Conversational analytics is the next-gen technology helping you extract what the customer says about your brand from many channels.
With the growth of artificial intelligence (AI) and machine learning (ML), you can now just rely on cloud or on-premise applications that can decipher all these customer voices in minutes. Conversational analytics is the technology behind these tools.
Read on to learn conversational analytics from the inside out. It will help you implement this technology in your business, develop managed services for other organizations, or become a developer of this technology.
What is Conversational Analytics?
Conversational analytics is using software that can go through various conversations from digital sources about your business. These conversations include social media posts, customer service phone calls/chats, business profile reviews, forum discussions, and more.
Essentially, this technology aims to read thousands of customer conversations with or about your business in a few minutes. Then, extract vital information that could help you improvise your product, service, or brand according to your customers’ liking.
AI and ML are the two main software development technologies behind conversational analytics. In AI, natural language processing (NLP) is the key algorithm behind such programs.
These advanced information technology tools and cloud computing capabilities help you to understand conversations in any form, like emails, phone calls, and texts.
Conversational analytics replaces the need for manual auditing of customer service calls, emails, and chats. AI software can scan through terabytes of conversations in minutes.
Also, the tools can collect various business data, like policies, risk assessment, etc., from other integrated apps and suggest immediate resolutions to the customers’ pain points.
If you are in the customer service industry, you will find abundant use of this information analysis technology. The service industry mainly uses the following two types of tools for conversational analytics:
- Voice conversation
- Text conversation
Businesses use this high-tech concept to analyze conversations with customers, employees, clients, vendors, etc. Organizations must follow the CCPA, GDPR, etc., privacy regulations while collecting conversational data from their target audience.
Why is Analyzing Conversations Important?
#1. Get the Nuanced Story
You might get a fragment of customer grievance and satisfaction from their online reviews. Still, the best place to get the most comprehensive story is their conversation with the customer service agents.
Many customers of all ages contact customer care, and companies will get a better view by analyzing their conversations. Besides offering you a detailed view of customer behavior and sentiment, it allows you to identify patterns and take action.
#2. Predict Customer Behavior
Every customer is different—how one will behave is impossible to predict completely. But you can identify patterns as you go through hundreds and thousands of customer conversations.
With its help, you will know what the customers need even before they know it. As a result, customers will have a better experience after contacting your customer support.
#3. Get Better Insights Than Customer Feedback
Only a small number of people who contacted your customer service will share feedback. In most cases, people with extremely positive or negative experiences find time to provide you with feedback.
For this reason, the data you get from the feedback might be skewed to the extremes. If you want to get accurate data on how customers feel about your brand and customer service, analyzing conversations is the best way to do that.
#4. Reduce Internal Workload
Conversation analysis is an automatic process done with the help of various applications. Hence, there is no need to appoint an employee to go through the conversations manually, which is time-consuming and hectic.
Instead, they can focus on high-value tasks that drive more sales and ROI.
On the other hand, the analysis lets you identify common questions or requests.
#5. Count on Their Own Words
Comments people make on your products and company are unstructured and brief. Therefore, it is not easy to analyze them for sentimental accuracy. Also, there might be a character or word limitation that makes it difficult for the customers to write what they feel.
In conversations, there are no such restrictions, and you can also analyze the sentiments properly from there.
#6. Get the Necessary Data from the Customers Themselves
The best way to improve customer experience is to gather data from all kinds of feedback. Whatever customer data you want to collect can be done from conversations involving their own opinion.
How Does Conversational Analytics Work?
The technology relies heavily on AI, particularly NLP. Apart from that, you need databases of text data, archives of phone calls, real-time integration with customer service operations tools, etc.
Artificial Intelligence
Using ML and NLP, software developers train their applications to understand written and spoken languages. For example, Google Assistant or Amazon Alexa are AI programs that can understand your spoken language and convert that into commands for the software.
NLP uses linguistics and phonetics concepts excessively. For example, the NLP algorithm breaks down spoken sentences into phonemes. These are sound units that help a machine distinguish millions of words.
The English language has 42 phonemes. Similarly, other languages have specific phonemes that an NLP algorithm utilizes to understand human languages.
Access to First-Party Data
Once the NLP is ready, you need to connect the program with a steady stream of customer data from several first-party sources.
Since you directly collect data from your customers through phone calls, emails, and chats, and they accept your privacy protection agreement, it is safer than third-party data sources.
Sentiment Analysis
The NLP program also comes with a sentiment analysis algorithm. The objective is to capture customer chats and phone calls that indicate the mode or intention of the customer.
For example, if the algorithm finds positive words like Amazing, Superb, Fantastic, etc., it means the user is happy. On the other hand, negative words like Useless, Not Good, Worthless, Junk, etc., mean the caller is not happy.
