Lidiia SuslovaによるEnder Turing コールセンター分析ソフトウェア

Lidiia SuslovaによるEnder Turing コールセンター分析ソフトウェアは、インターフェース・インタラクション・UXデザインコンペティションカテゴリーにおける非常に権威ある2023 A' Design Award 銅賞の優れた受賞作品です。

銅インターフェース· 2023

Ender Turing コールセンター分析ソフトウェア

デザイナー： Lidiia Suslova

クライアント: Ender Turing

インターフェースカテゴリーのA' Design Award 銅賞受賞者

22,531

認証済み受賞者

#149673の受賞ステータスを認証

サイトを訪問 A' Design Awardで見るプレスリリース受賞バッジ

カラー

デザイン

Ender Turing コールセンター分析ソフトウェアについて

Ender Turingは、コールセンターの品質管理を目的としたクラウドベースのソフトウェアプラットフォームである。リアルタイムダッシュボードにより、スーパーバイザーやマネージャーはライブ更新されるデータでパフォーマンスを監視できる。表示される指標には、通話量、平均処理時間、初回解決率、顧客満足度スコア、オペレーターの稼働状況が含まれる。通話およびチャットの自動事前スコアリングによって品質評価の時間を短縮し、柔軟なフィルター機能により、すべての会話ではなく少数のサンプルを抽出して確認することが可能である。スコアカードはエンジニアの支援なしに作成・編集できる。インターフェースは10言語以上で提供されている。

受賞

A' Design Award 銅賞について

Ender Turing コールセンター分析ソフトウェアは、創造性、独創性、鋭い洞察力で際立つ傑出したデザインに授与される、高い権威をもつ評価である A' Design Award 銅賞の受賞作品です。銅賞の受賞作品は、十分に巧みで印象的なほど創造的な工芸品であり、デザイナーの豊富な経験、想像力、機知を裏づけています。Ender Turing コールセンター分析ソフトウェアは、高く評価され、称賛され、愛される品であり、顕著にプロフェッショナルで革新的であることが期待されます。この A' Design Award 銅賞を受賞したデザインは、芸術・科学・デザイン・技術のベストプラクティスを取り入れており、細部への配慮に優れています。Ender Turing コールセンター分析ソフトウェアは、ニーズを見事に満たし、欲求に触れ、感情を魅了し、好奇心を引きつけます。

デザインの裏側

プレスキット

デザイナーが提供するクレジット、チームメンバー、技術的な詳細。

独自の特性: 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.
寸法とパッケージ: 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.
タグ: 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
製造技術: 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.
デザインの課題: 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.
リサーチ: 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.
インスピレーション: 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.
プロジェクト期間: 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.
オペレーションフロー: 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.
特許: 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