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Thamer Noori
Director of Industrial Security and Safety Dept.
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Kelly Karmann
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Country Manager

Sestek - Blog

Page: 1 | 2

Conversational Analytics: The Secret to Quality Customer Service

Poor customer service costs businesses about $75 billion annually. Given this tremendous loss, having poor customer service is not something you can ignore. To avoid ending up with a service your customers are unhappy with, you need to understand and answer their expectations effectively. And you can do this by listening to your customers, in other words, monitoring and analyzing their conversations.

Analyzing Customer Interactions

Customer interactions across different service channels include invaluable insights about customer behavior, the latest trends, and business productivity. To unveil these insights, you need to apply in-depth data mining methods, and thankfully, with today’s conversational analytics technologies, you can do that easily.
Conversational analytics, also called conversational intelligence, converts natural language conversations into a machine-readable format and extracts data from them. Using artificial intelligence and machine learning technologies, conversational analytics collects, analyzes, and makes sense of interaction data from multi-channels.

The heart of customer conversations: Voice Interactions

According to McKinsey, live voice interactions through a call center are crucial in providing high-quality customer service in a digital age. Therefore, analyzing call center interactions which can be defined as call center analytics, becomes more critical in evaluating the customer experience.
Call center analytics gives essential details about customer service through collecting, measuring, and reporting performance metrics within a contact center. To track call data, evaluate agent performance, and determine service quality, call center analytics uses the following criteria:

  • Average call abandonment rate
  • Percentage of blocked calls
  • Average waiting time
  • Average response speed
  • Average call time
  • Resolution of the request at the first call

It evaluates elements that directly impact the customer journey and gives businesses essential details about customer satisfaction, probability of customer loss, agent performance, campaign efficiency, etc.

What to look for when selecting an analytics solution?

Using analytics tools to uncover hidden insights in call center interactions is key to ensuring high-quality customer service. But not all analytics tools are created equally. To get the most out of this technology, there are some key features to look for:

  • High speech recognition accuracy for automatic monitoring and identification of script adherence, acoustic indicators, and sentimental features
  • Easy-to-use statistical comparison tool for instant identification of the granular differences between top-performing agents and others
  • Real-time sentiment analysis, real-time notifications to supervisors, and real-time triggers for API actions
  • Fast response times and query results
  • Intuitive solution supporting multi-tenancy for different teams, business units, and operation
  • A simple interface where users can enjoy visual query design without any coding requirement

Why is Knovvu Analytics the answer?

Knovvu Analytics collects 100% customer interaction data and transforms it into actionable business intelligence. By applying advanced data mining and quality management technologies, Knovvu Analytics helps decision-makers objectively evaluate business performance and make the right decisions to improve customer service.
Knovvu Analytics outperforms the competition with the following features:

  • Higher performance through faster response times and faster query results than the competition
  • Single solution supporting multi-tenancy for different teams, business units, and operations
  • Easy to use interface where users can create visual queries with no code
  • Objective analysis of script adherence, acoustic indicators, and sentimental features with AI-based quality management
  • Instant notifications on prohibited words, urgent customer inquiries, or regulatory issues with real-time analytics
  • Statistical comparison that identifies best-performing agents and reveals training needs for underperforming agents

Sounds like what you need?

If Knovvu Analytics sounds like what you need, fill out the form here. Our team will contact you soon to figure out how we can help.


Author: Sestek Marketing Team, Sestek


Publish Date: September 17, 2022

Perfecting the Airport Experience with Conversational AI


Remember those quarantine days when we were bored to death and wondered when we would be able to go on vacation again? And when we heard there’d be no travel limit anymore, we rushed to buy tickets to get revenge for those days we couldn’t travel.

It wasn’t just us who missed the good old days. The airline industry suffered COVID trauma, with over $200 billion lost, erasing nine years of earnings. Airports, as an essential part of the airline industry, weren’t any different. In 2021 alone, airports lost $83.1 billion globally.

From Trauma to Recovery: A brighter future


Now that vaccinations are working, travel restrictions have been lifted, and normalization efforts are ongoing, airports are moving away from turbulent times. Travel cravings and the rise of domestic and international flights boost optimism and move the industry forward.

People are ready to travel like it’s summer 2019, but what they expect from an airport experience has changed a lot. Since the pandemic hit, health has been among travelers’ top concerns. As a result, more are looking for touchless interactions to reduce or eliminate contact with staff.

In other words, they want self-service solutions where they can solve their issues without the need for live assistance.

Keeping up with Changing Customer Needs


Although people now try to avoid personal interactions as much as possible, their need to get information and guidance remains. Conversely, they need more empathy and care since they’re more likely to suffer post-covid travel anxiety.

So, offering solutions that enable seamless customer engagement through touchless experience is among the top challenges most airports face today. Fortunately, they have automation to ask for help. With conversational AI, airports can offer touchless customer service across multiple channels like IVR, web, and mobile. These technologies can provide 24/7 automated customer service without needing customer service employees.

The power of automation: Conversational AI


But what is conversational AI anyway, and how does it help businesses?

