Understanding your organization’s customer experience is vital for assembling the business intelligence insight your enterprise needs in today’s hyper-competitive marketplace.
After all, your business can always improve, and making customers happy will always be good for business. But understanding your customer experience means seeing your business through their eyes, and that is no easy task.
What is Customer Experience?
The term “customer experience” is used a lot in today’s businesses, and not always correctly. Often, it is used interchangeably with “customer service,” but that mischaracterizes both its importance and its effect. While “customer service” refers to the care customers receive from a business, “customer experience” refers to their perceptions of that business. It can include their perceptions of customer service, but it can also incorporate their experience purchasing a product or service or the marketing that brought them to purchase.
So, while “customer service” describes what your company does for a customer, “customer experience” describes how they feel about it, as well as how they feel about every other experience they have had with your business or brand.
This is a key distinction because it is a lot easier for a company to modify how they service their customers than it ever will be to change the way their customers feel about them. The battleground for customer experience is inside each customer’s mind, and nowhere else.
There is no doubt that improving customer experience can substantially increase a company’s bottom line. But improvement requires understanding, which is why customer experience is so important for companies to understand as fully as possible.
Methods of Exploring Customer Experience
In order to gather customer experience intel, most businesses just ask their customers. This can take the form of surveys or questionnaires, often given as a follow-up to the customer service activity. By way of example, you have probably received a follow-up text or email from a company immediately after calling them with questions about your bill or order.
These surveys are usually very brief, often only one or two questions. The most common question is, “how likely are you to recommend this product/service to others?”. This is because the “Net Promoter Score,” which determines a customer’s value as a referral source, is a metric that can also indicate a customer’s overall satisfaction, which is another important metric.
These and other important customer experience metrics are listed briefly below.
Key Customer Experience Metrics
- Net Promoter Score – describes the likelihood of referring new customers
- Customer Satisfaction – describes satisfaction with service received
- Customer Effort Score – describes how easy/hard the service/transaction was
- Lifetime Customer Value – how much will the customer spend overall
- Churn Rate – describes the rate of customer attrition
- Customer Response Time – the time it takes to engage a customer
- Average Resolution Time – the time it takes to resolve a customer’s issue
As the list above shows, many of the metrics businesses use to evaluate their customer experience come from basic sales and performance data. Only the top three are determined by actual customer input or are directly reflective of a customer’s frame of mind.
Further, the data gathered for those top three metrics are limited in their ability to provide useful insight. For example, a customer could be upset that the product they wanted was out of stock, causing them to rate their satisfaction and likelihood of referring as very low. But the cause of their dissatisfaction remains unknown within the data. Meanwhile, they will probably soon forget their frustration and maybe likely to refer their friends anyway. There is nothing that guarantees consumers will be truthful in responding to this kind of survey.
In order to really understand customer experience, one must look beyond what is stated to see how they really feel.
How Sentiment Analysis Reveals Customer Experience
That’s where sentiment analysis comes in. In a focus group setting, researchers will ask a series of questions to participants in the hope of gleaning insight into a brand, product, or service. While these questions can be taken at face value, researchers know better. They understand that focus group participants are often eager to please and tend to “sugar coat” their answers. So instead, researchers look for emotional clues and terms that better reveal hidden truths.
This qualitative data is then coupled with quantitative data from survey responses to provide a more complete picture of the overall customer experience. Because consumers know they are being evaluated, their responses alone cannot be fully trusted.
AI-Powered Sentiment Analysis
Fortunately, focus groups and surveys are not the only means of gaining valuable customer experience insights. Machine learning – also known as artificial intelligence (AI) – has progressed to the point where AI systems can not only interpret what’s being said, but also what the sentiment is behind the words.
AI-powered sentiment analysis allows computer systems to evaluate voice data for both literal and emotional content, offering businesses an exceptional look into the minds of consumers.
For example, CallCabinet’s Atmos call recording platform employs AI-driven Voice Analytics to digitally transcribe calls while analyzing changes in sentiment over the course of a call. Not only does this reveal when customers become happy, disappointed, angry, excited, or confused, but it actually does so within the context of the call itself. This allows businesses to see exactly what may have triggered the emotional response, providing unrivaled insight into their customer experience and what the business is doing to impact it.
AI-Powered Sentiment Analysis Data Source
AI-powered sentiment analysis is game-changing for enterprises looking to understand their customer experience not only for the depth of insight it provides, but also because it re-utilizes data that most enterprises already own.
CallCabinet’s Voice Analytics feature conducts real-time evaluation of voice data captured in Atmos’ call recording platform. Systems such as this are used by nearly every enterprise for regulatory compliance, quality assurance, or both. Since enterprises already own these rich pools of accumulated voice data (many regulations require archives to be kept at least 7 years), it’s foolish for them NOT to utilize this data source for customer experience analysis.
Even enterprises that utilize outmoded hardware-based premises recording platforms can repurpose their data by migrating it to the cloud-based Atmos platform.
As companies become more aware of the immense value locked up in their voice data, AI-powered sentiment analysis will likely become standard practice for understanding customer experience.