right priorities. So product development and customer support will be on the same page, and features will get developed (or possibly de-bugged) to meet the most important needs of the customer.

How does that happen? Feedback from every customer touchpoint is analyzed, from in-product surveys to emails. In this example, support ticket subject lines are auto-categorized and everyone from support to service to product to the c-level can see what issues are hot items to address.

Source: CXInsight™ Dashboard

Looking at the text analytics, it quickly becomes apparent that 15% of the support tickets are related to bugs that need to be addressed. On the proactive front, product could also delve into comments tagged “feature request” and focus on user concerns about UX/UI.

7. You need to demonstrate the ROI of your CX program.

Companies are eager to hop on the CX bandwagon, but it can still be a fight to get the proper resources to make a CX program thrive. You’ve probably already shown the C-suite the correlation between CX and revenue growth, but there’s pressure to squeeze a little more ROI out of what you’ve established.

Investing in a tool that pulls ROI from data is an expense. But it’s a more strategic spend than, and offers more immediate follow-up and action, than performing passive data review and organization. It’s also a moredirect value-add and much less expensive than hiring a third party human operation.

The cascading effects throughout the organization will increase ROI in the long-term as well.

Product teams can prioritize and build with evidence-based confidence.
Marketing teams will gain an understanding of different personas and see customers excited to spread the word about your business.
Support and operations teams will have early warning of potential issues and have more context when dealing with problems.

In the end, qualitative data is crucial to extracting value out of CX initiatives. Having more data from engaged customers should not be an obstacle.

Is this the point?

Are you seeing any of these 7 signs when you look at your company’s CX program? If so, do a cost benefit analysis. Typically, once your program has matured, the cost of tools that create actionable insights out of customer feedback are far cheaper than the cost of misaligned resources and long delivery times. Text and sentiment analytics make the resources you put into CX initiatives efficient, and turn the large quantity of unstructured data into an advantage by mining insight that would otherwise sit in limbo. Move this tipping point in your favor.

(Editor’s Note: this post is an update of a 2018 post)

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How to Create Meaningful Customer Experiences—Not Just Transactions

By: Andrew Park, VP, Customer Experience Strategy

October 27, 2020October 29, 2020

Conventional wisdom holds that customers shop the brands whose products and services best match their needs. But there’s more to the story than that. Even if it’s just a quick trip to the grocery store, customers seek something more profound from brands than a mere product: meaningful customer experiences.

There’s a lot for organizations to gain by orienting themselves around customers’ search for meaning. Experience programs can help them get there.

We’re going to go over exactly how companies can achieve that reorientation, create meaningful experiences for customers, and, ultimately, ride that heightened connectivity to the top of their respective verticals.

Right Audience, Right Problem

We touched on this in our last conversation about the importance of carefully designing your program before deploying it, but it’s worth saying again:

Some audiences are more worth brands’ time than others.

Sounds harsh, but let me explain. Some audiences offer context and solutions to problems that other groups may not even be aware of. Therefore, one of the first things brands should do to create meaning for their customers is consider the problems that can be solved by focusing on specific audiences.

This approach is vital is because it allows brands to hone in on customers’ “moment of truth.” This is the moment in which a customer finds significance in their interaction with a brand, not just a product or service.

What is preventing customers from finding their moment of truth? The answer to this question will dictate what you should design your listening program around.

Furthermore, that search will allow your company to create fundamental human relationships with customers. And those relationships will create positive buzz, build lifetime loyalty, and result in a much stronger bottom line.

Sharing the Love

Thinking how certain audiences can help solve business challenges is important, but it’s not the only step brands must take. Once a company’s experience team finds moments of truth, they absolutely must share the news across the organization! This sharing process is often called data democratization.

I really can’t say enough how important it is to share customers’ moments of truth. First, socializing that data across the organization gives every employee a glimpse of how their role affects the customer.

Second, sharing this intel makes it easier for brands to identify moments that matter out of mountains of experience program data. Ultimately, brands that intentionally democratize data from the beginning get so much more from their listening than companies who fail to design their strategy.

Listening Empathetically

The final key to creating meaningful customer experiences is on that is often overlooked: empathy. Empathy is the key to understanding moments of truth and, ultimately, business success.

