89% of brands will compete mainly on CX:

Voice of the Customer (VoC) Analytics can help

Wonderflow has developed a unified VoC analytics platform that can draw together 200+ customer feedback sources in an easy to use platform. Now you can examine the Voice of Customers from all over the world – both your own and those of competitors.

As a proud technology partner of Wonderflow, HIPSTO extended the invitation to produce our very first ‘guest blog’.

Find out why Voice of Customer (VoC) has become such a hot topic, and learn about the four main types of VoC analytics from the team at Wonderflow: Lucia Manetti, Halle Dang, and Michele Ruini.

Today, one negative review can tarnish the image of an entire brand. Businesses are, therefore, obligated to provide an excellent customer experience (CX).

Gartner states that nearly 9 out of 10 companies will compete mainly on CX. And although that may seem intimidating at first, opportunities in the competitive landscape are quite abundant.

For example, research shows that:

Experience-driven companies grow revenues 4-8% more than their competitors
Customer lifecycle value can increase 6 to 14 times more with outstanding experiences
Up to 55% more client retention can be achieved through Voice of the Customer (VoC) initiatives

Improving your customer experience isn’t a straightforward goal. What’s more, implementing a customer-centric strategy will take some serious listening skills.

Companies must tune in to what their customers are saying about their brand, products, and services to begin even thinking about improving CX.

That said, say hello to Voice of the Customer analytics.

In this article, we cover the main topics below in order:

What is Voice of the Customer (VoC)
Why should companies leverage advanced VoC analytics programs
How VoC analytics programs work
Four main method types of VoC analytics tools
Example of AI in VoC application: Wonderflow with HIPSTO

Read to learn more!

What is the Voice of the Customer (VoC)?

Voice of the customer (VoC or VOC), as defined by SixSigma:

“The customer’s voice, expectations, preferences, comments, of a product or service in discussion. It is the statement made by the customer on a particular product or service.”

In other words, VoC is the customer’s feedback.

With the rapid global growth in big data analytics, particularly artificial intelligence (AI), VoC programs are fast evolving. Hence, the term Voice of the Customer analytics.

Compared to standard methods of consumer feedback analysis (focus groups, interviews, surveys, etc.), VoC analytics is more technologically advanced and data-driven.

The AI-enabled approach involves a systematic process in collecting, preparing, and analyzing people’s raw words and unfiltered truths. The system can extract data from hundreds of digital sources.

Since customer reviews and ratings cannot become skewed towards your brand (or against it), VoC analytics makes for a powerful and effective business resource. There’s no reliance on self-selecting users, and there’s a lot of downright honest opinions out there anyways.

Why should companies leverage advanced customer feedback analytics to improve the customer experience?

The global customer analytics market is expected to grow to about $7.3 billion by 2023, at an unprecedented CAGR rate of 15% between 2017 and 2023.

According to Aberdeen, companies with a strong VoC game can increase customer retention by 55%. Also, they can experience a year-over-year reduction in customer service costs by 23% and a year-over-year increase in sales by 48%.

Thus, brands that invest in, or upgrade their existing, effective VoC programs can significantly improve many areas of business: sales, customer satisfaction, net promoter scores, loyalty, and CX.

Some of the key benefits of leveraging customer analytics include:

Better customer engagement: You’ll likely gain a deeper understanding of the effectiveness of your marketing strategies, plus your failed tactics, as a result of adopting the right customer analytics tool and one that meets your business needs. Further, creating more effective engagement with target customers.
Improved team productivity: Employees can feel more empowered to act on your brand’s product and service improvements based on buyers’ legit opinions.
Higher accuracy in data: When integrating the latest technologies with feedback analysis, smoother trial-and-error testing of new products is more likely to occur. The AI uses special techniques like natural language processing (NLP) to determine and source the right data and translate insights into proven results.
Increased revenue: You can observe significant upticks in sales as an indirect effect of designing better products around VoC data. In particular, when the technology allows you to pinpoint the users’ exact source of the issue.
Smarter competitive analysis: Advanced opinion mining solutions allow you to monitor and analyze other competing brands beyond learning about the end-users, granting you the bigger picture or full market overview.

How does a VoC analytics program work?

VoC analytics utilizes AI technology and requires little to no effort from humans.

With the help of advanced NLP, marketers, data scientists, customer experience professionals, and more can extract deep consumer insights and turn them into actions.

The system automatically scans the vast stream of reviews and ratings while picking and plucking specific information from all over the web.