Conversational AI lets customers naturally interact with systems in their own words. This technology uses natural language processing to make human-machine interaction easy in both written and verbal forms. With Conversational AI, businesses can offer enhanced self-service solutions for their customers.

Sestek Conversational AI solutions cover voice IVRs, chatbots, and virtual assistants. These solutions have been used successfully in different self-service automation projects in various industries. One of these successful projects is Istanbul Grand Airport (IGA), which uses Virtual Assistant to automate customer engagement.

Enhancing travel experience with Virtual Assistant: Istanbul Grand Airport (IGA) Case


With a 90-million passenger capacity, IGA is Turkey’s top global transfer hub. The airport needed an automation solution to handle increasing customer requests across multiple channels.

Using Sestek Virtual Assistant, IGA automated customer service and responded to customer inquiries 24/7, without the need for live agents, through its mobile app, WhatsApp, and IVR channels.

As well as providing customers with on-time and touchless engagement across multiple channels, the project also delivered many business advantages with visible effects on customer experience, time savings, and efficiency. With this technology, IGA;

  • answered more than 275.000 visitor questions,
  • saved 1.000 hours of agent time each month,
  • and observed an 18% improvement in NPS for the WhatsApp channel.

To learn more about this case study, click here

Transform your customer service with Conversational AI


Are you looking for ways to offer your customers a smooth experience across multiple channels while staying on budget? Does boosting customer service while cutting costs and improving efficiency sound like what you need? Then Conversational AI is the answer.

Just fill out the form here, and our team will get back to you with how we can help.


Author: Sestek Marketing Team, Sestek


Publish Date: August 16, 2022

CEO Interview: Knovvu is the Beginning of a New Era for Sestek


We already know that Sestek develops conversational AI solutions based on over 20 years of experience. After all this, Knovvu was born as a new fruit of this corporate expertise. So what was the insight behind creating such a brand?

In the field of customer experience, we know how important and difficult it is to provide the same quality of service in different channels for both the customer and the brand. In today’s customer service operations, quality means fast and accurate information. We, as Sestek, have been maintaining to produce solutions that can meet this need in our R&D centers for more than 20 years. Our aim with Knovvu is to consolidate all our solutions, which we have realized with this experience, under a single platform. While doing this, we continue to consider changing customer needs and developing new technologies.

Which changes take place in the field of Global Customer Service Automation, What trends do you observe that stand out? 

Today, customer interactions do not necessarily involve a live customer representative. Calls, inquiries and requests from customers; can be answered automatically through virtual assistants that can communicate in written and voice; so companies can improve in areas such as time, cost and efficiency. Furthermore, companies that correctly analyze incoming calls to customer service and orient their employees can make great improvements in customer experience. However, due to the high volume of calls, we think that it would be much more efficient if these analyzes were made by AI-based solutions instead of live employees.

AI-based solutions increase efficiency in customer experiences, sales and service operations, reduce costs and improve interaction between brands and customers. At this point, as Sestek, we step in and develop AI-powered products that companies can benefit from. As I mentioned above, we bring together these solutions on the Knovvu platform.

How Knovvu is designed to address emerging global trends?

We group our products on the Knovvu platform into 3 main categories: Virtual assistance, voice biometrics and interaction analytics. I would like to explain these concepts in brief details.

Virtual assistants today can answer one of four customer questions without the help of a live customer representative. Although this percentage may seem small at first glance, it means enormous savings and efficiency for a company that receives millions of customer calls.

With the technology of voice biometrics, a customer call can be shortened by approximately 20-30 seconds. Such a reduction per call represents a significant savings for call centers. This solution also provides a good experience for the customer, who goes through the security phase without being asked any additional security questions while talking to the representative and can find a quick solution to their query.

Finally, every customer call contains crucial insights that companies can use to improve their workflows. However, today, call centers cannot adequately analyze these calls due to time and human resource constraints. The technology of conversational analytics enables automation in this area, helping companies and their employees to uncover valuable insights.

We create value in these 3 areas with our AI-based solutions on the Knovvu platform.

Which services do you offer under the umbrella of Knovvu? In this sense, what are the points where you differ in the sector?

We bring together our services under 3 main headings on the Knovvu platform: Knovvu Virtual Agent, Knovvu Biometrics and Knovvu Analytics.

I would like to underline a few important points about Knovvu:

  1. Artificial Intelligence driven: Knovvu solutions are built on AI-powered technologies that will constantly learn the system they are involved in and improve themselves.
  2. Quick Response: Our users will have a faster response system than competing products in areas such as scenario editing, form design or instant reporting.
  3. Real-Time Performance: Instant improvements will be made in call center operations with sentiment analysis and instant notifications that allow real-time action to be taken in customer conversations.
  4. Simple and Easy Experience: Knovvu solutions are designed in such a way that users can easily understand and get maximum efficiency by revising the system without coding knowledge.
  5. Environment Independent Use: The Knovvu platform can be installed in the local environment of the institutions, or in all cloud (private or public) environments if desired.


What innovations and conveniences await a brand that cooperates with Knovvu?

