Whether you’re grabbing dinner with friends, staying at a luxury hotel, or purchasing a cup of coffee on the go, customer service always matters. After all, 91% of consumers are more likely to make another purchase after a positive customer service experience. The stakes are high for companies to offer quality customer experiences – about 61% of customers said they would switch to a new brand after just one bad experience.
It’s important that consumer-facing businesses know how their customers feel about their products or services. Thirty years ago, we relied on phone calls, mystery shoppers, and email surveys, hoping that our databases were current, and that people would respond. It was a spotty view at best.
As technology has evolved, we have many new ways to hear from and interact with our customers. As a result, it has become a constant endeavor to keep up with the deluge of customer feedback and gather it into useful formats and tools for analysis and action.
What we now call Voice of Customer (VoC) – the process of gathering feedback on customers’ expectations, preferences, and experiences – has advanced to a point that we not only can know more about customer experiences with our brands, but we can predict what they want and how they feel about us – even when they don’t outright tell us.
The evolution of VoC continues with new data sources and technologies constantly emerging to give us new and valuable insights. Here are some of the trends we’re keeping our eyes on in the year ahead.
Many companies are realizing that long surveys with a litany of questions and canned answers (from ‘Extremely Happy’ to ‘Extremely Unhappy’) result in a poor customer experience themselves!
As a result, many companies are focusing on simplifying the process of inviting customers to provide feedback. This means letting customers provide feedback about whatever they want, however they want. For example, Amazon simplifies the process to include an overall star rating, a title, an optional photo, and most importantly – a written review.
For customers who are all-thumbs when it comes to typing reviews, voice feedback technology is growing in popularity. For example, Edge of Texas Steakhouse uses a third-party technology to engage customers through links or QR codes across all their marketing channels, which allows them to provide feedback easily, any time, any place.
The ability to provide audio feedback in-app or via a device like Amazon’s Alexa has the potential to allow brands to increase the percentage of purchases for which they obtain feedback and improve the quality of the feedback. That’s because it’s based on what is important to the customer (not a series of standard questions on topics they may or may not care about).
The increase in open-ended written and voice feedback underscores the importance of natural language processing (NLP)-based text analytics in VoC solutions. With NLP, computers can understand and interpret human language in its written and verbal forms. This includes feedback from social media, chat bots, and voice texts, and can dramatically increase the amount of customer feedback organizations are capable of analyzing.
Although many packaged VoC solutions have been using text analytics for some time, the reality is that the abilities of many tools to analyze textual feedback is very primitive.
Regardless of whether it is a visit to a restaurant, an online purchase, a travel experience, or a purchase from a brick-and-mortar retailer, customers typically provide feedback on multiple aspects of their purchase experience. Our experience shows that the average customer providing open-ended feedback includes feedback on 3-4 different aspects of the interaction.
As a result, it is important for your analytics tool to be able to do the following:
To illustrate these points, consider this statement: “I ordered a burger, It was good, the fries were cold.”
Most customer feedback technology will capture the first part of this statement to find that the customer ordered and enjoyed a burger and miss the fact that the fries were cold.
As companies take their VoC capabilities to the next level, they must leverage NLP and other machine learning (ML) technologies to more completely and accurately analyze a larger percentage of unstructured customer feedback.
For more information about transforming unstructured data into customer insights, read our Voice of Customer Guide.
If you are a business where the customer physically visits your location for the purchase (restaurants, travel, lodging, brick and mortar retail, etc.), this often means that they’re using Google Maps to find you – especially for restaurants. Just in the United States, millions of daily searches in Google Maps include some variation of the phrase: Food Near Me.
Why is this important? According to Chatmeter, 41% of resulting clicks are to one of the first three listings. What’s more, 50% of consumers who conduct a local search on their phone visit a business within 24 hours.
Although Google doesn’t reveal the exact search algorithm, it is common knowledge that the quality, recency, and frequency of reviews factors heavily into the relative listing of each restaurant.
As a result, it is critical to analyze this feedback, respond to customers, and focus on improving all aspects of your operation to improve feedback going forward. Customer experience organizations should expect to see more integration with map applications, especially Google Maps, in future versions of their VoC solutions.
An online search suggests that 8 percent to 25 percent of dissatisfied customers will not voice their complaints to a company, yet most of them will share their experiences with friends and family and may favor a competitor for their next transaction.
For customer experiences that involve in-person interactions, computer vision and voice analytics have potential to identify these “silent customers” and further analyze the experience to determine if the customer was happy or unhappy.
This must be done in accordance with applicable privacy legislation, but the opportunity to leverage language used, tone, facial expressions, and body language can aid tremendously in understanding customer experience for those who do not provide direct feedback.
Organizations should expect to see more exploration of computer vision and voice analytics in the VoC space in the months and years ahead.
Today, many VoC solutions are siloed, channel-specific platforms that offer insights into a single sales channel or geography or a single type of feedback, such as social media or customer surveys. This data does little good when kept in silos.
For this reason, we will see more and more organizations working to integrate customer feedback data across multiple channels and geographies within a single environment, which can be incredibly powerful.
As organizations build integrated customer feedback repositories, it will be very important to maintain them behind the organization’s own firewall versus being locked within the walls of a third party. Considering that much of this data includes personally identifiable information (PII) that is or may need to be physically housed within certain geographies, this is becoming critically important.
Furthermore, having this rich dataset behind the company’s firewall makes it easier to integrate with other data assets, as well.
As the VoC market continues to evolve, organizations must evaluate whether they are prepared to see, analyze, and act upon the customer feedback data they receive.
It’s time to move beyond standard customer surveys and invite customers to provide feedback on their own terms. The more feedback we can capture and analyze, the more quickly and accurately we can respond to protect the customer relationship today and in the future.
Technology advances such as Wavicle’s ActiveInsights can help accelerate the integration of data from multiple silos and the use of artificial intelligence technologies such as NLP to derive contextual insights from customer feedback data. Now available in the AWS Marketplace, ActiveInsights leverages multiple AWS native services, including Comprehend, Athena, and QuickSight, to allow organizations to understand customer feedback from multiple sources at scale.
Through our data management and cloud migration services, we help clients build future-proof data architectures that capture and analyze data from any source and deliver business insights to facilitate smart decisions.