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We have entered a promising new era of computing, where advances in machine learning and artificial intelligence are creating a resurgence of interest in conversational interfaces and natural language processing.This boosts potential for conversation as the new mode of interaction with technology.
Conversational Systems are intelligent machines capable of understanding language and conducting a written or verbal conversation with a user. Their objective is to provide informed answers, assistance, help in direct channel interaction and possibly in real time.
Conversational systems are designed for conducting a conversation via auditory or textual methods, convincingly simulating how a human would behave and taking advantage of sophisticated Natural Language Processing and Understanding capabilities.
Reply framework Robotics for Customers introduced a Human centered design approach for conversational interfaces, aimed at creating “experience systems”, by humanizing processes and exploiting advanced technology. Besides, the Personality by design approach is the methodology aimed at shaping Bot personality, thus enhancing design of interaction styles, by humanizing Customer touchpoints and augmenting user experience.
Download Brochure about Conversational Systems and Reply’s Solutions
Conversational interfaces, based on voice interaction or chat, are rapidly spreading in common use. Messaging platforms allow interaction with chatbot while smart speakers (Amazon Echo, Google Home, etc.) have quickly spread today.
Reply develops conversational agents in the field of customer care and personal assistant. In the world of customer care, there are several applications of chatbots to increase the effectiveness of the help desk services. Personal assistants provide support in daily tasks.
Read more about Chatbots
E-commerce and retail trade continue to develop. In the past, ongoing development was often driven primarily by technological progress, but this is no longer the case, as the focus nowadays is often placed on the customers and their changing behaviour when it comes to buying.
Input/output devices for voice-supported shopping are already available and can be exchanged as and when required: the smart watch on the wrist, voice recognition in the car or even specialised voice assistants like Amazon’s Alexa or Google Home. If natural language cannot be used due to, for example, the current situation or the surroundings, informal written orders can also be placed via messengers such as WhatsApp or Telegram. Based on SAP Hybris as a Service (YaaS), Reply Voice Commerce provides a package that can be used to communicate with various services on the in/output devices via a generic interface.
Read more about Reply Voice Commerce
One of the first cases where the Robotics for Customers approach has faced Chatbots has been in the automotive industry, where online assistants have been conceived for product presentation and catalogue configuration.
The way people shop for cars has changed markedly
For the automotive sector, Reply is offering a chatbot solution to instantaneously and fully automatically handles customer interactions dealing with a wide range of topics such as car configuration, customer survey, after-sales service.
Chatbots have the potential to convincingly mimic human actors and even pass the Turing test
Reply has developed a new customer care service using the chatbot technologies for a leading multi-utility company. In addition to its ability to interact with a human speaker, the chatbot implements an end-to-end process that identifies and extracts the information required by the user from back-end systems, supplying this information in real time within the context of a conversation carried out in natural language. The interaction between the user and the chatbot is immediate and does not require login procedures, resulting in a marked improvement of the service offered to the customer.
The New Challenge for Contact Centre Automation
In recent decades, we have witnessed the emergency of an increasingly robotic society and the growth of complex artificial intelligence. We are in a world where the conversation is the interface and the personality is the new User Experience.
Conversations with bots follow the same rules as communication between people
Data Robotics, defined as the set of technologies, techniques and applications required to design and implement a new automation process based on self-learning and artificial intelligence technologies, facilitates the introduction and integration of automation into organisational processes. Thanks to the application of “smart” technologies Intelligent Process Automation guarantees an improvement of Robotic Process Automation, facilitating the evolution from solutions that handle straightforward and recurring tasks, to new paradigms based on machine learning techniques.
While Data Science produces Insights, Machine Learning produces Predictions. C-level executives should think about applied Machine Learning in the upcoming stages of prediction and prescription.
Recommendation Systems deal with a particular form of Intelligent Information Filtering, aimed to extracting value by finding similarities among users and/or items and generating a ranked list of proposal tailored to an end-user’s preferences.
Image and video Recognition is a great task for developing and testing Machine Learning approaches. The field of Natural Language Processing is shifting from statistical methods to neural network methods.
Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future—for example, by helping credit-risk officers at banks to assess which customers are most likely to default or by enabling telcos to anticipate which customers are especially prone to “churn” in the near term (exhibit).
Prescription is the most advanced stage of Machine Learning, because it is, after all, not enough just to predict what customers are going to do; only by understanding why they are going to do it can companies encourage or deter that behavior in the future. Technically, today’s machine-learning algorithms, aided by human translators, can already do this.
Some samples of Reply’s experience:
Machine Learning for Fraud Fighting and Insurance Fraud Detection via Unsupervised Learning Methods
From an organization perspective, a Recommendation System can be built on a small amount of data, which can be as diverse as an organization may hold in specific business domains.
Reply introduced a unified development strategy for Recommendation Systems which allows different kinds of services to be developed and implemented in a straightforward manner, from prototypes to production environments. The data processing engine is provided by advanced analytics, typically built using AI-driven and Machine Learning capabilities. Download Brochure about Recommendation Systems and Reply’s Solutions
Some samples of Reply’s experience:
Bringing Recommendation Engines in Production Environments and
The Banca Mediolanum Case
Reply adopts innovative Deep Learning techniques for the recognition of images and videos. These techniques, based on neural networks (eg Convolutional Neural Network) allow the use of networks pre-trained on general datasets, or the creation of customized networks on specific datasets. It is so possible to realize recognition engines that allow the identification of specific objects and / or features in videos and images, and the characterization of the sentiment of facial expressions, too.
Reply already developed projects in these field, including the recognition of sentiment in the customer care field, augmented reality (recognition of specific objects to guide the application logic), visual verification of anomalies, the counting of objects on the shelves, etc.
Language identification is a Machine Learning technique that allows not only the conversion between voice and text, but also the understanding of the meaning of the text itself. Deep Learning, and specifically frameworks like TensorFlow, are used today to create sophisticated learning models.
Reply has gained the skills for the integration of smart speakers and the know-how necessary for the realization of language recognition models based on the most advanced platforms. Semantic search engines together with machine learning algorithms support the identification of the most relevant results to the requests.
Featured Business Case: Natural Language Processing across the Automotive Value Chain