Catering to customers’ search for meaning is neither a program luxury nor a saying you put on a wall sign. It’s a strategy that builds transformational brand success and the meaningful, emotional relationships that can sustain it indefinitely.

I go into greater depth about the importance of designing your experience program before listening in my article on the subject, which you can read here. Thank you!

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Text Analytics & NLP in Healthcare: Applications & Use Cases

This article explores some new and emerging applications of text analytics and natural language processing (NLP) in healthcare. Each application demonstrates how HCPs and others use natural language processing to mine unstructured text-based healthcare data and then do something with the results.

Healthcare databases are growing exponentially, and text analytics and natural language processing (NLP) systems turn this data into value. Healthcare providers, pharmaceutical companies and biotechnology firms all use text analytics and NLP to improve patient outcomes, streamline operations and manage regulatory compliance.

In order, we’ll talk about:

Sources of healthcare data and how much is out there
Improving customer care while reducing Medical Information Department costs
Hearing how people really talk about and experience ADHD
Facilitating value-based care models by demonstrating real-world outcomes
Guiding communications between pharmaceutical companies and patients
Even more applications of text analytics and natural language processing in healthcare
Some more things to think about, including major ethical concerns

NLP in the Healthcare Industry: Sources of Data for Text Mining

Patient health records, order entries, and physician notes aren’t the only sources of data in healthcare. In fact, 26 million people have already added their genetic information to commercial databases through take-home kits. And wearable devices have opened new floodgates of consumer health data. All told, Emerj lists 7 healthcare data sources that, especially when taken together, form a veritable goldmine of healthcare data:

1. The Internet of Things (IoT) think FitBit data)

2. Electronic Medical Records (EMR)/Electronic Health Records (EHR) (classic)

3. Insurance Providers (claims from private and government payers)

4. Other Clinical Data (including computerized physician order entries, physician notes, medical imaging records, and more)

5. Opt-In Genome and Research Registries

6. Social Media (tweets, Facebook comments, message boards, etc.)

7. Web Knowledge (emergency care data, news feeds, and medical journals)

Just how much health data is there from these sources? More than 2,314 exabytes by 2020, says BIS Research. For reference, just 1 exabyte is 10^9 gigabytes. Or, written out, 1EB=1,000,000,000GB. That’s a lot of GB.

But adding to the ocean of healthcare data doesn’t do much if you’re not actually using it. And many experts agree that utilization of this data is… underwhelming. So let’s talk about text analytics and NLP in the health industry, particularly focusing on new and emerging applications of the technology.

Improving Customer Care While Reducing Medical Information Department Costs

Every physician knows how annoying it can be to get a drug-maker to give them a straight, clear answer. Many patients know it, too. For the rest of us, here’s how it works:

You (a physician, patient or media person) call into a biotechnology or pharmaceutical company’s Medical Information Department (MID)
Your call is routed to the MID contact center
MID operators reference all available documentation to provide an answer, or punt your question to a full clinician

Simple in theory, sure. Unfortunately, the pharma/biotech business is complicated. Biogen, for example, develops therapies for people living with serious neurological and neurodegenerative diseases. When you call into their MID to ask a question, Biogen’s operators are there to answer your inquiry. Naturally, you expect a quick, clear answer. At Biogen Japan, any call that lasts more than 1 minute is automatically escalated to an expensive second-line medical directors. Before, Biogen struggled with a high number of calls being escalated because their MID agents spent too long parsing through FAQs, product information brochures, and other resources.

Today, Biogen uses text analytics (and some other technologies) to answer these questions more quickly, thereby improving customer care while reducing their MID operating costs. When you call into their MID, operators use a Lexalytics-built search application that combines natural language processing and machine learning to immediately suggest best-fit answers and related resources to people’s inquiries. MID operators can type in keywords or exact questions and get what they need in seconds. (The system looks like this illustration.) Early testing already shows faster answers and fewer calls sent to medical directors, and the application also helps new hires work at the level of experienced operators, further reducing costs.

Hearing How People Really Talk About and Experience ADHD

The human brain is terribly complicated, and two people may experience the same condition in vastly different ways. This is especially true of conditions like Attention Deficit Hyperactivity Disorder (ADHD). In order to optimize treatment, physicians need to understand exactly how their individual patients experience it. But people often tell their doctor one thing, and then turn around and tell their friends and family something else entirely.

