Voice Of Customer Analytics and Analysis – The What, Why, And How
Voice of the Customer Analytics (VoCA) is about understanding how your customers feel about your products and services after using them, as well as what they expect from your company. Customers’ expectations are defined by this process, and products are developed to meet those needs.
In Voice of Customer Analytics (VoCA), unstructured data, such as reviews and social media posts, is analyzed to extract relevant customer insights using data science and analytics tools.
Customers are increasingly becoming more skeptical in their purchasing decisions. They want to hear what others have to say about the product and see proof before they buy.
Customer analytics is one of the best ways to combat this hesitancy in purchase. This e-book discusses the voice of the customer program, why you should do it, and how to make it successful for your business.
4 Aspects of Voice of Customer Analysis
- Needs of customers
- A structure that is hierarchical
- A priority list
- Performance perceptions of customers
What is Voice of Customer Analysis?
Voice of Customer (VoC) Analysis leverages data science and analytics. Information can be gathered from unstructured data, such as product reviews and data collected from social media sites.
Key questions to seek answers to before building your voice of the customer strategy
When developing your voice of the customer strategy, it’s important to answer these three questions:
- What is the problem I’m trying to solve?
- Who is the customer and what is their current situation?
- What do they want or need?
If you can provide a solution that addresses the customer’s current issue and improves their life, you have a chance to market your product or service.
Each question should be well thought out and will help determine the issues customers are most concerned about. Companies can also use their in-house customer service or customer relations departments to collect feedback.
This is the most common way companies obtain feedback, but they should make sure they are asking customers what they think.
How do you know if your customers are content with your offerings? You need to ask them and measure the results.
Your customers are less likely to recommend your product or service to others, so you should be able to determine whether they’re happy with it. By following the three rules above, you can cover the basics of marketing.
Voice of Customer (VoC) Analytics For Real-time Results
Companies that use this type of analysis will use the feedback they receive to create new processes and improvements and to determine what customers need. This information is compiled into data that can be analyzed to produce real-time results.
Voice-of-customer analytics helps businesses understand what customers want, and it is important to have a plan in place to address those needs.
Net Promoter Score - Sentiment Analysis
Importance of Voice of Customer Analysis in Businesses
Why do businesses require voice of the customer analysis? Voice of the Customer can offer a treasure trove of statistics. Understanding it and acting on that understanding lead to buyer satisfaction and increased revenue.
A Gartner study found that gathering consumer feedback can increase the success rate of cross-selling and upselling by 15-20%. Also, businesses with a VoC program spend 25% less on customer retention than those without.
A study has revealed that businesses that engage in a VoCA program – not just gathering VoC data but also enthusiastically taking measures and responding to this feedback get:
- up to 43.2% new customers
- 25% increase in cross-selling and up-selling
- a 15% increase in average pro
The VoCA program can benefit the company in many ways and, in today’s world, is a necessity for any company offering services.
Voice of Customer Processing by Topic Modelling Technique
Topic modelling is an NLP technique that helps us automatically discover and extract topics from a given text, where ‘topic’ can be defined as an abstract set of words, phrases, and sentences that are connected to something precise. Topic modelling is an unsupervised technique.
This means the model can identify topics in the text based on patterns, without having to predefine the exact topics you want.
Keyword Extraction with Voice of Customer Analytics Tool
Keyword extraction/detection or analysis is a technique in NLP that automatically extracts the most frequent words and expressions from a piece of content.
Text sentiment analysis tools, such as keyword extraction and intent-based sentiment classification, offer deeper insights from Voice of Customer (VoC) data, helping you get into the heads of your consumers.
Using voice-of-customer sentiment analysis smooths out data and provides all groups with a complete view of patterns and events across the customer experience.
Reviews Detection
Before buying, people often read both positive and critical reviews and look at the rating distribution to gauge how genuine the product is.