As a customer interaction automation platform, Knovvu is positioned to serve industries with high customer interaction, such as retail, telecom, banking and insurance. Today, it is very important for companies that a solution enables central management, omni-channel access and can work in any local/cloud environment, and Knovvu comes to the fore with these features. The Knovvu platform enables better management of customer interactions with the help of automation.

What are the future goals of Knovvu?

Knovvu is the beginning of a new era for SESTEK. Our vision is to be a global technology company that improves the experiences of our business partners, their customers, and our own employees. I think we have come to a good point with what we have done in the last 20 years on this journey. Today, we serve more than 200 corporate clients in 16 countries.

To talk about our strategic goals, our primary objective is to expand our network in the global market. While doing this, we want to continue to deliver our products as software (SaaS) that can be accessed from the cloud environment to our current and potential business partners at home and abroad.

Last year, we started the company-wide DevOps transformation process. Thus, we have made our products in a way that can easily adapt to needs and innovations and respond quickly. At the end of the day, our goal is to make the lives of both our business partners and their customers easier by using artificial intelligence technology more and more efficiently in our solutions.


Author: Sestek Marketing Team, Sestek


Publish Date: July 6, 2022

Employee Experience, Customer Experience, Total Experience: How are They All Connected?

A chain is only as strong as the weakest link. If one part of something is weak, it jeopardizes the integrity, quality, or effectiveness of the whole. The idiom is applicable to almost all cases composed of more than one part. Let’s take a car, for example. Its main objective is to ensure that people or objects reach from point A to point B in a short and safe manner. It consists mainly of an engine, transmission, battery, alternator, radiator, front axle, front steering and suspension, brakes, muffler, tailpipe, fuel tank, rear axle, and rear suspension. No matter how powerful its engine is, an incorrect setting or malfunction of one of these parts will cause the car to run incorrectly or not start at all.

The main economic objectives of a business are profit earning, creating customers, acquiring and increasing market share, innovation, utilization of resources, leveraging productivity, etc. To achieve these objectives, a business needs a strong sales operation and a strong total experience (TX) structure that supports this operation. Total-experience is a business strategy that aims to create a better, holistic experience for everyone who engages with a brand (customers, employees, users, partners, etc.). No matter how good and powerful the product is, a problem that occurs in one of the links that make up the TX chain, for example, in the employee experience (EX), will disrupt the entire customer journey and will lead to a decrease in the company’s sales ratios. The statistics below confirm this:

  • According to a recent report, disengaged employees cost UK companies around  £340 billion each year.
  • Organizations that score in the top 25 percent on employee experience report double the return on sales compared to organizations in the bottom quartile.
  • Companies with highly engaged workforces outperform their peers by 147% in earnings per share.

If you want higher sales figures, faster revenue growth, and profitability you have to provide an employee experience that makes your sales team feel valued, driven, and engaged.

To Be Successful in CX, First Consider Improving Your EX

The leading actors of the customer experience scene are the employees of a business. Therefore, a business whose goal is better customer experience should also focus primarily on better employee experience. Employee experience can be defined as the sum of what people encounter and observe during their tenure in an organization.

There is no doubt that today, a significant part of employee-customer interactions takes place over contact centers. The metrics used for evaluation of a contact center such as average call abandonment rate, percentage of calls blocked, the average waiting time, the average speed of answer, average response time, first call resolution, occupancy rate, service level, customer satisfaction, etc. have a direct effect on the customer journey and are mostly related to employee experience.

Here clearly shows that for a business determined to strengthen the TX chain and in return boost sales, it is vital to monitor contact center activities wholly, to evaluate and analyze the monitored data accurately and to intervene and provide coaching timely. On the other hand, as we put in our previous posts, making it fulfilled with manual evaluation tools is impossible. Growing volume and the increasing importance of customer-agent interactions compels the implementation of conversational AI and Analytics solutions as the most efficient tool for monitoring, evaluating, analyzing, and coaching.

As a global supplier of various conversational solutions, Sestek offers a robust and AI-powered tool for analyzing agent performance as well. Sestek Agent Performance Analytics provides an automated solution to boost agent performance. This automated reporting interface generates evaluations on all recorded customer interactions, providing supervisors with crucial data to improve customer and agent experience. Using intelligent and customizable evaluation forms, advanced call filtering systems, and a user-friendly interface, the Sestek Agent Performance Analytics system will help reduce operational costs while improving call center performance.

The most recent success story of Sestek Agent Performance Analytics was with ING Turkey. ING Turkey, a part of ING Group, one of the largest financial institutions globally, was targeting to increase sales performance effectively to manage its call center operations with more than 200 agents. ING Turkey was searching for a solution to evaluate 100% of all interactions and effectively analyze them for actionable results to improve agent performance and increase sales revenue.

Using Agent Performance Analytics, ING Turkey monitored, evaluated, and analyzed 100% of all customer conversations and gained valuable insights to improve both customer experience and agent performance. The results were quite satisfactory:

  • 9% increase in sales conversations and 25% increase in profit per agent:

Agents’ script adherence was monitored and analyzed. Detailed feedback and guidance were provided to improve the sales performance of agents.

