How to Set Up Your Voice of Customer Methodology

How to Set Up Your Voice of Customer Methodology

In order to best serve your customers, you need to listen to the voice of customer (VoC).

Also known as “buyer persona” or “customer’s voice”, VoC is the process of capturing customer feedback to better understand how customers use your products and services, and analyze overall customer satisfaction, in order to improve products and services, increase customer retention, and, ultimately, your company’s bottom line.

You need to analyze customer feedback to get into the heads of your customers, follow the entire customer journey, and get a solid handle on customer expectations, so you know what your company does great and what you may need to work on.

  • Customer support teams use VoC data to anticipate the needs of customers and help them solve problems before they come up – with FAQs and well-designed onboarding programs.
  • Product teams use VoC methodologies to inform product development and understand what product features work particularly well and what features a product may be lacking. In fact, Productboard’s 2020 Product Excellence Report tells us that 52% of leading product managers say their teams’ new products and features are inspired mainly by customer feedback, yet only 1 in 10 feel that they actually use all of the available feedback sources.
  • Marketing teams use VoC data to follow campaigns on social media and throughout the internet. Machine learning text analysis techniques, like topic extraction and sentiment analysis allow you to follow your brand and your products wherever they may be mentioned and automatically analyze customer feedback to understand if the comments are positive or negative and which topics or aspects of your business they’re referring to.

So that each team in your company can make the most of customer feedback, you’ll need to have a clear voice of customer methodology in place.

Voice of Customer Methodology

Voice of customer methodology is how your business collects customer feedback to inform your Voice of customer program. It is the assortment of voice of customer tools, techniques, and sources that you use to gather and analyze customer feedback to get an end-to-end understanding of the voice of customer.

There are a number of relevant voice of customer methodologies and VoC examples to gather the data you need. But let’s focus on the top 10 voice of customer methods.

Top 10 Techniques for Collecting VoC Data

  1. Surveys
  2. Online reviews
  3. Email
  4. Live chat, online chats, chat bots
  5. Social media
  6. Website behavior
  7. Interviews
  8. Focus groups
  9. Customer success team
  10. Feedback forms

1. Surveys

Surveys are often the quickest route to the VoC data you need. And online survey tools, like Typeform and SurveyMonkey make them easy to customize, easy to perform, and easy to collect. Net Promoter Score (NPS) surveys allow you analyze responses to find out whether your customers are Passives, Detractors, or Promoters. And general customer satisfaction (CSAT) surveys aim to understand a customer’s overall happiness with a company or its products and services.

You can ask simple close-ended questions that seek quantitative responses (Yes/No, “on a scale of 1 to 10,” multiple choice, etc.). Or you can dig into customers’ feelings and opinions with open-ended questions that allow them to answer in their own words, offering data you may have never even considered.

Open-ended responses can be more difficult to analyze than quantitative data, but with the help of machine learning text analysis tools, you can automatically analyze them for much more accurate results than humans could ever produce.

MonkeyLearn is a SaaS text analysis platform with powerful tools to plug into your voice of customer methodology that can automatically analyze surveys and all manner of voice of customer data.

Try out this AI survey analyzer, for example, that automatically sorts survey responses into categories or aspects: Customer Support, Ease of Use, Features, and Pricing. You can also run this same survey response through an AI sentiment analyzer, which tags text as Positive, Negative, or Neutral.

2. Online reviews

There are dozens of online product review sites full of VoC data that’s ripe for the picking, from general product evaluation sites, like Consumer Reports, TrustPilot, and ConsumerSearch, to software review sites, like TrustRadius, Capterra, and G2, to dining/travel reviews, like TripAdvisor, Yelp, and Google My Business.

These are sites built to give consumers the opportunity to leave their unguarded opinions, so you should definitely take the opportunity to listen to VoC about your business. And e-commerce sites, like Amazon, usually allow reviews, which can be a huge drive (or drain) for any business.

amazon

Techniques like aspect-based sentiment analysis can gather data relevant to your company and automatically analyze it for a minute-to-minute, real-time understanding of the customer sentiment of your business and your products and services.

3. Email

You’re already gathering email data from customer service interactions, complaints, suggestions, and more. So you should make sure you analyze this data with machine learning techniques, rather than only giving it a brief read through. There are, undoubtedly, recurring themes and subjects that properly-trained text analysis tools will be able to uncover.

Furthermore, email analysis will help you understand if your customer support is performing to your expectations. You’ll be able to find which agents may be lagging behind others, and discover new training possibilities. You can also email surveys directly after support interactions or other points on the customer journey.

4. Live chat

chat

Live chat is similar to email, but offers an even quicker option for customer survey opportunities. You can send single-question, quantitative CSAT surveys directly after purchase, after onboarding, after cancelation, etc., to get a regular understanding of pain points.

Although they don’t offer a massive wealth of information, customers are likely to click a simple Yes/No or multiple choice response, and you can give them the option to enter open-ended responses with pop-ups, like “What else would you like us to know?”

