As adoption of AI in contact centres is increasing, so is the depth of its technical capabilities which evolves with every new dataset - and we are still only scratching the surface.
According to recent estimations, 2.5 quintillion bytes of information are generated every single day, with 90% of the world’s data created in the last two years alone, according to Forbes. While the statistics are mind-boggling, they’re just the tip of the big data iceberg, with areas like voice data remaining relatively unexplored.
How many important business phone calls does your company make each year? And how much could you learn about your customer service capabilities from each call recording? With readily available technology now able to interpret speech at a granular level, companies are increasingly able to mine their calls for valuable information to feed into AI.
Studies in the US found that 90% of customers prefer to resolve customer service issues by phone. Each conversation is an untapped opportunity to discover what worked and what didn’t in order to improve the customer experience.
For nearly 15 years, NPS has been the ‘voice of the customer’, but this relies on customer participation and generally only yields a low response rate. Voice data analytics from contact centres can identify every customer with a negative experience, providing businesses with the opportunity to fix it.
Customer retention and loyalty are challenges for every business, especially those in the services industry. Having started my marketing career in a retention role, churn prediction is key to retaining customers as well as cross/up-selling.
These models are generally based on usage/purchase behaviour and possibly NPS results as well. The missing element is voice data from customer interactions. Imagine being able to build a churn prediction model based on what was said during an interaction, rather than just what the customer used or purchased. These previously untapped insights are now more readily available for marketing teams and data scientists to create models and tools to have a real impact on not only customer experience but business profitability as well.
With the addition of voice data, powered by call recording, contact centre analytics can provide basic information like average wait time, the number of transfers, which items were said or selected within the IVR, average handling time and frequency of particular call drivers. Voice data analysis on a large scale allows organisations to unpack their calls to reveal insights on a more sophisticated level. These Speech Intelligence tools can also help contact centres map customer sentiment across their call databases, teams, locations, time of day, responses to certain campaigns, and so much more. These new insights can help contact centres identify which teams certain calls should be routed to, where to send dissatisfied customers and which teams require training.
Contact centre agents will also be able to access predictions and trends in their customers’ behaviour, intentions and requirements across the call database. Detailed customer profiles will hold information from all communication channels to track all customer interactions. And insurance and financial fraud detection software may even be able to analyse voice patterns for risk indicators.
What’s next?
Going forward, AI will be crucial for marketing teams to deliver the best customer experience through the back-end data insights that are used to fuel the front-end customer interactions. Without the appropriate data, AI will not help.
As Industry 4.0 extends the layer of smart technology across all industries, marketers should be looking to digitise voice data in the cloud and capture the valuable insights of important business conversations – straight from the horse’s mouth.