Introduction to Responsible AI: The CallMiner Research Lab Responsible AI Framework
The CallMiner Research Lab Responsible AI Framework outlines definitions and concerns, as well as some of the driving questions, leading to deeper con...
May 22, 2018
Technology developments move so quickly that it’s easy to miss out on improvements that could make a real difference to business performance. Artificial Intelligence (AI) and Machine Learning (ML) probably fall into this category. The recent CallMiner and IPI Contact Centre Performance Summit highlighted that contact centres could be missing out on these valuable uses and performance improvements of interactions analytics, as they find themselves at different stages of technology adoption.
This blog provides the next set of practical advice gathered during the Summit on how to improve your contact centre performance IQ. You can read the first two blogs here: Is Your Contact Centre in the Eye of the Storm? and Do you know how to turn your contact centre from a cost to profit centre?
Below I’ve included three emerging use cases that can help you take your Interaction Analytics programme to the next stage and convert your contact centre into a predictions centre for the whole business.
If you’re anything like me, there will have been times when you’ve wanted to predict the future. Well now you can if you use AI and ML to accelerate finding ‘patterns’ in the huge volumes of data associated with customer interactions captured in the contact centre. You can use these ‘patterns’ for predictive scoring and discovery of key business insights.
Interaction analytics tools have the capacity to automate call categorisation and scoring. By adding AI and ML you will speed up the process of identifying patterns. As a result, you will be able to:
This kind of enriched insight presents great benefits for resource planning, sales and marketing campaign planning or manufacturing and service planning. It can help to drive sales and collections revenue. It can also protect customer subscription revenue, by identifying the likelihood of customer churn so you can prevent it from happening.
Fraudsters are constantly developing new and innovative ways to commit fraud. So, relying on agents to recognise malicious attacks is a high-risk strategy. You will need to use
technology to improve your defences. Fraud analytics is a combination of speech analytics technology and voice biometrics that can be used to detect and predict the likely occurrence of fraud in contact centres.
Fraud analytics works because it can draw on insight created by analysing 100% of calls using interaction analytics. As a result, it is possible to identify patterns of speech, behaviours, emotional acoustics and key words and phrases that are indicative of fraudulent behaviour. Voice biometrics are used to compare voices of customers with voices from identified fraudsters.
Fraud-specific tags are automatically applied to suspicious conversations thanks to language patterning capability. These interactions are then scored, tracked, and assessed based on the information provided by the interaction analytics software. If new incoming calls are identified as likely to be fraudulent, the caller’s voice can be compared with voices of previously identified fraudsters, while conversation’s transcript is used to spot the occurrence of the fraud-specific patterns of words or phrases. This information is then passed to agents in the form of alerts to indicate there is a high risk that they are dealing with a fraudulent caller. It can also be used for fraud-identification training and coaching purposes.
By implementing fraud analytics, contact centres can help prevent the business suffering significant reputational damage and financial losses. Just as importantly, fraud analytics can help to protect customers personal data from fraudsters. With GDPR on the horizon, being able to protect personal data is now more important than ever.
When I talk to people about analytics they usually think I mean an activity that takes place after a call is over. So, while they accept that this post-call analysis can help to improve future agent behaviour, they don’t see it as something that can influence the outcome of a live call. Which brings me to the next use of interaction analytics combined with AI and ML – real-time analytics.
Real-time analytics works by ‘streaming’ transcripts of conversations in real-time. This enables key language patterns or acoustics, that are present or missing, to be identified so that if action is required, agents and supervisors can be provided appropriate information in the form of alerts or next-best-action guidance. For example, agents who forget to read a specific part of the script that assures regulatory compliance, would receive corrective guidance on their dashboard. Supervisors can also be alerted to any calls in real-time that look like they are deteriorating so they can take the necessary action to achieve a successful call outcome.
This means that a bad call should be a thing of the past. Imagine the difference that would make to you call centre. Imagine the difference it will make to agent satisfaction because they know they will always have customers leave a call happy.
That’s the emerging power of interaction analytics. I’ve identified three ways it can deliver value. What other ways do you think you could harness this predictive power?
Join us May 23 for our webinar, ! If you can’t make it, register and get the replay link will be sent to you after.
Learn the latest uses, benefits and keys to successful implementation of customer interaction analytics.