Alex, a contact center executive with a Life insurance company attends a customer request call and commits to something not part of the insurance coverage. A few months later, the customer files a suit and the company is at loggerheads to settle the issue.

Incidents such as the above occur often due to errors committed by contact center agents over the phone. Unfortunately, it is nearly impossible to track and analyze every single call to point out errors and avoid the damage. Most contact centers randomly choose a few calls for the quality check and assess them manually making it very cumbersome process.

There is the dire need for standardization of processes in contact centers to avoid communication errors and customer confrontation, and Speech Analytics plays a key role in this.

rsz_standardization

Let’s observe a few examples –

Compliance Adherence: Compliance adherence is a must for contact centers without which they are liable for hefty fines. Speech Analytics helps monitor and analyze every interaction between the agent and customer to check for anomalies. Sometimes, agents either commit something not part of the deal to customers or fail to voice out the disclaimer statement both of which can create issues for contact centers. By analyzing and pointing out these specific errors Speech Analytics helps contact centers standardize on what can be communicated to customers and what should be avoided.

Upsell & Cross Sell: Since Speech Analytics ‘’observes’’ the conversations between agents and customers it helps formulate the upsell and cross-sell strategies based on customer behavior, emotion, and key phrases used on the call. For example, phrases such as ‘’satisfied’’, ‘’pleased’’, ‘’happy’’, etc., will trigger agents to undertake to upsell and cross-sell activities and phrases such as ‘’disappointed’’ and ‘’not happy’’ will prompt agents to restrain from selling and focus on building lost faith.

Another way Speech Analytics helps standardize and improvise on cross and upsell activities are analyzing those calls that led to the successful cross and upsell closures and provide valuable insights from these calls to train agents.

Sentiment Analysis: The latest developments in Speech Analytics helps contact centers gain visibility into the sentiment analysis of customers. Sentiment analysis is the key to customer care management as it helps to analyze the emotion of customers – be it positive, negative or neutral, and the intensity of emotion. Analysis of customer emotion or sentiment can be shared across organization firstly, for agent training and secondly, to help sensitize the sales and marketing teams in how they could interact with customers at other customer touch points.

QC is an integral contact center performance measure. By effectively capturing keywords and phrases, Speech Analytics helps in effective call calibration, reducing the number of calls to be monitored and eliminating redundant QC processes. Subsequently, there is a reduced burden on QC managers, supervisors, and agents paving way for effective and efficient call monitoring and training process.

Reducing AHT: The lesser the average handling time better the efficiency and outcome of contact centers. Speech Analytics coupled with AI and Machine Learning can help predict customer behavior based on emotion, tone, and keywords used. This helps in training agents to proactively handle customers in a much smoother manner, thereby eradicating confrontations that ultimately results in reduced AHT.

Speech Analytics helps capture the essence of customer interaction while providing valuable insights on customers. It is these insights that help contact centers standardize processes and redefine contact center operations leading to added value and enhanced customer relationships.

To know more about how Speech Analytics helps contact centers achieve operational efficiency, click Speech Analytics for Contact Centers & BPOs – Revitalizing Customer Experience for the whitepaper. 

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