Investing in a speech analytics solution is a big decision for any company as it is generally linked to considerable expenses. Nevertheless, more and more enterprises today embark on this opportunity. As practice shows, they can expect a return on investment in just three to twelve months.
In one of our recent blogs, we have already retold you about the numerous benefits the speech recognition and analytics software brings to organizations. Among them are the exceptional customer experience, reduced expenses, increased revenue, and minimized customer churn.
In practical terms, the solution makes it possible to reduce time spent on speech transcription and quicker perform a comprehensive analysis of the mined data.
Prior to exploring how to evaluate and choose speech analytics software, let’s recall what speech and voice analytics actually includes and how it is used to elevate customer experience or customer interactions in general.
What is Speech Analytics?
Speech analytics can be defined in many different ways, but should be primarily understood as a specialized technology that enables the process of extraction of relevant data from a real-time conversation or a recorded audio file.
This technology automates a range of operational processes, including call recording, speech recognition and transcription, search by keywords and extraction of actionable insights, conversation quality management, behavioral analysis, and other forms of data analysis.
With the aid of speech analytics solutions, all of these processes are carried out at a higher speed and at a more sophisticated level than by any human-powered data mining system.
In fact, in the context of a modern-day front office or contact center, the manual collection and analysis of information are hardly imaginable. This antiquated approach would certainly require the mobilization of significant human and financial resources, making the process terribly cost-inefficient. Call analytics benefits, on the other hand, are too compelling.
Speech Recognition and Transcription
Among all of the processes mentioned above, speech recognition and speech-to-text transcription remain some of the most difficult to tackle. In the most common implementation, computer algorithms identify phonemes in a speech by comparing them with the ones existing in a particular language.
This method tends to deliver the most accurate results and is sometimes complemented by recognition of pre-defined bigrams, trigrams or whole phrases for quicker processing. The pauses made in conversations are often used in order to distinguish speakers from one another, especially when analyzing phone conversations.
Once the recognition is complete, the program can transcribe the conversation into text and use all the accumulated data to conduct an analysis.
Once the speech recognition is complete, additional analytics technology makes a thorough analysis of the conversation content possible. For example, the transcribed text can be searched for predetermined keywords or phrases.
On top of that, the speech and voice analytics software can collect meta-information about each conversation such as call duration and response speed.
Going beyond the conversation basics, some programs can also incorporate voice analytics technology and scrutinize voice tonality. By evaluating the audio patterns, they can draw conclusions about the emotional context of the customer-agent interactions.
All the collected statistical and analytical data can be further presented in a convenient table format or visualized through graphics and charts. Comprehensible by any person, the findings of the speech analytics program can be immediately put to work in a business setting.
This way, words said by a customer over the phone are transformed into valuable enterprise findings: it’s one of the major benefits of speech recognition technology.The collected data gives companies actionable insights into the customers’ interests, preferences, feelings, and intentions, providing an opportunity to learn not only about what clients say but also how and why. Click To Tweet
Key Questions to Evaluate Speech Analytics Software
If your organization is planning on getting a speech analytics tool for your front office, consider conducting thorough research prior to making a concrete purchase. This will give you an opportunity to identify the right solution that is ideal for achieving your business goals.
Not to get lost in the sheer number of options available in the market keep in mind 5 main questions while assessing the quality of the offered speech analytics product.
1. Does the Solution Offer Real-Time Speech Recognition?
Real-time processing of speech is an important feature of any speech-to-text transcription and analytics service. Having recognized predefined keywords in real-time conversations, the service displays the relevant information on the agent’s desk, successfully guiding him or her through an uneasy or important talk with a customer.
Due to the use of scripts and knowledge bases inbuilt in speech analytics software, a front office or call center can achieve higher first-call resolution and an increased level of sales.
It is certainly possible with most of the speech recognition and analytics services to review call transcripts after the conversations have already taken place.
But the output will be more meaningful if an issue is appropriately addressed while the speakers are still on the phone and not once a customer has already hung up and chosen a competitor’s product.
