Voice of Customer Data Analysis

How Organizations are Using Voice of Customer (VOC) Analysis Tools to Improve Customer Support Services

“This call may be monitored for quality assurance.” This automated phrase is guaranteed to make us tap our foot impatiently. We want to reach a human quickly to ask a question, voice a complaint, or get information about services. We might not consider that, in recent years, this ubiquitous phrase has moved beyond monitoring customer service and representative performance. Companies use Machine Learning software and Natural Language Processing (NLP) to perform comprehensive voice of customer (VOC) analysis. These tools examine calls for voice timbre, stress levels and even the keywords in conversation. We reveal much more than what we merely say. Companies are using the findings of voice data analysis to improve customer support services in some very innovative ways.

Examining the Voice of Customers for Real-time Speech Analysis

Businesses earn a competitive advantage through loyal customers. In an attempt to get an edge on the competition, it’s estimated that 70% of enterprises in 2018 increased their investment in real-time customer analytic solutions, such as voice of customer analysis tools. The biggest advantage of using real-time speech analytics is that this technology recommends the best solution while the call is taking place! It does this by recognizing specific trigger words in the conversation. For example, if a customer makes a reference to a competitor’s product, the rep can top it by saying, “We have a similar product with a promotional offer running right now…” Proactive customer service strategies will improve customer relations, while serving as a shining testament to the responsiveness of your business.

Additionally, this technology can send alerts to supervisors who can intervene when a call is going south. Traditional methods that provide post-call analysis miss out on the opportunity to address the problem in the moment. By the time customer service analyzes the call, they have already lost the opportunity to connect with the customer and address their concern. However, post-call analysis still serves a useful purpose to improve customer support services for future calls.

Automatically providing agents with the best answers to a customer’s problem reduces wait time and errors. With real-time solutions, customer service representatives reduce the average wait time. Since customers receive a resolution in their first call, voice data analysis also drastically cuts the rate of call-backs. Another benefit is that businesses can leverage analysis results to recommend soft skill enhancements for individual reps. After all, how you say something is as important as what you say. New technology can monitor for intonation and speed as well as talk time.

Performing Voice Sentiment Analysis

In all sci-fi man vs machine films, sentient software is trained to pick up on human emotions and react programmatically. Of course, distinctions can become blurred as these humanoids take over. However, the original premise of these films might not be so far off. Today, voice analysis not only transcribes conversations into text, but can also analyze conversations for long pauses, speech inflections and stress levels. This ability is a turning point in AI-driven algorithms. The amount of voice data generated by a contact center each day is too much for any manual analysis to be possible. Capturing this information and extracting meaning from it can reveal much about customers’ frustrations, anger and happiness.

Sentiment analysis uses a scoring mechanism to quantify reactions and opinions for specific products, services, company brand and even competitors. There are challenges though, particularly since AI can’t distinguish nuances such as sarcasm as humans can. Machines overcome this challenge in Natural Language Processing by factoring in stress signals and rate of speech. Including Machine Learning into these algorithms means that the algorithms become more accurate with time.

Most companies use sentiment analysis to monitor spikes in brand sentiment across social media channels. In customer service, businesses use sentiment analysis to monitor agent performance. Software detects and signals signs of frustration or satisfaction in the early stages of the call. Combined with voice identification, representatives can target repeat customers with personalized sales processes.

Call Script Optimization and VoC Analysis

Voice assistants such as Amazon’s Echo and Google Home are giving organizations insight into how far voice analytics can take a business. You can learn the behaviors of a household based on usage patterns; then, companies monetize these patterns through marketing opportunities. Voice data analysis in customer service is no different. Voice data analysis triggers alerts when reps deviate from a call script and can identify if it is a positive or negative outcome. What works for successful agents can be used to effectively alter call scripts and extend value to all customers. However, the value does not stop there. Voice analytics can be used to monitor executive calls to ensure that effective call behaviors are all working in tandem.

For practical applications of this new technology,  the insurance giant MetLife, stands out for the results they have seen in a short timeframe. They deployed voice of the customer data analytics for three of their customer-call teams and found that customer satisfaction increased while call duration dropped. They expect to continue this trend as they roll it out to more of their support teams. With more businesses turning to speech analysis software, customers might soon be greeted with a changed message: “This call will be monitored for tone and emotion.”

Companies are quickly realizing the benefits of using customer voice data analysis to improve their support services. This technology can help optimize customer service representative performance, reduce call duration, and increase customer satisfaction. If you would like to learn more about Machine Learning and Natural Language Processing (NLP), the team at Sertics is here to help.

With Sertics’ software as a service solution, users can create a custom data lake that allows for storage of both structured and unstructured data. Since voice data analytics generates a lot of unstructured data, a data lake solution is necessary to glean the insights that can be discovered through Data Visualization and Predictive Analytics. To find out how you can utilize Sertics in your business strategy, contact the team today.

Shane Long is the President of Sertics and SevenTablets, two companies that drive impactful digital innovation for their clients. Shane is an enthusiastic leader with more than 20 years of experience in hardware and software development, SaaS products and mobile technology. He holds an engineering degree from Texas A&M University, as well as a master's in finance from Southern Methodist University.