A Lexalytics (an InMoment company) data scientist used our text analytics and natural language processing to analyze data from Reddit, multiple ADHD blogs, news websites, and scientific papers sourced from the PubMed and HubMed databases. Based on the output, they modeled the conversations to show how people talk about ADHD in their own words.

The results showed stark differences in how people talk about ADHD in research papers, on the news, in Reddit comments and on ADHD blogs. Although our analysis was fairly basic, our methods show how using text analytics in this way can help healthcare organizations connect with their patients and develop personalized treatment plans.

Facilitating Value-Based Care Models by Demonstrating Real-World Outcomes

Our analysis of conversations surrounding ADHD is just one example in the large field of text analytics in healthcare. Everyone involved in the healthcare value chain, including HCPs, drug manufacturers, and insurance companies are using text analytics as part of the drive towards value-based care models.

Within the value-based care model, and outcome-based care in general, providers and payers all want to demonstrate that their patients are experiencing positive outcomes after they leave the clinical setting. To do this, more and more stakeholders are using text analytics systems to analyze social media posts, patient comments, and other sources of unstructured patient feedback. These insights help HCPs and others identify positive outcomes to highlight and negative outcomes to follow-up with.

Some HCPs even use text analytics to compare what patients say to their doctors, versus what they say to their friends, to identify how they can improve patient-clinician communication. In fact, the larger trend here almost exactly follows the push in more retail-focused industries towards data-driven Voice of Customer: using technology to understand how people talk about and experience products and services, in their own words.

Guiding Communications Between Pharmaceutical Companies and Patients

Pharmaceutical marketing teams face countless challenges. These include growing market share, demonstrating product value, increasing patient adherence and improving buy-in from healthcare professionals. Lexalytics customer AlternativesPharma helped those professionals by providing useful market insights and effective recommendations.

Before, companies like AlternativesPharma relied on basic customer surveys and some other quantitative data sources to create their recommendations. Using our text analytics and natural language processing, however, AlternativesPharma was able to categorize large quantities of qualitative, unstructured patient comments into “thematic maps.” The output of their analyses led to research publications at the 2015 Nephrology Professional Congress and in the Journal Néphrologie et Thérapeutiques.

Further, AlternativesPharma helped customers verify assumptions made by Key Opinion Leaders (KOLs) regarding the psychology of patients with schizophrenia. This theory was then documented in collateral and widely communicated to physicians. (Full case study)

More Applications of Text Analytics and Natural Language Processing in Healthcare

The above applications of text analytics in healthcare are just the tip of the iceberg. McKinsey has identified several more applications of NLP in healthcare, under the umbrellas of “Administrative cost reduction” and “Medical value creation”. Their detailed infographic is a good explainer. Click the image (or this link) to read the full infographic on McKinsey’s website.

Meanwhile, this 2018 paper in The University of Western Ontario Medical Journal titled “The promise of natural language processing in healthcare” dives into how and where NLP is improving healthcare. The authors, Rohin Attrey and Alexander Levitt, divide healthcare NLP applications into four categories. These cover NLP for:

Patients – including teletriage services, where NLP-powered chatbots could free up nurses and physicians
Physicians – where a computerized clinical decision support system using NLP has already demonstrated value in alerting clinicians to consider Kawasaki disease in emergency presentations
Researchers – where NLP helps enable, empower and accelerate qualitative studies across a number of vectors
Healthcare Management – where patient experience management is brought into the 21st-century by NLP used on qualitative data sources

Next, researchers from Sant Baba Bhag Singh University (former link) explored how healthcare groups can use sentiment analysis. The authors concluded that using sentiment analysis to examine social media data is an effective way for HCPs to improve treatments and patient services by understanding how patients talk about their Type-1 and Type-2 Diabetes treatments, drugs, and diet practices.

Finally, market research firm Emerj has written up a number of NLP applications for hospitals and other HCPs, including systems from IQVIA, 3M, Amazon and Nuance Communications. These applications include improving compliance with industry standards and regulations; accelerating and improving medical coding processes; building clinical study cohorts; and speech recognition and speech-to-text for doctors and healthcare providers.

