More than facts: Recognizing customer sentiment

Sentiment analysis in customer service

Do you know how your customers feel? Keep an eye on the mood of your customers and respond proactively to praise and criticism.

09/10/2023

An estimated 347 billion emails are sent worldwide every day. In customer service, the email communication channel continues to gain ground: growth of 18 percent is forecast by 2025.

Customer service inboxes receive countless emails every day that are waiting to be answered. How can employees set priorities here? What happens when emails from dissatisfied customers arrive - shouldn't they be processed more quickly? Which emails can be answered a little later?

Keeping track of the flood of emails and then filtering them according to the mood of the customer is simply impossible for employees in large service teams with several hundred or thousand emails per day.

But processing customer inquiries chronologically is no longer up to date! After all, contact with customer service is often emotionally charged on the part of the customer - both positively and negatively.

The new smartphone gave up the ghost after a very short time and now all the data is gone? The jeans have shrunk by half despite the cold wash cycle? The energy provider has already communicated the third price increase in a row?

Hardly any customer will be left cold and many will vent their anger. But how do you deal with these messages, which may contain a lot of potential for escalation? And how can they be quickly identified in the mass of incoming emails and dealt with swiftly?

The solution: AI-based sentiment analysis

But what is sentiment analysis?

In a sentiment analysis, the AI recognizes the mood in which a written request is formulated. Based on such sentiment recognition, communication with customers can be individualized and tailored to their needs. THE success factor on the way to greater customer satisfaction.

Why is sentiment analysis useful in customer service? ...isn't a simple text-based analysis enough? No!

An example: A customer writes "As a long-standing customer, I am very disappointed that you have increased the shipping costs by 20 percent and will have to reconsider my ordering behavior with you in the future."

In a simple text analysis, an AI might first read "long-standing customer" (aka satisfied customer) and classify the entire message as positive.
During the context-based sentiment analysis, the AI recognizes the potential for conflict in this email. It also recognizes that a quick response from a service employee is required in order to continue to satisfy the disgruntled customer.

You can use sentiment analysis in addition to your usual process handling.

How does sentiment analysis work?

Step 1: Document receipt & analysis

Just like in the normal workflow, a context-based analysis is carried out in a matter of seconds during sentiment analysis. Based on previously defined rules, the software recognizes whether it is a neutral message, praise or a complaint.

Step 2: Routing

As soon as a critical message has been recorded, the inquiry is routed to the responsible person in customer service. This allows employees to recognize directly whether the customer is dissatisfied, for example, and respond immediately. The main advantage of this is that employees can respond directly and proactively to customers. Especially if the customer is already impatient or annoyed. This additional process is then worth its weight in gold.

Step 3: Proactive response to customers

The last step is the response and, if necessary, an action in the system. There are various ways to do this. Employees can proactively respond to the customer's mood. For example, they can respond to an angry message with a voucher or discount. If praise has been given, service employees can take the opportunity to ask for a rating or recommendation. For example, to receive a positive Google review. Another option would be to send discount vouchers for the next order or even to pass on to friends or family. This will not only show appreciation to the customer. In the best case scenario, you can also gain new customers.

Responses can be automated flexibly and as required

In order to respond proactively to inquiries, various options are available for sentiment analysis:

Option A: semi-automated response

The employees process the request and receive suggestions with various possible answers. An appropriate selection can be made here and, if necessary, supplemented individually.

Option B: fully automated response

The processing of requests is fully automated. The request does not have to be forwarded to the employees, but can still be viewed in the system at any time. This saves a lot of time for employees and shortens the waiting time for customers.

Human judgment is a key factor, particularly in the case of critical or emotional issues. Experience has clearly shown this: The human & AI duo is unbeatable for top customer service. AI alone cannot achieve this.

The AI takes over the analysis of all incoming messages within a few seconds - something a human would never be able to do in the same amount of time. The human brings in the personal component and decides in the end: Which measure or which response is appropriate?

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How to react mye Customers looking for change?
Have you communicated a price increase to your customers? Or removed a product from your range? Then you may be interested to know how your customers react to this. Are the complaints an exception? Or do they accumulate and represent a general mood? You can create queues for this (e.g. "Price increase complaint"), into which all messages on the specified topic are routed. This way, you always have an overview of how acute the topic is.

How can I respond proactively to praise or complaints?
Determine in advance what offers you can make your customers when they contact you with praise or criticism. For example, can you grant a discount? If so, the software can be configured so that a discount code is directly generated and stored in the system in addition to the response text modules in the event of a complaint. Or in the positive case: you receive praise. Simply thank them with a personal discount code or thank your friends? Motivate upselling measures or a review. All of this can be configured in advance. Discount codes or links to review pages can be inserted automatically using text modules.

Are customers always dissatisfied? - Sentiment history provides information
In addition to individual ratings, the sentiment history can be used to evaluate the sentiment progression. The history graphically shows directly in ReplyOne which sentiments the AI has recognized from the individual contact points. An automatically generated 360-degree history shows service employees all previous interactions with customers and the associated documents at any time. This gives them an overview of everything at all times. With a sentiment analysis, you can keep an eye on the mood of your customers - and react quickly and proactively. Customers feel understood and customer satisfaction increases in the long term.

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