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Chatbots Why are they trending, how can they be utilized and how do they work?

The better the question. The better the answer. The better the world works.


01

Introduction

02

Key trends

03

Market potential

04

Case Study 1: WASTY

05

Case Study 2: Cuoca Rita

06

Why EY


Chatbot Market Trend ✓

“Chat bots will power 85% of all customer service interactions by the year 2020” Gartner

“Chat bots will be responsible for cost savings of over $8 billion annually by 2022, up from $20 million in 2017” Juniper Research

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Voice of the leaders

Satya Nadella, Microsoft CEO

Chatbots will fundamentally revolutionize computing. Bots are the new apps.

Ted Livingston, Kik CEO and founder

Mark Zuckerberg, Facebook CEO

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Chat apps will come to be thought of as the new browsers; bots will be the new websites. This is the beginning of a new internet.

Messaging is one of the few things people do more than social networking.


The market for chatbots is experiencing rapid growth $23 billion ~60%

80%

- 59% of US millenials (18-34) and 60% of generation X (35-55) have interacted with a chatbot [1]

- 80% of businesses want chatbots by 2020 [2]

Facebook Messenger launches chatbot support

- Estimated potential annual salary savings in US customer service positions [3]

27,8 % - Estimated CAGR for global chatbot market, 2016-2024

[4]

Google trends – «chatbots»

Relative amount of searches for keyword «Chatbot» (Google Trends)

But – what is a chatbot? Page 5


A chatbot is a service, powered by rules and sometimes artificial intelligence, that you interact with via a chat interface* Where are they applied?

How do they work?

How are they accessed?

A chatbot service can be applied to a variety of industries and application areas.

A chatbot has information about a certain domain. A user can obtain information through interaction with the chatbot.

A key benefit with chatbots is that they are available on the platforms people are using, as well as on company websites.

Examples are:

The conversational complexity of chatbots depends on their underlying logic, which varies between

Examples are:

Industries

Banking

Retail

Healthcare

simple rule based structures

Application areas

and extensive use of artificial Intelligence. Customer support

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E-commerce

Productivity

What separates a bot from a chatbot? A bot is any system that can solve some problem automatically, based on rules or triggers. Some key aspects separating bots from chatbots are Functionality

Chatbot

Understands natural language Has context-specific knowledge

Interacts with users

Example: Some Twitter bots can publish content automatically, but they don’t have any of the functionalities listed above.

Bot


A Chatbot has a simple yet powerful architecture

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Chatbots can be classified by the range of their conversation domains and the way they respond Domain of conversation Open domain

Nearly impossible

General AI

Closed

Open

You can ask a limited set of specific questions. An example is a company’s FAQ page.

You can ask questions about any topic, and expect a relevant answer. An example is Apple’s Siri.

Response generation Retrieval based

Closed domain

Rule-based

Smart machine

► Maps user input to suitable answer in database

► Generates suitable response to user input

► Uses a heuristic to understand input

► “Translates” from an input to a suitable output

► Only works in closed domains

► Also works in open domains, although complexity increases

► Does not make grammatical errors when responding ► Can utilize machine learning

Retrieval based

Generative based

Source: «Chatbot conversation framework» by Mark Clark

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Generative

► Low cost, easy to develop ► Widely used

► Can make grammatical errors, especially for longer sentences ► Typically require large amounts of training data ► High cost, hard to develop ► Not widely used. Area of research


01

Introduction

02

Key trends

03

Market potential

04

Case Study 1: WASTY

05

Case Study 2: Cuoca Rita

06

Why EY


Several trends and events point towards a growing business potential for chatbots Changing customer expectations and behavior

Advances in technology

Apps are losing ground

Advances in artificial intelligence

Users spend most of their time in just five apps, and are likely to delete new apps after using them only once. Also, the average number of apps on each phone is decreasing.

Recent advances in Machine Learning and Natural Language Processing have greatly increased the potential capabilities of chatbots.

Major tech companies are getting involved

Everyone is on one or more chat platforms

Both Microsoft and Facebook announced support for 3rd party chatbot development on their platforms in 2016.

The four biggest messaging apps now have more users than the four biggest social networks, and the number using chat platforms is steadily growing.

Chatbot technologies are becoming more available

People want things to happen now

A growing number of chatbot development tools with varying complexity and ease of implementation are hitting the market, lowering the chatbot creation barriers.