Some More Things to Consider: Data Ethics, AI Fails, and Algorithmic Bias

If you’re thinking about building or buying any data analytics system for use in a healthcare or biopharma environment, here are some more things you should be aware of and take into account. All of these are especially relevant for text analytics in healthcare.

First: According to a study from the University of California Berkeley, advances in artificial intelligence (AI) have rendered the privacy standards set by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) obsolete. We investigated and found some alarming data privacy and ethics concerns surrounding AI in healthcare.

Read – AI in Healthcare: Data Privacy and Ethics Concerns

Second: Companies with regulatory compliance burdens are flocking to AI for time savings and cost reductions. But costly failures of large-scale AI systems are also making companies more wary of investing millions into big projects with vague promises of future returns. How can AI deliver real value in the regulatory compliance space? We wrote a white paper on this very subject.

Read – A Better Approach to AI for Regulatory Compliance

Third: The “moonshot” attitude of big tech companies comes with huge risk for the customer. And no AI project tells the story of large-scale AI failure quite like Watson for Oncology. In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center to develop a new “Oncology Expert Advisor” system. The goal? Nothing less than to cure cancer. The result? “This product is a piece of sh–.”

Read – Stories of AI Failure and How to Avoid Similar AI Fails

Fourth: “Bias in AI” refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. Algorithmic bias in healthcare AI systems manifests when data scientists building machine learning models for healthcare-related use cases train their algorithms on biased data from the start. Societal biases manifest when the output or usage of an AI-based healthcare system reinforces societal biases and discriminatory practices.

Read – Bias in AI and Machine Learning: Sources and Solutions

Improve Your Understanding: What Are Text Analytics and Natural Language Processing?

In order to put any tool to good use, you need to have some basic understanding of what it is and how it works. This is equally true of text analytics and natural language processing. So, what are they?

Text analytics and natural language processing are technologies for transforming unstructured data (i.e. free text) into structured data and insights (i.e. dashboards, spreadsheets and databases). Text analytics refers to breaking apart text documents into their component parts. Natural language processing then analyzes those parts to understand the entities, topics, opinions, and intentions within.

The 7 basic functions of text analytics are:

Language Identification
Tokenization
Sentence Breaking
Part of Speech Tagging
Chunking
Syntax Parsing
Sentence Chaining

Natural language processing features include:

Sentiment analysis

Entity recognition

Categorization (topics and themes)

Intention detection

Summarization

Lexalytics’ text analytics and NLP technology stack, showing the layers of processing each text document goes through to be transformed into structured data.

Beyond the basics, semi-structured data parsing is used to identify and extract data from medical, legal and financial documents, such as patient records and Medicaid code updates. Machine learning improves core text analytics and natural language processing functions and features. And machine learning micromodels can solve unique challenges in individual datasets while reducing the costs of sourcing and annotating training data.

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Wootric ranks #1 in ROI in Experience Management | G2 Grid Report

Note: Wootic was acquired by InMoment in January 2021. The Wootric product lives on as our Professional Plan.

Wootric, the CX management platform for maximizing customer lifetime value (CLV), has been recognized as a High Performer in the G2 Crowd Grid Report for Experience Management for Fall 2020 and Winter 2021. Wootric also outperforms the category on all satisfaction measures including ease of use.

Notably, Wootric, which seeks to drive business outcomes from customer experience efforts, has the fastest payback in the category.

Wootric is ranked #1 in ROI (Return on Investment)

“Companies should expect a financial outcome for their investment in CX,” said Jessica Pfeifer, Chief Customer Officer of Wootric, “Our turnkey approach means that our customers quickly understand user sentiment at the moments that matter, and analytics surface ways to immediately improve retention and engagement. It is gratifying to see our customers’ success reflected in our ranking.” In the G2 report, Wootric averages 9 months to return on investment, versus an average of 19 months for others in the experience management category, including Qualtrics and Medallia.

Experience management platforms help businesses bridge the gap between the experiences they believe they are delivering to customers and the experiences customers are actually receiving. They enable organizations to collect feedback from their customers with surveys that measure net promoter scores (NPS), customer satisfaction (CSAT), and customer effort scores (CES). By combining and analyzing customer feedback from multiple channels, experience management software offers companies a holistic view of their customers’ experiences and how those experiences are impacting the business.

