If your contact center is looking to uplevel everything from its operations to its revenue generation capabilities, speech analytics will be a crucial part of the solution.
Speech analytics, which refers to the transcription and analysis of customer calls, is gaining traction across industries for its positive impact on key performance indicators such as average handle time (AHT), first call resolution, and customer satisfaction.
This versatility and potential for deep impact is one of the reasons Observe.AI focused on perfecting its speech analytics technology in its suite of conversation intelligence capabilities, causing G2 to name Observe.AI a leader in speech analytics and quality assurance in its Summer 2022 report.
Here’s how speech analytics works and its benefits for contact centers.
First things first: What is speech analytics? Speech analytics software uses speech recognition, natural language processing, and machine learning to convert the spoken words of customer conversations into text. The software can then analyze this text to provide insights into customer sentiment, preferences, and needs.
Speech analytics tools offer real-time analysis of voice recordings, providing an instantaneous feedback loop for contact center agents and promoting continuous improvement. This allows agents to better understand customers' needs and adjust the conversation accordingly, improving both performance and customer satisfaction.
Speech analytics is a subset of conversation intelligence—it’s specifically the call recording and transcription part of the process that transforms call center interactions into business results.
Conversation intelligence is the larger umbrella that includes the end-to-end contact center operations:
Speech analytics is one piece of the puzzle to allow organizations to extract the kind of insights that drive business results.
At its core, speech analytics software collects and analyzes the data from customer conversations and provides aggregate data through dashboards, reports, and call transcripts. Dashboards provide contact center agents and managers with real-time insights into call volume, agent performance, customer sentiment, and other metrics. The transcripts offer accurate, searchable records of customer interactions that agents and managers can use for training and quality assurance purposes.
There are three phases to speech analytics and each plays a key role in reaching the desired objective of surfacing deep contact center insights, trends, and metrics from each call that can be used for strategic business decisions and contact center improvements.
Before entering the first phase, 100% of voice calls and their metadata are injected into the speech analytics software. That kicks off the following process:
There are numerous benefits of speech analytics depending on the size of organization, industry, volume of agents, and many other factors, but the most common and evergreen benefits are the following:
Speech analytics has numerous use cases, demonstrating its versatility in contact center operations. Financial services companies can use speech analytics to identify and prevent instances of fraud or unauthorized disclosures. Healthcare providers can use speech analytics to ensure compliance with HIPAA regulations and improve patient outcomes by identifying valuable care insights. Retailers can use speech analytics to improve omnichannel experiences and identify opportunities for upselling and cross-selling.
Here's an example of a voice call after analysis with speech analytics. You can see instances of call opening, negative sentiment, supervisor escalations, and call closers exactly where they took place.
Regulatory compliance is paramount across all industries, most notably in financial services, insurance, and healthcare. Strict legislation exists to help ensure the protection of customer data. As a result, monitoring mandatory compliance dialogues and categorizing voice calls relevant to specific compliance regulations is mission-critical.
The beginning of a conversation is important from both a customer experience and a compliance standpoint. It's incredibly important for contact centers to optimize call openers to improve CSAT, mitigate compliance risk, and improve conversion rates for sales calls.
The end of a conversation is also important for customer experience, and it also is an opportunity to both better confirm how the call went and create next steps.
Supervisor escalations are a strong indicator of a negative customer experience or an organizational inefficiency. Escalations in any contact center are costly due to the amount of time and resources required to resolve them.
Customer sentiment analysis is an indicator of how people feel about a brand, its products, and its customer service.
Operational efficiency is critical for improving critical contact center KPIs, all contributing to lowering average handle time (AHT).
When looking to implement a speech analytics solution, contact centers should prioritize 4 key features:
You want a solution developed specifically for the contact center environment, which accounts for things like noise, low audio quality, and poor microphones.
The solution should also expertly leverage Natural Language Processing (NLP): the branch of AI focused on “understanding” text and spoken words. A common use of NLP is in interactive voice response (IVR) systems for customer interaction, as well as in question answering, text classification, and information retrieval with features like automatic suggestions. Using NLP, customers can interact with a company’s automated systems using natural speech.
Avoid black-box solutions that can’t be tailored to your specific needs or updated with new information. Observe.AI uses a proprietary AI engine to recognize (and allow you to update) critical business phrases customized by you. Examples include brand names and specific compliance terms.
It’s important to note that recognition of these specific business terms is a key feature in a speech analytics solution because this feature contributes to transcription accuracy. At the end of the day, speech analytics is useless unless transcriptions are accurate. As they say, “garbage in, garbage out:” If your transcriptions aren’t correct and complete, the data and insights you pull from them will be unhelpful—or, in the worst-case scenario, downright harmful. After all, the impact of low-quality transcripts exponentially increases when you’re factoring in thousands of calls. Not to mention the potential negative impact on compliance.
“Don't take 80% or 90% [transcription] accuracy at face value. What matters most is if the context behind the words is captured and critical business terms are recognized,” explains Jithendra Vepa, chief scientist at Observe.AI. “These words and the intent gathered from tonality, frequency, rate of speech, and more become the foundation to deciphering key moments and sentiments from conversations.”
Your solution should be able to detect the meaning and emotions behind the words. To do this, the best solutions use Natural Language Understanding (NLU), which is a subset of natural language processing (NLP). Using NLU, AI-powered solutions like Observe.AI can precisely determine the speaker’s intent, regardless of how they’ve expressed it.
Again, your solution should be adaptable. It should include regular transcription improvements and an easy-to-use feedback loop to ensure the highest level of accuracy at all times.
This is where Natural Language Generation (NLG) can really help. Another subset of NLP, NLG enables AI to produce natural-language text responses to users based on data input. Not only can NLG provide agents with real-time notes and suggestions during a call, but it can also summarize calls and produce detailed post-call summaries, alerts, and coaching.
Speech analytics has driven QM processes to grow more automated, more accurate, more efficient, and more relevant to the agents themselves. It has had a massive benefit on organization leaders, supervisors, and the contact center agents themselves, impacting customer experience, compliance, and learning and development. A speech analytics solution drives: