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How can text analysis improve your customer service? Part 1

September 19, 2018
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When thinking about customer service, we all know that emails are a very big part of daily conversations with customers. As much as it seems easy for the customer to just send an email and wait until their problem or request gets solved, there are several things that can make this process not as smooth as expected.

In this article I’ll tell you about two different cases we faced with our customers, both showing very common problems that occur in customer centres if dealing with emails. The cases are somewhat similar but illustrate well, how ineffective customer service influences business and how the problems can be solved with text analysis.

Use case I

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 longer waiting time may not be a crucial issue, but 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 the 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 hurry buying. Therefore, instead of losing new possible customers because of slow service, their emails should be prioritized and maybe even 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, prioritizes sales emails and serves those between 10-30 minutes after receiving them.

This is a case we had with one of our customers and as a result after starting to categorize and prioritize their emails, their sales handling efficiency improved 30-50%, their customer experience was better and buying attempts got a quick service. And as the expected increase in sales revenue followed.  

Use case II

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 automatically forward emails to the right type of team.

As a result, the service agents in this company didn’t waste time anymore on the job they don’t like to do, bringing with it raise 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 I introduced today, are perfect examples of how to achieve all that. For businesses with the 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.

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