Planning and designing a VoC analytics program typically involves these six general steps:

Identify your department’s specific problem, objectives, and questions
Collect and sort the data
Select the appropriate type of analysis method (more in a later section)
Analyze and troubleshoot to solve the problem
Make inferences and predictions
Act

Four main types of VoC analytics programs

Customer feedback is most commonly written (textual-based). Other formats of user reviews are audio, visual, and video.

Therefore, various types of methods exist using VoC analytics. The four main ones include:

1. Text Analytics

Also known as text analysis (mining), this procedure is the most standard and common form of VoC analysis. It involves the scanning of words. More text analysis examples exist than the other three subsequent methods.

Text analytics is the latest and most advanced level of text mining. It refers more to AI-based software tools to help companies perform more efficient and accurate customer review analysis. The user essentially studies the face value of words, such as grammar and the connection among the different word meanings.

More recent advancements have led to such tools offering prescriptive and predictive analytics, the power to suggest actions for users to consider and predict the impact of outcomes based on their decision.

Additionally, online data sources vary greatly; news articles, web pages, discussions, conversations, Google reviews, blogs, social media threads, surveys, call transcripts – just to name a few.

2. Sentiment Analytics:

Another high-level feedback analysis method is sentiment analytics. It also involves the automatic processing of many structured and unstructured texts, but this approach goes a little deeper than the former. It grants the user more insights into the customer sentiment.

In other words, text analytics show us the meaning of words, and sentiment analytics offer the emotions conveyed behind the meaning.

In general, sentiment analytics tools categorize an element of text by sentiment type (positive, negative, neutral). Aggregated scores then show the overall sentiment score (e.g. for a given article).

3. Social Media Mining:

A method focused specifically on the context of social media. Popular platforms like Hubspot can automatically ‘comb’ for texts in any social channel, its comments, posts, over selected accounts, or entire platforms, all to understand what the customers are really saying. Plus, such tools can go beyond social media metrics and analyze the entire customer journey.

The primary purpose is usually to find a specific brand and/or product mentions, thus involving the critical practice of social listening. As new algorithms become available, social media mining evolves.

Consequently, systems will extract text strings from media formats, such as videos, audios, hashtags, embedded texts, and on-screen logos.

4. Video Content Analytics:

The most recent VoC analytics method to emerge (which can also be categorized as content analytics) is video analytics or intelligent video analytics. Deep learning algorithms are helping to transform this area, and the technology enables video analysis platforms to copy human actions. In turn, this culminates in a paradigm shift.

As its name further suggests, video analytics go beyond text mining and can pick up on human sentiment and motions (facial expressions).

Take, for example, facial recognition analytics software, Zenus. Their novel ‘ethical AI’ collects, analyzes, and reports on sentiment by scanning moving content, such as YouTube, while avoiding confiscating individual identity.

This kind of awe-struck advancement in behavioral analytics serves as a valuable business tool to gather deep consumer insights to improve operations, particularly in security, retail, transportation, and healthcare.

AI In Action: The integration of Wonderflow and HIPSTO to help improve CX

In a specific real example proving the value of AI in Voice of the Customer analysis, software solutions like Wonderflow and HIPSTO are collaborating to help redefine CX.

As a unified AI-driven text analytics tool, Wonderflow does all of the aforementioned mining tasks, if not more. Users can analyze and understand everything in one place, and the system sources data from over 200 different channels.

The common challenge in text mining often involves extracting data from complex platforms. This is especially the case with Chinese and Russian digital networks, making scraping ratings and reviews from their websites nearly impossible.

Consequently, product marketers or data analysts cannot extract the information they need regarding specific customers or demographics.

Utilizing HIPSTO’s proprietary new approach, Blind Vision, which takes extraction methods to the next level, has allowed Wonderflow to combat the web scraping issue.

How it works

To best summarize how Wonderflow and HIPSTO work together, we asked Luca Galasso, a Full-Stack Developer at Wonderflow, whose team works closely with our technology partner. Luca explains:

“Long story short, Wonderflow provides HIPSTO the sources from which we want to retrieve customer feedback, and they periodically send us a snippet containing all that information. If we’re going to modify the source list, we can easily do it with almost zero effort.

With HIPSTO in our tech stack, our users will be able to go through all their reviews and ratings from highly complex areas, specifically Chinese and Russian e-commerce sites.

The integration of their product with our own has been fast and smooth, and it’s now almost effortless to retrieve new information from our selected sources.”

– Luca Galasso, Full-Stack Developer at Wonderflow

This is a game-changer for companies, allowing them to leverage the kind of valuable information that will enable them to really understand their targeted overseas customers. More so, it will allow them to use predictive analytics to receive suggestions about business actions and the potential impact of those decisions.

If you are truly listening to the customer, you can take actions that genuinely improve the customer experience.