Now, once you combine all these in one cloud application, you get enormous power to understand your customer effectively. You can modify your service to make them happy without breaking the bank.
Some conversational analytics tools are so powerful that they inform customer service team leaders of any real-time negative incidents on calls or chats. Hence, the manager or supervisor can assist the support agent in delivering a delightful experience to the caller.
Benefits
#1. Locate Customer Pain Points
Customer satisfaction is the primary driver for business success. Unless you find out their pain points, it becomes impossible for any company to address them and retain customers.
The most crucial benefit of conversation analysis is helping you identify the causes and triggers of customer frustrations. Thus, it becomes easier to address the issues as soon as possible while companies can take necessary steps to prevent those.
#2. Better Rates of Sales and Conversion
Every business aims at better traffic conversion and sales. That’s why you need to analyze the customer conversation.
It lets you know about the features users are asking about the most. If someone is not happy about certain functionalities of your product or service, you can learn that tool from the analysis data.
#3. Get Better Insights Into UX
With conversation analytics data, you can get insights that will make you understand the entire customer journey. It also makes you aware of the customer sentiment changes during the journey.
As you can learn about the actionable insights into the digital and on-the-phone experience of customers, you can use it to improve user experience.
#4. Making Informed Decisions
Every business decision you make should be well-informed and backed by evidence. Since your services are aimed at satisfying the customers, there can be no better evidence than customer conversation.
Go through the analytics data to find out what the customers want in your products to make decisions about the next range of products or updates you are about to bring into the market.
#5. Real-Time Monitoring of Agents
The support agents are the representatives of your company who deal with your customers. Some conversation analytics tools are also capable enough to offer insights into the agents’ real-time performance.
Businesses can use this data to train customer care executives by figuring out their strengths and weaknesses. Also, the same data can be used to develop an improvised strategy for dealing with different customers.
#6. Boost Support Center Productivity
Analyzing the conversation in a support center (call and chat) also enables you to make the system more productive. Here, one can also use the analytics data for better categorization and routing.
It shares insights on specific agents being good at handling certain issues. Thus, companies can route chats and calls from customers more effectively.
Actionable Use Cases
#1. Collecting Feedback From Many Channels
A single conversational analytics tool can cover all mediums you use to exchange words with your audience. So, you can collect actionable insights from customer feedback from chats, social media comments, tweets, phone calls, emails, business reviews, and so on.
For instance, customers excessively report a product or service issue in various channels. The tool can instantly analyze these bursts of comments, understand the issue, and recommend you intervene with a resolution.
#2. Product Trials
If you are an SMB or startup and can not afford the full-scale release of a product/service for trial, a conversational analytics tool can help you.
For example, you can roll out the product/service among a small group of customers. Then monitor their comments, feedback, and engagements on various platforms. The NLP algorithm will help you gather positive, neutral, and negative sentiments.
Then, you can statistically measure whether or not the rollout will be successful.
#3. Virtual Customer Service Assistant
A pain point for the customer service industry is repeat callers. It happens when the first agent does not handle the caller effectively.
A conversational analytics AI analyzes various dialogues and monologues of your business and consumers.
When it notices any caller calling the customer service team multiple times, it can flag the incidents to the managers. Then, an experienced customer support agent can delicately handle the issue.
#4. Compliance in Call Centers
Frauds involving credit cards, debit cards, SSNs, and identity are some of the big challenges for any call center. Businesses can handle such frauds efficiently and affordably using a conversational analytics tool.
The algorithm analyzes all calls, emails, and chats in real-time. Whenever it detects any pitching of credit card, debit card, or SSN information from a customer, it can immediately flag the incident.
Then, your call center auditing and compliance team can intervene to stop customers’ sensitive data from getting public.
#5. Lead Assessment
Marketing teams can save a lot by analyzing leads through conversational analytics. The algorithm will help your team analyze the prospect’s sentiment about your brand.
If the analysis finds anything negative, you can stop pursuing the lead, as it will not convert.
#6. Personalized Marketing
A conversational analytics algorithm can work closely with a marketing tool that sends emails, texts, IVR phone calls, WhatsApp messages, etc., to customers.
For instance, a customer contacted your agent about an upcoming smartphone that you are launching. After the call, upon receiving a trigger from the algorithm, your marketing CRM can send a personalized email with a checkout link for the phone on the launch date.
Hence, customers can buy the device in just one click, and you have ensured multiple lead conversations.
Final Words
Conversational analytics is a great approach to harness customer data for business growth. However, you must ensure that you are ethically capturing conversations with consumers, employees, or vendors.
Declaring that the chat, call, or reviews may be saved to understand the needs is a great way to avoid any privacy regulation breaches.
So far, you have learned this fast-growing business data analytics tool from a foundation level. You can now apply this technology in your business effectively and securely.
Next, you can check out customer loyalty and retention software to harness more revenue from the existing customer base.