How do your customers really feel about your brand?
A brand is more than just a company logo or slogan – it’s the thoughts and emotions people experience when they see your logo or hear your company name. For companies, establishing a strong brand can be the difference between success or failure and one of the most critical factors in building a brand that people know and love is to deliver truly exceptional customer experiences.
The way a brand is perceived correlates directly with company trajectory, employee satisfaction, and company growth. However, gauging this perception is inherently subjective and difficult to measure reliably, especially on a large scale. Thankfully, sentiment analysis tools are designed to do what most people would think is an impossible task: quantify and understand what your customers are thinking and feeling about your brand.
What is Sentiment Analysis
Also known as opinion mining or emotion AI, sentiment analysis is the analysis of customer conversations to measure customers’ affective states. These customer sentiment analysis tools typically use various forms of data collection to look for signs of emotion in customer-employee interactions. This information is analyzed by data and VOC teams to prioritize workflow and make strategic decisions. By having the ability to monitor your brand and product reputation, companies are more equipped to foster customer loyalty and excel in the market.
How Does Sentiment Analysis Work?
All types of sentiment analysis share the same overarching goal: understanding customer attitudes. However, these analyses can range from simple measures, such as survey responses, to advanced analysis of customer conversations. They typically measure polarity, urgency, intentionality, and/or emotions. Customer service calls are the most direct contact businesses have with customers, so it’s important to prioritize the quality of these conversations.
The most common survey-based brand sentiment analysis is customer Net Promoter Score. A post-call NPS survey may ask customers the following:
“How likely are you to recommend (business) to a friend or colleague?”
The customer ranks their response on a 1 (not likely) to 10 (very likely) scale. If the average of all responses to this survey is 8, then a company could infer that customers perceive them positively. NPS surveys remain a key data point for estimating company growth, but only captures polarity.
If a company is interested in more detailed data, they may use computational linguistic tools to measure conversations between customers and frontline employees. Companies have become increasingly reliant on machine learning that mimics the way humans understand emotions. These technologies analyze the words being used (natural language processing) to understand emotion. As an application of sentiment analysis in real life, if a customer says the phrase “I am so tired of dealing with this”, then the software would detect frustration from the customer. These findings can be used on a small scale – this customer is frustrated so we should reduce their premium – or a large scale – many customers are experiencing frustration, so we should change our policy.
Traditional sentiment analysis tools provide a massive amount of data to companies, and, just like the human brains that they’re modeled after, improve over time. By using this data, companies can make quick and informed decisions to increase customer satisfaction and loyalty. However, these tools only capture the words being spoken and oftentimes miss out on the meaning behind them.
Pitfalls of Traditional Sentiment Analysis
Imagine the sentiment analysis example of a customer calling their insurance company to report damages from a fender bender. At one point in the conversation, they say “I can’t believe this” to an agent. The customer may be expressing anger about the severity of the damages, or they may be excited about how much the insurance company is covering. If a sentiment analysis system were to only analyze words, this powerful statement would not be useful. The words being used don’t clearly indicate the polarity or emotion being expressed, but the way in which it is said provides important context.
Cogito: Signal-Based Sentiment Analysis
At Cogito, we use signal-based sentiment analysis to capture unconscious communication signals within conversations. Our “honest signals” are the intentions, thoughts, and feelings that are infused into human conversations. Humans indicate opinions by altering elements such as pitch, tone of voice, and rate of speech. In fact, the way we say things is often more meaningful than the actual words that we say.
Signal-based systems are able to process hundreds of data points at the millisecond level and provide key insights to employees. Dialog, Cogito’s AI coaching system for the call center, converts these findings into real-time feedback for agents. Agents can use this feedback to deliver higher-quality customer service in every phone call and track their performance over time. Supervisors receive a summary of this information and use it to coach their teams and identify top performers. Lastly, site leads and call center executives can use trends to make informed, strategic decisions to foster customer loyalty.
Cogito Represents The Future of Sentiment Analysis
Signals-based analysis provides unparalleled insight. By combining Cogito’s signal-based analysis with real-time word recognition, Cogito provides the ultimate sentiment analysis system. By combining the power of lexical and non-lexical communication cues, companies will have a robust understanding of their customers’ attitudes. Analyses of this magnitude are set to have a profound impact on the call center industry. The more you can understand how customers perceive your brand, the more your company will thrive.
Interested in seeing first hand what the future of sentiment analysis looks like? Request a demo.