Voice of customer analytics

What is Voice of Customer analytics?

Whenever your customers express an opinion about the experience they’ve had with your company – either directly or indirectly – they’re adding to an overall data set referred to as Voice of Customer (VoC).

If Voice of Customer, then, is the collective term for the opinions and feedback you gather from your customer base, then voice of customer analytics is how you’ll make sense of what you learn. VoC analytics is the process of using digital CX and contact center platforms to aggregate customer sentiment and turn it into insight that can fuel proactive change across your business.

When you run voice of customer analytics, you’re main aims are to:

Uncover customer needs

When you analyze feedback from every touch point – like surveys, reviews, social media channels and customer service calls – you’ll better understand your customers’ main requirements, priorities, and desired outcomes. With that insight, you’ll be in a better position to refine your product offerings and improve the overall customer experience.

Find (and fix) pain points

Customer feedback is a key source of insight around problem areas and experience gaps. VoC analytics helps you find and fix those pain points along the customer journey, as well as common technical issues or other sources of customer frustration.

Understand customer satisfaction

Collating and analyzing feedback data allows you to monitor overall satisfaction levels, as well as measure sentiment towards specific products, features, or touchpoints. Sudden changes in things like NPS or CSAT, for instance, can indicate emerging issues that need to be tackled.

Drive product or service improvements

Analyzing Voice of Customer data can help you prioritize your development efforts for maximum customer value. Feedback might point to new features to add or remove, for instance – or even suggest a gap for new products to fill.

Fine-tune your marketing efforts

Your customers’ perceptions might not match up with your marketing campaigns, which can lead to a dangerous disconnect. Analyzing what your customers think can help you align your messaging in a way that delivers greater impact.

Ultimately, the goal is to understand what your customers think, how they feel, and what the likelihood is that they’d recommend your business or products to others – and to use that information to improve things over time.

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Why is Voice of Customer analytics important?

Understanding what your customers are saying about you is one thing, but really analyzing sentiment and opinions through genuine data science is how you’ll connect the dots between what’s causing negative feedback and what you need to do to change things.

In and of itself, that should naturally sound like an important practice, but there are very real business benefits backing things up. Voice of customer analysis is all about drawing conclusions that can lead to customer experience improvements, and those improvements are known to drive revenue and retention.

Key voice of customer stats

Voice of the customer analysis can help you:

1.    Keep customers for longer

• Forrester research shows that customers are 2.4x more likely to stick with brands who can listen and solve problems quickly
• Bain & Co. cites that voice of customer analytics programs can boost retention by 55%

2.    Boost sales

• Customer-centric brands report 60% higher profits than those who don’t put the customer experience first.
• Collecting and analyzing customer feedback can increase cross-selling and upselling success rates by as much as 20%.

3.    Become a market leader

• 72% of customers share a positive experience with six or more of their peers, which helps build a market-leading reputation.
• 86% are willing to pay more for products that come from brands with a great customer experience.
• 65% say that a great customer experience supersedes great advertising in terms of importance.

How do you Analyze Voice of Customer data?

Analyzing voice of customer data relies on being able to collect a robust amount of it – and that means using digital customer experience management suites that can track what your customers are saying no matter where they’re saying it.

Broadly, we group voice of customer data into four groups:

Structured feedback data

Structured VoC data is the kind that naturally lends itself to metricization. Surveys that score your business as part of the CSAT or NPS framework, for instance, produce a score that’s easy to track over time.

Unstructured feedback data

Unstructured data, on the other hand, is harder to quantify without the right tools. Included here are things like conversational analytics, where AI-powered tools can listen to conversations and apply values for sentiment, effort and intent to what’s being said, as well as agent script compliance.

Solicited feedback data

Solicited feedback happens wherever you proactively ask customers for their input. That’s usually in the form of surveys or feedback boxes on your website or app. A CSAT survey, for example, is a great example of explicitly soliciting customer feedback.

Unsolicited feedback data

You’ll be able to find unsolicited customer feedback in your company inbox and across the web – specifically on third-party review sites and social media channels. Any time a customer talks about your brand, that’s unsolicited feedback that you’ll want to try and capture and analyze. Because you haven’t explicitly asked for it in a set format, unsolicited feedback is usually unstructured.

Okay, so how do you capture and analyze all that data? Doing so comprehensively relies on having software suites that can do the hard work for you…

Contact Center Analytics

Your contact center is a goldmine for voice of customer analysis and data. Every day, your customers are telling you what they think of your products and services, but without the right tools in your tech stack, you won’t be able to aggregate all those messages in a way that can be combed for insight.

Great contact center analytics suites, then, incorporate conversational analytics tools to scour calls, emails, texts and live chats for customer effort, intent and emotion – and use what they learn to build a comprehensive voice of customer picture.

In practice, that means having a centralized, one-stop-shop for analytical insight that’s able to spot emerging trends and highlight customer issues while they’re still in the process of arising. Is your customer program working? Do your products have recurring defects? Are your digital experiences landing as they should? Contact center analytics will be able to tell you by collating the voice of the customer from every inbound interaction.