Overall, the implementation of the real-time speech recognition and analytics software helps companies ensure clients’ satisfaction and in the long term minimize their attrition.
2. How Accurate Will the Extracted Data Be?
Choosing a speech analytics solution for your business, make sure that it performs transcription in an accurate manner. The accuracy, in this case, is assessed based on the Word Error Rate (WER) metric, which entails the estimation of the total number of words, substitutions, deletions, and insertions in the transcribed text in comparison with the reference.
The industry standard WER is 8% as estimated by the veteran Microsoft scientist Xuedong Huang.
In the provided below video, we explain in detail how WER is calculated and give several vivid examples to demonstrate how the speech-to-text transcription is performed in real-time.
Recognizing human speech is certainly not an easy task for digital programs. Not only do they have to solve the difficulty of separating the words of speakers from the background noise, but they also need to distinguish the speeches of people talking.
Another challenge is to identify separate words, which constitutes a serious problem if the system transcribes the speech of a person who runs all words in one stream. Donald Trump’s statement provided in the video above serves as an example of such speech.
Similarly, regional accents and dialects can throw off many speech transcription platforms. A high recognition rate in this case can be achieved by means of syntax and semantics analysis, but to carry it out the system has to be trained to work with these categories.
In view of this, when choosing a speech recognition system, try to find the one that has blistering brainpower to handle all the language peculiarities.
3. What Insights Does Speech Analytics Provide?
When looking for a speech analytics tool, choose a solution that will take you beyond the traditional analysis of the calls’ contents and common causes.
The ideal speech analysis should produce truly deep and meaningful insights into the accumulated data, exposing the underlying trends and patterns. It is an ultimate advantage if the system also allows the measurement of tone and speech volume. These two features can be used effectively to assess the emotional context of the conversations.
The purchased by your company solution should have another important feature, and that is the ability to combine the extracted insights into something more visually comprehensible than the lengthy mediocre reports.
Intelligent speech analytics applications offer a great variety of graphs and presentation formats. Their main purpose is to provide actionable insights, namely those that can be easily interpreted by both high executives and junior staff members and immediately put into action.
4. How Strong Is the Solution’s Power of Search?
Having the information about the calls collected and analyzed is a big step forward. But what you ultimately want to get is an opportunity to manipulate this data, by building queries and categorizing it in accordance with your own requirements.
A good solution always offers an opportunity to select several search criteria to get a better understanding of the discovered information. The power of search is, therefore, an important criterion that should not be overlooked when purchasing a speech recognition and analytics solution.
5. How Difficult Is It to Integrate the Solution with the Existing System?
For an optimal result, the purchased by your company speech recognition and analytics solution has to be easily integrable with your existing IT infrastructure. Ask your vendor whether he will be able to customize the solution for you.
A fully integrated software enables a rich user experience and helps to prevent the alternations between several different interfaces, such as the one of the speech analytics tool and the telephony setup in your office.
Even after the system has already been deployed, there might arise a need to change some settings. For example, if you wish to expand the system’s dictionary by adding business-specific terms or tune it to allow the recognition of new languages and geography-based accents.
Thinking ahead, ask your provider already at the purchase stage whether it will be possible to improve the system performance for you in the future or whether this can be done at the user level.
Speech-to-text transcription and analytics solutions are undoubtedly taking over the digital world. They open up new horizons for companies, enabling them to dig into the massive data array accumulated by their front offices and to extract the insights invaluable for performance management and continuous growth of their businesses.
According to the research undertaken by Opus, 247 out of 500 decision-makers (49%) have adopted speech analytics tools in their organizations. Approximately 83% of these respondents achieved the initially estimated ROI within 12 months, with 1/3 receiving the expected payback in as short as 6 months.
In this blog post, we have attempted to guide you through the difficult process of choosing the right speech recognition and analytics solution for your business. The provided list of questions, which have to be answered at the outset of this process, is by no means exhaustive.
We are however convinced that they constitute a great starting point and will enable you to find a speech-to-text transcription and call analytics solution perfectly matching your company’s needs.