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Every day, contact centers gather masses of conversational data in the form of call recordings, chat logs, and transcripts.
This data contains invaluable information about what customers want, when and why they are calling, and what companies could be doing to serve their customers better.
Almost every contact center has access to this unstructured data created from thousands of customer interactions. The challenge is turning this data into a structured format to expose actionable insights that can transform how you do business.
Structured data is data that has been categorized in such a way that it can be analyzed.
This might look like transcriptions of call recordings broken down by intents (what a caller wants), values (such as the number of people on a reservation, dare, or time), or specific conversational turns (customer’s responses to specific questions).
This allows clients to draw conclusions from customer data such as – 3/10 calls are about order tracking, or 20% of tables booked on Thursdays are for 2 people.
Structured data can be gathered in a number of ways, some more labor-intensive than others…
Agents typically record certain information as part of After-Call Work (ACW). This typically includes writing notes to summarize the call, categorizing the reason for the call, and updating details in the CRM.
After-call work impacts call handle time and is susceptible to human error. As agents rush to write notes and select a reason for call from a dropdown of 200 options, it’s easy to see how mistakes can be made. Erroneous data is, at best, useless and, at worst, damaging.
Third-party speech analytics projects capture data from recorded customer conversations and transcribe it into text to extract insights such as keywords, customer sentiment, key phrases, and topics.
After processing the data, the insight is presented to the contact center to help identify trends and patterns in customer behavior, coach agents, and improve the customer experience.
Voice assistants can understand caller intent, answer FAQs, complete transactions, and route calls to the right people. In order to do this, every spoken utterance must be transcribed as text which can, in turn, be processed by machine learning models. The machine learning models must then categorize (or structure) the data in order to understand the meaning behind the utterance and decide on the best possible course of action.
Because of this, voice assistants automatically turn unstructured data into structured data without the need for speech recognition projects or after-call work.
Access to real-time structured data enables organizations to react faster to unforeseen risks and issues, such as website problems, current affairs, or a rise in specific product inquiries.
With this insight, decisions quickly become data-driven, not based on assumptions.
Structured conversational data enables companies to improve the following three areas:
High volumes of calls about specific problems may highlight issues in other areas of the customer journey. For example, if a large number of customers are calling in to reset the password for their online accounts, it may be that the reset password functionality is not working as expected on the website.
By highlighting peaks in specific queries and issues as they arise, companies can fix friction in the customer journey efficiently and reduce the likelihood of the problem impacting wider business operations.
Customers will call in when products or services fail to meet expectations.
Customer feedback plays a vital role in developing a company’s service offerings. It needs to be communicated in a way that the business can use.
For example, a logistics company receives a steep increase in calls about rescheduling deliveries because customers don’t have the option to ‘leave their parcel with a concierge.’
A simple IVR may show that a large percentage of callers wish to reschedule deliveries, but more detailed structured data will show that the source of the problem is limited delivery options. Changing the service offering better serves the customer’s needs and alleviates pressure on the contact center.
Excessive pressure is just one of the reasons employee turnover in contact centers remains 30-45% above the average of other occupations.
With structured conversational data, contact center leaders can identify areas where they are receiving a higher proportion of calls and move resources around to support the teams that need it most.
By making sure the right teams have the correct number of staff, you can expect each agent to have more time to spend with customers, delivering a higher standard of service and building long-lasting customer relationships.
This insight also highlights where contact centers would benefit from automated processes.
For example, a voice assistant can answer all FAQs while an agent manages more complex queries.
Peaks in call volume are inevitable, but preparing for them remains an ongoing challenge for many organizations. Contact centers can be overstaffed and pay for agents to do very little or understaffed when they need agents the most.
Access to real-time data allows contact center leaders to react faster to peaks in volume by identifying rising trends in calls about specific topics, enabling you to respond more quickly to unforeseen issues.
Contact center leaders can leverage reporting of these busy and quiet periods for better future scheduling.
During busy periods, agents have less time to take notes, whereas voice assistants can capture conversational data during every call.
By highlighting common issues or topics that lead to increased average handling times and call volume, contact center leaders can identify where agents need more training to better respond to customers.
Automating the process of gathering conversational data saves contact centers valuable time. Decreasing the time between insight and action will differentiate one organization’s customer experience from another.
The sooner companies can deploy a voice assistant on a focused contact center use case, the sooner it can start gathering data.
Contact center leaders can leverage this data to inform the next deployment phase and encourage buy-in from internal stakeholders by building up a case for further automation supported by accurate customer data.
Structured conversational data plays a fundamental role in understanding why customers are calling and how wider business operations impact the customer experience.
Manually processing conversational data no longer serves organizations that want to meet the increasing expectations of their customers.
Taking an automated approach to gather conversational data will enable companies to:
If you want to turn your conversational data into actionable insight, get in touch with PolyAI today.