Speech Analytics is a process of analyzing the voice of the customer, consisting of collecting the numerous recoverable, identifiable and reusable information on the interactions between this customer and a company.
Speech Analytics is traditionally used in customer relations by companies to increase their knowledge of the Voice of the Customer and is today an essential lever in customer relationship management, particularly in call centers.
Speech Analytics can go further by being combined with Quality Management or Quality Monitoring, i.e. the analysis of the advisor's voice in addition to the customer's, thus validating the quality control and compliance control of the interaction between customer and advisor.
BATVOICE AI: EMOTIONS, SPEECH ANALYTICS and quality monitoring
What is Speech Analytics?
The basics of Speech Analytics: definitions, KPIs, use cases for customer relations, perspectives for the future...
Definition of Speech Analytics
The future of Speech Analytics
The field of Emotion & Speech Analytics now makes it possible to integrate an additional dimension into Speech Analytics: human emotion. Compared to a classic written transcription system combined with keyword search, the "emotional" factor provides customer relationship managers with a finer understanding of call motives or customer irritants.
While Speech Analytics analyzes the words you pronounce, Emotion & Speech Analytics technologies analyze the non-verbal content beyond speech (or prosody): this concerns the way customers and advisors interact, including the physical symptoms that indicate emotions such as strong anger or delight, in order to link them to subjects and call motives in order to prioritize actions to improve company processes, products and services, or even advisor arguments and speeches.
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Or do you simply want to see what Speech Analytics
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DEMONSTRATIONcan do for your customer relationship management?
LEADING CUSTOMER RELATIONSHIP CENTERS TRUST US
The challenges addressed by Speech Analytics
Reduce costs
-300 K€
Time spent by advisors and supervisors on low value-added calls digitized as a priority.
Improve productivity
-30%
Less time spent on your Quality processes: calibration, picking, evaluation and debriefing.
Save time
2X less
Time to perform each Quality Monitoring evaluation and as much time saved to analyze the results and help advisors in their training.
The glossary of Speech Analytics in customer relationship centers
CRC: Customer Relationship Center
CRCs, Customer Relationship Centers, are a service dedicated to processing incoming requests from customers and/or prospects, formulated through one or more communication channels (telephone, email, live messaging, chatbot...). The term "customer relationship center" refers to a set of technical and human resources whose missions are focused on customer information, after-sales service or even sales.
In France, the quality of customer relationship centers is validated by obtaining NF Service Relation Client (NF 345) certification, which is based on international standards ISO 18295-1 & ISO 18295-2. This certification was created in 2004 by customer relationship professionals and AFNOR to provide consumers with a benchmark for the quality of service provided.
FCR: First Contact Resolution
The FCR (First Contact Resolution) rate is measured by dividing the number of calls resolved upon initial contact by the total number of calls made to the relevant customer relationship center (or by the number of initial calls if you wish to exclude repeat calls from your calculation).
Managers view FCR as a key performance indicator in customer relationship centers because it reflects not only the efficiency with which teams handle incoming requests but also a reduction in the number of low-value calls (repeat calls), leading to increased customer satisfaction and lower service costs.
Churn Rate
The churn rate (or attrition rate) is a retention indicator used to assess the extent of customer departures, although this indicator can also apply to employees (equivalent to employee turnover or staff turnover). The churn rate is primarily expressed as a percentage and is calculated as follows: by dividing the number of customers lost over a given period by the total number of customers over the same period (NB: if you want to calculate turnover, replace customers with employees).
CSAT: Customer Satisfaction
CSAT (Customer Satisfaction) is a customer relationship center indicator, typically measured as a percentage, obtained through dedicated questionnaires. CSAT is measured as follows: the number of satisfied (score of 4/5) and very satisfied (score of 5/5) customers divided by the total number of customers surveyed in the CSAT survey.
Although CSAT is used in many scenarios, it is important to study it alongside other performance indicators to gain a representative picture of the customer experience. Responses to a CSAT questionnaire, administered by email or telephone, can be influenced by the quality of the previous interaction between the customer and the advisor. For example, a dissatisfied customer may simply not respond to the questionnaire, whereas a satisfied customer will take the time to respond, which biases the sample being studied.
For more representative results of your customer base, it is recommended to combine CSAT with NPS or to implement Voice of the Customer analysis.
ESAT: Employee Satisfaction
ESAT (Employee Satisfaction) is an indicator used by customer relationship centers to measure the satisfaction of their employees, expressed as a percentage and obtained through dedicated questionnaires. Measuring ESAT is particularly useful in sectors with high employee turnover/churn.
ESAT is measured as follows: the number of satisfied (score of 4/5) and very satisfied (score of 5/5) employees divided by the total number of employees surveyed in the ESAT survey.
