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Conversational AI has become the driving force behind digitalization projects. Businesses use this technology to automate customer-facing touchpoints on any channel. Conversational AI reduces costs and increases efficiency by automating repetitive tasks and allowing human agents to focus on more crucial tasks. Besides, enabling customers to engage with technology in a much more natural way ensures an enhanced experience.
One of the biggest challenges that companies face when building or buying a conversational solution is intelligence. An intelligent system can offer a human-like conversation and understand multiple ways in which the same information is being phrased. The intelligence of a conversational AI solution relies heavily on the design of the dialog flow. This flow is the brain of the solution. But also, as important as the brain, is the user experience. Here are four hints to design a smart conversational AI solution.
1. Know Your Audience
To understand your customers’ expectations, you need to know where they are coming from. Try to define your audience by considering their demographics, such as age, gender, profession, geography. Try to answer these questions: Who are they? Students or employees? Which age group(s) do they belong to? What are their habits? How about their language and tone? What kind of sentences would they use? Would they prefer short and direct communications, or would they enjoy longer conversations? Be familiar with the way they speak; the phrases and slang they use. Also, consider their preferences and habits. Knowing all these details helps you design conversations that your customers can easily engage with and be happy with the result.
2. Intent Recognition is Vital
Offering a system that answers simple FAQs is not enough for today’s customers. Customers want to say simply what they want and to be understood by the systems. They don’t want to lose time with detailed queries. So, businesses need to offer solutions that not only understand what customers say but also understand what they mean. And this is possible with the intent recognition technology. This feature understands the meaning behind customer queries with high accuracy. If a query is ambiguous, the AI will ask additional questions to make sure. This results in a human-like dialog between customers and machines.
3. The Heart of The Conversation: Dialog Flow
To ensure a natural and smooth dialog, you should build conversations that sound more human and less machine. While building your dialog flow, focus on language details. Consider your audience, the language, and the tone they use every day, and build your dialog flow accordingly.
Make sure that you keep the conversation short by only asking the necessary questions. Keep the prompts short, and don’t confuse your customers by offering multiple options at once. Be concise. Don’t reply with ten lines of information when two will do. Never forget that customers might change their minds during a dialog and ask for something totally different from what they had initially asked for. You should be ready to interpret these changes and instantly adapt to them.
4. Respect Your Customers
Customer satisfaction must be at the center of your dialog design. Customers always prefer to interact in their way. So, you shouldn’t force them to engage in your standard format. Let them be free to choose. When engaging they can use formal language or everyday language. And your AI solution should be able to adapt. This is possible with NLP-based conversational solutions.
Time is the most valuable asset. Respect your customers’ time. Offer solutions that smoothly integrate with different channels. By doing so, you can save them from repeating themselves whenever they change their engagement channel. Build a system that can pick up the dialog from the channel your customer left off. In short, put yourself in your customers’ shoes and design conversational AI solutions that you would enjoy interacting with.
To learn more about designing smarter conversational AI solutions, download our “Conversational AI E-book” by filling the form below.
Author: Çağrı Doğan, Accessible Products Consultant, Sestek
Today’s customers expect smooth journeys.
They want to interact with brands easily, at any time, at any channel; contact centers, chatbots, messaging apps, smart assistants. And while doing this, they expect to be understood fast. They want to be understood before they open their mouth. They want to be understood not only by humans (customer reps) but also by machines. The answer to this expectation is Conversational AI.
Publish Date: May 25, 2021 5:00 AM
In the past few years, advances in artificial intelligence led to the widespread use of Conversational AI. The rise of the technology continues thanks to its successful use cases in both consumer and enterprise applications. According to Research & Markets, the Conversational AI market generated $3 billion and is predicted to reach $15 billion in 2024, advancing at a 30% CAGR.
The Rise of Conversational AI
The rising demand for AI-powered customer support services, positive return on investment (ROI) for companies deploying Conversational AI solutions, and an increasing number of solution providers in the market are effective in this growth. So, the adoption of AI in the enterprise sector is increasing. According to Gartner, 31% of CIOs have already deployed conversational platforms, representing a 48% year-over-year growth in interest. Conversational AI is implemented across various use cases, including customer service, sales support, human relations, employee engagement, customer engagement, retention, and more.
What does Conversational AI Offer?
Today’s customers expect smooth journeys. They want to interact with brands easily, at any time, at any channel; contact centers, chatbots, messaging apps, smart assistants. And while doing this, they expect to be understood fast. They want to be understood before they open their mouth. They want to be understood not only by humans (customer reps) but also by machines. The answer to this expectation is Conversational AI.
Natural Human-Machine Interaction
Combining technologies like natural language processing (NLP), speech recognition, and text-to-speech, Conversational AI enables smooth interaction between customers and machines. The technology allows customers to naturally interact with systems in their own words via speech or writing. Conversational AI provides a personalized and enhanced experience for customers. Customers can complete various tasks simply by speaking to systems as if they are speaking to a human.
Reducing Costs and Enhancing Experience
Keeping costs minimum while offering high-quality customer service is among the biggest challenges that businesses face. Conversational AI automates routine customer service tasks by allowing customers to self-serve. This helps companies reduce operational costs while increasing efficiency. Offering enhanced customer service also provides an effective differentiation tool for businesses. Conversational AI leads to higher customer satisfaction and greater customer loyalty. This means a sustainable competitive advantage and a positive brand perception from customers.
3 Steps of Conversational AI Deployment
Deploying Conversational AI for the sake of “everybody else is doing it” might be the worst thing you can do for your business. Boston Consulting Group’s latest study shows approximately 70% of organizations fail in their attempts for digital transformation. You will need a well-thought strategy before you take any action. Following the steps below might help you build and implement a result-oriented conversational AI strategy.
Step 1: Set your end goal
So, you are not implementing Conversational AI to jump on the bandwagon. Then, try to discuss within your company (within your team) the following questions:
⦁ What do we want to achieve with implementing AI? What is our end goal?
⦁ How can AI serve our business objectives?
⦁ What are the main pain points of our customers that we think AI can help solve?
⦁ How will this solution help them?
⦁ How can we set up KPIs to monitor progress?
Step 2: Select the right vendor.
Developing AI solutions within your company will take a serious amount of time and effort. When there are AI vendors working on these solutions for more than decades, it would be wise to get some outside help.
But choosing the right vendor is important. While deciding on the technology provider, make sure that they have the following capabilities:
⦁ Technology and industry-specific expertise
⦁ UX-oriented approach
⦁ Competence in professional services
Step 3: Phase the plan
⦁ Bringing together your team with your technology provider’s team to determine requirements.
⦁ Prepare checklists on specifications, installation requirements, and KPIs beforehand.
⦁ Testing technology specifications to see if specifications are implementable in practice.
⦁ Launch internally before offering it to your customers to complete user and security testing and apply necessary fixes on time.
⦁ Now your project is live, and your customers can start interacting with your solution.
⦁ Monitor customer behavior and get as much feedback as possible to detect improvement needs.
⦁ The success of any project depends on objective performance evaluation.
⦁ Continuously monitor and analyze your efforts to measure the effectiveness of the solution and define your next steps for improvement.
⦁ You can use Conversational Analytics tools such as Speech, IVR, and Bot Analytics for an in-depth evaluation.
To learn more about leveraging self-service automation and enhancing the customer experience with Conversational AI technologies, download our “The Conversational AI Playbook” by filling the form below.
Publish Date: April 1, 2021 5:00 AM
From mobile devices to smart homes and websites to virtual assistants, conversational platforms are everywhere we touch. By using voice as the most natural form of interaction, conversational-AI transforms any platform into a helpful assistant.
