Remember when people used to refer to the phrase “voice of the customer” as a metaphor?
We treated it as a kind of a concept that companies explored to make better products.
Now, we can literally hear and understand what customers are saying with smartphones, chatbots, voice assistants, smart home appliances, and other IoT devices. This data, like many other types of customer feedback, is precious for any company.
The amount of this data saved by companies has reached tremendous amounts thanks to chatbots and voice-first devices. With companies having more opportunities to record spoken and written conversations than ever before, they are now struggling to process everything they’ve collected.
That’s where conversational analytics comes in.
What is Conversational Analytics?
Conversational analytics is the process of studying large volumes of textual and audio data recorded as a conversation between a user and a device.
Studying both language and audio insights provide unprecedented insight into people’s emotions, needs, and goals. Tons of that data collected by voice-controlled devices and chatbots can help with finding emotion- and sentiment- related insights.
Why do companies care so much about them?
Well, for one, emotion is the number one factor in customer loyalty. The experience provided by a company makes a customer or user feel a certain way, and this has a tremendous impact on their loyalty.
But wait, how to analyze that conversational data to find these insights?
Here are some of the best ways.
3 Ways to Leverage Conversational Data for Companies
1. Text-Based Emotion Analysis for Customer Service
This is the technology for chatbots and other connected devices using text as the primary mode of communication. Recent advances have made it possible to identify the emotion, communication style, and the sentiment just from the text that a user sends the chatbot.
There are two major tools that make text-based emotion analysis a reality:
- machine learning
- Natural Language Processing (NLP).
By detecting certain words, words combinations and recognizing underlying emotions, chatbots can:
Provide Adaptable Customer Support
Conversational customer service featuring a chatbot using emotion analysis would be more effective for serving customers. For one, chatbots would change their answers based on the input from customers and provide more appropriate responses.
With emotion analysis, a chatbot can segment the audience based on the conversation, e.g. “happy” customers, “angry” customers,” etc. This data can help with focusing the effort of support agents on customers who are likely to churn because of the negative experience.
Connecting Unhappy Customers with Agents
If a chatbot recognizes an angry or otherwise unhappy customer right away based on their responses, they will immediately redirect them to human agents. As a result, these customers will receive quality support the first and won’t have to wait a long time for someone to respond.
2. AI Chatbots to Help Local Governments with Citizen Engagement and Participation
Chatbots’ area of application isn’t limited to companies. Their ability to handle hundreds and even thousands of conversions makes them an important part of many smart city infrastructures.
Here’s the list of top smart cities in the world.
For example, governments use chatbots to connect with citizens: send them weather, traffic, and other updates, as well as inform them about other city-related events. In turn, they also collect some responses from citizens.
In fact, gathering citizen responses is a critical consideration for city authorities because they need as much feedback as possible to improve their services. The list of the typical smart city challenges includes many feedback-related issues: low awareness of government initiatives, lack of collaboration between local governments and citizens, etc.
By allowing to communicate with the authorities, the conversational surface like a chatbot:
- Improves citizens satisfaction with the local government
- Saves costs by eliminating the need to organize in-field citizen surveys
- Responding to common information requests (Between 80 and 90 percent of the North Carolina Innovation Center IT help chatbot’s requests from citizens are for help changing a password)
- Increases citizen engagement and participation by making it easier to communicate with the authorities
- Improves the ability of the local government to anticipate problems and make better decisions on issues involving the citizens.
Integrating a solution like a smart city AI chatbot is a good idea that will be welcome by most.
“Most citizens are already familiar with conversational platforms like Apple’s Siri, Google’s Google Assistant and Amazon’s Alexa,” says Bettina Tratz-Ryan, research vice president at Gartner. “It will be easy for government CIOs to use similar conversational agents — or chatbots — to drive more citizen-centric services for their smart cities.”
3. Conversational Analytics to Help Enterprises Reach their Goals
There are many more applications for conversational data on top of customer support. Each of these contributes to the ability of smart enterprises to meet their goals.
Script Improvement for Sales Department
Conversational analytics can identify shortcomings and issues of a sales agent’s script. For example, it can find negative emotions, long pauses, or other issues evoked by certain phrases to help team leaders adjust the scripts.
Case Study a logistics company was looking to reduce customer churn. The management proposed a new offer where B2B clients could reduce their logistics costs by choosing to remove the services of their choice. After reviewing the conversational analytics report, the marketing team found that clients reacted negatively to the words ‘value’ and ‘channels.’ On the other hand, their responses to “savings” and “personalized” were positive. The company decided to change the script accordingly and started offering the new deal. By using the words that led to positive reactions, they were able to reduce churn.
Targeted Training of Personnel
By analyzing calls and conversions with customers, conversational analytics can help with identifying patterns and topics that employees are struggling with the most. This information can help managers and leaders to come up with more effective and/or personalized training tracks.
Reduced Operational Costs
Both textual and voice-enabled conversational interfaces allow companies to engage in more dialogs with customers. Increasing the number of self-service options leads to reduced costs on customer support and other areas with increased reliance on human involvement.
IBM, for example, claims that chatbots can save big with chatbots because they can answer up to 80 percent of routine customer queries. As a result, the companies can speed up response times while avoiding expanding their support department and cutting costs by 30 percent.
Designing and Launching Better Digital Products
Conversational AI experiences can help with finding out if a company is pursuing the right opportunities. For example, by having a chatbot inside a newly developed digital product or a website chatbot, the company can collect more user feedback. It does not only allow constantly improving the product itself, but also helps to develop an effective strategy and create engaging content using the best paper writing site, build social media presence, and come up with relevant ideas for marketing campaigns.
Recognizing people’s emotions and intentions is becoming more important for businesses of all types. Nowadays, they have multiple options for collecting conversational data and information, both textual and voice-based, but the sheer amount of intelligence is already enough to extract some valuable insights.
As you can see, using conversational analytics, it’s possible to get some valuable information for advancing business, improving public service, reducing operational costs, and, most importantly, increasing customer loyalty. The role of analytics will increase in the nearest future, as more and more organizations will benefit from implementing conversational interfaces.