  • 20% decrease in customer complaint calls:

Possible complaint calls are detected by analysis with speech analytics, and the majority were resolved by the proactive complaint management system.

Sestek Agent Performance Analytics helped ING to effectively train agents, improve their performances and increase the sales revenue significantly. Click to check out our success story here.

Please visit this link for more details and case studies about Sestek Agent Performance Analytics.


Author: Çağrı Doğan, Sestek Marketing Team, Sestek


Publish Date: May 16, 2022

Speech Analytics Come to Rescue for Better CX

The customer experience (CX) refers to the interactions a business has with customers at every channel related to marketing, sales, and customer service. In general, customer experience is the sum of all interactions between a customer and a brand.

The customer experience is not just a series of transactions; It is also about emotions. What do current or potential customers feel or think about the brand? At each touchpoint, customers’ thoughts will be positively or negatively affected. So, there are important decisions to be made at each touchpoint, and these decisions will affect the experience and ultimately the success of businesses.

Why is Customer Experience So Important? 

As products and services become similar and competition heats up, customer preferences are now based on experiences with the company rather than on specific product features and functions.

As customer experience becomes the main competitive edge, businesses must ensure that their customer experience strategy delivers personalized and satisfying interactions at every customer touchpoint. These interactions have an increasing impact on customers’ overall perception and impression of brands. This makes the customer experience critical to success.

The statistical insights, we’ve read from the article Top 40 Customer Experience Statistics To Know in 2021  on customer experience, say a lot about the significance of CX.

You can find a few of them below:

  • 86% of professionals, engaged in or leading CX, expect to compete based on CX
  • $35 billion is lost every year by US businesses in customer churn caused by avoidable CX issues.
  • 83% of executives feel that unimproved CX presents them with considerable revenue and market share risks.
  • 74% of consumers are at least somewhat likely to buy based on experiences alone.

In the light of all the figures, we can say that effectively listening to customers and acting fast to close the loop on their issues are becoming more and more vital for businesses. However, the high volume of customer interactions makes it impossible to manually review and analyze them accurately. The manual reviewing process can only focus on a fraction of interactions and is far from providing holistic results.

How to Overcome the Challenge: Speech Analytics 

With the help of AI (artificial intelligence), businesses can apply in-depth analysis to customer interactions across multiple channels. These analyses include not only textual and statistical details but also emotional data. With advanced features like emotion detection and sentiment analysis, businesses can gain valuable insights to make wiser decisions.

Call centers are still at the forefront of customers’ preferred channels for interaction. Especially during the pandemic, the call traffic at this channel increased significantly. This shift calls for greater care in choosing the right tools to monitor, evaluate and analyze customer experiences.

Choosing the right speech analytics solution is very important for the correct evaluation of the vast data obtained through customer interactions. The success of the solution for speech analytics has a direct impact on the call center activities such as:

  • Call center monitoring and reporting
  • Agent performance analysis and training
  • Customer satisfaction and service quality analysis
  • Automation of quality monitoring processes
  • Crisis management
  • Competition, market, and campaign feedback analysis
  • Identification of cross-sales opportunities

As a global technology company helping organizations with Conversational Solutions be data-driven, increase efficiency, and deliver better customer experiences; we provide a very competent and proficient tool for speech and text analysis. Recently, we deployed our speech analytics product for Halkbank, the third-largest bank in Turkey.

The analytics solution implemented for Halkbank’s call center monitors all (100%) customer-agent conversations and evaluates in these following parameters:

  • Customer/Agent Anger Ratio
  • Agent Speech Duration & Speed
  • Agent Interruption/Overlap Rate
  • Agent Silence Rate
  • Agent Waiting/Hold Time During Call

This solution offers actionable insights to quality management teams to coach and guide agents accordingly to improve the service level. The implementation resulted from the inputs below:

  • 25 seconds decrease in ticketed call duration
  • 10% decrease in silent rates
  • 5% decrease in hold time
  • 75% improvement in customer anger ratio

The solution implemented for Halkbank also stands out with its high accuracy rate of 94% for speech-to-text conversion.  This high accuracy rate enables quality management teams to discover customer pain points and frictions more accurately and it also provides deep insight on how to support agents for improved performance. Click to check out our success story here.

Please visit this link for more details and case studies about our solution for speech analytics.


Author: Çağrı Doğan, Sestek Marketing Team, Sestek


Publish Date: April 13, 2022

Voice: Still the Most Natural, the Most Comfortable and the Safest

As in our previous post (From Single-Use Bots to Intelligent One-for-All Bots), the scope and scale of bots is expanding day by day. Furthermore, bots, whose capabilities have increased and diversified through artificial intelligence and machine learning, also facilitate the provision of more inclusive and accessible services for people of different ages, cultural backgrounds, disabilities, gender, temporary and permanent impairments, race and socioeconomic status.