The use of voice-activated digital assistants is increasingly becoming common in cars as well.
According to recent research by Market Insight Reports, AI in the automotive market is expected to be appraised at USD 12 billion by 2026.
The Transformation of Infotainment Systems
Before the proliferation of conversational systems, infotainment systems were popular in-car systems.
The in-vehicle infotainment system had its origin in the 1930s, but the first-ever car radio, named ‘Motorola’, was introduced in 1950. After several advancements in the automotive industry, during 1970−1977 automotive cassette tape player was introduced. The integrated GPS navigation system was introduced by Toyota in 1987, followed by other players in the following years.
In the late 1990s, remote diagnostics came into the picture and after 2003, vehicle health reports became an inclusive part of connected car services. In the late 1990s, smartphone technology also evolved and around 2004−2006 smartphone connectivity for in-vehicle infotainment was introduced. By the end of the decade, alternates for in-vehicle smartphone usage, such as large display screen that includes services like audio, visual, e-mail, vehicle diagnostics, navigation and compatibility of mobile apps came into the picture. After the 2010s, we started to see more voice-activated systems in cars. The rise of voice-based assistants also accelerated the adoption of conversational systems in vehicles.
What does In-Car Conversational AI offer?
The convenience of conversational systems in cars is undeniable. Letting the drivers operate all in-car systems by voice enhances the driving experience and increases security by minimizing distraction.
Enhancing Security with Conversational AI
Driver distraction is an important road safety issue. National Highway Traffic Safety Administration (NHTSA) estimates that in 25% of accidents in the US driver distraction is the main reason for an accident. This means just in the US, 1.2 million incidents each year happen because of driver distraction.
Modern cars with advanced infotainment systems often need more cognitive attention, causing more distraction. That is why researchers are searching for better ways to manage distraction by improving the interaction between the car and the driver. And conversational AI technology offers an effective solution. The technology allows hands-free interaction via natural speech. Advancements in speech recognition and natural language processing technologies enable an ongoing conversation between the driver and the user. This ensures an uninterrupted driving experience, which increases security.
Enhancing Driving Experience
In-car conversational AI applications enable users to interact with voice, the most natural interface. The hands-free nature of this technology provides a convenient experience for drivers. So, drivers can accomplish various tasks without taking their hands off the wheel. This makes voice-enabled assistants more of a must-to-have than a nice-to-have for cars.
Conversational AI transforms current in-car infotainment systems into easy-to-interact digital assistants. By speaking to these systems, drivers can accomplish various tasks and have a fun driving experience: Making a phone call, receiving navigation directions, sending a text or email, learning the weather forecast, and so on. Drivers can do all these things only by speaking to their in-car assistant.
Implementing the Technology
As the adoption of in-car conversational AI rises, more companies will be offering this technology as a given service for their customers. That is why offering these technologies alone will not be enough to differentiate from the competition. As more brands provide such technologies, they will need to find new ways to differentiate from their rivals. Here is a checklist for automotive brands to offer an effective in-car conversational system:
- Set Your End Goal
Deploying Conversational AI for the sake of “everybody else is doing it” might be the
worst thing you can do for your business. You will need a well-thought strategy before you take any action. To draw a roadmap, you need to set your goal first. Then, try to discuss within your team
the following questions:
- What do we want to achieve with implementing conversational AI? What is our end goal?
- How can conversational AI enhance driver experience?
- What are the main pain points of drivers that we think AI can help solve?
- How will this solution help them?
- How can we set up KPIs to monitor progress?
- Select the right vendor
Implementing conversational AI solutions is a serious decision that requires a serious amount of time and effort. Collaborating with an expert vendor would make this process easy and seamless. So, while deciding on the technology partner, you’ll work with look for the following capabilities:
- Expertise on NLP: The performance of a conversational AI system depends on the NLP engine. The language, the linguistics of phonetic spelling, dialects, cultural nuances, and domain-specific terminology determine the effectiveness of NLP engine. So, make sure that you’re working with a vendor who has experience in these areas.
- UX-oriented approach: UX is all about how a product or solution fits user expectations. In other words, the success of conversational AI depends on its ability to provide a great UX, which is directly related to dialog design skills. To ensure a natural and smooth dialog, you should build conversations that sound more human and less machine. This requires expertise not only in linguistics but also in contextual capabilities. So, your technology provider should have experience in designing smarter dialogs and eventually, smarter systems.
- Competence in professional services: Conversational AI projects require a rigorous approach. Continuous monitoring and improvement are necessary to ensure an enhanced driver experience. While selecting your technology provider, consider their capabilities in professional services, including customizations, training, implementation, and post-implementation support. Make sure that your technology provider understands your motivation and offers a project management approach accordingly.
- Phase the Plan
- Prepare: Determine the requirements by bringing together your team and your technology provider’s team. Prepare checklists on specifications, installation requirements and KPIs beforehand.
- Test: Test technology specifications to see if they are implementable in practice. Apply as many internal tests as possible before offering the technology to your customers. So, you can complete user and security testing and apply necessary fixes on time.
- Monitor: After your project goes live, monitor driver behavior and get as much feedback as possible. These will help you to determine what you need to do to improve UX.
- Evaluate: The success of any project depends on objective performance evaluation. This requires continuous monitoring and analysis. Conversational Analytics tools can help you measure the effectiveness of the solution and guide you through your next steps for improvement.
Sestek and In-Car AI
At Sestek, we offer omnichannel conversational AI technology with a wide range of use across multiple channels, including voice IVRs, chatbots, virtual assistants, and intelligent platforms. Lately, we collaborated with TOFAŞ, the leading automotive manufacturer in Turkey. Together we will develop an in-car voice assistant, which will be a first in the industry. Our Conversational AI technology will enable a dialog between the driver and the virtual assistant. Drivers will be able to interact with the assistant by natural speech. The assistant will be able to make sense of what is said and return with the necessary answers, and when it needs additional information, it will be able to request detailed information by asking various questions to the driver.
The assistant will provide route and road status information, recommendations specific to the driving characteristics of the user, and support driving safety with instant verbal warnings. In this way, a more advanced driving experience will be possible in terms of safety, convenience, and comfort. The most remarkable difference of the application from known virtual assistants is that it can analyze many instant data to be taken from the car. This continuous feedback and analysis will be used to support the driver. To learn more about this project, please click here.
Author: Çağrı Doğan, Accessible Products Consultant, Sestek
Publish Date: March 9, 2021 5:00 AM
Conversational technologies transform the customer journey. By allowing customers to use their own words to interact with systems, conversational technologies offer the most natural communication method. And the conversational journey starts with speech recognition technology.
Speech Recognition (SR), also known as automatic speech recognition (ASR), catches spoken words and phrases and converts them to a machine-readable format. This is the first step to let users control devices and systems by speaking instead of using conventional tools such as keystrokes or buttons.
Why is Speech Recognition important?
As the first step, the accuracy of speech recognition is key to a successful conversational journey. If you cannot accurately translate voice into text, you cannot understand what your customers are saying, and you will not be able to solve their problems. The accuracy of SR increases the efficiency of self-service applications and allows companies to deliver improved customer experiences. Since SR is the core technology that empowers conversational solutions, the success of a conversational system depends on the capabilities of its SR technology. In other words, to ensure a smooth conversation between machines and the customers, a comprehensive Speech Recognition solution is crucial.
To offer an effective conversational product, make sure that your SR solution ;
- has a high recognition accuracy
- offers advanced natural language support
- supports multiple languages and accents
- easily integrates with multiple technologies like AI, natural language processing (NLP), and machine learning (ML)
- has a flexible structure that supports omnichannel deployment
How Sestek SR stands out
20 Years of Know-How
Sestek SR is the product of Sestek’s 20 years of experience in building highly accurate speech solutions. Since day one, we have been working hard to make our technology more accurate and robust. Empowering Sestek Speech Recognition with the latest technologies like neural network (NN) improves its recognition accuracy and as an R&D company, we have been investing in this for a long time.
