The absolute first approach to sentiment analysis was pretty straightforward. To start with, lists of words were drawn up - which indicated negative and positive word associations in adjectives form. Then the customer’s comments were stacked up against the categories and listed accordingly.
Now naturally, dividing them into two categories - negative and positive - was overly simple. Over time the approach expanded and became more refined, more sentiments started getting taken into account. Sentiments around the intensity of the customers’ feelings, if they would invest in your business again, would they spread the word around your business, etc.
1. What is the Scope of Customer Sentiment Analysis?
2. How Does Customer Sentiment Analysis Work in a Contact Center?
3. Top Benefits of Employing Sentiment Analysis in a Contact Center
4. What to Look for in a Contact Center Sentiment Analysis Solution?
Sentiment analysis mechanisms can be applied at multiple levels of scope, but there are three main scopes on which it is applied, namely -
Sentence level sentiment analysis - It helps in obtaining sentiments off a single sentence
Document-level sentiment analysis - It helps obtain the sentiment from a whole paragraph or document
Sub-sentence level sentiment analysis - It helps to obtain the sentiment from a sub-expression inside a sentence.
While the scope of sentiment analysis and its obvious use cases are very high across industries, let us concentrate on the contact center side of it in this article.
A common order that is followed to add sentiment analysis in the contact center consists of these steps:
1. The prospective customer raises a ticket – They send in a support query through any passive channel such as chat, email, or social media.
2. Artificial Intelligence notes the expression of the text – Algorithms are set to analyze sentiments to look at specific phrases and keywords which determine the customer’s emotions.
3. The business rules get triggered based on the customer state - The business rules such as prioritization or ticket routing get initiated based on the analysis of customers’ sentiments.
4. Context-specific personalized support – The agents can understand the emotional state of the customers and cater to their needs in an empathetic manner to avoid any aggravation and offer them the best possible service in time.
With the work now attended to, let us shift our focus on something that truly concerns your business - the benefits that you would derive at the back of adding a sentiment analysis solution in your contact center.
Happy customers are the easiest to sell. And customer sentiment analysis mechanism is your way to identify them just in time to upsell them your other offerings. Along with this, it will also help you know when not to upsell to a client and lose them forever.
Sentiment analysis mechanisms do not just help your human agents in getting trained but also your chatbots. A well-designed system can train your chatbots in recognizing and responding to the customers’ moods and can help you know whether the issue has to be escalated or if would it be solvable on the bot’s end itself.
Being in the line of business that deals majorly with customer interactions you are always in dire need to do continuous agent monitoring. You always have to be on your toes in terms of seeing how empathetic your agents are and where they stand in terms of emotional intelligence. A rightly devised sentiment analysis mechanism, which is made by customer service experience experts, contains deep analysis of all the interaction details agent-by-agent wise. Meaning, you can keep a constant eye on how your agents are performing.
Sentiment analysis gives your management the provision of a quick escalation. Any potential issues get nipped right in the bud. With the help of the mechanism, all the calls coming in from disgruntled customers are identified and solved in real-time. This ensures that no customer leaves your business unsatisfied and unheard.
In any regular chat session, the agents more often than not find themselves dealing with multiple clients. Now keeping track of how they all are feeling is not easy. But the sentiment analysis technologies present in the market today, like the ones developed by C-Zentrix, make it easy for you to keep track of the real-time charts of how the customers are feeling. And the moment you see a drip, you can shift your attention there.
Now that we have looked at the scope, working, and benefits of having a sentiment analysis mechanism integrated into the customer contact center, it is now time to look at the last stage - one where we see how to finalize the right sentiment analysis solution.
Almost all the calls that come into a contact center happen because customers have issues that need to be solved. But it does not mean that ALL the interactions would be negative. And the right contact center solution will know the difference and show that it is unique to the business. Average accuracy achieved by state-of-the-art sentiment analysis models on benchmark datasets was found to be 85%, indicating their ability to classify sentiments correctly in most cases.
Several contact center sentiment analysis solutions which are operating in the market today are not able to tell the sentiment difference between phrases like - “That was a bad response” and something like “I did not like that response.” The optimal solution would undertake a holistic approach that does not base the sentiment analysis across a single phrase or word and thus would be able to accurately detect negative sentiments of seemingly similar statements.
You will more likely be able to take advantage of the contact center’s sentiment analysis if it is automated. You should look for a completely automated solution that delivers sentiment scores straight to the report or dashboard without calling for a definite manual inspection and documentation.
The right solution will get you the names of the teams, top-performing agents, and groups in the context of sentiments. This, in turn, will help you enable the sharing of best practices and would serve as the indicator of agents or teams who need quality evaluations.
The ideal solution will not just give you the sentiment score of the contact center, but it would also enable you to slice and dice the sentiment data with key metrics such as average hold time, call duration, retention rate, or product line. It would also let you integrate the sentiment info with the agent and customer effort score.
The benefits of the integration of customer sentiment analysis in a contact center are significant. With NLP and text mining, sentiment analysis applies to all types of interactions in an omnichannel world. With sentiment analytics on the supervisor dashboard of the contact center, companies can take quick steps to retain an unhappy customer and arrest business loss
Author Bio: Akanksha is an IT enthusiast, having an experience of 7 years in marketing and promotion. She has experience in varied industries, in positions working across the line of marketing. She is a day dreamer, an avid reader and a crazy traveler when she is not working.