Tone analysis for customer interactions

Till date, call centres remain to be the most important touch point in any organisation. If you are a business owner in this day and age, you cannot over-stress on the value of a flawless agent- customer interaction, simply because these conversations directly impact business revenues as they are often the redressal chambers for customer complaints, and hence prized opportunities for delivering the perfect customer experience.

What ails call centres is similar to the burning issue in almost all industries today – the responsibility of optimally managing data. Data presents Customer Service Managers with the potential for quality checks and employment of best practices – but there is simply too much to analyse manually! While it may be possible to manage quality for a single executive by listening to previous phone recordings, it is absurd to do this process for the astronomical amount of calls that go in and out of a call centre during the course of a single working day.

So what’s next? How can you make the voice of your customers work for you? How can you scientifically mine unstructured call data and come up with insights that will help you set best practices to ensure complete customer satisfaction against every call?

Sentiment and Tone Analysis using Natural Language Processing

Taking the call centres by storm, are the Sentiment and Tone Analysis applications that use Natural Language Processing technology to detect the tone, mood, emotion in the voice of your customer. Based on this analysis, ticketing/scores are made which in turn can be used by Customer Service Managers for quality controls, implementing compliance, reducing risks and customer attrition and mostly, ensuring stellar customer experiences.

Sentiment Analysis: Sentiment Analysis works to answer the question “How do they feel?” for telephonic customer interactions. Platforms such as Cognitive Insights use deep learning AI technology to analyse customer responses for various attributes:

· Identifiable ‘entities’ within customer responses which can be linked to actionable steps. These are words/nouns mentioned during the conversation that may point out to a specific problem. For example, for a telecom company trying to fight their attrition rates, it might be helpful to know under what circumstances their customers mentioned their competitor’s names – was it during billing information, refunds or a threat of defection? Analysing these trends may help the company focus on what’s really ailing them.

· Looking for ‘themes’ in conversations. This is an exercise in inferring the “feel” of the conversation even if the trigger words are not mentioned. For example, SA system employed at an airline should be able to detect buzzwords and phrases such as “being stood up”, “been here forever” and so on and link it to “airline delays”.

· Finally, Sentiment Analysis plays a vital part in identifying “categories” in conversations, that is, analysing data for detecting the exact context of the problem. For a retail chain, this may be “customer service”, “stock piling” and so on.

Tone Analysis: It is almost natural for a customer to call in frustrated. But it is important to analyse the tone progression of the client through the conversation. If the customer sounds dissatisfied at the end of the conversation, it isn’t good news for you.

The Cognitive View platform analyses the customer’s voice for tones such as, frustrated, sad, satisfied, excited, polite, impolite and sympathetic. A thorough analysis for these tones from the voices of both the agent and customer can lead to actionable insights for Customer Service Managers based on questions like: do the agents need more training in content/communication styles? Are there any recognisable patterns in the voices of successful agents? If so, can these be replicated en masses? Are the tones of agents predictive of the results of the conversation? And so on.

What’s in it for you?

Employing a Sentiment and Tone Analytic platform such as Cognitive Insights can be game-changing for your business in more ways than one:

· Help deliver personalised interaction: based on group and individual interactive trends analysis

· Help Prioritise Calls: Judging from tonality for levels of dissatisfaction and urgency

· Improve Call Centre Productivity: Reducing costs due to lesser attrition and spends on support staff, better returns from each call made, better customer retention and compliance protection

· Improve Customer Experience: As a result of precise call data monitoring, best practices are employed towards ensuring excellent customer experience

Sentiment and Tone Analysis Technologies are fast becoming prerequisites for a smart and productive call centre, functioning at its peak capacity.

As AI and Natural Language Processing gets more and more precise, conversations will be micro-managed and dealt with more and more personalised treatment, ensuring better returns for the company and memorable experiences for the customer.