End-to-end Conversational Journey
Sestek SR is the core technology behind our main products such as voice IVRs, virtual assistants, and conversational analytics. Moreover, Sestek SR is a component of our omnichannel automation solutions. Meaning when you implement Sestek SR once, you can benefit from the technology at any channel you are willing to build conversational solutions for your customers.
Tailor-Made for Different Verticals
Each business has different priorities when it comes to offering the best customer service. Each business needs specific solutions rather than one-size-fits-all ones to build the right conversational journey.
Sestek Speech Recognition’s highly customizable structure enables us to build a tailor-made conversational solution for each company. The technology can be trained with specific language models according to industry and vertical needs.
Difficult to Build Difficult to Implement
Building highly accurate speech solutions in house might take significant time and effort. Collaborating with experienced vendors saves more than money, it can contribute to the awareness within your organization. But this requires a close relationship with your technology provider. Your technology provider needs to understand your needs fast and offer intelligent guidance with proven processes and advanced tools. Sestek offers end-to-end professional services, including strategy building, application design, deployment, testing, and optimization. Our team’s expertise relies on hands-on experience in speech tech, gained from 20 years of developing conversational solutions. This may be our most significant differentiator to our global competitors’ deploy and forget approach.
SR Accuracy Test
Sestek SR is the product of our continuous R&D efforts. We optimize our product with the latest technologies and methods in a way that increases recognition accuracy.
Lately, we developed a new model where we used a neural network as a technological leap. And to measure the success of this model, we tested the accuracy of our speech-to-text engine. We compared our engine with Google and IBM’s SR engines.
For manual testing, we used two sets of random data from call center recordings, two sets of recordings of medical articles. For automated testing, we used 3 YouTube videos.
In the manual test, recordings were listened to and labeled all the automatic transcribed words/phrases as correct/wrong and calculated final word-error rates within the data set. WER (word-error-rate) is a common metric for SR engines; it is the ratio of the total word of error (substitutions, deletions, and insertions) to the total number of words in the reference. The smaller the ratio, the more accurate the engine.
The first table shows the results of manual calculation, and the second one shows the result calculated automatically using the reference text. Here are the results:
As seen above, our NEW approach provides nearly 30% improvement for accuracy.
With these numbers, we are not suggesting that we are certainly better or the rest is certainly worse. The speech recognition process includes calculating and optimizing millions of parameters over a vast search space, and it is hugely stochastic (what we engineers call as the pattern that may be analyzed statistically but may not be predicted precisely). A vendor’s SR engine can perform better than others for a specific recording, but the same engine can perform worse for another one.
We are simply suggesting that our SR technology can easily compete with billion-dollar vendors such as Google and IBM.
Speech recognition is among the leading technologies used in conversational automation. The performance of this technology plays a crucial role in the success of conversational customer services. By offering an easy-to-use and advanced conversational system, businesses can improve customer experience. That is why choosing the right speech recognition technology is an important decision to make. Sestek offers an effective solution not only with its advanced technical features and high accuracy rates but also with 20 years of know-how and distinctive professional services. Click here to test our Speech Recognition technology for the following languages; Turkish, English, Flemish, French, Russian, and Turkish.
Publish Date: October 10, 2020 5:00 AM
Customer satisfaction is the key factor behind the success of a business. The more satisfied a customer is, the higher the chances they become loyal customers. This means they will stay with your brand and spend more than others. Therefore, keeping customer satisfaction as high as possible is important for the sustainability of a business.
Improving customer satisfaction requires understanding customer expectations better. This is possible with continuous listening and monitoring. By doing so, businesses not only figure out what customers expect but also detect their pain points, which show up as complaints.
Call centers are the primary customer service points that handle customer complaints. Complaint management is a tough task for call center teams. Providing on-time feedback and reducing the number of complaints is important.
Speech Analytics offers an effective solution for complaint management. The technology analyzes 100% customer interactions and provides call center managers with insights into customer satisfaction, agent performance, and service quality.
The steps below can help call centers to reduce customer complaints and increase customer satisfaction with Speech Analytics:
- Detect the problem
With manual evaluation methods, only a small ratio of recorded calls can be evaluated. With such a limited evaluation, it is almost impossible to detect complaints. On the other hand, Speech Analytics analyzes 100% of the calls and allows supervisors to pinpoint the calls that include complaints.
- Find the root causes
Detection of the behaviors that cause customer complaints is the primary step. Speech Analytics allows call centers to take one step further by showing the real reasons for these complaints with root-cause analysis. This analysis lets managers compare dates, agents, agent groups, queries, and voice channels to identify and respond to common problems.
- Take action
Features like statistical comparison and automatic evaluation allow supervisors to generate in-depth reports about agent performance. They can transform evaluation results into agent feedback and training material to improve agent performance. So, they can guide agents through enhanced customer service.
Here is how one of our dear customer Credit Europe Bank Russia reduced customer complaints at its contact center by 35%
As one of the leading financial services providers in Russia, Credit Europe Bank is featured in Forbes TOP 10 Banks in Russia List. The bank was searching for solutions to increase the efficiency of its customer service operations.
CEB Russia was targeting to increase efficiency for its call center, collections, customer care, telemarketing activities. The bank needed to monitor and evaluate inbound/outbound customer calls to gain insights on how to increase call quality, agent performance, collections performance, sales revenue and to reduce customers` complaints executing preemptive actions. This required an automated quality management approach due to the vast amount of calls that cannot be fully evaluated with manual monitoring methods.
The Results After Speech Analytics
- 35% decrease in customer complaints
- 25% increase in customer satisfaction
- 2X Increase in sales at mobile banking channel
Publish Date: August 12, 2020 5:00 AM
The world’s leading research and advisory company, Gartner includes Sestek in its Market Guide for Speech-to-Text Solutions, published in April 2020.
Sestek was listed under Broader NLP Suites and Services of the Platform and Services category. This category covers the vendors who have the most well-developed value-added services and differentiate with speech features, the ability to deliver edge-based models, domain customization, and system integration support. This confirms Sestek’s leading position in the crowded conversational AI market.
Recognition accuracy is among the distinguishing features of Sestek’s Speech-to-Text technology. Offering high accuracy rates in more than 15 languages, including English, Spanish, French, Russian, Turkish, and Arabic, Sestek provides frictionless experiences both for end-users and for business units.
Sestek’s CEO, Professor Levent Arslan, says, “Speech-to-Text is the core technology that empowers our conversational solutions like Conversational IVR, Chatbot, and Speech Analytics. Our vast vertical market experience in financial services, retail, telecom, and healthcare helps us deliver tailor-made projects in a fast and highly accurate manner. We are proud to be recognized as a leading technology provider by Gartner.”
To see the summary of the report, please click here.
Publish Date: May 13, 2020 5:00 AM
Download our free e-book to find out.
Can technology find a cure for COVID-19? It is still too early to give an answer to this question, although researchers are working on it.
If we change the question as “Can technology help us to handle these difficult times?” We don’t need time to find an answer. The answer is obviously, “Yes.” Because like a godsend, technology is helping us transform the way we work and the way we live.
Being prepared with a digital workforce and digital technologies paid off. Thanks to advanced digital technologies, millions of people easily adapted to the changes due to social isolation concerns. Schools managed to switch to online classes so that education was not interrupted. Millions of employees continue to do their job without leaving their homes. And many companies continued to serve their customers without needing physical contact points.