Omnichannel analytics

Complimenting the contact center side of things is any tool offering true omnichannel voice of customer analytics. True omnichannel analytics is able to look at anywhere across the web where customers may be voicing their opinions, understand what’s being said, and turn it into metrics that can be easily compared and tracked over time.

Examples here include third-party review sites where customers give your products written ratings and posts on social media sites that might voice complaints – alongside the conversations you’ll have with your customers in your contact center.

Surveys

Surveys remain a fantastic source of voice of customer feedback. Here you’ll be proactively seeking their opinion on a variety of topics, as well as industry standard formats like measuring customer satisfaction via CSAT, or their likelihood to recommend with NPS.

CSAT

CSAT surveys ask customers how satisfied they are with a given experience (like purchasing a product or dealing with customer support). This is usually recorded on a five-point scale.

NPS

Your Net Promoter Score is determined by asking customers how likely they are to recommend your business to others, on a scale of one to ten.

From an analytical point of view, surveys are best deployed digitally from software that’s not only able to easily collate and store responses, but also turn them into insights for you.

Ultimately, the best tools for the job here are the ones that proactively run voice of customer analysis without the need for complicated, manual data science. That means going beyond just gathering data – interpreting it as both metrics and useful action points.

Customer experience management software like that offered by Qualtrics®, for example, can turn all that data into simple, understandable insights – insights that can help you proactively close experience gaps as they arise.

Understanding regression analysis

Regression analysis compares different variables against one another to help companies understand how changes in one area impact others. For example, a ‘regression’ could show how making different changes affect customer satisfaction scores. Here, our core (dependent) variable would be our CSAT score and the other (independent) variables would be what we test.

Let’s say a software company surveys customers monthly on a 1-10 scale about product satisfaction. They average 8.2. They then make improvements to fix bugs and usability issues based on what their voice of customer feedback data tells them. The next month, customer satisfaction scores increase to 9.1.

Regression analysis quantifies the relationship between those product changes (just one of many possible independent variables) and the satisfaction score (our dependent variable). It might show, for example, that for every unit of improvement made, CSAT increases by 0.9 points.

In other words, regression analysis pinpoints the connection between cause and effect. Companies use regression to guide their decision-making – like how increasing call center script compliance by 10% might raise customer retention by 2%.

Most businesses know they have to identify the key drivers to improve their customer experience or showcase ROI in some way, but many don’t know how to actually begin. Regressions, then, are a powerful analytics tool for any voice of customer program because they help turn VoC data into quantitative insights.

The good news is that, with the right contact center and conversational analytics suite, this process is made a lot easier. While choosing the right dependents can be challenging, good VOC analytics software can help your data science teams take raw inputs and turn them into a workable regression model.

Voice of Customer analysis examples

Let’s take a look at how voice of customer analytics can help businesses make informed decisions across a range of verticals:

Sentiment

Tracking sentiment (positive, negative, or neutral) in customer surveys, reviews, social media and call transcripts will help you gauge consumers’ overall satisfaction and emotional response.

Example:

A hotel chain analyzes online reviews and finds negative sentiment has increased around cleanliness. This prompts them to retrain housekeeping staff on cleaning procedures.

Keywords

Identifying common keywords in open-ended feedback is a great way to understand priorities, concerns, and desires.

Example:

A software company reviews user feedback and their software suite flags frequent mentions of “navigation issues.” As such, they prioritize improving the UI and menu layouts in their next release.

Satisfaction scores

Scoring metrics like CSAT from surveys helps quantify satisfaction in relation to products, services and customer support interactions.

Example:

A retail bank sees their survey CSAT scores related to contact center interactions have dropped from 4.5 to 4.0 in a quarter. They investigate by interaction type and offer coaching to support agents.

NPS

Analyzing Net Promoter Scores (showing user likelihood to recommend) can help gauge brand loyalty and satisfaction.

Example:

A home appliance maker surveys customers and sees their NPS declined from 50 to 30 over a year. They dig deeper to understand why fewer customers would recommend them now, and their customer experience management suite flags that this decline is linked to a decline in product reliability.

Pain points

Reviewing customer complaints, negative social posts and service interactions across every channel to understand problems and frustrations.

Example:

A B2B accounting software firm looks at its user feedback forum and sees many complaints about its new invoice design. They roll back the design changes to resolve this pain point.

Demographics

Linking feedback data to customer demographics like age, location, gender, etc. can help identify trends that are otherwise tough to spot.

Example:

A luxury hotel brand analyzes its guest satisfaction data and sees lower satisfaction among international guests – particularly around the food on offer. They customize amenities and services to better cater to this demographic’s tastes.

Journey stages

Segmenting feedback by the customer journey stage (awareness, consideration, purchase, onboarding, active use, or renewal) can help pinpoint exactly where common issues lie.

Example:

An online clothing store finds higher returns from new customers purchasing in their first month. They adapt their recommendations engine to better cater to first-time buyer preferences.