Make the most of voice of customer interactions with AI
By Punit Sedani, Product Manager, Content Guru
How important is it to collect customer voice interactions?
It is very important for organisations to collect customer voice interactions, to understand how customers interact with their business. However, the quality of information being collected is currently a larger issue than the importance of collection itself, and needs rapid change. It’s all down to the technology behind the quality of voice interactions.
Traditional Dual-Tone Multifrequency (DTFM) doesn’t hit the customer experience mark. Customers often end up bouncing to and from menu options trying to find the right person to speak to. Natural Language Processing (NLP) makes it a lot easier for the end customer to be routed to the right person, have a much better conversation and achieve first call resolution.
By collecting NLP interactions, it becomes easier to understand why a customer has contacted the business, and not just collect basic information such as what queue they ended up in. For example, businesses may find that lots of people who got through to the ‘Billing’ queue actually wanted to get in touch because they were billed incorrectly. Companies can offer further details on their website or provide customers with alternative communications channels to pre-empt contact centre call traffic for routine or repetitive issues.
Through NLP, customer voice interactions give businesses context beyond DTMF that allows them to understand why a customer has been routed to a queue and provides them the option to take action on that reason of interaction.
How can organisations use these to increase business and satisfy customers?
Organisations can utilise the insights they gather from customer voice interactions to understand why the customer has connected and use this intelligence to inform the way they handle their query. This means that there will be a higher possibility of ‘first call resolution’ which increases customer satisfaction. It also reduces the time in Interactive Voice Response (IVR) for the customer, as well as the total time on hold if the customer has to call back multiple times.
How can businesses prevent the squandering of unstructured and unsolicited feedback?
Businesses implementing voice-focused Artificial Intelligence (AI) are able to ensure they utilise customer feedback efficiently and effectively. By using AI which transcribes speech to text, companies can build a database of customer conversations. Sentiment analysis can be performed on this data to categorise it according to whether the customer was satisfied or unsatisfied with the service they received. This turns unstructured voice data into searchable organised feedback which can be used for quality analysis of agent interactions with customers.