And AI was on the stage as always. Conversational AI technologies enabled us to reach brands easily whenever we need them. We continued to get the high-quality service as we used to do simply by interacting with a chatbot, a virtual assistant, or a speech-enabled IVR system.
A crisis means a shaky ground for your brand’s image. If you can’t provide your customers with what they need on time, this might damage your brand. On the other hand, offering your customers consistent self-service across any channel, they prefer would help you to turn the crisis into an opportunity for your business. And to achieve this, you can get help from Conversational AI.
As Sestek Marketing Team, we prepared a playbook to guide you through your Conversational AI journey. By downloading our free e-book, you will learn about the definition of Conversational AI, along with technologies supporting it. You will also see an industry snapshot that defines today and tomorrow of the technology. You will dive into the benefits of conversational AI, with a list of products that include this technology. And the final section of our e-book which was designed as a playbook aims to guide you through the implementation of the technology in your own business.
Publish Date: May 4, 2020 5:00 AM
With any tool, technique, method or system they developed, humans lead to reorganization of the natural, spatial, temporal conditions which created and defined them. Let’s take AI, for example.
There is no field where AI does not interfere, interact, lead to change, or improve. Of course, one of the most important issues of our life is health. And the use of artificial intelligence in health has already started to transform this field.
Physicians have been performing analysis, diagnosis and treatment for hundreds of years. They accumulate and convey what they know and experience verbally and in writing. This is how medical science / art / profession has evolved and continues to evolve. Of course, medicine is not an isolated field, developments in the fields of biology, anatomy, physiology, etc. have led to the development of medicine. Moreover, the development of engineering disciplines, the development of many fields from genetics to imaging, from biomedical devices to hygienic issues have greatly contributed to the development of medicine and human health.
Especially, the amount of data growing day by day and the increase of analytical applications will contribute to the development of analysis, diagnosis and treatment methods. As the work done previously by human mind is done through the algorithm, error rates will decrease, sensitivity will increase and as a result, more lives can be saved, life expectancy will be longer, health quality will increase, health spending will decrease.
Especially if AI comes into play, human errors will decrease, sensitive diagnoses beyond the human mind will be made, the best treatments can be developed based on the data collected worldwide, even preventive measures can be taken based on the predictions, and recommendations and actions can be produced to eliminate diseases.
Medical Solutions Powered by AI
We can already talk about many medical solutions powered by AI. The first examples that come to mind are applications related to personal health assistance. One of them is ADA. Ada’s core system connects medical knowledge with intelligent technology to help all people actively manage their health and medical professionals to deliver effective care.
Another one is Apple’s iOS Health. This health app consolidates data from your iPhone, Apple Watch, and third-party apps you already use, so you can view all your progress in one convenient place. You can see your long-term trends, or dive into the daily details for a wide range of health metrics.
The use of artificial intelligence in medicine is no longer a myth. Now, the greatest assistant of doctors in every field are algorithms, machine learning systems and robots equipped with many abilities…
AI revolutionizes health as it does in every area of our lives. Health services worldwide are also significantly affected by this change. Machine learning and AI affect physicians, hospitals, and all other health-related areas.
According to Eric J. Topol’s article published in the journal Nature Medicine, everyone in the healthcare industry, from specialist doctors to first aiders, will use artificial intelligence technology in the near future.
According to GE’s projection, the artificial intelligence market for the health sector will exceed $ 6.5 billion by 2021. Considering that 39 percent of decision makers in the health sector plan to invest in machine learning and predictive analysis systems, this figure will increase further in the coming years.
How will AI Contribute to Our Health?
So, how will AI, ML and algorithms create changes in hospitals and contribute to our health?
We can say that the most benefited area is and will be the diagnosis of diseases. Accurate detection of diseases requires years of medical education. Diagnosing even after this training, is challenging and time- consuming. In many areas of medicine, the fact that the demand for specialists has exceeded the supply puts physicians in stress, and the diagnosis of diseases is further delayed.
Machine learning - especially deep learning - algorithms have made great progress in the automatic diagnosis of diseases recently, making the diagnostic process cheaper, easier, and more accessible.
Machine learning is useful in the following similar areas, where the diagnostic information examined by physicians is digitized:
– Lung cancer and stroke diagnosis by analyzing computed tomography scans
– Determination of the risk of sudden heart attack by analyzing electrocardiograms
– Classification of lesions by analyzing skin images
– Determination of diabetic retinopathy indicators by analyzing eye images
Thanks to the abundant data available in these areas, algorithms can be as successful as specialist physicians on the diagnosis. The only difference is that algorithms can diagnose in a very short time and can do this cost-effectively from anywhere in the world.
AI is especially popular in the field of Radiology. More than two billion chest X-rays are taken each year in the world. According to the researches, AI algorithms are more successful than people in evaluating these X-rays and diagnosing diseases. In addition to X-ray films, these algorithms are used in all kinds of medical imaging systems such as CT, MR, echocardiogram, and mammography, and results are obtained at speeds up to 150 times compared to humans.
According to studies, physicians spend much more time on data entry and desk work than they do actually talking to and engaging with patients. When processes like data entry and analysis of test results are automatedAI systems will alert and inform doctors about potential problems, enabling them to be more interested in patients and interpret signals more healthily. Considering that the world population is getting older and the need for a doctor is increasing, every second gained can lead to the survival and prolongation of many people.The question of whether AI or physicians are also on the popular side of the issue. In emerging countries such as China where there is an acute shortage of trained doctors, “Doctor vs. machine” competitions are very popular. This is illustrated by the Chinese TV broadcast of the brain tumour diagnosis and progression prediction competition between a team of 25 expert doctors against the Biomind artificial intelligence (AI) system. The 2:0 win of the AI over the humans in analyzing brain images gained high visibility in China.
AI-supported Surgery & Drug Development
Another area where artificial intelligence is used in medicine is surgery. AI systems can guide surgeons during the operation by analyzing patient data before surgery. Systems can also combine data on past surgeries and develop new and more effective surgical techniques. Researches show that complications are reduced by five times, and hospital stay is reduced by 21 percent in AI-supported operations.
Another field that uses artificial intelligence is drug development. Developing drugs is a very expensive process. The majority of analytical processes during drug development can be carried out much more effectively by machine learning. This will shorten years of work and reduce millions of dollars of investment.
AI is successfully used in all four basic stages of drug development:
– Determining the targets to be intervened
– Identifying potential drug candidates
– Acceleration of clinical trials
– Finding biomarkers for the diagnosis of the disease
AI-supported Personalized Treatment
The last area powered by AI on which I want to talk about is Personalized Treatment. Different patients react differently to medications and treatments. Therefore, personalized treatment is critical to prolonging patients’ lifespan. However, it is not easy to identify the factors used to determine which treatment method to choose.
In the article of Doctor Bertalan Meskó, who describes artificial intelligence as “the stethoscope of the 21st century,” it is stated that AI will make the “uniform” treatment history and suggest personalized treatments, therapies, and medications.
Machine learning can automate this complex statistical study and identify indicators that will be used to determine the patient’s response to a particular treatment. The system learns this by cross-evaluating similar patients by comparing the treatments and results applied to patients. The resulting predictions can make it easier for doctors to determine which treatment to apply.
For example, colorectal cancer patients in Brazil usually refuse the surgical removal of the colon because of cultural reasons. That’s why oncologists turn to methods such as radiotherapy and chemotherapy. However, only 20 percent of patients respond positively to these methods. So, how will it be determined which patient is in this 20 percent group? Here, deep learning algorithms come into play. Algorithms scan the data of patients and determine the appropriate treatment method in a short time and accurately.
