Automation of data capture and analysis reduces operational complexity, and thus improves margins. Naturally, as part of the effort toward understanding the customers better, speech data from service calls should be able to help organizations add a new dimension to enterprise analytics. Using the additional component of voice, industries like Retail, Banking, Telecom and others can add depth to their analytics. It is also possible to transform services to voice-based menus once machines can understand human language.
As the technology can be designed to mine intricate data such as words, dialects, emotional responses, and match them with browsing, business leaders must prepare for something new. Here’s how different industries stand to benefit.
A contact center is vital in retail, but it should be able to turn it into a profit center by improving analytical efficiency and decreasing overheads. As interactions with customers ensue on a regular basis, there is an opportunity to reduce the burden on executives through voice-recognition technologies. Some improvement had come from early developments like event-triggered SMS interaction on offers and more, but Speech Recognition technology will help reduce the cost borne during service calls and provide companies what is required to analyze the calls. With enough data, product and service feedbacks, and how agents dealt with them, companies can assess their service quality more accurately.
The financial sector is required to maintain strict protocols, individually, for large consumer bases. Processes such as collections in the credit department and other day-to-day services are often based on tedious sessions for agents and time-consuming menus for customers. Deploying speech analytics implies the financial company can ensure little compromise on sensitive customer data and prevent fraud.
Utility services such as telecom have to rely on communication channels for verification calls on many occasions. Although with appification, there seems to have been great improvements in service overheads, further improvements can come from speech recognition systems, which require customers to simply “speak” to verify that services are provided or they have registered their complaints. Whether or not a step ahead of appification, one can imagine the entire service being call based, and not app based, where the customer delivers the speech-based information for machines to develop the necessary workflow before executives are assigned the tasks accurately.
4) Travel and hospitality
Travel bookings can be tedious, often forcing passengers to let go of better deals. An industry that has, for the most part, been phone and app-based, can use speech analytics to reduce overheads in booking services and improve customer experience according to tastes, cultural preferences, etc., at the individual level. Naturally, Speech Recognition technology with the multiple-language feature is useful for the industry. The brand affinity of customers is significant for growth in this sector. Capabilities to get closer to “the voice of the customer” becomes easier when analytics and speech recognition combine to offer the most potentially profitable strategies.
5) Healthcare and medicine
Although not prevalent yet, using voice data to mine health-related information about the caller will be possible in the near future. Discovery of issues such as the presence of a virus, or state of mind such as anxiety, and general wellbeing will be useful to ensure care for patients who might be traveling or living far away.
The future is in Speech Analytics
The applications made possible after mining data from speech audio are widespread. It is one of the necessary steps in creating an efficient workforce, which will make scaling look easier and easier. Consumer-driven industries stand to benefit their customer-service units with speech analytics through better information, transparency, efficiency, and responsiveness.
Click to access our whitepaper publication which talks about how easy it is for enterprises to deploy and operate Speech Analytics.