There is no doubt that algorithms are one of the main actors shaping our present and future experiences. It has effects on our past experiences as well. An artificial intelligence application that successfully deciphers destroyed or missing Ancient Greek inscriptions and seems much more successful than human beings in this task (DeepMind AI beats humans at deciphering damaged ancient Greek tablets) is just one example among many others. It was also an artificial intelligence application that revealed that the work “Samson and Delilah”, which has been on display at the London National Gallery for 40 years, allegedly belonging to Peter Paul Rubens, is fake (Was famed Samson and Delilah really painted by Rubens? No, says AI).

As can be seen from these examples, algorithms are already more adept at distinguishing fake from real than the human beings who created them. On the other hand, bots equipped with superhuman abilities gain the ability to pretend to be human, and this enriches the possibilities of people to pretend and to show oneself different from what they really are. For example, machine learning and artificial intelligence technologies make it possible to clone a person’s voice and imitate their speech by making use of a person’s speech recordings. In this way, it is possible for people who have lost their voice or already passed away to continue to speak via their own voice, albeit through a digital interface. Similarly, an actor can voice different projects at the same time by cloning his own voice and increase his income (dubbing artist and actor Tim Heller explains that by cloning his voice, he can do many things simultaneously, such as animating cartoon characters, voicing books and documentaries, speaking in video games, and voicing in movie trailers). Similarly, it is possible for a singer to have his/her cloned voice sing in languages he/she does not speak.

What if behind the familiar and reliable voice on the phone there is a hand you wouldn’t want to shake?

Every new technique we develop makes us go beyond what we achieved previously, using much less effort and fewer resources. However, the impact of developments is not limited to our good and harmless abilities. The criminal world also (FBI says profits from cybercrime hit $3.5 billion in 2019) develops and diversifies its methods by making use of new techniques. The news about people being deceived or defrauded via phone or e-mail by strangers no longer surprises any of us. However, deepfake technology, which can make the cloned image or voice do the desired movement and say what is wanted, also increases the possibility of encountering a bot with the appearance and voice of someone we already know, and this sounds quite frightening.

If he/she is not nearby, hearing his/her voice over the phone can be considered the surest way to understand that we are communicating with someone we know. The incident of a bank manager is a case in point. In early 2020, a bank manager in Hong Kong received a call from a man whose voice he knew (he was the manager of a company he had spoken to before). The manager had good news: his company was about to make an acquisition, so the bank had to allow some money transfers of $35 million. A lawyer named Martin Zelner was hired to coordinate the procedures, and the bank manager could see emails from the manager and Zelner in his inbox and verify which amount of money was to be sent where. The bank manager started making the transfers, thinking everything looked legitimate. What he didn’t know was that he had been tricked as part of an elaborate scenario where the scammers used “deep fake” technology to clone the company executive’s speech.

The first recorded case, which is thought to have used the voice cloning technique, took place in March 2019. A fraudulent transfer of 220,000 EUR  (Related article from The Wall Street Journal) was requested in an incident where scammers used AI-based software to imitate the voice of a CEO, and what cybercriminal experts described as an unusual case of AI used in hacking.

It may be frightening that criminal world actors come up with more sophisticated and complicated scenarios by taking advantage of new technologies, but it is not a healthy and logical reaction to refrain from using these technologies. It is highly probable that we would still be living in caves if our ancestors had only focused on how unsafe the life outside was and had not thought of developing appropriate protective methods and tools. On the other hand, it is impossible for us to protect personal computers against external attacks by examining the files one by one; nor does it seem possible for us to protect ourselves against increasingly sophisticated fraudulent methods by being more vigilant or by continuing to use outdated security methods. For instance, if a multi-factor authentication system or a voice biometrics solution equipped with passive authentication and effective fraud prevention capabilities, capable of operating independently of language and accent, and of distinguishing whether the voice is reproduced by digital means, were used in the examples above, it would be highly likely that the cases would have been prevented from producing undesirable results.

In fact, voice, like fingerprints or iris, is a uniquely human trait. This paves the way for voice to be used as a powerful authentication tool. Unlike PINs, passwords and answers to challenge questions, voice biometrics can’t be compromised without the knowledge of the voice’s owner. This is one of the factors that makes voice verification much more secure. However, it is not possible to do such an analysis by manual listening. The conversational biometrics solutions we developed as Sestek analyze the voice based on over 100 parameters. The solutions in question have playback manipulation detection functionality. This means that the solution will detect whether the party on the other end of the phone is actually speaking or is playing a voice sample. When a recorded voice sample is played, the technology can detect and report the situation by using the synthetic voice detection feature. The system, which can detect even known fraudsters with its biometric blacklist detection feature, uses a voice change detection algorithm to determine if a user’s voice has changed during a conversation. ING Turkey, one of our customers using this technology, shortened the average call time by 19 seconds for calls requiring identity verification. Thanks to this saving, they reduced operational costs and increased customer and representative satisfaction.

For details on our conversational solutions, please visit


Publish Date: December 20, 2021

From Single-Use Bots to Intelligent One-for-All Bots

The software which performs repetitive, automated and predefined tasks is named as “bot”. When we hear the word “bot”, it is highly likely that most of us first think of chatbots. Probably the first thing that comes to mind when we say ‘chat’ is the correspondence through internet applications. However, we all know that every healthy individual belonging to the human family can be an actor of chat action by signing or speaking and later, on writing.