This is why genuine reviews are of paramount importance to competing brands, as they reflect the quality of their products and attract more customers only if most reviews are positive.
No wonder brands struggle to earn positive reviews; however, ideally, they should take note of the critiques and work to address them to improve their image.
Voice of Customer Analysis Examples
Let’s say we obtain top words related to a topic as “chicken”, “pasta”, “shrimp”, “sauce”, “dish”, “salad”, “good taste”, etc. It is clear that the topic is related to food. Now, the dominant topic for a review is the one associated with the highest weight.
For example, consider the review mention “I had a similar issue to another reviewer. They don’t apply their COVID-19 policies fairly to everybody. We were constantly asked to move in and out of the restaurant, while others stood in large groups inside.
They seem enforce strictly on some and ignore others. Frustrating experience. To top it off, the food was subpar for the price. Bland, tasteless. Horrible time.”
Radar Chart Sentiment Analysis Tool
Now, for a 5-topic NMF model, the results for the above review will be:
0.0174Covid Support + 0.0099Food + 0.0098Customer Service +
0.0013User Experience + 0*Reservations
Named Entity Extraction (NER) can be used for multiple purposes and, when combined with sentiment analysis, can offer useful insights.
For example, by extracting location mentions from review text, a business can categorize reviews by location, allowing a relevant branch to address issues. Apart from that, negative reviews that mention a competing organization can also provide insight into what customers are dissatisfied with and how the organization can improve its service.
Any statement or piece of text can be said to have a sentiment or an emotion attached to it. For example, “I had a nice day today!” expresses a positive emotion, whereas “I didn’t like today’s dinner!” expresses a negative one. Likewise, in an e-commerce scenario, if you are pleased with a product you bought online, you will leave a positive review and may even recommend it to others.
However, if you receive a faulty piece, you may complain about it. It is crucially important for the brand as well as the e-commerce platform to keep track of the sentiment of the reviews to stay posted on the brand’s overall image, or something that we call “Net Promoter Score” (NPS). This kind of analysis is required for brands to identify where they lack growth and to recognize trends in what their consumers like or dislike about the product.
By building a routine of such analysis, they can not only address their inadequacies but also strengthen their strong opinions. This is why sentiment analysis is vital today.
Sample examples of voice of the customer for each class:
Positive Review:
“Clean store with staff always stocking stuff on shelves. Friendly staff that want to be helpful.”
Negative Review:
“Horrible service to a police officer who was waiting for her food while other consumers were assisted.”
Neutral Review:
“I only got cheesecake to go, but I was happy with it. I’ve eaten there in the past, and it was just ok.”
Voice of Customer Analytics (VoCA) Engine Suite includes tools like:
- Sentiment Analysis
- Fake Review Identification
- Topic Identification
- Net Promoter Score
- Named Entity Recognition
6 Steps of Building a Top Voice of Customer (VoC) Analytics program for your business:
- Find a question
- Collect and formulate data
- Choose your VoC analytics tools
- Investigate and troubleshoot
- Drawing conclusions
- Taking proper action
13 Voice of Customer Analysis Methods
What are the voice of the customer analysis methods and techniques? There are many voice-of-customer analysis methods that companies can use to conduct VoC analysis.
Businesses often use a combination of techniques to ensure they’re getting the most from their voice-of-customer analysis.
Let’s look at 13 voice of the customer analysis methods you can use for data collection.
- Take Customer Interviews
- Take Online Customer Surveys
- Add Live Chat
- Keeping watch on Social Media
- Focus on Website Behavior
- Pay attention to and Recorded Call Data
- Scan Online Customer Reviews
- Do In-Person Surveys
- Track Net Promoter Score
- Meet and Focus Groups
- Focusing on Personalized Emails
- Provide a Dedicated Feedback Form
- Do an analysis of Customer Behaviour and UX
Get this free e-book to understand what VoC analytics is, and how voice of customer (VoC) analysis can help your business.
