Speech analytics driven by AI is speech recognition software that works using natural language processing and machine learning technologies. With speech analytics in call centres, you can convert live speech into text. After that, the program evaluates this text to reveal details about the needs, preferences, and sentiment of the customer.
In contact centres, speech analytics tools helps:
- Analyse voice recordings.Provide feedback for agents. Improve customer experience.Increase sales.
How does speech analytics driven by AI differ from the traditional one? What benefits can contact centres and businesses receive from it? Find the answers in this article.
How does AI-driven speech analytics differ from traditional?
They differ in several key aspects:
Key components of AI-driven speech analytics
Here is a list of common technologies driven by artificial intelligence. They are being used to optimise and improve the performance of contact centres and the applications they run:
Artificial intelligence is a branch of computer technology that develops computer programs to solve complex problems by simulating behavior associated with the behaviour of intelligent beings. AI is able to reason, learn, solve issues, and self-correct.
Machine learning is a subsection of AI that teaches computers through experience rather than additional programming. It is a method of data analysis that, without the need for programming, finds patterns in data and forecasts future events using statistical algorithms.
Natural language processing allows a computer to understand spoken or written language. It can analyse syntax and semantics. In determining meaning and developing suitable answers, this is helpful.
For example, it processes verbal commands given to intelligent virtual operators, virtual assistants that staff work with, or voice menus. Sentiment analysis is another application for this technology. More advanced natural language processing can “learn” to take into account context and read sarcasm, humor, and a variety of different human emotions.
A part of natural language processing called natural language understanding enables a computer to comprehend written or spoken language. Grammatical structure, syntax, and semantics of a sentence can all be examined using it. This helps in deciphering meaning and creating suitable answers.
Predictive analytics uses machine learning, data mining, and statistical analysis techniques to analyse data and identify relationships, patterns, and trends. One can create a predictive model using such data. It forecasts the possibility of a given thing happening, the tendency to do something, and their possible consequences.
How does speech analytics work in contact centres?
Software for speech analytics gathers and examines data from conversations with customers. Transcripts of phone conversations, dashboards, and reports can all be created using the gathered data.
Agent productivity, customer satisfaction, call volume, and other metrics are all shown in real time to contact centre management through dashboards. Call transcripts are recordings of conversations in text format used for training and quality control of service.
Speech analysis is most often carried out in the following stages:
#1 Interaction recording
A recording of a conversation that needs to be analysed.
#2 Separating the audio tracks of interlocutors
It enables you to more clearly pinpoint issues. For example, if the paths intersect in a conversation between a manager and a client, one interlocutor interrupts the other.
#3 Converting speech to text
This step helps to obtain a text version of the conversation that will be used for subsequent research.
#4 Text transcript
Different text processing techniques are applied to the resultant text to examine it. These include of finding tags and themes, marking words and phrases, and assessing the tone of the text. The program also processes terms, dialogues, and discussion.
#5 Data classification
By terms, topic, tone of emotion, or other parameters.
#6 Data visualisation
By charts, graphs, heat maps, and other visuals. The program will clearly show the results achieved.
#7 Data analytics
During this phase, judgments are made, trends are found, important discoveries are highlighted, and data is interpreted.
The system allows you to record calls and create detailed, complete reports, which will allow you to identify errors in work and find additional points of growth. This information will help develop the project and increase the average bill with the right choice of promotion tools and budget savings.
How can AI-driven speech analytics help businesses?
Depending on the company size, industry, size of the contact centre, and other factors, different benefits of speech analytics will come to the fore. The universal advantages are the following:
Increasing the number of verified calls
Quality control teams in call centres check an average of two to four operator calls per month. Businesses may quickly validate up to 100% of calls with speech analytics.
KPI fulfilment tracking
Various interaction metrics can be analysed with the use of speech analytics:
- Request escalation ratesOut-of-script behaviourCustomer satisfactionAverage call handling time, etc.
Speech analytics tools are able to pinpoint the areas in which agents’ quality scores are lagging. Following that, it offers useful data to boost productivity.
Instant feedback
Supervisors may provide agents individualised feedback more quickly with faster analysis and 100% call coverage. Many contact centres have begun implementing AI assistants to give agents real-time suggestions.
Improved operational efficiency
Speech analytics reduces the time for verification processes. Contact centres can handle large call volumes and enhance operational efficiency with its help.
Large-scale customer self-service capabilities for common queries are provided by speech-to-text and text-to-speech voice assistants. Resources for agents to handle more complicated scenarios are freed up.
Personalised learning
Programs for individualised agent training can be developed by managers and workforce development teams. Because each agent’s call performance and attributes are advanced assessed, it becomes feasible.
Higher customer service quality
Speech analytics offers thorough insight into the requirements of the consumer. Teams can find elements of a satisfying customer experience by using sentiment analysis. Or indicators of a negative customer experience to influence the customer experience and lifecycle.
Problem identification and management
Words and phrases used in consumer interactions can be found via speech analytics. Problem-call information can be instantly sent to supervisors by email or instant messenger. Managers are able to address challenging issues in a timely manner because of notifications. After that, they use reports and dashboards to evaluate the effectiveness of their decisions.
Customer sentiment analysis
Speech analytics can determine a speaker’s emotions at a given moment by considering speech characteristics such as voice volume and pitch. Contact centres can use this information to determine a customer’s general opinion of the business.
What difficulties could you expect when using AI-based speech analytics?
Data privacy and security
Contact centres handle a large amount of personal and financial information. There is a risk of data breaches, unauthorised access, and misuse of customer information, which can lead to regulatory penalties and a loss of customer trust.
How to address:
Contact centres need to put strong data security procedures in place. These are the following:
- Data encryptionStrict access controlsRegular security audits, etc.
It helps identify and address vulnerabilities. Also, you can employ solutions with built-in security features.
Cost of implementation
AI-based voice analytics implementation can need a large financial outlay. Such costs include the following:
- Purchasing softwareIntegrating new systems with existing infrastructureTraining staffOngoing maintenance and support
How to address:
Contact centres should start with an ROI analysis. They ought to project possible cost reductions as well as increased income. Phased implementing modifications can assist in distributing costs. It lessens the financial load in the short term. You can also implement cloud-based solutions—it lowers up-front expenses because these are usually pay-as-you-go.
Technological complexity
Deploying advanced AI technologies and their integration with existing systems can be technically demanding and require specialised knowledge.
How to address:
Implementation complexity can be decreased by collaborating with seasoned suppliers that have a solid track record. These vendors can provide end-to-end services, including integration, training, and ongoing support.
The bottom line
Statistics show that mundane duties take up almost half of a contact centre agent’s working hours. The introduction of modern speech analytics services significantly optimises processes and allows you to obtain analytical data. Based on this data, you can develop a strategy for the further development of the company and improve relationships with customers, forming their loyalty.
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