Call Center Automation and Contact Center Automation
Save money and improve customer satisfaction with contact center automation and insight.
When it comes to call center automation and contact center automation, we hear a lot of hype about how AI technology, such as chatbots, is advancing to minimize the amount of contact a customer has, so does that mean that the human customer services agent will become obsolete?
Well, underneath the hype of AI, the reality is that chatbot development has plateaued, and the understanding matured.
Alongside this limitation is the fact that customer interactions themselves are becoming more and more complex as customers learn to resolve more simplistic issues by themselves. There are also more communication channels as customers might try to resolve issues by a website, then chat, then phone.
The current best practice is to deploy rules-based text analytics to make sense of customer interactions. Yet it is not reliable as it is based on keywords and needs to be highly curated in a ‘rear-view-mirror’ fashion and it doesn’t have any quantitative metrics to show what the accuracy is. It also can’t be used for predictive or prescriptive analytics. Whilst it has solved the simplest queries, it cannot organically grow to deal with the next level and so reaches a plateau.
Machine learning is an alternative, often applied to classify text based on the computer ‘learning’ the common patterns to predict and classify without necessarily guessing keywords or using templates. The elegance of this solution is unfortunately mired by the complexity of the task which requires data scientists to spend a great deal of time training, building and testing predictive models, let alone cleansing the data in the first place. Again, this has reached a plateau due to the resource constraints.
PrediCX™ generates machine learning models but when it’s not sure about something it asks a human to help validate in a highly efficient way, thus speeding up the process and increasing accuracy. It is really mimicking and supercharging human judgement on an automated, industrial scale. OL overcomes the disconnects because it doesn’t require any data scientists: The training and interaction can be done by a non-data scientist guided by OL. It is really marrying the best of both the human intelligence and artificial intelligence worlds. This can help to make sense of the disparate data and break down the corporate silos to affect a seamless, structured data stream which translates to better customer experience analytics.