How Robotic Process Automation Makes Contact Centers More Efficient

Automation isn’t new. Technologies like Interactive Voice Response have been around for a long time. But while advancements like these have reduced costs for the contact center, they’ve also managed to annoy customers. In the case of IVR, callers often get stuck in menu loops or struggle with systems that don’t understand what they’re saying. Enter robotic process automation.

Robotic Process Automation and Artificial Intelligence

Contact centers are in the business of serving the customer, and in an effort to improve the customer experience, technologies are always emerging. Robotic process automation (RPA) is one of them, automating tasks and freeing up agents to personally handle complex issues. RPA uses Natural Language Processing, which is related to artificial intelligence, an even more advanced type of automation that can make human-like judgments about tasks.

Interactive Text Response for Customer Service

Interactive Text Response (ITR), more casually referred to as chatbots, goes hand-in-hand with the increasing popularity of messaging apps. Brands that want to improve the customer experience are making themselves available on chat – and it’s working. More than 70% of 1-800-Flowers’ chatbot orders come from first-time customers, and the company’s commitment to new tech has attracted tens of thousands of users. Chatbots are more effective than IVR because text input is easier for the system to understand than spoken language. AI can then be used to gain a deeper understanding of what the customer is saying, accounting for the different ways a customer may phrase a sentence or question.

Sample Phone Call with RPA

RPA can also be used with phone calls, not just chatbots. Here’s an example of how RPA can help with a live call:

• Jane calls to speak with an agent.
• Your RPA takes the call and authenticates Jane by confirming her account number and call-in PIN.
• Your RPA analyzes Jane’s account and sees that she has an open ticket and that she’s just been on the website to look at the status.
• Your RPA says something like, “I see that you have an open ticket with us. Is that the reason for your call?” Jane confirms that this is the reason for the call.
• Jane is transferred to an appropriate live agent.

Contact center technology like RPA can help customers solve their issues more quickly, but it can also provide much-needed support to agents by making them more efficient.

4 Contact Center Tips for Forecasting and Analyzing Data

Picture this: there’s a sudden spike in call volume, but you don’t have enough agents to handle it. Wait times increase and customers become dissatisfied. You get on top of the problem as quickly as possible and scale your workforce up to handle the demand. Soon, call volume evens out again, and now you’re over-staffed and draining your budget.

Improving forecast accuracy can limit these scenarios. Data, history and experience, combined with your own judgement and common sense, make forecasting much more accurate and predictable. A quality system will combine historic data with real-time data for accurate forecasting.

Here’s how to improve your forecasting:

1. Choose quality forecasting software.

Your forecasting software should gather historical data from the past two years to show you daily, monthly and seasonal patterns and trends. It should then monitor performance, document results, and continue to measure and evaluate data on a recurring schedule. Most importantly, your software should repeat this process ­– the repetition is what makes the forecasting so accurate and dependable.

2. Look at both data overviews and specific segments.

Look at historical data, which will give you an overview of NCO and handle time. Also view data in hour, day and month formats. Continue to break data down to view it differently – turn monthly forecasts into daily forecasts, daily into hourly, and hourly into half hour views.

3. Compare one month to the same month last year.

Point estimates are too simplistic an approach when it comes to contact center forecasting. A point in the future won’t necessarily match the same point in the past, even if it’s the same hour, day and month of the year. You have to look closely to determine if any data is out of the ordinary, and a good start is to compare this year’s month to last year’s month (i.e. January 2018 to January 2017).

4. Don’t ignore aberrations.

Investigate data that’s exceptionally high or low to figure out if it was caused by a one-off event or if you should be prepared for a regular occurrence. Situations that affect call volume include:

• Billing cycles
• Business mergers
• Change in hours of operation
• Competitor activity
• Holidays
• Marketing campaigns
• New technology implementation
• Planned maintenance sessions
• Weather and natural disasters

Balance customer demand with staffing numbers to keep costs low while managing wait times and ensuring customers satisfaction.