NPS: Net Promoter Score
NPS or Net Promoter Score is a very popular indicator among customer relationship professionals that measures a customer's likelihood of recommending a solution to others. This indicator, a true barometer of the relationship and customer experience, is valued by professionals because it is obtained through a single question, the answers to which provide information on both customer satisfaction and loyalty.
NPS is measured using a dedicated questionnaire that asks respondents to rate, on a scale of 0 to 10, their likelihood of recommending the product/service in question to others. Respondents giving a score of 0 to 6 are considered detractors, those giving 7 or 8 are considered neutral/passive, while those giving 9 and 10 are considered promoters. NPS is calculated by subtracting the percentage of detractors from the percentage of promoters in the sample surveyed.
CES: Customer Effort Score
Similarly to NPS, CES is characterized by the formulation of a single question to obtain information on the state of the customer relationship, by asking the customer to evaluate the effort required to resolve their issue. Generally, CES will be measured using a choice between 5 to 7 degrees of agreement (from "strongly agree" to "strongly disagree") with a statement such as: "The resolution of your dispute was easy to handle."
The CES metric is used in customer relationship management, as well as in improving the customer experience (CX). CES can thus help in preventing customer churn.
CES is then calculated by subtracting the percentage of people with different degrees of disagreement from the percentage of people with different degrees of agreement with the statement.
QOS: Quality Of Service
QOS (Quality Of Service) measures the level of service quality provided by the customer relationship center to its callers. Often, QOS is measured using a combination of several individual metrics such as the FCR rate, the volume of calls processed daily, or the average call handling time (ACL).
Thus, QOS allows for the objective determination of the level of service to be provided by an outsourcer and is a fundamental topic when defining the SLA (Service Level Agreement). During the service provided by the outsourcer, the implementation of quality monitoring processes (which can now be automated using artificial intelligence) allows for the validation of the application of the QOS conditions contractually agreed upon.
SLA: Service Level Agreement
The SLA (or Service Level Agreement) is the contract that describes the level of service provided to a customer by the supplier. The purpose of this type of contract is to identify and quantify the characteristics and different metrics of the service to be provided in order to ensure the customer's quality of service. For example, for customer relationship centers, a particularly common SLA clause consists of a commitment to answer 100% of incoming calls within X seconds (to be agreed between the two parties).
The SLA is particularly important for companies that outsource their customer relationship management, especially in the case of call centers. It ensures consistent levels of responsiveness, relationship building, and customer satisfaction across a company's entire customer relationship, regardless of the channel used to handle complaints.
ASA: Average Speed of Answer
The ASA (Average Speed of Answer) is a crucial metric for the operational efficiency of customer relationship centers and their customer experience. This indicator assesses the time it takes an advisor to answer an incoming call, from the moment the customer is placed on hold until the moment an advisor picks up to handle their request.
If it is too high, the ASA may indicate a lack of efficiency among the advisor teams. New methodologies (digital tools, management, internal organization, etc.) can then be implemented to reverse the trend.
ACL: Average Call Length
The ACL (Average Call Length) is also a crucial metric for customer relationship centers and their operational efficiency. This indicator assesses the time it takes an advisor to handle an incoming call, from the moment the customer is connected to the advisor until they hang up after their request has been processed. If the ACL is too high, this may indicate a lack of efficiency among the advisor teams, which harms the customer experience. It is then useful to set an ACL target allowing teams to assess and manage the workload distributed across the floor.
To calculate it, divide the sum of the durations in seconds of each incoming call by the number of incoming calls and you will obtain the ACL expressed in seconds for your customer relationship center.
CPC: Cost Per Call
The CPC (Cost per Call) is a fundamental indicator of the operational efficiency of your customer relationship center. This indicator is calculated by dividing the total cost of all calls handled by the total number of calls made, and its average value varies greatly depending on the business sectors studied.
Many customer relationship centers set a certain amount as a CPC target and direct their customer relationship improvement action plans towards a reduction in this CPC (which is even more true for CRCs with sales targets). However, be careful not to take the CPC as the only indicator, since a very low CPC may indicate a low investment in customer relationship management and poor service quality. It is therefore essential to use this indicator in combination with other indicators, whether they relate to cost efficiency or advisor efficiency.
Call Abandonment Rate
The Call Abandonment Rate, similarly to CES or FCR, is one of the indicators of customer experience quality used to assess the difficulty of resolution, by measuring the number of customers who were put on hold and then hung up before being taken care of. This indicator is critical to avoid as much frustration as possible for the end customer, although it should be noted that this indicator varies according to usage: for example, a customer calling for technical support will on average show greater resistance to waiting than a customer contacted for a commercial offer. Generally, it is acceptable for a customer relationship center to have a call abandonment rate of around 5%.
The Call Abandonment Rate is generally expressed as a percentage and is calculated as follows: subtract the total number of calls handled from the total number of incoming calls, then divide this number by the total number of incoming calls.
Channel Mix
The Channel Mix is an indicator measuring the contact channel used by the customer to reach an advisor. Modern customer relationship centers do much more than just handle incoming calls and must manage requests made via SMS, chats on websites, or social networks. The Channel Mix thus allows you to assess the origin of incoming requests in order to then adapt your customer relationship center to the main types of incoming traffic.
The study of the Channel Mix over several years has notably made it possible to establish the general trend according to which voice channels such as call centers are decreasing in importance (still heavily used by large groups but much less present in small businesses) while so-called "self-service" support channels such as chatbots are expanding rapidly. But the decline in voice channels does not necessarily translate into a decline in the use of the Voice of the Customer (VoC), whose semantic analysis principles can also be applied to so-called "self-service" channels.
Existing use cases for Speech Analytics
Ensure the quality of speech in contact centers
Speech Analytics makes it possible to validate the conformity of interactions en masse with regard to the quality grid established beforehand in the contact center. It also makes it possible to identify "best practices", contributing to the training of advisors.
Reduce customer churn and team turnover
On the customer side, Speech Analytics prevents customer churn by identifying at-risk behaviors. On the advisor side, it facilitates skills development and guarantees an impartial evaluation.
Sales conversion and upselling
Speech Analytics makes it possible to identify the keys to success and the obstacles to sales in order to subsequently identify opportunities for cross-selling, or even for adapting pre-existing offers.
Improving the customer experience
Customer irritants are more easily identified and reported to dedicated teams, which ideally places user feedback at the center of the customer relationship improvement process.
Need advice on implementing Voice of the Customer analytics & Quality Monitoring in your business?
Or do you simply want to see what Speech Analytics
can do for your customer relationship management?
DEMONSTRATIONcan do for your customer relationship management?
Testimonials
Our customers say it best
How to test Batvoice AI?
The team responds to your account creation requests directly from the chat, or through your preferred method of contact.
What technical efforts are required?
Batvoice AI is compatible with all telephony solutions present in call centers. We will need access credentials to your recording platform. The solution is compatible with all protocols (API, SFTP, etc.).
What results can we expect?
The thousands of hours of feedback activated by Speech Analytics, multiplied by each customer, opens a wide range of high value-added benefits to our customers: increase in sales, conversion rates, upsell and cross-sell, prediction of customer churn risks and reduction of churn, improvement of customer experience and customer satisfaction, assurance of call quality and compliance. Depending on your role and objectives, you can help your supervisors and evaluators improve your quality processes using Automated Quality Monitoring or improve your customer knowledge through the Voice of the Customer.
What efforts by business teams are required?
Batvoice first contacts your account administrator and helps you configure your users' accounts, then offers you a roadmap with two workshops, before giving you access to your new knowledge base of best practices, methods, and tools to make your Speech Analytics project a success. Your teams master the tools and are operational in a few weeks thanks to the dedicated training content of our Batvoice Academy.
Users of Auto QM © Quality Monitoring see the gains in time and efficiency from the first minutes of use. Batvoice adapts to your quality processes and allows you to easily migrate your internal grids, evaluations, and user accounts.
Users of the Voice of the Customer have key information on customer expectations to launch actions and monitor their impact. Strong team involvement around the continuous improvement of the brand and brand image, the organization and processes is necessary to reap all the benefits.
Users of Auto QM © Quality Monitoring see the gains in time and efficiency from the first minutes of use. Batvoice adapts to your quality processes and allows you to easily migrate your internal grids, evaluations, and user accounts.
Users of the Voice of the Customer have key information on customer expectations to launch actions and monitor their impact. Strong team involvement around the continuous improvement of the brand and brand image, the organization and processes is necessary to reap all the benefits.
What is this technology?
Batvoice AI is based on technologies developed 100% internally by our R&D department: speech recognition (speech-to-text), natural language processing, affective & social signal processing, predictive analysis. This in-house development is part of our vision of responsible and ethical AI as well as the growing importance of data sovereignty, values shared with the VoiceLab, of which Batvoice is a co-founding member.
Is it GDPR compliant?
Yes:
- Your information message at the beginning of the call to your customers remains unchanged: "this call may be recorded for quality and training purposes."
- Batvoice automatically anonymises the personal data contained in the audio of the calls and in their transcription.
- The solution is available either in the "Cloud" or "On premise".
- Cloud data is processed in the European Union in accordance with GDPR policy.
- Your information message at the beginning of the call to your customers remains unchanged: "this call may be recorded for quality and training purposes."
- Batvoice automatically anonymises the personal data contained in the audio of the calls and in their transcription.
- The solution is available either in the "Cloud" or "On premise".
- Cloud data is processed in the European Union in accordance with GDPR policy.
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