Lidiia Suslova의 Ender Turing 콜센터 분석 소프트웨어

디자이너 Lidiia Suslova의 작품 Ender Turing 콜센터 분석 소프트웨어은(는) 매우 권위 있는 2023 A' Design Award 동상의 인터페이스, 인터랙션 및 사용자 경험 디자인 부문에서 탁월한 수상작입니다.

동인터페이스· 2023

Ender Turing 콜센터 분석 소프트웨어

디자이너: Lidiia Suslova

클라이언트: Ender Turing

인터페이스 부문 A' Design Award 동상 수상작

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Ender Turing 콜센터 분석 소프트웨어 소개

Ender Turing은 콜센터 품질 관리를 위한 클라우드 기반 소프트웨어 플랫폼이다. 실시간 대시보드를 통해 감독자와 관리자는 실시간으로 업데이트되는 성과 지표를 모니터링할 수 있다. 표시되는 지표에는 통화량, 평균 처리 시간, 최초 통화 해결률, 고객 만족도 점수, 상담사 가용성이 포함된다. 통화와 채팅에 대한 자동 사전 점수 산정은 품질 검토 시간을 단축하며, 유연한 필터 기능을 통해 모든 대화 대신 소규모 표본을 선택할 수 있다. 스코어카드는 엔지니어링 지원 없이도 생성하고 편집할 수 있다. 인터페이스는 10개 이상의 언어로 제공된다.

수상 인정

A' Design Award 동상 소개

Ender Turing 콜센터 분석 소프트웨어, 이 작품은 A' Design Award 동상 수상작으로, 창의성, 독창성, 영민함으로 돋보이는 뛰어난 디자인에 수여되는 높은 권위의 인정입니다. 동상 수상작은 충분히 숙련되고 인상적으로 창의적인 작품으로, 디자이너의 폭넓은 경험, 상상력, 기지를 입증합니다. Ender Turing 콜센터 분석 소프트웨어, 이 작품은 높이 평가받고 감탄과 사랑을 받는 사물로서 두드러지게 전문적이고 혁신적일 것으로 기대됩니다. 이 동상 수상 디자인은 예술, 과학, 디자인, 기술의 모범 사례를 담고 있으며 세부에 대한 주의로 훌륭합니다. Ender Turing 콜센터 분석 소프트웨어, 이 작품은 필요를 훌륭하게 충족하고 욕망에 닿으며 감정을 매료하고 호기심을 불러일으킵니다.

디자인 비하인드

프레스 킷

디자이너가 제공한 크레딧, 팀원 및 기술 세부 사항.

고유 특성: A real-time dashboard allows supervisors and managers to monitor call center KPIs and agent progress with live updates. It displays metrics such as call volume, average handle time, first call resolution, customer satisfaction scores, and agent availability. This helps identify areas of improvement, detect issues, and optimize call center performance through proactive management and data-driven decision-making, leading to improved efficiency, customer satisfaction, and business outcomes.
치수 및 패키징: The primary function of Ender Turing engine is to help users create and train AI models that can understand and generate human-like language. The platform provides a range of NLP capabilities, including text classification, sentiment analysis, entity recognition, language translation, and text summarization, among others. Users can build custom models using Ender Turing's drag-and-drop interface or integrate pre-trained models into their applications using the platform's API.
태그: Artificial intelligence (AI), Natural language processing (NLP), Machine learning, Text classification, Sentiment analysis, Data processing, Text summarization API, Cloud-based platform, Language modeling, Text mining, Call center, Scoring
생산 기술: We used a data-driven design approach to gather feedback for our design, which involved observing and interviewing customers of the product. All of our decisions were based on identifying user pain points and areas of frustration. As a result, we developed a product solution that eliminates most pain points for call center supervisors, increases their productivity, reduces stress and frustration, and features an intuitive UX interface covered with more than 10 language options.
디자인 과제: Experiments were done to test hypotheses and validate design decisions. We found that our users are non-technical and prefer a more prominent communication style. We avoid using complex logic constructions and heavy interface decisions. Our UX solutions are continuously simplified.
연구: As we maintain ongoing communication with our end users, including data center supervisors and stakeholders, we have collected data through a range of research methods, such as interviews, surveys, and observations.

This has allowed us to gain valuable insights into usesr needs, preferences, and behaviors, which will inform the design of effective solutions that meet their specific requirements.
영감: Call centers handle up to 1000 calls per agent per day. Measuring and analyzing this data with metrics similar to those used by humans is crucial to improve client service and call center productivity. A productive call center saves businesses revenue and promotes polite and friendly communication with customers, enhancing a business's reputation, improving customer satisfaction, and increasing their overall quality of life.
프로젝트 기간: The project started in June 2020 in Tallinn, Estonia. The Design for the Project started at January 2022 and still developing and enhancing. Product was nominated as winner at Latitude59 pitching competition in May 2022, Tallinn, Estonia and took 2nd place at Infoshare Startup Contest where been selected among 500 startups from all over the world in October 2022, Gdansk, Poland.
운영 흐름: Our solution provides a 20-fold increase in speed for scoring calls and chats. This is made possible by automated AI pre-scoring and call center quality assurance. Using flexible filters, you can randomly select only a small number of calls and chats for quality monitoring, rather than monitoring all of them.

Moreover, our system allows for flexible setup of scorecards with no limitations. This means that you will no longer need engineers or Excel sheets to modify or create new scorecards.
특허: Scientific publications:

1. Transferability Evaluation of Speech Emotion Recognition Between Different Languages
January 2022
DOI:10.1007/978-3-031-04812-8_35
In book: Advances in Computer Science for Engineering and Education (pp.413-426)

Ievgen Iosifov
Olena Iosifova
Oleh Romanovskyi
Vladimir Sokolov

2. Automated Pipeline for Training Dataset Creation from Unlabeled Audios for Automatic Speech Recognition

July 2021
DOI:10.1007/978-3-030-80472-5_3
In book: Advances in Computer Science for Engineering and Education IV (pp.25-36)

Oleh Romanovskyi
Ievgen Iosifov
Olena Iosifova
Vladimir Sokolov

3. Analysis of Automatic Speech Recognition Methods

January 2021
Conference: Cybersecurity Providing in Information and Telecommunication SystemsAt: Kyiv, Ukraine

Olena Iosifova
Ievgen Iosifov
Vladimir Sokolov
Oleh Romanovskyi
Igor Sukaylo

4. Sentence Segmentation from Unformatted Text using Language Modeling and Sequence Labeling Approaches

October 2020
DOI:10.1109/PICST51311.2020.9468084
Conference: 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T)

Ievgen Iosifov
Olena Iosifova
Vladimir Sokolov

5. Methods and components of natural language processing

August 2020 Adaptive automatic control systems 1(36):93-113
DOI:10.20535/1560-8956.36.2020.209780

Olena Iosifova
Ievgen Iosifov
Oleksandr Rolik

6. Techniques Comparison for Natural Language Processing

June 2020
DOI:10.5281/zenodo.3895814
Conference: 2nd International Workshop on Modern Machine Learning Technologies and Data ScienceAt: Lviv-Shatsk, Ukraine

Olena Iosifova
Ievgen Iosifov
Oleksandr Rolik
Vladimir Sokolov