Wootric specializes in customer experience management for high growth B2B and B2C software-as-service and companies in digital transformation. Over 1200 brands worldwide are understanding and improving the post-acquisition customer journey with Wootric’s CLV-focused approach.

Enterprise users also gave Wootric the top rank for usability and easiest admin

“We understand that in order to have an impact, CX champions must engage stakeholders, democratize insights, and ensure data is at the fingertips of frontline teams in real-time,” says Prabhat Jha, CTO of Wootric. “Their needs drive our roadmap — whether we are talking about our native integrations with modern tech stack players like Salesforce, Intercom, Hubspot, and Segment or our flexible Voice of Customer analytics hub that can be customized to meet the needs of numerous stakeholder teams like Product, Support, Success, and Customer Insights.”

The G2 Crowd Grid Report for Experience Management (Fall 2020) is a quarterly report that shows how the leading customer experience management solutions stack up to one another based on customer satisfaction and market presence. G2 Crowd’s scoring methodology blends data from user reviews and a vendor’s market presence, taking into account their social impact and market share, to generate the results for their Grid Report. Once scored, a vendor falls into one of four categories: Leader, High Performer, Contender, or Niche player.

This robust piece of research material should be read by CX practitioners and anyone evaluating a customer experience management solution. Learn exactly how each of the 26 companies included in the report received their score, their highest and lowest-rated features, satisfaction ratings, and more. Sign in and view this research on G2 to see:

Additional Data. Compare payback period (ROI) by vendor.
The G2 Crowd Grid visual
The Grid scores that determined each vendor’s placement
Side-by-side feature comparison
Methodology behind the scoring process

Learn how Wootric can help you improve customer lifetime value. Book a consultative demo today.

Build an Integrated CX Program

2024 Gartner CX Magic Quadrant for VoC

2024 Online Reputation Benchmark Report

Step 1: Listen

Customer Satisfaction Score (CSAT)

Customer Effort Score (CES)

Step 2: Learn

Customer insights through machine learning

Link CX metrics to business outcomes

Step 3: Act

Get CX data into everyone’s workflow

Optimize your product with CX

Close the loop at scale

CX program fundamentals

Step 1: Listen

Begin With Net Promoter Score (NPS)

Choose Your First Survey Channel

Step 2: Learn

Segment Your CX Data

Identify Themes in Customer Comments

Step 3: Act

Close the loop with customers

Loop closing in practice

Evangelize CX data

The B2B focus: from customer to end-user

12 ways to “consumerize” B2B customer experience

1. Hospitality

2. Financial Services

3. Medical Affairs and Pharma

4. PR and Advertising

5. Retail

The NPS survey

Collect NPS Data with a Survey

How to Calculate NPS: The NPS Calculation Process

What is the NPS Formula, and How Does it Work NPS Calculations?

Interpreting Your NPS Score

Utilizing Customer Feedback

Creating Additional Questions for Your NPS Survey

Ongoing Voice of the Customer

A quick primer on AI training sets

Training model process

Manual tagging: an inconsistent truth

Tag definitions vary

Getting to a good training model

Ready for autocategorization

1. You have a mature or quickly-maturing CX program.

2. You receive 500+ comments per month (or you’re headed there.)

3. You’re sitting on a goldmine of feedback, but unable to get actionable insights.

4. Manual feedback organization & categorization is insightful, but painfully slow.

5. Your CX program lacks a real-time issue detection system.

6. Your internal teams aren’t agreeing on CX priorities.

7. You need to demonstrate the ROI of your CX program.

Is this the point?

Right Audience, Right Problem

Sharing the Love

Listening Empathetically

NLP in the Healthcare Industry: Sources of Data for Text Mining

Improving Customer Care While Reducing Medical Information Department Costs

Hearing How People Really Talk About and Experience ADHD

Facilitating Value-Based Care Models by Demonstrating Real-World Outcomes

Guiding Communications Between Pharmaceutical Companies and Patients

More Applications of Text Analytics and Natural Language Processing in Healthcare

Some More Things to Consider: Data Ethics, AI Fails, and Algorithmic Bias

Improve Your Understanding: What Are Text Analytics and Natural Language Processing?

Wootric is ranked #1 in ROI (Return on Investment)

Enterprise users also gave Wootric the top rank for usability and easiest admin

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