Yes, we, as a member of the human family, have been doing this for so long that we can say chat is one of our unique and natural features. As it is one of the common features of the entire human family, we don’t need something like “chatman” to name any group in this family. Undoubtedly, in more and more situations in our daily life, we come across conversational algorithms or chatbots more often; for example, when buying plane tickets or clothes from an online store. Although chatbots are now appearing in almost every aspect of our daily lives, the history of bots in this field is not very long. It was generally implemented with the aim of making users feel that they are chatting with a real person; a computer, program, algorithm or artificial intelligence that enters into a dialogue with a person is called a chatbot. The surge in the use of chatbots, whether simple or more advanced artificial intelligence applied, has been driven by the enormous expansion of the Internet and especially social networking sites.

One of the oldest and best-known chatbots is a program called Eliza created by the Artificial Intelligence Lab at MIT between 1964-1966. After that date, we see many chatbot projects implemented as a result of research and development studies. However, they can be used publicly and globally in the 2000s with the widespread use of the Internet. Today, a significant part of customer service is carried out through chatbots. Business Insider estimates that the global chatbot market, with a volume of $2.6 billion in 2019, is expected to reach $9.4 billion in 2024, with a compound annual growth rate of 29.7%. The report also suggests that the highest growth in the chatbot implementation will be in the retail and e-commerce sectors, driven by the increasing demand for seamless omnichannel experiences.

Today, simple chatbots, whose capabilities are limited to answering FAQs in a certain field, are being replaced by chatbots powered by artificial intelligence, machine learning, natural language processing, speech recognition and analysis, speech synthesis and voice biometrics technologies. In this way, multifunctional/capable chatbots can be developed that can express themselves more humanely, understand various expressions implied by different styles and terms, and adapt the language they use to the style of the people they are communicating with.

We are now in a world where chatbots are the main actors in the field of corporate communication. Their use not only helps organizations improve their own potential, but also means a more enhanced user experience. With bots powered by technologies such as Speech Recognition, Speech Synthesis, Voice Biometrics, Natural Language Processing, Artificial Intelligence, voice and text analysis, Sestek, where I used to work on different projects for many years, can easily design customizable chatbots for the needs of organizations. Based on Sestek’s 21 years of conversational technologies development experience; with the solutions currently being used by 275 institutions in 20 countries; self-service rates are increasing significantly and the workload and stress on the agents are relieved. For example, according to September 2021 data, 250,000 customer interactions have been carried out via the Whatsapp bot utilized by Hepsiburada, Turkey’s leading e-commerce platform. With this application, 14% increase in self-service rates and 10% reduction in contact time with live representatives can be achieved. You can watch TR AI Week session titled “The Magnificent Duo: Artificial Intelligence and Self Service” by Taner Timirci (Hepsiburada, Chief Operations Officer) and Selin Özbalmumcu (Sestek, Sales Manager for Turkey) for details.

Another milestone project is the virtual assistant named Selim, which developed by Sestek for KUVEYT TURK and used by their 2.4 million customers in 12 months, has reached a success worth mentioning in recognizing what is said (intent recognition rate) with an accuracy rate of 97%. Selim was able to answer 6.2 million questions from 2.4 million users in 12 months, and the rate of connecting KUVEYT TURK customers to live representatives decreases by 29%. For details, you can review the case study titled (ACCELERATING DIGITAL TRANSFORMATION WITH VIRTUAL ASSISTANTS).

At the end of the day, the digital world continues to flourish, with the growing data flow rates and the increasing amount of data we collect in the digital network. With the advancement in artificial intelligence and machine learning, bots are perfecting their ability to process the data in question. Considering that bot assistants can make a reservation for a hairdresser or in a restaurant without making the person they are talking feel they are communicating with a bot, (Google Assistant calling a restaurant for reservation), we can say that we are confidently moving towards a future where a bot, which is initially intended to be used in a certain area, can be easily adapted to work in different areas as its processing power, knowledge and capabilities improve. In a nutshell, bots are expanding the boundaries we set for them day by day. One can surmise that in near future we will be able to define a bot as “a tool that is able to chat as one of its unique and natural features”, and there will no longer be a need for a special name like “chatbot”. Moreover, in the not-too-distant future, thanks to the “unsupervised learning” capability, which we have already started talking about a lot, for example, a bot initially programmed for customer relations will be able to train itself with the knowledge it receives in that field and can serve beyond its original purpose such as a sales assistant bot or HR bot when needed.


Author: Çağrı Doğan, Accessible Products Consultant, Sestek


Introduction to Chatbot Design


Building The Right Chatbot To Ensure Better Customer Experiences.
Today, chatbots are a significant part of customer services. From reserving a ticket to completing a banking transaction, they can offer any service that a live agent can. But being able to do these tasks might not be enough for enhanced customer service. Because customers not only expect high-quality services but also personalized experiences. According to recent research, more than 63% of customers expect personalization as a standard of service. So, chatbots need to answer this need to ensure an enhanced customer experience. Why do chatbots need personalization? Today’s customers seek more than functionality. They expect authentic connections with businesses. And the only way to build a strong and emotional bond between customers and chatbots is personalization.


