Data Silos and Inconsistent QA Standards

[Jahangir147]

Call data was scattered across multiple systems, preventing a holistic view of customer interactions. Teams struggled to access and analyze data efficiently without a centralized tagging and tracking system. Additionally, inconsistent quality assurance (QA) processes led to variations in service delivery as different teams evaluated calls based on various criteria.

The lack of standardized QA metrics meant that agent performance assessments and compliance tracking were inconsistent, impacting overall service quality.

These inefficiencies slowed call analysis, affected decision-making, and hindered customer experience, creating an urgent need for an AI-powered tagging and automation solution.

How Convin’s AI Disposition Transformed Call Identification
Convin introduced AI-powered automation to resolve these inefficiencies, enabling real-time tagging and categorization.

1. Automated Quality Assurance (Auto QA) for Faster Audits

Convin replaced manual audits with AI-powered automated call estonia mobile database evaluations, significantly reducing the time and effort required for quality checks.

Instead of auditors manually reviewing a fraction of calls, Convin’s AI Disposition analyzed 100% of interactions, automatically tagging and scoring them based on predefined quality parameters.

How Convin Reduced Workload by 40%

Automated Call Scoring: AI evaluated calls against key compliance and service benchmarks, eliminating the need for manual scoring.
Custom Audit Templates: Predefined rules tailored to the client’s needs ensured consistent and accurate quality assessments.
Real-Time Insights: AI instantly flagged non-compliant or critical calls, allowing supervisors to focus only on priority cases.
Elimination of Manual Sampling: Instead of reviewing a small percentage of calls, Convin provided full coverage, ensuring no insights were missed.
By automating repetitive audit tasks, Convin allowed QA teams to shift their focus to strategic improvements, reducing workload by 40% while enhancing accuracy and efficiency.