Do you Know Your Return on Investment from CX Transcription?


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It’s All About the Data: How Automatically Transcribing Your Conversations Multiplies Your CX Strategy

Most CX organizations have invested millions of dollars in transcription but are hard pushed to show the ROI. The promises of yesteryear technology never materialized and some companies even had a negative ROI. So, let me take you on a journey of how to rethink transcription powered by artificial intelligence to deliver CX transformation.

Over the last decade we’ve increasingly absorbed information through consumer cloud services like Facebook, TikTok, Amazon, Twitter and Spotify-like services. Audio, video and images absorb our time to create a richer experience. Culturally, it has overtaken all traditional forms of communication. We’ve adapted to these thoughtfully designed formats and platforms that are ultimately powered by artificial intelligence (AI).

These platforms have one thing in common: The capturing of people’s data. From emotions, sentiment and opinions through likes, comments, retweets, duets, stitches and shares. All for one purpose: Gaining advertising eyeballs in new and creative ways that maximize fees these platforms can charge businesses.

Despite being rich in data, the customer experience industry hasn’t been able to fully utilize the various types of historical and real-time data—from voice, chat, text or video —to improve the customer experience. However, with AI native transcription, designed specifically for contact center environments, companies can apply capabilities similar to the major consumer cloud players.

What Do You Need to Know About AI Transcription?

There are three important things to understand about great AI transcription. First the word-error rate, or accuracy rate of the transcription. Knowing, and testing, the quality of the transcription is the engine that makes everything downstream work. If you have poor transcription, it will result in poor quality. Without high-quality, real-time transcription, the data becomes inferior and pollutes services such as bots and frustrates agents.

Speech recognition accuracy is a second key element. Ensuring that the speech recognition is designed for the environment it is operating in, is essential. Generic speech recognition cannot decipher context and business-specific taxonomy that is critical to each business and the efficacy of agent facing technology designed to aid in real-time.

The third key essential for real-time transcription is low-latency. Latency matters because of the need to reduce delays in transcription. When you’re supporting agents with real-time transcription it’s essential to empower them with a natural flowing conversation.

So how can AI transcription provide such a major ROI?

Real-time transcription captures words, jargon, emotions, sentiment and opinions from customers. That voice and transcription data can feed machine learning algorithms that learn to accurately provide insights that negate the needs for customers to complete surveys, providing robust feedback from every customer interaction.

Through telephone calls, IVRs, chatbots, messaging—all this data can be processed through speech recognition, natural language processing and machine learning to synthesize all conversations at scale—and in real-time—enabling a business to leapfrog their CX operations to gain insight and actionable data that can drive improvements in operations and revenue.

Here are ways that adopting automatic transcription can deliver value that improves the customer experience and reduces costs.

Agent Training and Ongoing Coaching
There’s a notably high attrition rate among new employees across the CX industry as we’ve all experienced. There’s also the added pressures of a dispersed, work-from-home (WFH) environment. Proper onboarding, ongoing training/coaching, and employee engagement in these settings are new challenges for this industry. For most companies the problems of absenteeism and attrition in a WFH setting will likely get worse if it’s not addressed, so having technology that is designed for this environment is critical.

As agents undergo a steep learning curve, with varying degrees of training rigor, the underperformance of some agents starts to show. In fairness, it’s a lot of training to take in during a relatively short period of time. Scientists and researchers have shown that the human brain has a limited capacity to process information, and this capacity is severely affected by stress, internal and external distractions. Designing AI technology that mitigates these variables, enables agents to use tools, such as real-time transcription that aids them in their work and reduces cognitive load, so they can focus on what they do best: being human with customers. In live situations, transcription can be the engine to alert supervisors of coaching needs by agents, so they can help them in real-time.

Insights that Deliver Sentiment, CSAT, NPS for a Voice of the Customer Program
Accurate, contact center-designed transcription captures the words that are missed in the gap between 80% accuracy and 90% accuracy and are often the words that matter most to each specific industry. These are the words, and terms, that are specific to a business and are critical to unlocking the value.Having data that isn’t easily accessible or navigable has little value; but when algorithms are applied to the data, the data can be used in meaningful ways.

Why High-Quality AI-Powered Transcription Designed for Contact Centers Matter
Why High-Quality AI-Powered Transcription Designed for Contact Centers Matter

Sentiment Analysis
In addition to the low percentage of customers who fill out surveys, customer feedback surveys have a problem with bias: customers are more likely to respond to a survey when having either a positive or negative experience, thus heavily skewing results to positive and negative feedback. By collecting more information from every call, speech sentiment can be a way to reduce bias and provide a more comprehensive measure of the customer experience to measure real-time attitude and opinion regarding the service customers receive, to equip organizations with intelligence to make swift shifts in agent coaching. As more agents work from home, access to live sentiment can be a great way for supervisors to support agents in real-time without needing to be in the same office.

  • CSAT and NPS Prediction can be achieved via transcription and sentiment analysis, mitigating the need for surveys.
  • With high-quality transcription, companies can classify intent at a useful level of detail, not just at the beginning of a customer call, but all intents over the course of the customer interaction.
  • A business can also spot correlations between different issues—for example: callbacks or sentiment by agent, intent, or length of call
  • Having real-time transcription enables companies to highlight incoming trends and anomalies in customer conversations.

    The ability to automate the detection of anomalous interactions in real time… more tapping agents on the shoulder in hope that unusual trends can be explained. This unlocks data-driven insight into opportunities to fix problems, respond to competitive offerings, or collect feedback on new product launches… a few examples.

  • Automate summary notes, providing cleaner data for analysis and better records for future customer contact. Operations can help agents be more productive with automated disposition summary notes, one of the most tedious processes for agents to increase productivity and improve agent satisfaction, as they skip a step most don’t enjoy.

Automate Quality Management

Contact Center Supervisors have an arduous job as it is, and in the culture of improvement that underlies operations supervisors spend time listening, evaluating and understanding how agents are performing. The small percentage of calls that are chosen to represent the effort of each agent, don’t always represent the overall performance of agents. Feedback that doesn’t reflect an agent’s work, if negative, can demoralize an agent and increase absenteeism.

However, with machine learning models trained on a company’s transcription data, an organization can understand at both a macro and agent level each person’s work. This provides the opportunity for supervisors to have meaningful conversations with agents—based on data— that can help improve the performance of agents. It also strengthens quality management and compliance with increased visibility to drive performance gains.

So, consider putting your call data to work to give you a full-picture view of your business starting with automatic transcription driven by artificial intelligence.


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