Demystifying Customer Support Analytics

Let’s begin with a rapid fire test:

• How would you rate the level of customer support that your business offers?
• How quickly are your customers getting their tickets resolved?
• Which support channel is the most effective for case resolution?

Now these are some questions that you need to answer if you are serious about customer support.

Why?

Because U.S. companies lose more than $62 billion annually due to poor customer service. And this was in 2016! The number would have now crossed $80 billion!

Effective customer support relies heavily on data. How can you make something better if you don’t know where to begin?

This is where support analytics comes in. The goal here is to:

• Keep customers happy
• Make support agents more productive
• Enable managers to make smarter decisions

Effective support analytics aims to provide a 360° degree view across all support channels.

Let’s take a look at different analytics techniques that you can employ to make customer support effective: 

Descriptive Analytics

This is the What Happened phase of data analytics.

All your support channels collect massive amounts of data which can be used to provide insights. Let’s take an example of one of our customers—a leading manufacturer of activity trackers.

They had multiple support channels because of which their data was scattered. This resulted in the customer not having adequate visibility into the performance of their support channels. Our team of analysts integrated the customer support experience and enabled the customer to dive deeper into their support channels and agents.

This helped them address the What Happened phase. They could now focus on resolving issues faced by their customers.

Predictive Analytics

This is the What Could Happen in the Future phase of data analytics.

The data collected by your support channels can be used to foresee scenarios. Predictive analytics is an advanced analytics technique that relies on historical and current data, and machine learning to forecast trends, behaviors etc.

There are different predictive models that can be used like decision trees, logistic regression, Naïve Bayes classifier and more. These models help fill information gaps that descriptive analytics might leave out. Moreover, they enable you to predict customer satisfaction and anticipate the needs of your customers.

Prescriptive Analytics

This is the What’s the best course of action phase of data analytics.

Prescriptive analytics combines both descriptive and predictive analytics and aims at seeking the best solution from different choices.

Prescriptive analytics, though relatively new, sees application in customer support. It can help you optimize your support community and identify the decisions that you need to take to provide better customer support.

Acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one! That’s why providing effective customer is absolutely important.

After one negative experience, 51% of customers will never do business with that company again — New Voice Media.

Having a 360° view across all your support channels can enable you to:

• • Improve Customer Retention
  • Reduce Churn
  • Increase NPS

And more…

Want to know more about Support Analytics? Get the datasheet.