How can text analysis of emails improve your customer service?

Text analysis Feelingstream

When thinking about customer service, we all know that emails are a very big part of daily conversations with customers. It seems easy for the customer to send an email and wait for a reply. Several things can make this process not as smooth as expected. If no text analysis is done and everything is manual, email handling is more complex and time-consuming than it needs to be.

In this article we will tell you about two different cases we faced with our customers. Both show very common problems that occur in customer centres dealing with emails. The cases are somewhat similar but illustrate well, how ineffective customer service influences business and how text analysis can solve the problems.


Use case I – more sales with text analysis of email content and prioritization


As a customer, we always expect quick answers and solutions to our problems. Since there are many other customers as well, it may take longer to get a reply than we would like to. In some cases, the long waiting time may not be a crucial issue. In other situations, it might be a game-changer.

For example, let’s say I want to buy a new iPhone as soon as possible and I ask for an offer from three different companies.

There is a high probability that I choose the one that made me the offer first, especially if I’m in a hurry buying. Therefore, instead of losing new possible customers because of slow service, their emails should be prioritized to maybe surprise the customer with a quick service.

How to do this? The solution here would be that the company adapts automatic categorization of customer emails. Prioritizing sales emails and serving those between 10-30 minutes after receiving them will make a big difference.

This is a case we saw with one of our customers. As a result after starting to categorize and prioritize their emails, their sales handling efficiency improved 30-50%. Their customer experience was better and agents serviced buying attempts quickly. There was an expected increase in sales revenue.  

Use case II – automated email routing to the correct team


Another one of our customers struggled with getting incoming sales and regular service emails mixed between two teams (sales and customer service). It took extra time daily for both sales and service people to read and forward irrelevant emails. On one hand, it wastes valuable working time. On the other hand, it also made the employees unhappy, as salespeople hate service cases and want to sell and vice versa with the service people.   

How to solve this problem? The first solution is similar to the previous case – automatic content categorization – to detect what the email is about. After this automatic forwarding of emails to the right type of team.

As a result, the service agents in this company didn’t waste time on the emails they didn’t like to deal with. This led to a rise in employee satisfaction. Last but not least, thanks to immediate sales emails handling the sales revenue increased between 5-15% per agent.

Every company’s priority should be satisfied customers, happy employees and getting better business results. These two uses cases we introduced today, are perfect examples of how to achieve all that. For businesses with a big workload in customer service, text analysis is the way to go.   

Is this something your company could also benefit from? Contact us and let’s see how we can be helpful. Read more about finding ways to make your business more efficient with the help of AI.

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