AI and the Coronavirus
It is obvious that AI makes remarkable contributions to healthcare. And a question comes to mind since it is high on the agenda: What about coronavirus? Although the spread of the virus is a very recent development, AI-powered applications for virus diagnosis have already appeared. AI company Infervision launched a coronavirus AI solution that helps front-line healthcare workers detect and monitor the disease efficiently. Imaging departments in healthcare facilities are being taxed with the increased workload created by the virus. This solution improves CT diagnosis speed, they claim. Chinese e-commerce giant Alibaba also built an AI-powered diagnosis system. They claim it is 96% accurate at diagnosing the virus in seconds. Let’s hope that AI contributes to the development of an ultimate solution to stop the spread of the disease.
The global willingness to use artificial intelligence and robots is increasing.We can say that the main factor in this increase is the desire for faster, intuitive and low-cost health services. Trust in technology is critical for increased use and acceptance; however, ‘human relations’ remains a key component of the health care experience. So, it looks like we will be able to get the most effective results when we combine the power of AI with humans.
Publish Date: March 28, 2020 5:00 AM
ProPublica’s survey had revealed that the risk assessment algorithm named COMPAS and AI behind the system tends to identify blacks as more risky than whites.
The famous trolley dilemma on ethical philosophy asks: “would you kill one person to save five?”. In this question, you are asked to imagine you are standing beside some tram tracks. In the distance, you spot a runaway trolley hurtling down the tracks towards five workers who cannot hear it coming. Even if they do spot it, they won’t be able to move out of the way in time.
As this disaster looms, you glance down and see a lever connected to the tracks. You realise that if you pull the lever, the tram will be diverted down a second set of tracks away from the five unsuspecting workers. However, down this side track is one lone worker, just as oblivious as his colleagues.
So, would you pull the lever, leading to one death but saving five?
This is the crux of the classic thought experiment known as the trolley dilemma, developed by philosopher Philippa Foot in 1967 and adapted by Judith Jarvis Thomson in 1985.
The trolley dilemma allows us to think through the consequences of an action and consider whether its moral value is determined solely by its outcome.
The trolley dilemma has since proven itself to be a remarkably flexible tool for probing our moral intuitions, and has been adapted to apply to various other scenarios, such as war, torture, drones, abortion and euthanasia.
Of course, there is not a single correct and moral answer to this question about how people think when deciding on an action. However, it is estimated that many people answered this question as “yes, I pull the lever, I can sacrifice one worker to save the lives of five workers”. Also, this answer can be found moral by many people.
Today, apart from philosophy, this dilemma is brought to the agenda by adapting to artificial intelligence. Although there are no AI implementations that can think like a human and make moral judgments, it is often expressed by scientists that we’re approaching this. Of course, how these dilemmas can be solved by AI is of utmost importance. Especially considering that driverless cars will come to traffic in the next ten years, Though not expected of it, AI is thought to have to make some decisions and achieve moral results. On the other hand, it is often mentioned that the possibility of artificial intelligence applications and robots equipped with AI can pose a greater danger than leaving people unemployed. The danger is racist and sexist bias and prejudices in decisions made by AI. Research on the results of AI algorithms used in a number of experiments and decision making processes gives an idea about the magnitude of this danger.
Recently, A research conducted by MIT is particularly remarkable. In this research, the application of artificial intelligence, which is expected to recognize and distinguish the thousand photos uploaded to it, differentiates whites in a perfect way, But, When it comes to blacks it starts to make a big mistake. When the person in the photo is a white man, the software is right 99 percent of the time.
But the darker the skin, the more errors arise — up to nearly 35 percent for images of darker skinned women, according to a new study that breaks fresh ground by measuring how the technology works on people of different races and gender.
Research shows that speech examples used to train the machine learning application is likely to lead to bias. Such problems with the technology have been evident in popular tools such as Google Translate. Recently, while translating Turkish to English Google Translate matched a number of jobs and situations with men and some with women (for instance the sentence “o bir aşçı” translated as “she is a cook”, the sentence “o bir mühendis” is translated as “he is an engineer”) and of course the sexist bias of these translational content has been the subject of debate.
As most of you may remember, a recent example of the biased AI is An AI application developed by Microsoft. In 2016, Microsoft launched the chat application called Tay, which learned human behavior using artificial intelligence algorithms and interacted with other users on Twitter with what she learned. Tay was designed to learn to communicate with people and tweet with data provided by other users on Twitter. In sixteen hours, The tweets she created with the data she collected from Twitter users became sexist and pro-Hitler. On March 25, 2016, Microsoft had to close Thai by apologizing to all users for these unwanted aggressive tweets.
In the text of the apology, Microsoft stated that “artificial intelligence has learned with both positive and negative interactions with people” and therefore “the problem is as social as it is technical”. In fact, this seems to be the highlight of an entire discussion. It can also be clearly seen that although Thai was taught very well to imitate human behavior, she was not taught to behave correctly or morally.
As all these examples clearly show, The racist, sexist, or in some cases status bias produced by artificial intelligence arise from the data sets used to train AI. The datasets used by artificial intelligence algorithms are of course collected from the internet, which is the biggest resource. For example, Microsoft’s Thai who tries to tweet and interact with people in this way, or Google Translate are trying to learn the words, how and with which other words they are used together, so they try both to capture the meaning and to produce answers using natural language against what they understood. Artificial intelligence establishes some relationality through its algorithm while it’s learning which words, how and with which other words these words are used statistically in the datasets provided from the internet. These can sometimes be relationalities whose cause is not understood by human. But in any case, these are not artificial intelligence produced by itself, but the relationalities that exist in the data set that it uses. Therefore, it can match feminen pronouns with cooking, cleaning or secretarial jobs and masculine pronouns with engineering. In other words, the issue appears not as the prejudices of artificial intelligence, but as data sets used in the learning processes of algorithms. That is; racist and sexist content of the internet where this data is collected makes AI produce biases.
As said in the Microsoft statement, social causes rather than technical reasons lie at the root of the problem. While AI learns with the data produced by real people, it can learn to behave like a human, it can analyze the data much faster than the human mind, but at last it cannot learn whether this behavior is right or wrong. But on the other hand, do people always act “good” and “right” in the real world? Maybe, As those who claim that artificial intelligence is not biased, AI produces the most realistic results, but expectation is to see the most suitable results for an ideal world. Considering that there are inequalities and prejudices in the world we live in and the historically produced data is biased, there is no surprise that AI applications also make biased decisions and have real world bias in their decisions. On the other hand, while answering the question “Would you kill one person to save five people?”, It is not unlikely that AI would take into account the race and sex or status of these people, that is, making the dilemma deeper.
Humans shouldn’t be a single source in AI Training
Maybe it is not a very good idea for artificial intelligence to learn merely from people. It is certain that alternative learning ways for artificial intelligence, data sets that are meticulously prepared, cleaned from prejudices and bias as much as possible or algorithms showing how the AI came to which result and how will allow us to progress on these problems. When these are possible, there may be some things that people can learn from AI. Then, It may also be possible for us to negotiate the trolley dilemma and its variations with AI.
Publish Date: February 7, 2020 5:00 AM
We saw a tremendous increase in the use of voice technologies in 2019. Conversational AI, voice recognition and NLP were among the popular technology concepts of the previous year. It is not hard to guess that the rise of voice technologies will continue in 2020. But we still have some questions to answer: What kind of use cases will we see? Which technologies will keep their growth? In short, what will 2020 mean for voice technology? We tried to answer these questions below. Here are the seven trends that will drive voice technology in 2020.
- Conversational AI will be part of business strategies
Today, brands know that voice as a natural interface not only means easier transactions for customers but also higher efficiency for their operations. That is why an increasing number of businesses incorporate conversational AI technologies into their strategies, and this will continue in 2020.
Conversational AI will transform into a must-have feature from a nice-to-have innovation project. Businesses will use voice as a significant differentiator. But offering voice-based solutions will not be enough to differentiate. Customer experience will still be the underlying key factor. In other words, the ones who offer personalized voice experiences will be one step ahead of competition.
- Voice interface design will gain importance
Voice is the interface for anything smart. Businesses already discovered the power of voice interface. And this year they will do more to benefit from this power. Although voice-enabled technologies are hitting the mainstream, there’s room for improvement in terms of the user interface. Users are expecting more natural dialog flows while interacting with devices.
To design effective interactions, understanding how people naturally communicate every day is important. In other words, designers need to consider the fundamentals of voice interaction and design dialog flows in a way that answers users’ high expectations.
- Voice commerce is here to stay
With the increasing use of conversational platforms, voice search is becoming mainstream. This new method offers practical experience for customers. Within a matter of seconds, customers can search for a product and verify their purchase by simply speaking to a voice-integrated device.
With the maturity of voice assistants and improvements in conversational technologies, 2020 will see a noticeable increase in voice commerce. Businesses will work hard to adopt this new form of commerce. They will take voice search seriously and include voice search optimization tactics in their strategies. More to do for marketing teams to explore this new channel!
- Voice-activated wearables will expand
Voice plays a vital role in the expansion of wearables. Voice technology transforms these devices into voice-activated ones and helps users to get the most out of these devices.
We have seen watches, earphones, headsets, fitness trackers as common wearables. With 2020, we will see new forms of wearables. Smart jewelry, such as rings, wristbands, watches, and pins, are among them. These new devices can engage with voice assistants. So, they can offer the same skills many of these assistants offer.
In 2020, increasing use of augmented reality technology will also influence the expansion of wearables. We know that Apple is working on a new AR headset. Similarly, major technology vendors are expected to enter this field by developing their own wearable devices.
- New platforms and applications for voice
The increasing use of voice technologies will increase the need for new platforms for voice-enabled applications. Apple is expected to launch a new platform that will enable developers to create voice-based apps for Siri.
The number of Skills and Actions for voice assistants such as Amazon Echo and Google Assistant will also increase. These features will support not only voice assistants but also new use cases, including earbuds and in-car applications.
All these developments will allow brands and third-party developers to have a presence on voice platforms. So, voice technology will continue to be an effective channel for consumer apps.
- We will see a content transformation
People starting to see voice assistants as parts of their daily routines and use voice search increasingly. That is why more publishers create voice-based content to engage with their target customers.
This trend is expected to continue in 2020. Content strategies include voice as a new format. And not only publishing and media companies but also popular brands will adopt this new form of content.
The increasing use of voice content will influence marketing too. We will see more examples of voice-based advertising. Voice dialog ads will be used to offer interactive marketing experiences for customers.
From consumer applications to enterprise solutions, voice technology has been used as a tool for transformation. By converting devices into voice-controlled systems, voice technology ensures a practical use for customers while offering self-service advantages for businesses. People are getting more used to voice-based systems, and more businesses include voice-enabled technologies in their portfolios. So, it is obvious that voice will continue to be an essential part of our lives in 2020.
Publish Date: January 23, 2020 5:00 AM
VITAL STEPS TO GET THE BEST OUT OF SPEECH ANALYTICS
Here’s the bottom line. Speech analytics is a must-have solution if you are willing to increase the effectiveness of your contact center. DMG Report’s estimation shows us that the adoption rate of speech analytics on a worldwide basis was 35% as of 2019, assuming that there were 19,5 million contact center seats at the end of 2018. Close to 7 million seats benefit from this technology.
However, you must have a clear vision of what you’ll do with such a solution like this. Most of the customers can’t get enough of speech analytics because they don’t know what to do and how to do with the valuable data they have in their hands.
I’ll try to give a couple of tips that can be useful if you have, or plan to have speech analytics;
WHAT TO DO
First, you must define what you expect to do with speech analytics. These expectations generally lead you to three basic outcomes;
- Cost optimization
- Increase Agent performance
- Increase customer satisfaction
So, you must think of what to do to reach these outcomes. For example, ATT (Average Talking Time) analysis leads you to take actions for reducing overall ATT averages and these actions’ outcome is cost optimization. Setting up AQM (automated quality management) forms and integrating them with your agents’ performance management leads you to agent performance increase. Thus, you must have a couple of clear and outlined plans before jumping on the speech analytics world.
HOW TO DO IT
To get the most out of speech analytics, you’ve cleared your targets and now you know where to go. To reach your destination, there are 3 steps that you must go thru:
Preparation of data: You have a huge amount of transcribed data, that’s brilliant. You can see every call’s transcription by double clicking on it and yes, it is very fancy. However, the data is still huge, and you must structure your data to make sense of it. Speech analytics products offer different features like queries, topics, categories and by using these features. You can find the complaining customers and see in which call category your customers are complaining the most or see which of your calls categories have the highest ATT averages.
This is important because that you can extract specific types of data sets from thousands, or sometimes millions of interactions.
Getting deeper: Imagine that you’ve found out you have the highest ATT averages in a certain call category. Now what? Most of the speech analytics users waits for the speech analytics systems to hold their hands and take them to the action phase by themselves. In truth, speech analytics only shine a light on your path to action phases. You have to plan and take these steps.
After you have found a lead like above, you can use the root-causes analysis features of speech analytics products to dig out the insights. At this step, speech analytics show you what to find such as the words/sentences that are being used the most. Or point out thee conversational metrics like hold duration averages, silence ratios etc. You are to decide which makes sense or which doesn’t. Because no other product or team knows your operation better than you.
Take action: So, you’ve found the main reason behind the call category which has the highest ATT averages. Let’s say that agent desktop applications work very slow and because of that you’ve seen the average hold duration is high, agents generally use phrases such as “my screen is slow” etc. Now it’s the time to act. That’s where you shake hands with speech analytics and thank it for its services, because now the ball is in your court to fix this problem with your IT department. You have solid proof about the problem, and it is your responsibility to organize related teams on how to fix it. If you take action, observe and see that the average ATT for this certain category has been decreased, congratulations, you’re ready for your next mission with speech analytics.
Author: Fahrettin Yılmaz, Presales & Partner Enablement Consultant
Publish Date: November 25, 2019 5:00 AM
How likely am I to cancel my bank account if I decrease the number of my routine transactions? Or what if I pronounced “I don’t like this” or “your competitor X does it the other way” to an agent during a call center conversation? Compared to an average customer, probably I would pose a higher risk of leaving this company. Maybe if the agent had the prior knowledge of me being risky, he/she would present a special promotion to me during the conversation and prevent this possible churn.
Considering that gaining a new customer requires much more effort and resource than keeping an existing customer, why take the risk of churn if there is a possibility to be alarmed before it happens?
Analyzing Customer Behavior
Collecting and analyzing past customer behavior is a need for sure. From small entities to large corporates, many organizations use these data to improve their services and enhance customer experience. However, it is not enough for today’s interaction dynamics. Companies have to be informed not only about the customer’s past actions but also about the customer’s possible behavior soon. It wouldn’t be wrong to say that being informed about the customers’ past actions is something; however, using this information to predict customers’ future behavior is a game-changer.
How Predictive Analytics Helps?
According to Markets and Markets, the Predictive Analytics market size is expected to grow from USD 4.5 Billion in 2017 to USD 12.4 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22%. The main reason for this expected growth is being associated with the increase of the companies’ interests for forecasting future.
Predictive Analytics plays a critical role in predicting customer actions before they occur. It uses historical data to identify possible future behavior with the help of statistical algorithms and innovative machine learning techniques. For instance, historical data of customers that canceled their membership in the past would be a prior ingredient to train reference models for the “churn prediction” case. These models, which are trained for different scenario cases, can be used to make comparisons with the newcomer data. Thus, it would be possible to see if customers matching with these patterns and to flag the interactions accordingly.
Predictive Analytics can also be used to detect fraudulent behavior before any serious damage is inflected. Companies can notice unusual activities in time that may cause incidents ranging from credit card fraud to fake identity calls.
Sestek Predictive Analytics
Besides typical historical data such as transaction data, demographic data, etc. the call center conversations also give valuable information about behavioral patterns. As an AI-based analytics company working on speech analytics, Sestek can create extensive prediction scenarios.
Sestek’s Speech Analytics solution currently analyzes acoustical and textual information of 1 out of 4 contact center calls in Turkey, which makes the case easier to expand this knowledge to get further insights about future customer intent. Sestek records, transcribes, and analyses the calls between the customers and the call center agents by its own tools and combines these outcomes -like the speaker’s emotional behavior and tone- with further customer data to build reference prediction models for Sestek Predictive Analytics solution. These prediction models are trained for specific cases according to the organization needs. So far, our studies showed that Predictive Analytics can be beneficial for churn, fraud, and collection scenarios. It can be used to provide real-time agent guidance, next-best-action recommendations, or optimal marketing/sales offers for the benefit of both the customers and the organizations. Each new data bolsters up the machine learning algorithms and becomes more and more reliable compared to the instincts of agents.
Sestek Predictive Analytics is an ongoing project that has been financially funded by The Scientific and Technical Research Council of Turkey (TUBITAK). We “predict” that this new member of our analytics family will complement our solutions suite and present more comprehensive insights and actionable results for our customers.
Author: Tuba Arslan Kır, Sestek R&D Coordination Team Leader
Publish Date: October 31, 2019 5:00 AM
As AI continues to rise, it radically changes how work gets done. From consumer goods to telecommunications, from healthcare to financial services, an increasing number of businesses use AI to improve productivity.
This brings along a question: Will AI replace humans?
Researchers say no.
Harvard Business Review’s survey found out that companies which optimized collaboration between humans and AI obtained better performance results in terms of speed, cost savings, revenue, and other key operational measures.
According to Accenture’s report, entitled “Reworking the Revolution,” higher investment in AI and human-machine collaboration could increase revenues by 38% and boost employment by 10% by 2022.
So, it looks like AI is not here to replace us. Instead, we can get the best out of this technology by collaborating with it. Many industries have already combined humans with AI technologies to ensure efficiency and contact centers are among them.
How Contact Centers combine AI and humans?
As the flagships of customer services, contact centers were considered as cost centers. To overcome this, organizations searched for effective ways of cutting costs without sacrificing customer experience. AI-based self-service solutions were the answer.
AI-based self-service solutions enable call centers to automate various tasks with the help of the latest technologies, including chatbots, virtual assistants, conversational IVRs. These technologies enhance customer experience by shortening transaction durations and offering fast and practical answers to customer needs. These self-service automation solutions include various forms of human-AI collaboration to ensure enhanced experience and higher efficiency.
In many call centers today, a customer starts to interact with an AI solution to accomplish a task. This might be a chatbot, a virtual agent, or a conversational IVR menu. Thanks to developments in natural language processing technology, these AI solutions have advanced conversational capabilities. Unlike traditional versions that can only give simple yes-no answers, today’s conversational AI technologies can understand what customers really mean and offer the right solution accordingly.
This means many interactions can start and end with an AI solution without the need for a live agent.
On the other hand, in some applications, AI technologies can provide real-time guidance to agents. So, they don’t need to search for specific information while providing support to customers. AI technologies show the necessary information and save agents from causing long wait times for the customers.
So, when implemented in call centers, AI-based self-service solutions help agents by decreasing their workload and allowing them to spend less time on operational tasks and focus on more crucial tasks.
Human-assisted AI applications aim to help AI technologies to become more accurate in less time. Because the success of AI heavily depends on the data and training methods that are used to fine-tune it, human assistance can help speed up this process.
When implemented in call centers, these applications boost the benefits of AI by eliminating the drawbacks of possible inaccuracies. For example, call center agents step in when AI shorts fall in understanding a customer need or providing an answer accurately. This not only prevents an error that might result in customer dissatisfaction but also trains the system in the long run. Each human intervention helps AI technology to be trained and offer better answers in the following interactions.
A patented technology that combines AI with agents
Sestek’s patented Seamless Agent technology is an example of human-assisted AI. This technology supports AI solutions such as chatbots, conversational IVR, and virtual assistants with live agents. Seamless Agent depends on the idea of preventing any mistakes which AI could make with the help of agents.
For example, when a customer is interacting with a system, and the system detects a customer phrase with low-confidence recognition values, it sends them to a human agent for assistance. The agent then corrects or verifies the decision within seconds before sending it to the customer. This provides a seamless and flawless experience without the customer, even realizing it.
In a short period of time, the need for live agents is minimized due to the increased learning capabilities of the system. The more the system learns, the less assistance of a live agent is required.
Join Our Live Demo
To learn more about Seamless Agent technology, register to our live demo, which will be on Wednesday, August 28th, 2019, at 2:00 PM Istanbul time (+03). At this live demo, our Pre-Sales Director will explain in detail the features of our patented Seamless Agent solution and how it enables the perfect collaboration between agents and AI for that ideal customer experience.
Publish Date: August 19, 2019 5:00 AM
When it comes to audio forensics, many of us can easily imagine a scene where some serious-looking guys listen to an audio recording while looking at an audio waveform on a computer screen.
Thanks to increasing media coverage about dramatic court cases and popular fictional entertainment series like Crime Scene Investigation, everybody is now familiar with audio forensics.
What Is Audio Forensics?
As a field of forensic science, audio forensics combines audio engineering and digital signature processing techniques to evaluate audio data as part of a legal proceeding or an official investigation.
Before being used as a piece of evidence, audio data is evaluated in terms of its authenticity, any modifications it includes, and its relevance to the goals of the investigation. Audio evidence can be obtained from different resources, such as an acoustical recording system (such as a cockpit voice recorder), a call center recording, a voice mail message, or a surveillance tape acquired during a criminal investigation.
Audio Forensics Tools
Voice is a biometric identifier because, like fingerprints and retinas, each voice is unique to one individual. Therefore, a person’s voice can distinguish her from others, making it possible to identify a person by comparing her voiceprint with the recorded voiceprints of other people.
Audio forensics tools use voice biometrics technology to analyze voice and assist forensics experts in their crime prevention and investigation efforts.
By using these tools, forensics experts can:
- Determine whether a voice belongs to a specific person
- Test to see if a recording has been edited or altered
- Compare a target speaker with a database of possible candidates
- Accurately match an individual’s identity with the audio evidence content
- Verify an individual’s identity with an audio recording
Benefits of Forensic Voice Analysis
Accurate Investigation Results
Forensic voice analysis solutions answer law enforcement and crime prevention needs by offering comprehensive forensic audio mining capabilities. These audio forensics tools contribute to securing justice by providing courts with proven biometric identification results.
With advanced voice biometrics features, forensics experts can easily detect samples of speech in an audio recording and identify speakers in just moments, no matter the gender, language, accent, or speech content involved. These biometrics features include:
- Speaker identification, which confirms or disproves the identity of an individual by analyzing audio evidence
- Speech-silence detection, which automatically detects speech or silence in audio samples and labels different sections appropriately as one or the other
- Formant verification, which allows for one-to-one comparisons of formant distributions of audio recordings
- Speaker diarization, which differentiates between multiple voices in a single-channel recording of speech
- Gender identification, which automatically detects the gender of the speaker
Together, these features ensure identity verification with high accuracy.
