Introduced in 1958 and discontinued after only three years, the Ford Edsel is perhaps the most notorious product flop in the history of American industry — the name itself has become synonymous with failure. In truth, the Edsel was a decent but not very attractive car with lots of interesting innovations. The bigger issue was that Ford completely miscalculated the market.
Most notable for its unusual vertical grill, the Edsel featured a 375-horsepower V-8 engine, ergonomically designed controls, self-adjusting brakes, push-button transmission and a “rolling dome” speedometer. However, it was introduced smack in the middle of a global recession when folks were looking for smaller, economical and fuel-efficient cars.
What do your customers want? It could be the single most important question any business can ask about its operations. To get the answer, more and more organizations are implementing sentiment analysis technologies within their contact center operations.
Sentiment analysis, sometimes known as opinion mining, is a subset of call center analytics. It refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify and quantify consumer opinions. It allows organizations to monitor and evaluate not only what customers are saying, but also the tone of their voice. It can also be used to extract subjective information from text records, survey responses and social media posts.
Tractica forecasts that worldwide revenue from sentiment and emotion analysis software will increase from $123 million in 2017 to $3.8 billion by 2025, with customer experience, customer service and market research rated as the top three use cases.
Forrester’s 2019 U.S. Customer Experience (CX) Index illustrates the importance of being able to understand the underlying emotions and sentiments in customer feedback. The survey found that emotion plays a critical role in differentiating brands and has a bigger impact on brand loyalty than effectiveness or ease of use, regardless of industry. That’s why it’s critical to evaluate not just a customer’s feedback, but also the underlying feelings.
Sentiment analysis algorithms are designed to evaluate conversations and messages for particular words, phrases, voice inflections, rate of speech and other cues to evaluate the amount of stress, frustration or anger the customer is experiencing. Machine learning capabilities help refine the algorithm and evaluations over time.
Follow the Cues
Even the customer’s choice of communication channel can be an indicator of sentiment. For example, customers who are angry or frustrated are more likely to choose a real-time channel such as voice in order to convey their irritation, while those who aren’t particularly emotionally invested in an issue are more likely to use non-real-time channels such as email or web chats.
Sentiment analysis not only provides insight into what customers think about your brand or your individual products, it can also help you understand how your customer service representatives are performing. Comparative analysis of sentiment scores can help identify agents who need more training and those who may need to be reassigned or replaced.
Mitel has incorporated sentiment analysis into its industry-leading contact center solutions by leveraging Google’s Dialogflow, a development suite for creating conversational interfaces and chatbots for websites, mobile applications, messaging platforms and IoT devices. Dialogflow runs on Google Cloud Platform, which Mitel is using to create smarter contact centers through robust artificial intelligence, machine learning and reporting capabilities.
Understanding what your customers want and don’t want is key to keeping them happy and building brand loyalty. As a Mitel Platinum Partner, IPC has deep expertise in planning, implementing and supporting Mitel contact center solutions. Contact us to learn more about using sentiment analysis in your contact center to gain a deeper understanding of your customer base.