Abstract
The business sector now recognizes the value of
Conversation Intelligence in understanding patterns, structures
and insights of authentic conversation. Using machine learning
methods, companies process massive amount of data about
conversation content, vocal features and even speaker body
gestures of spoken conversations. This study is a Work-inProgress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights.
Conversation Intelligence in understanding patterns, structures
and insights of authentic conversation. Using machine learning
methods, companies process massive amount of data about
conversation content, vocal features and even speaker body
gestures of spoken conversations. This study is a Work-inProgress (WIP), aimed to modeling the dynamics between sales representatives and customers in business-to-business (B2B) sales calls, by relying solely on the acoustic signal. To this end, we analyze 358 sales calls at the Discovery phase. To model the conversations, we compute a basic set of acoustic features: Talk proportions, F0, intensity, harmonics-to-noise ratio (HNR), jitter, and shimmer. The plots of each acoustic feature reveal the interactions and common behavior across calls, on one hand, and within calls, on the other. The study demonstrates that using delta metrics to assess the interactions leads to new insights.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE 5th International Conference on Big Data Intelligence and Computing |
| Pages | 152-157 |
| Number of pages | 6 |
| State | Published - 2019 |
| Event | The 5th IEEE International Conference on Big Data Intelligence and Computing: DataCom 2019 - Kaohsiung, Taiwan, Province of China Duration: 18 Nov 2019 → 21 Nov 2019 |
Conference
| Conference | The 5th IEEE International Conference on Big Data Intelligence and Computing |
|---|---|
| Country/Territory | Taiwan, Province of China |
| City | Kaohsiung |
| Period | 18/11/19 → 21/11/19 |