Publish Date: November 11, 2021

Chatbot? Virtual Assistant? Digital Assistant? What’s The Difference?

Conversational technologies have been on our agenda for a long time, and these technologies are expressed with various concepts such as bots, virtual assistants, digital assistants, chatbots, etc. Is it really necessary to have so many terms that we witness new ones being added every day? Are these increasingly confusing concepts that important?

As someone who has spent 20 years in the technology field and believes that communication is a multi-channel experience, I have never liked the term “chatbot” since I first heard it. If we look at the history of this technology, which has become increasingly popular since 2016 and presented as if it were a new discovery; we can see that its foundations were laid in 1950 with the discovery of the “Turing Test” by a world-renowned mathematics professor named Alan Turing. The Turing Test determines whether a machine can demonstrate human intelligence. If a machine can engage in a conversation with a human without being detected as a machine, this means it demonstrates human intelligence.

The Evolution of Customer Service Automation

In fact, in the process triggered by the Industrial Revolution, the basic expectation of human beings from technology was the more effective use of machines in many fields that require manpower. The increasing use of machinery in the customer service area, which we, as Sestek, also focus on, has often resulted in customer dissatisfaction, especially in the first examples in the past.

Primitive versions of voice recognition, bots, and similar technological solutions started to be used in the 1990s. Although they provided cost advantages to companies, they were insufficient in terms of customer experience and therefore led to customer dissatisfaction. In recent years, rapid development in AI technologies such as deep learning, machine learning, etc., increased the success of such technologies positioned in the customer service automation field.

The Real Matter is the Right Experience Design

Bot, chatbot, virtual assistant, digital assistant… Whatever the name given to technology, the basis of a successful self-service solution lies in the correct design of the customer experience. Instead of only cost-oriented designs, bots that offer human-like conversations -except reflecting their emotions as humans do- and provide 24/7 service have entered our lives.

I’m sure that some people reading this piece will say, “When we call or text banks, we don’t want technologies that don’t make us talk to real people.”. But believe me, those who deal with a properly designed system integrated with the right technologies, within six months at the latest, report a better satisfaction result than their previous experience.

In this regard, I would like to explain with an example why technology has gained an unstoppable speed. You may think it would be more practical for a 70-80-year-old user to tell customer service about his problems with a live representative. But, let’s imagine that the virtual assistant provides personalized service to that person as soon as he calls customer service, understands everything he says, and performs his transactions five times faster than in the past. In such an experience, both the service recipient and the service provider will be happy.

An Undeniable Need: “Omnichannel”

We have seen in the pandemic that our service demands have almost completely moved to the digital environment, and our communication experience has changed irreversibly. One of the most important issues when positioning bots here is to offer an “omnichannel,” that is, a “single experience” independent of the channel. Namely, you get service from an e-commerce company, and when you contact customer service, you expect it to give you the same experience from every channel. When you call the contact center, submit a support ticket on a website, or communicate with WhatsApp or a voice assistant, you have only one expectation: You want your request or problem to be understood and answered accurately as soon as possible. At this point, there are many solutions that look like “chatbots” at first glance but can’t actually offer this experience. These solutions, which work independently of other customer channels, remain unaware of the dialog initiated by the customer in a different channel. This means that the customer has to express herself again and again in every channel, thus causing loss of time and customer dissatisfaction.


Whether it is called “chatbot,” “virtual assistant,” or a different term, these technologies, which are at the center of customer service automation today, are based on the principle that users receive service by communicating with systems. When designed correctly, these technologies enable transactions in a much shorter time for users. With the automation they provide, they offer cost advantages and efficiency to businesses. Regardless of the purpose of use, these technologies need to be developed based on customer needs and introduced to customers in a way that best meets their expectations. In other words, the customer is at the center of the business, and only the work carried out based on this fact can be successful.


Publish Date: September 21, 2021

The Evolution of Machine Learning: Explainable AI


Once a scene in science fiction movies, artificial intelligence is a natural part of our daily lives today.  When we open a film from Netflix or follow a profile on Instagram and get movie or profile suggestions in just as we want, not to mention Amazon’s real-time pricing or Google search’s semantic ability.

Companies invest in their talents and technologies more and more every day to get the best performance, precision, and accuracy from their algorithms to provide the information that users are hoping to find.

According to Ritu Jyoti, the Vice President for AI Research at International Data Corporation (IDC), AI has become the top topic of all industries for its versatile application areas and resilience - and pandemic only magnified the effect. The same report mentions that the expected growth in revenues for AI solutions will reach $500 billion by 2024.