Forensics experts are racing against time, because every moment counts in a criminal investigation. Forensic voice analysis tools help law enforcement and security experts save essential time in prosecuting suspects.
Audio forensics tools analyze up to hundreds of audio files in just a few minutes. This far outpaces the amount of evidence a human could review in the same amount of time. Users may compare several audio recordings at the same time, including any audio evidence that is relevant to the investigation. Rather than listening to each one individually—which could take hours and hours—users can quickly narrow in on a suspicious individual’s identity in moments.
With fast, visualized voice biometrics, forensic voice analysis tools enable experts to assess audio evidence at a glance. Thus, they can complete voice treatment and speaker identification in record time, saving hours of time that would otherwise be spent listening to recordings in full.
Practical Tool for Experts
Audio forensics tools aid in criminal investigations by providing a fast, simple, and accurate speaker identification process. They ensure easy-to-use and practical audio analysis for forensics experts.
The tools allow experts to organize and refer to any audio files relevant to their investigation. They also offer multiple archiving features that allow users to archive any analyses in case they are needed in the future.
By offering this practical application, audio forensics tools help experts to complete voice treatment easily, saving not only time but also energy. With this minimized workload, experts can give their full attention to the investigations, which will increase their chances of success.
Sestek Forensic Voice Analysis
Sestek Forensic Voice Analysis is a biometric audio forensics solution. The solution analyzes audio evidence accurately by applying voice biometrics technology in a way that makes it easier to work with audio evidence. It assists forensics experts and security organizations complete voice treatment and speaker identification processes accurately.
Thanks to Sestek’s 19 years of experience in the speech technology industry, the solution provides highly accurate and reliable voice analysis results.
To learn more about Sestek Forensic Voice Analysis, please visit our product page.
Publish Date: January 20, 2019 5:00 AM
Attracting New Customers Is Expensive
It is always cheaper to keep your current customers than attract new ones. According to the Harvard Business Review, acquiring a new customer is anywhere from 5 to 25 times more expensive than keeping an existing one. This is why you need to find better ways of retaining your customers. One of the best ways is to increase customer engagement, because engaged customers are great brand advocates. They are also repeat buyers who have a direct influence on profitability.
According to Gallup, a global analytics and advice firm, customers who are fully engaged spend 23% more in terms of wallet share, profitability, and revenue than the average customer, so investing in customer engagement builds a strong brand with loyal customers.
Increasing Customer Engagement with Conversational Technologies
Customer engagement is about enhancing the customer experience and encouraging customers to interact. It is also about influencing customers in ways that build long-term relationships.
To increase customer engagement, organizations need to enhance the customer experience by:
- Offering high-quality solutions
- Answering customers’ needs on time
- Being reachable on any channel
- Providing personalized solutions
Conversational technologies are great tools for increasing customer engagement. These technologies include speech recognition, text-to-speech (TTS), natural language processing (NLP), and voice biometrics.
Conversational technologies use voice as a natural interface to facilitate human-machine interaction. Thus, they empower intelligent automation solutions that enhance customer experience and engagement through smart self-service.
By using these technologies, you can cut costs without sacrificing customer satisfaction. With the automation they provide, conversational technologies decrease customer service costs, the need for human workers, and the working hours spent on conventional service approaches.
Conversational technologies can be integrated into any channel, and they are available 24/7. This enables customers to reach your organization any time from whatever channel they prefer. Always being available means uninterrupted service for customers. By using conversational technologies, you can provide effective omnichannel self-service for your customers.
Empower Customers with Qualified Self-Service Through Natural Dialog
NLP-based technologies enable users to interact with any system by using their own words instead of conventional interfaces. These technologies ensure a natural dialog between users and systems and can be used in IVRs, chatbots, and virtual assistants.
By implementing NLP-based natural dialog technologies in your solutions, you can empower your customers to help themselves. These technologies understand your customers’ natural speech and intent and offer them the solutions they need any time from any channel they like. For example, when implemented in IVR systems, conversational technologies allow users to navigate across menu options via natural speech. Time-consuming touch-tone and agent-assisted menu navigations are replaced with everyday language. Customers can reach the right self-service menu option by stating their needs quickly and easily, in their own words.
Another use case for these technologies is chatbots and virtual assistants. These popular applications draw their strength from NLP technology. With the help of NLP, both applications understand the intent and meaning behind users’ statements with high accuracy, answering customers’ questions with ease, no matter how complex they are.
As intelligent automation solutions, natural dialog technologies can help you to increase customer engagement by:
- improving and optimizing business operations
- reducing average handle times
- offering simplified and personalized self-service
- enhancing the customer experience
- ensuring consistent self-service across multiple channels
Enhance Security with Voice Biometrics
Today’s customers are deeply concerned about security, and given the security threats that exist, they are not wrong. Traditional security measures like PINs, passwords, and security questions are poorly equipped to stop the growing incidence of fraud and identity theft.
As a conversational technology, voice biometrics offers an effective security solution. The technology verifies users’ identities via each user’s voice. Everyone’s voice is unique, just like fingerprints and irises, which makes voice authentication far more secure than traditional security measures.
The technology not only increases security but also enhances the customer experience. Conventional security measures like PINs, password, and security questions can be time consuming for customers—and sometimes easy to forget. Unlike these methods, voice biometrics enables reliable identity verification in a matter of mere seconds.
Voice biometrics automates security processes. By using this technology, you can optimize your security processes by replacing manual identity verification. This significantly reduces the number of security steps and time involved in the verification process.
Voice biometrics is a smart approach to identity verification. The technology contributes to customer engagement by:
- saving customers from complicated questions and easy-to-forget passwords
- providing a fast and easy authentication method
- offering simplified and personalized self-service
- increasing security and ensuring data protection
- reducing average wait times due to manual identity verification
Know What Your Customers Think About You with Smart Analytics
To provide your customers with what they are looking for, you need to listen to them. Effectively listening to your customers on their terms and acting on what they say are keys to effective customer engagement.
Customer interactions include a wealth of invaluable insights: the level of customer satisfaction, likelihood of churn, agent performance, campaign effectiveness, and more. However, the sheer volume of these interactions makes it impossible to manually review and analyze them. Manual review can process only a fraction of interactions and is far from providing objective evaluation results.
Interaction analytics solutions, also known as Voice of the Customer, can help you overcome this challenge. With these automated approaches, you can apply in-depth analytics to recorded customer interactions across multiple channels. These analyses include not only textual and statistical details but also emotional ones. With advanced features like emotion detection and sentiment analysis, you can gain valuable insights into how your customers feel.
By applying smart analytics to recorded customer interactions, you can identify and measure the drivers of customer behavior. Acting on customer feedback allows you to implement effective business strategies that improve self-service processes, staff performance, and customer experience. The result is happier and more deeply engaged customers.
Smart analytics solutions help you to gain intelligence from customer interactions by allowing you to:
- capture and analyze customer feedback
- discover what your customers care about most
- understand your customers’ needs and pain points
- gain actionable insights and act on these insights to enhance the customer experience
- improve customer experience and engagement
As Sestek, we will be sharing insights into the effective use of conversational technologies at the AVAYA Partner Summit 2019, which will take place on 4–5 December 2018 at the Event Center of the InterContinental Hotel, Dubai Festival City.
Visit our booth to learn more about Sestek’s conversational technologies, including Natural Dialog, Voice Biometrics, and Voice of the Customer.
To learn more about the Avaya Partner Summit, please visit the event website.
Publish Date: December 4, 2018 5:00 AM
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