Speech analytics is essentially an artificial intelligence-powered tool used by speech analytics companies. The program does several tasks, including monitoring voice-based interactions between a customer and your customer care representatives. It then finds and pulls insights from that conversation that would otherwise be overlooked and could be used to help a business make better-informed decisions.
You probably have this information on speech analytics(What is AI speech analytics?), but there’s certainly a lot more than goes into it. What you didn’t know is speech analytics companies have deficiencies and not perfection as vendors in the space promote it. If you plan on or already have implemented speech analytics, it is best to know both the pros and cons of the solution.
This blog series will unpack what is ai speech analytics. and cover the details no other company will tell you when selling the software to your company.
Speech analytics has drawbacks that you can’t afford to ignore, even if they can dramatically improve call center processes by unlocking important information. Information that can help improve conversion probability, generate higher-quality leads, increase customer satisfaction, and give a chance to reconnect with lost opportunities.
Most business owners presume that integrating it into their systems will solve every problem they have at the call center. This popular perception emanates from how companies in this space sell their software. You, however, need to recognize that these firms are in business to push their product and will tell you about the positives while glossing over or completely avoiding the negatives. If this were everything the suppliers said they were and without any negatives, don’t you think all call centers implemented the technology and would be running smoothly across the globe?
You will often hear and read about how speech analytics companies will upscale your business by giving you 100% accuracy in transcription and analysis. Not even the best software on the market can deliver this. When setting up the software, you are required to provide an initial extensive selection of audio samples with different agents on the line and on different days or weeks. This is what the software will compare against future calls to analyze speech.
But as you know, customers are different, and so are call center representatives. Many things will affect the accuracy of the results, most of them beyond your control. Even if you have the best equipment and the most competent customer care agents, things like stuttering, mumbling, accent diversities, speedy talking, recording quality, and background noise from the customer’s end will always be out of your control.
According to a survey, 80% of customers get satisfaction when their emotional needs are catered to, even if they call to get a solution to a problem regarding a product or service. This means emotion detection is significant to contact centers as it determines the success rate of calls.
A Voice Analytics component known as sentiment analytics is used to identify the emotions presented in call data. It enhances customer satisfaction in the long run by offering a more accurate depiction of a client’s mood. Our experience is that this capability is moderately accurate at capturing true sentiment.
Some organizations’ (Speech Analytics companies) salespeople will oversell and tell you how efficiently and completely their software detects emotions and sentiments. We focus our efforts on sentiment analysis to identify interactions where there may be negative sentiment, however, our focus is to then use human validation to ensure the accuracy of the sentiment detection.
This software’s current state of sentiment recognition is fairly above average and depends on a multitude of factors, including a mix of phrases, relative volume, and tone change. But the truth is that no software at the moment can identify emotions based only on conversational interactions.
Analyzing the terms and expressions that your consumers use when they’re angry or unhappy can help you identify their sentiments. You might be shocked by how this varies depending on your customers’ geographical location and ethnicity.
Additionally, some words the software regards as positive can be used sarcastically to imply a totally different thing, but the tool won’t detect that.
Here is a practical example;
Agent: Sorry, we are unable to solve that at the moment, but we are working to rectify the issue as soon as possible.
Caller: Oh great! Now I have to sit and wait because I don’t know how long to use my app.
This statement is sarcastic and depicts how the customer’s schedule will be disrupted by the issue they are facing, but no software is smart enough to detect this. Speech analytics will instead pick the keyword ‘great’ and conclude that the customer was satisfied (As in most speech analytics companies). In our solution, we would detect the “sorry” and “unable” in the agent’s speech and flag this interaction for follow-up by a human reviewer to determine if, in fact, the customer was unhappy or happy.
Simple voice and word detection is a basic application of speech analytics frequently employed in call centers. Primary integration and setup may cost more than expected, even though this technology is able to analyze 100% of interactions, it is truly complex to configure it to be more effective and economical than the human-based alternative.
It is expensive to integrate this kind of information because it requires significant labor for analysis of large volumes of calls to determine the correct information to be codified in the software to enable automation. This labor expense continues for the life of the implementation (if done correctly) as human review and course correction of the automation are required to maintain accuracy and confidence in the solution output.
Additionally, many speech analytics-only tools require a very sophisticated “coder” familiar with arcane syntax to program the platform to be successful. This can be a very expensive resource to maintain internally or buy specialized services from the open market.
Because of these complexities and costs, the benefits of the Speech Analytics solutions “only” are not realized, either at the beginning or in the long term as the accuracy of the analytics degrades.
Unless the organization is aware and willing to make the investment in expensive and complex setup and maintenance the benefits won’t be realized by investing in costly speech analytics tools that offer minimal functionality. Because of this, it’s critical to weigh the benefits and known and any potential unforeseen costs against each other. If going down the path of a speech analytics “only” solution, be sure and ask the provider relevant questions as needed to avoid future frustrations and unanticipated costs.
Organizations typically face various challenges as they work to comprehend a customer’s direct voice.
First, they use arbitrary manual call-sampling techniques that only sample 2% of all conversations, which can be statistically valid sample sizes overall for the organization, but lead to deficient raw data sets to drive coaching and development of individuals.
Second, most Speech analytics companies leverage speech-to-text techniques, but the quality of these tools restricts the amount of meaningful data they can capture.
To ensure the accuracy of the analytics, you may need to engage one or more speech analysts who will listen to and analyze recordings for a significant amount of their time and turn those interactions insights into meaningful improvements in the speech analytics system(Unlike most speech analytics companies). However, this frequently fails due to cost or lack of time and resources
Unlike other approaches to using speech analytics as a standalone solution(that all speech analytics companies do), Call Criteria combines its automation solution with human validation and verification. Call Criteria has over 700 characteristics defined in our library that can be analyzed by a combination of automation and human validation. Our solution overcomes the hidden reality that there are a huge number of false positives and false negatives that require human validation to ensure accuracy and trust in the output of speech analytics solutions.
Call Criteria closes this gap in voice analytics, which most solutions don’t address, by combining our human QA’s with our automated speech analytics powered by Artificial Intelligence. Choose efficiently before deploying any speech analytics companies.
Companies frequently work with multiple speech analytics suppliers (Speech analytics companies) over time in the hopes of fully automating their contact center quality control, only to be let down. Call Criteria is aware that speech analytics can only improve your contact center to a certain extent and will never tell you otherwise.
To set our organizations up for success, our quality assurance team will inquire about any gaps in your script and scorecards to ensure all the guidelines are followed.
Additionally, we recognize that the Artificial Intelligence (What is Ai Speech Analytics) component of our solution will require iteration. We focus on having a great solution initially, but by using our human reviewers we have the ability to identify areas of improvement. The solution will continue to get better with time once it is trained in the team’s patterns, such as missing specific sections of your scripts that our Human QAs have verified.
What sets us apart from regular providers is our incorporation of Human QAs. One of the deficiencies of speech analytics is the inability to detect human emotions such as sarcasm, excitement, or anger. Here, we combine human QA and Artificial Intelligence to counter such problems, ensuring the best possible results.
The precision of the human ear combined with the speed of artificial intelligence allows us to give more than 99.5% accuracy at a cost lower than what you will get with other providers.
Call Criteria provides unmatched, high-quality, cost-effective QA service for contact centers. Get in touch with us to learn more and stay tuned to the next series of this set of blog posts that will unpack what speech analytics companies won’t tell you.