AI is known for its “black-box” nature, lack of transparency while offering almost limitless possibilities for developers and scientists who train AI decision systems based on a specific domain with providing no visibility or rationale behind it. This can be negligible when we talk about getting movie suggestions from Netflix, but if this system is used for disease diagnostic, or sentence a criminal subject, the crucial need for explaining AI is inevitable. Moreover, today’s industries and governments require these technologies to make sure that users or customers trust the AI-based systems when making decisions; they have the right choice with the help of AI. Therefore, finding an approach and developing algorithms that transform black-box systems to glass-box systems are significant. It will be possible for people to trust machines on their decisions by understanding how they think and why they choose what they choose. This is where explainable AI comes into play.

What is Explainable AI?

Explainable AI (XAI) is a suite of machine learning techniques that produce more explainable models. The main goal of XAI is to explain how algorithms come up with a decision and which factors affected their decision points and eventually ended up with that solution. It is an emerging field that sits at the intersection of different focuses: transparency, causality, bias, fairness, and safety.

XAI progress has affected by three accelerating factors:

  • Increasing ethical concerns. The growing need for transparency, required by laws like GDPR about how personal data is used.
  • As explained with examples in the introduction, before putting trust in machines’ decisions, humans need to be convinced. And that would be only possible with the explainability of AI systems.
  • Better human-machine synergy. Machines are part of our daily lives more than ever and enhanced our life with their wide range of functionality and increased intelligence. So, it is important to create an environment where both humans and machines are working together.

XAI was first introduced in 2004 by Van Let et al. to explain a game simulation that they developed to train the US army. Although it is a game, it is especially designed to better train the soldiers without losing the effectiveness of the education material. Full Spectrum Command consists of users (soldiers) and non-player characters that AI controls. After a mission is done, users can click on subordinates and ask questions within the system or review key aspects presented by the case. This study also significantly influenced the gaming industry and its technologies.

Understanding Explainable AI

A simple illustration is shared below to understand the main concept of XAI. There are few highlights worth mentioning. First, today’s section of the picture represents how the classical mechanism of ML works. The system gets training data, applies ML processes, and concludes or makes a recommendation according to how the model is trained. It is also important to notice that there is one-way interaction with the system’s user; this shows no explainability in this system. The user only sees the final decision made by the system.

On the other hand, there are explainable models and explainable interface layers in the XAI concept instead of the learned function. Explainable models are responsible for taking the task and offering a recommendation or an action; the interface is responsible for justifying the cause of why the system has made that decision. Then the user makes a final decision based on the explanation, so there is a two-way interaction between the system and the user. Explainability interfaces can benefit from Human-Computer Interaction (HCI) techniques to generate effective explanations.

Figure 1: Comparison of AI and XAI concepts proposed by DARPA

Some Application Areas

Explainable AI has a wide variety of application areas, including healthcare, finance, insurance, law, etc. Here healthcare and law case studies are briefly shared to give a holistic overview of how XAI contributes to industries.

Explaining Medical Diagnosis

While describing XAI, it is mentioned that XAI is deployed to convert a black box into a white box. No matter how advanced the decision model is, there is always a need for a human in the loop for approving that model gives the right decision and handles unexpected scenarios. The medical domain is a suitable topic for describing such cases. Convolutional neural networks used to interpret medical diagnostics by using computer-aided diagnostic systems. Patient data collected through Magnetic resonance imaging (MRI) and computed tomography (CT) scans and existing diagnostics archives were processed, and the model trained to make accurate diagnostics by looking at the patient’s scans. In this way, the research team trained the model to identify disease patterns by looking at the existing atlas.

XAI in Legal

Explainable AI has great potential in legal applications. Courts can benefit from XAI for its pragmatic and transparent approach like judicial reasoning, bottom-up approach while making decisions, case-by-case considerations of delicate cases, and even stimulating the paperwork required for legal settings and audiences. It is also believed that XAI helps law to become more transparent and become independent from private actors by becoming more publicized. Judge is the main consumer of XAI algorithms recommendations and decisions. Taking reasonable explanations for sentences, the likelihood of that crime will occur, due processes, etc. require explanations from XAI models. As real-life cases, data collected, and model is trained by the collected data; common law of XAI will be created. This law can be used to compensate the explanation requirements by criminal, civil, administrative law settings, or it can be used by judges, juries, defendants, etc.

Final Thoughts

Explainable AI emerged as an answer to the increasing need to understand machine learning and AI better. Being able to comprehend AI will help us build better human-machine collaboration and contribute to a transparent approach where ethical concerns and trust issues can be resolved easily.


Author: Gülşah Keskin, Product Analyst, Sestek


Publish Date: July 13, 2021

Making Conversational AI Smarter: 4 Hints to Design an Intelligent Conversational AI Solution

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.

Learn More
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


The Conversational AI


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

How to Deploy Successful Conversational AI Projects

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

Transformation of Driving Experience: Tips for Implementing Conversational AI in Automotive Industry

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:

  1. 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?
  1. 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.
  1. 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

The Pillar of a Successful Conversational Journey: Speech Recognition

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:

Manual Measurement

Automated Measurement

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.

Learn More

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

Reducing Customer Complaints with Speech Analytics


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:

  1. 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.

  1. 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.

  1. 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%

The Customer

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.

The Problem

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

Gartner Recognizes Sestek’s Speech-to-Text Technology

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

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