And you should always save your chat data for further analysis to uncover recurring pain points and find out what language, words, phrases, etc., work best to put your customers at ease.

5. Social media

Social media often offers some of the purest VoC feedback because it’s completely unsolicited – customers simply feel compelled to leave a comment.

Social media comments are purely open-ended, so you’ll need machine learning tools to help out with the analysis, if you don’t want to hire dozens of employees to spend thousands of hours scouring the internet.

Web scraping tools can extract exactly the information you need or you can connect directly to sites like Twitter and Facebook with APIs.

Then you can put MonkeyLearn’s text analysis tools to work. Take this tweet, for example:

Tweet: "After over a year of having an iPhone 11 Pro Max and trying to fit that horrible TV in my pocket I am over-correcting and switching to the iPhone 12 mini – I cannot wait to have the ability to text using one hand"

Social media comments (and other customer opinions) like this, may contain multiple opinions in a single statement, so you first need to break them into individual opinions before analysis. After proper training, your opinion units may look like this:

OPINION UNIT 1: After over a year of having an iPhone 11 Pro Max and trying to fit that horrible TV in my pocket

OPINION UNIT 2: I am over-correcting and switching to the iPhone 12 mini

OPINION UNIT 3: I cannot wait to have the ability to text using one hand

Next, we can run these opinion units through a sentiment analyzer:

Test with your own text

Results

TagConfidence
Negative96.3%

The first comes up as easily “Negative,” although the next two would need a custom-trained model to truly perform to the specific needs. Ultimately, your analysis could look like this:

OPINION UNIT 1: Features, NEGATIVE

OPINION UNIT 2: Features, NEGATIVE

OPINION UNIT 3: Features, NEUTRAL

6. Website behavior

Analyzing website behavior is great for quantitative analytics, to follow click throughs, purchases, time spent on sites, etc., and is particularly useful when joined with qualitative data from open-ended responses and social media comments.

Tools like Google Analytics, Hotjar, and CrazyEgg can help you understand the customer experience (CX) from a purely mathematical perspective. Website behavior VoC will show you how your customers interact with your website or app, compared to how they say they perceive it, allowing you to back up customer statements with data or counterbalance preconceptions with new data.

7. Interviews

Voice of customer interviews can be time-consuming and difficult to analyze, but can be especially rewarding because they allow the interviewer to follow a customer’s train of thought that wouldn’t normally have been dug into.

Customer interviews are like surveys, but they aim to elicit even more information from the respondent. Just be sure that the interviewer is properly trained for the task, so you don’t end up with leading questions and, ultimately, skewed data.

8. Focus groups

Focus groups are similar to interviews because they allow customers to follow their trains of thought in wholly new directions. They can be even more powerful, however, because they allow customers (or potential customers) to bounce ideas off of each other, sometimes offering new ideas for products, features, etc., that even a well-trained product team couldn’t have envisioned.

Furthermore, conducting open-ended questionnaires within a large group allows the group to come to a consensus, a vote as it were, so you can get an idea of what new concepts truly have legs.

9. Customer success team

Customer success teams dig into customer service, Customer Effort Scores (CES), sales and marketing, etc., to gather data about CX and funnel all of the information together with the goal of improving overall customer satisfaction and making it easier for customers to get exactly what they need from a company.

You can use this data to improve CX and follow the lead of companies like Zappos that put the customer at the center of everything they do.

10. Feedback form

Targeted surveys work great and can usually get the information you need. But sometimes your customers may have feedback that doesn’t fit neatly into a predetermined questionnaire, and you simply need to give them the opportunity to voice their opinion – whatever it may be.

You should make it easy for customers to leave VoC in simple comment forms, without the need to wait on hold or have their email passed from employee to employee. You just need to allow them to be heard, like this AirBnb form:

asana

This gives customers the option to give feedback about any subject they see fit.

The Wrap Up

That’s a lot of VoC methods and techniques to choose from, and you’ve seen a bit about how to analyze the feedback. The main point to take home here is that you should be gathering VoC feedback from as many sources with as many methods as possible.

And once you’ve set up your voice of customer methods, you should implement a customer feedback loop to (1) gather VoC feedback, (2) analyze your feedback, and (3) act on your results. This includes “closing the feedback loop” to let your customers know that you’ve heard their feedback and, in some cases, have instituted changes based on their complaints or suggestions.

VoC analysis allows you to get into the heads of your customers, so you can learn how to best serve them to increase customer retention and decrease churn. Find out the major customer pain points, improve customer support, and increase profits.

Take a look at MonkeyLearn’s suite of text analysis tools to see what they can do for your VoC techniques and methods. You can even try them out for free before you buy.

Or request a demo to learn how to set up your VoC process from start to finish.

Rachel Wolff

March 22nd, 2021

Posts you might like...

MonkeyLearn Logo

Text Analysis with Machine Learning

Turn tweets, emails, documents, webpages and more into actionable data. Automate business processes and save hours of manual data processing.

Try MonkeyLearn
Clearbit LogoSegment LogoPubnub LogoProtagonist Logo