ROBOTICS FOR
CUSTOMERS

Reply’s new framework on Customer Engagement driven by Data

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Customer Robotics

WHAT IS ROBOTICS FOR CUSTOMERS?

Reply has developed its own Robotics for Customers approach in the context of Data-Driven Customer Engagement. Robotics for Customers is a framework built on two foundational pillars: Recommendation Systems and Conversational Systems.

The synergy between these advanced capabilities determines a rich and unified perspective on:


  • A new approach to Customer Insights, thanks to Machine Learning and Advanced Analytics
  • A new touchpoint aimed at improving Customer Engagement, thanks to Natural Language Processing and Understanding
  • A new way to get interaction across channels, thanks to a tailored Customer Journey design

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Customer Robotics

LET’S GO DEEPER ON RECOMMENDATION SYSTEMS

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.

The framework Robotics for Customers introduced a unified development strategy for Recommendation Systems which now allows those kinds of services to be developed and implemented in a straightforward manner, from prototypes to production environments. 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. The data processing engine is then provided by advanced analytics, typically built using AI-driven and Machine Learning capabilities.KNOW MORE!

Customer Robotics
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WHAT ARE CONVERSATIONAL SYSTEMS?

Conversational systems are intelligent machines capable of understanding language and conducting a written or verbal conversation with a User. Their adoption is aimed at improving Customer Experience by steering human-machine interaction. 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.

The 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. KNOW MORE!

Customer Robotics

ROBOTICS FOR CUSTOMERS: A NEW FRAMEWORK!

Reply developed the framework Robotics for Customers as a comprehensive approach for developing strategic, Data-Driven Customer Engagement services. One major objective of the framework is to make information, which is typically hidden and fragmented inside enterprise data assets and IT platforms, actionable.

From this viewpoint, the framework Robotics for Customers was conceived to provide suitable support to any business process. It mainly addresses Personalized Services, to be provided to Users within their preferred digital channels.

The framework model is built by adopting a cross domain and multi disciplinary approach and by leveraging expertise about strategic consultancy in several domain contexts and market scenarios. The framework Robotics for Customers harnesses from one side technical competences and practices, on the other side strategic and business consulting experience in order to fulfill end-to-end solutions. KNOW MORE!

Customer Robotics

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Customer Robotics

Conversational Systems

Best Practice

Conversational Systems in the Automotive industry

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 Banca Mediolanum case Data Analytics Laboratory and implementation of a Recommendation Engine  0

Financial Services

Case Study

The Banca Mediolanum case: Data Analytics Laboratory and implementation of a Recommendation Engine

With the aim of experimenting with an Advanced Analytics approach, the Data Analytics laboratory initiative launched by Banca Mediolanum involves a partnership between the Marketing Research team and Reply for the development of advanced data analysis mechanisms and the design of proactive services, tailored to the customer’s needs.

Conversational Systems

Best Practice

Personality by Design

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.

Human Centered Design The biggest obstacle to customer centricity is your organizational culture 0

Conversational Systems

Best Practice

Human Centered Design: The biggest obstacle to customer centricity is your organizational culture

With the current hype on customer centricity going strong, a lot of companies are asking Reply to help them turn their product and service development towards a more human centered one.

Recommendation Systems

Best Practice

Reply's approach and methodology for bringing recommendations in production environments

Reply has developed the framework Robotics for Customers which allows customers to build a time-to-value Recommendation System that can be easily integrated into any existing platform.

Using Deep Learning and Knowledge Graphs to anticipate customers’ needs 0

Recommendation Systems

Best Practice

Using Deep Learning and Knowledge Graphs to anticipate customers’ needs

There is no mystery behind traditional collaborative algorithms: they simply try to suggest similar content to what we have previously watched, or what other users with similar tastes to us have been watching. Yet we can go even further using Deep Learning and Knowledge Graph methods that leverage contextual and unstructured data.