The Ultimate Guide To Voice of Customer Analytics" class="wow_main_float_head_img">

The Ultimate Guide To Voice of Customer Analytics

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Voice of customer analytics is everything about understanding how your customer feels about your product or services and the expectations they have from you.

Competition has never been so fierce and transparent in any business domain. But, on the contrary, it’s never been easier for customers to share their experiences or compare your products or services with your competitors. 

Convinced? 

These days, customers share their feedback and experiences not just with their friends and family but with the world. Customers expect brands to be all ears to them and interact with them directly and anywhere. The most successful companies keep customers, wants, and needs as the focus of their every business decision. 

Here, the Voice of Customer analytics program comes into the big picture. Voice of customer analytics is everything about understanding how your customer feels about your product or services and the expectations they have from you.  

Voice Of Customer Customer Experience  - It’s Worth

With social media dominating every nook and corner, just one viral review is all t to burn the brand entirely. According to Gartner Research, 89% of companies compete solely on customer experience, and considering this, the onus comes on businesses to deliver unparalleled experiences.

This opportunity is huge. Research by Bain Co. has shed light on its unskippable value: 

  • VoC customer programs result in 55% greater client retention.
  • Companies and brands that integrate customer experience raise their revenue 4-8% more than their competitors. 

To put in all, investing and improving your customer experience offers colossal value. But, to make concise and practical changes, it’s essential to listen to your customers. And to truly listen to your customers, you need to understand the communication they are having about your products, services, and brands. 

Step into the Voice of Customer Analytics. 

How To Collect VoC?

Every VoC process starts by collecting customer feedback. The most accessible voice of customer data source from surveys, whereas social media curates another source of VoC data. For example, users send 350,000 tweets every minute and post approximately 5 billion Facebook comments every month

How long will it take a human to analyze this data? How to draw the connection between the themes expressed in Facebook comment # 1,907 and Twitter tweet #33,90? And, remember, reading is just the beginning.  

Here is where the Voice of Customer platform empowers you to get value from data sources.

How To Use Voice of Customer Platform to Build an Effective VoC Analytics Program? 

To get real value, you must ensure that your Voice of Customer analytics program generates accurate and trusted insights. And to make those precise and reliable insights, you should follow the data analytics process. 

Always remember that hasty decisions and rough actions lead to significant failures. 

 

  • Begin with Question

 

An efficient VoC analytics program focuses on answering the questions. So, before you start analyzing anything, clearly find the questions you look forward to answering. Also, keep in mind the questions you ask should inform the data you collect, the analytics tools you select, and the type of analyses you perform. 

If still you are perplexed, think about your business goals and what insights would lead to good decision-making. 

 

  • Collect and Arrange Data 

 

It’s time to collect data. 

Above all, the data collected should be suitable for your question. For instance, a brand-related question might look for a mountain of tweets that mention your company. A product-related question could lead you to gather voluminous customer satisfaction survey responses. 

Here are some familiar data sources for Voice of Customer analytics programs:

  • Survey responses
  • Facebook comments
  • Support tickets
  • Online reviews
  • Chat conversations
  • Tweets
  • Emails
  • Call transcripts

 

  • Select the Right Voice of Customer Tools

 

A barebone survey analysis is not enough to compare complex trends over time. 

For example, suppose your objective is to use insight extracted from social media comments as a proxy for customer survey responses. In this case, you’ll require a flexible voice of the customer platform that provides rich reporting and personalization. 

Select a reliable partner with deep experience solving problems like yours to get the best ROI. 

Above all, don’t make a hasty decision. Choosing the wrong tool often leads to a failed analytics project and wasted resources. 

 

  • Analyze The Data 

 

In data analytics for VoC, the reports produced should focus on answering the initial questions. 

Considering the technical perspective, you’ll be more interested in reporting on themes, topics, and entities discussed in reviews and the sentiment expressed towards each. Other questions might focus on categorization or intention extraction. 

 

  • Outline Conclusions

 

In outlining conclusions, some insights are self-evident. Though, other revelations may surprise you. And, in some scenarios, you can find answers to questions you hadn’t thought to ask at first. 

Simply put, unexpected insights extracted from actual customer reviews demonstrate how a data-driven Voice of Customer analytics can stand out from the crowd. 

 

  • Right Step Leads To Improved Customer Experience 

 

In some scenarios, the course of action will be clear. Sometimes, it’s difficult to define the best course of action. 

However, by collecting and comparing reports into dashboards, you can discover the insights you need to work on to improve your customer experience. 

… and Off You Go! 

With this ultimate guide, you have the imperatives you need to start with VoC programs. But first thing first, define your audience and your objective, ask the right questions at the right time and track your progress and your customer’s opinions when integrating changes. 

But, don’t forget that the simplest mistakes often result in significant errors in data analytics. So, ensure to take the right course of action and make your company stand out and grow better. 



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