Contact center support is the keystone of great customer experience, the one factor that 96% of customers swear influences purchase decisions. To run a successful contact center, it is vital to monitor the calls for quality assurance purposes. Streamlining call center QC processes with AI-powered tools and solutions improves customer satisfaction. Therefore, brand reputation & revenue see a positive impact that too at a 70% lowered operational cost.

The traditional QC processes include basic systems that record metrics like hold time, call volumes, handling times, etc., and recording the calls. Specialists analyze these to pick up sentiments, troubleshooting aspects, and ways to improve interaction quality. Using AI in call center quality management can broaden it to omnichannel insights besides making it more efficient.

Monitoring calls to Check Quality?

Old school contact center analytics has been heavily focused on speech analytics. However, the modern customer follows a brand on many channels before picking up the phone. Omnichannel analytics tools like aura365™ can help track the customer across the complete journey.

In traditional call monitoring, a certain amount of sample calls are picked per batch to determine to monitor the batch call quality.

In order to determine the sample size, different variables like the total number of calls made by each agent and allowed standard deviation are used. If all these variations sound like gibberish to you as it is to me, you can use this contact center QC sample size calculator.

Ideally, a QC score requires high confidence of 95% and up, but that will also drive up your sample size per batch. Typically, if you have a call volume of 50 per day across three agents, you’d have to monitor 306 calls every month. Sounds high, right? Most contact centers under-sample their quality assurance recordings, not even properly analyzing 20 calls in some cases.

Since every call doesn’t get monitored, it can be difficult to consistently keep track of the same agents’ performance. It will also be hard to figure out how long poor quality practices have been happening when the call volumes build up over time, and in a worst-case scenario, the entire call recordings have to be pulled to know the exact quality.

Using AI in call center quality control allows an exponential increase in the number of calls that can be monitored. The software not only gleans more insights when it picks up data across more channels but tirelessly processes repetitive information, unlike human analysts. It can free them up to focus on more strategic tasks.

How aura365™ Makes Your Contact Center More Efficient

What if your team could gather the inputs and KPIs by analyzing every call? That is what the VoC tool, aura365™, precisely allows you to do.

The software allows QC agents to rate the calls and then perform sentiment analysis on both voice and text data. It can pick up common keywords and other significant features like silences with the machine learning capabilities. This helps it discover deep insights to improve the customer interaction processes, identify cross-selling and upselling opportunities based on real-time data.

The output can be used to optimize upstream processes by sending feedback to product and process improvements across the organization. At the downstream level, the insights are critical for training agents. aura365™ handles the largest possible sample for the highest confidence results and costs lower than manual sampling in the long run.

You can give your team, and managers access to a quality monitoring system that will optimize their performance. It can improve the quality and consistency of your customer experience while making it easier to adhere to compliance standards as well.

Book a demo to see aura365™ – the AI-powered omnichannel analytics tool in action.

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