The value of conversation intelligence in deepening the insights of authentic conversations is a common ground nowadays between researchers and the business community. The rapid development of big data algorithms and technology enables massive amounts of data and meta-data processing, including content, vocal features and body gestures. This study is based on 358 business-to-business (B2B) sales calls at the discovery stage. We propose a model to capture the dynamics of acoustic gaps between the sales representatives and customers by relying solely on the acoustic signal. We extract basic features from the acoustic signal: speech proportion, fundamental frequency (F0), intensity, harmonics-to-noise ratio (HNR), jitter and shimmer. We focus on the differences between the four speakers' role-gender groups (e.g., female-representative with female-customer). We found significant differences in the behavioural patterns of the dynamics between these four groups. The study demonstrates that using delta metrics to assess the interactions leads to new insights.