Companies spend millions of dollars surveying their customers and doing random quality sampling of interactions in an attempt to measure customer satisfaction, often reported via NPS Score (Net Promoter Score). They then attempt to coach their customer service agents to improve their score, while simultaneously trying to reduce the cost of supporting these same customers. The traditional method of performing Quality Management / Coaching in the contact center is based on random selection, manual listening/scoring, and coaching on the findings. Scoring an agent’s overall performance based upon 4-6 calls a month, versus the 500-600 calls they typically take, is not representative of overall performance. Subjective scoring from another person is also unfair – since each evaluator will introduce his/her own biases to the evaluation. The result is an ineffective evaluation process that’s not trusted by front line employees and fails to drive meaningful improvement in NPS or operating costs. There’s a better way. Use Artificial Intelligence and Machine Learning to automatically score EVERY interaction for Customer Satisfaction and to evaluate and score the agent’s performance on key behaviors that directly link to Customer Satisfaction Scores (NPS). Evaluate and score every agent on EVERY interaction, whether by voice, chat, or text. Agents and Supervisors then have the ability to identify the specific behaviors in need of improvement and can self-coach or be coached by their supervisors on these key skills. Join ConvergeOne and NICE-Nexidia to understand what we’re seeing in the industry today, and how to drive out cost and increase employee and customer satisfaction in your contact center.