According to IDG research, 80% of data will be unstructured by 2025. These predictions open up endless possibilities for leveraging unstructured data to drive business growth.
In the context of customer experience and contact centers, unstructured data is at the core of every customer conversation, be it over the phone or in writing.
By utilizing AI and machine learning tools that process and analyze unstructured data, organizations can dig up loads of insights with multiple use cases.
These insights can reveal the agent’s performance or how customers feel about your service or product, without the need to ask them for feedback explicitly.
Unstructured data is the most valuable type of feedback as it is unsolicited and free from bias. But how exactly can organizations get to this precious information?
Machine Learning to the Rescue
In a digital-first era, millions of interactions are documented in emails, text messages, and chat conversations. Add just as many phone calls to that already enormous number, and you get a mammoth task of analyzing each interaction.
Missing out on data hidden in these interactions can lead to customer churn, low performance and revenue loss. In order to track down and process each interaction from every channel, organizations can employ one of the following machine learning technologies:
1. Automatic Speech Recognition
Automatic Speech Recognition or ASR uses machine learning or AI to process human speech and transform it into readable text. In recent years, ASR technology has reached almost human accuracy levels, making audio and video data more accessible.
ASR also lets callers perform self-service tasks, such as checking account balances or authenticating their identity. It is the most useful technology for identifying the reason behind the call and using this information to route the call to the right agent.
2. User or participant analytics
Also called conversation analytics, user analytics can measure and show data such as talk time, emotion, sentiment, politeness and more. It can also monitor the length of silence in a call or the speaker’s pace.
The use cases for user analytics can be agent performance, giving a closer look at the conversation dynamics of the top-performing agents. Further, user analytics can boost customer satisfaction as it offers insights into customer sentiments and enables organizations to take action to improve outcomes.
3. Content Analytics
Similar to user analytics, content analytics deals with the contents of calls, analyzing key topics of discussion, or giving a brief summary of the call. This reduces the time needed to go through each call and pinpoint key themes, allowing immediate action.
4. Domain Analytics
Domain analytics links user and content analytics to process industry-specific data. For example, in a webinar environment, user talk time will be measured differently from a sales call.
5. Future user preferences
Analyzing a number of conversations over a period of time gives an overview of user preferences with regard to the preferred communication channel or product requests. This technology can also help with spotting upsell opportunities and improving the overall customer experience.
Eager to take conversation analytics to the next level? Find out how Symbl.ai can help you make the most out of unstructured data by visiting this page: https://symbl.ai/solutions/
