Are You Using These 3 Speech Analytics Features to Improve Silence Time Drastically?

Silence time in debt collection calls is often a significant factor affecting the consumer experience and the overall success of the call. As per our internal analysis of 65 million calls in 2022, 3.5% of calls witness unnecessary hold time and a silence block of 90 seconds. Interestingly, calls with no silence block and hold time had a higher agent productivity of around 10%, which explains why long periods of silence can decrease the effectiveness of any collector.

You must understand the root causes of silence time and take proactive steps to improve the consumer experience and thus increase collections. Agent training, process improvements, and the use of an advanced speech analytics platform like ICAP can monitor call effectively and provide insights to reduce silence time. Here are some speech analytics features you can use to improve silence time. These features can help you identify extended periods of silence, which may suggest a lack of knowledge on the part of the agent or excessive multitasking and information search, both of which can increase call duration, operational costs, and negatively impact customer satisfaction.

1. Speech-to-text transcription for detecting silence block(s)

Speech-to-text transcription, the process of converting spoken words into written text, can be used to improve silence time by identifying periods of silence (silence block) within recorded calls. Once the call is transcribed, it can also provide additional context around the silence, such as the tone of the conversation before and after the silence.

Once automated, transcription can then be used to evaluate agent performance by measuring factors such as their average silence block per call, average hold time, etc. This information can be used to identify areas for improvement for agents to improve the overall consumer experience. For instance, if your QA analysis reveals that silence block hits right when the consumer asks for certain account information, you can take steps to improve your information search system.

2. Keyword and phrase detection for specific reasons for silence

Keyword and phrase detection can help your QA team identify specific reasons for silence in recorded calls. This speech analytics feature will help you identify keywords and phrases that may be associated with specific actions or events during the call, such as the consumer waiting for a response, the agent placing the call on hold, or the agent taking notes. This will eventually enable you to better understand the reasons for silence and take proactive steps to reduce it.

3. Sentiment analytics for a more nuanced reason behind the silence

In the context of silence time in debt collection calls, consumer sentiment analysis can help determine a more nuanced root cause of the silence. If the silence is a result of a negative consumer experience, such as frustration or confusion, you can use this information to train your agents on empathy, and the right tone (and pitch) of voice. On the other hand, if the consumer is satisfied with the interaction and the silence is a result of a satisfied pause, you can use the same information to improve the call flow and make the call more efficient.

Cut silence time by 10% with the power of speech analytics

With Provana’s cutting-edge speech analytics platform, ICAP, you can effectively categorize each type of call and determine the ideal length for each call category through thorough analysis. This will allow you for the identification of unusual silence blocks and any trend in silence time which can then be further investigated on an individual agent level or by comparing the current call duration with historical data. The results of this analysis can further provide your training team with valuable insights into successful call practices. To cut your silence time by 10%, click here.