Every customer conversation contains valuable information to improve your service, improve agent performance, understand customer expectations and reduce compliance risks. However traditional call centre QA often involves only a random selection of calls being listened to which can mean problems are missing.
Using AI driven QA, you can get 100% coverage of all your customer interactions. But this alone will not improve quality. You also need to ensure you are getting accurate scores, actionable insights, consistent scoring criteria and ways to actually improve based on the findings.
ConvoZen AI offers automated quality management & conversation analytics to help improve your service.
Why 100% Call Coverage Isn’t Everything
Traditional QA teams within a call centre often use random sampling to save time and resources. Whilst it can show problems with individual calls, it can miss out on general problems that could be affecting your whole team.
For example, if you only look at a few calls you may find that one agent isn’t making a required disclosure. But if you look at all calls you may find that there is never a disclosure made for that particular campaign or type of call.
Reliable QA should give you insight into why things are happening so you can avoid them in the future.
Your AI QA should really help to provide consistent scoring, risk identification and continual improvement.
How to Build a Reliable AI QA with ConvoZen
1. Set Scoring Criteria
Whilst AI can help provide a way to analyse conversations, you need to make sure you’re measuring the right metrics. If there isn’t a clear criteria set up, you’ll struggle to get actionable data from the scores.
Create a custom QA scorecard based on your goals to improve customer satisfaction, compliance, sales conversions or to reduce repeat calls.
You may want to score based on –
SOP Compliance – did the agent follow the correct process?
Compliance – were there any required disclosures or verification?
Communication – was the agent clear, professional and empathic?
Resolution – was the customer issue resolved correctly?
Sales – was the agent asking the right questions and overcoming objections?
ConvoZen can offer a custom QA scorecard.
2. Score All Conversations Consistently
Once you have scoring criteria set, you can use our Automated Quality Management to score all your customer conversations including calls, chats & emails. We use transcription & AI to determine the quality, sentiment, context & compliance.
This will help you to analyse your conversations.
You can see if your agents are always completing verification, explaining policies correctly & resolving customer issues. You can then compare performance across individuals, teams & time frames.
The key benefit here is that you’re getting continual QA instead of just a one off. However it’s important to validate the AI scores with real examples.
3. Identify Compliance Risks Early
If you aren’t compliant you run the risk of customers complaining & potential risk to your business. If you’re only using manual QA you’re less likely to identify all the issues with required disclosures, banned phrases or missing checklists.
Using our automated QA you can ensure you’re checking for any compliance issues.
When implementing this you should focus on the important aspects such as required disclosures, verifying the customer, obtaining consent & using the correct communication.
You should investigate the evidence to see if there was really an issue.
This will help you improve compliance within your call centre whilst focusing on the conversations that need it.
4. Improve Based on Scores
Whilst it’s great to know there was a poor performing call, it’s much more valuable to know why & help improve the agent.
Using our quality management & performance analytics you can see where you need to coach your agents. You can identify common issues & how individuals are performing differently to the team.
For example, if an agent is struggling with overcoming objections you can look at the conversations and coach them. If multiple agents have issues then this could highlight a training issue or a lack of clarity around the process.
Effective AI coaching should focus on specific behaviours, provide evidence from the conversation & show improvement in performance.
This takes QA from just judging employees to helping them improve professionally.
5. Use Insights to Improve
Having scores is great but knowing how they tie in with the wider business is even better.
Using our conversation analytics you can identify common customer issues, resolution issues, sentiment & trends. This can help you to make decisions outside of QA.
For example, if customers are repeatedly complaining about unclear pricing then the sales team may need to improve their communication. If there are lots of repeat calls then there may be issues with your support process. If there are common objections during sales then this could show there is room for improvement in how you explain your products.
Track the right KPIs such as first time resolution, customer satisfaction, repeat calls, compliance & conversions. This will help you see if you’re actually seeing improvements based on your QA scores.
How to Successfully Implement AI QA
To ensure you’re doing QA reliably, start with a focused pilot.
Audit your current QA processes to see where there are gaps. Set up your scoring criteria, link your communication channels & test the AI against manually scored conversations.
Once you’ve validated the results you can start to increase coverage. Set up regular checks on flagged calls, scoring accuracy, coaching & customer feedback. As your policies, products & customers change, update your scoring criteria.
Most importantly ensure you’re involving your QA professionals to help with complex cases. Whilst AI can provide more coverage & show trends, you still need people to manage your quality.
