- Define goal: Map your QA process to business goals
- Build a QA quality culture and behavior framework: framework centered around improving the customer experience by focusing on the effort the customer makes, minimizing conduct risk and providing the most positive customer outcomes
- Remove the perception of QA evaluation as being a punitive process
- Pay close attention to correlation between QA scores and customer survey scores – note: Only 20-25% of customer satisfaction with a call is determined by the content of their conversation with agents
- Progressive businesses are moving on from tick box, features and benefits format to customer engagement / active listening, matching and outcomes
- The QA team should be regarded as experts in driving the customer experience agenda across the organization and be regarded as an integral part of the company value chain
- Business Intelligence Team uses the data gathered through quality process for providing the business with performance insight & predictive analytics
- QA should inform product and product governance: Undertake product reviews / what do customers like, dislike
- The QA and compliance teams need to work hand-in-glove; ensuring compliant customer interactions for both new and existing customers: regulatory rules are now given
- Red flag process vigorously enforced
- Senior Executives need visibility: Customer experience, product / marketing / managing risk
- Build processes and systems to share information across the business: Share Voice of the Customer feedback with other departments
- Hold regular round tables with Product, Marketing, Sales: highlight trends, risks, feedback
- Influence the organization: don’t operate in a silo
- Include agents and team leads in the calibration process
- Calibrate based on expertise, not deviation from average or arbitrary targets
- Use Calibrations to refine QA forms and remove ambiguity
- Look to your teams to identify and share anecdotal feedback: sometimes it is OK to work in the grey
- Keep your QA forms fresh and relevant
- Deep-dive analysis of the data to identify trends, insight and areas that require greater focus
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