In today’s fast-paced, digital environment, people are used to having information instantly available, and don’t want to spend time using tedious means of communication.

Companies face increasing global competition Companies need to cut costs and be efficient

New business opportunities

Rapidly changing business landscape

Chatbots are good at doing repetitive, simple tasks, and are therefore a good tool for automating business processes. This leads to cost reductions or reallocation of human resources.

Chatbots create new possibilities in communicating with customers. An example is that they allow simultaneous one-on-one marketing with millions of people.

Being a technology leader in the market can lead to first mover advantages. Also, being a laggard with adapting new technologies may lead to loss of competitive edge.

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Apps are losing ground, while messaging platforms are gaining popularity Predictions for 2020

27

Average number of apps used in one month by a typical consumer[5]

23

Percentage of apps that are abandoned after being used once [6]

<5

Number of apps that are used heavily by the average consumer[7]

2.5

Billion people are on social networks in 2017 [8]

41

Percentage of US Millenials who would be “truly satisfied” if they could connect with companies through messaging or SMS [9]

67

Percentage of businesses surveyed who believe chatbots will outperform mobile apps in the next five years [10]

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80 % of businesses will have employed a form of chatbot automization[11] Only 15 % of interaction with customers will be through human employees[12]

The four biggest messaging apps have more users than the four biggest social networks.

1 968

1 000

600

319

1.200

1 000

889

868

Million active users, April 2017 (Statista.com)

«Messaging is one of the few things people do more than social networking» - Mark Zuckerberg


All the major tech giants provide powerful NLP and ML tools for chatbot development ►

Used by more than 100 000 developers.

Encapsulates NLP algorithms, configuration data, performance and tuning, letting developers focus on apps and devices’ core features.

Microsoft cognitive services offer powerful AI tools in the fields computer vision, speech, NLP, knowledge extraction, and web search.

LUIS is an NLP tool which interprets sentences in terms of the intention they convey and the key entities present.

The tools integrate with Microsoft Bot Framework, enabling creation of powerful chatbots.

Watson has APIs for language, conversation, speech, vision and data insight which add cognitive functionality to applications and services.

Available for developers on IBM Bluemix.

Intended for enterprise applications.

Helps customers engage with their customers and employees through common instant messaging and chat clients, like Facebook Messenger and WeChat, or by adding conversational features to their mobile apps.

Api.ai acquired by Google in September 2016 and is now Dialogflow

Provides pre-built knowledge packages across a diverse collection of topics built from over two and a half billion user queries.

Facilitates speech recognition, NLP (intent recognition and context awareness), and conversation management.

Enables building of conversational voice & text interfaces powered by the same deep learning technologies as Alexa.

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Alexa provides capabilities, or skills, that enable users to interact with devices in an intuitive way using voice.


Chatbots can provide a competitive edge Chatbots can help businesses in…

Examples – see the appendix for further details «Julie»

…cutting costs

According to Business Insider, chatbots can potentially automate 30% of US customer service tasks. They estimate that this constitutes saving of $23 billion in the US alone. [13]

…discovering new opportunities Compared to passive forms of advertisement on the web, like bannes, clips at the start of YouTube videos or Facebook ads, chatbots allow for a more interactive way of marketing where the brand can communicate directly to the consumer. Chatbots make simultaneous one-on-one marketing with millions of people possible. Chat interactions with consumers can help advertisers collect interesting data, and to see and analyze how users interact.

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US railroad company «The National Railroad Passenger Corporation» (Amtrak) started developing their customer service chatbot «Julie» in 2012. According to NextIT, who provided the platform Julie was built on, the savings from Julie amount to 1 million USD annually in service email costs. [14]

«The Official H&M chatbot» Clothing giant H&M has launched a chatbot on messaging app Kik. The chatbot uses information about the user to suggest outfits, and learns about the user’s fashion style. It even tries to adapt its style of speech to different demographics.


Chatbots have benefits compared to current solutions that fulfill the same purpose Telephone

Drawbacks for current solutions ►

Changing customer expectations and behavior

Businesses to be more Companies need face increasing efficient global competition

Benefits gained by involving chatbots

Difficulties that remain when involving chatbots

Advances in technology

Lower acceptance for waiting lines. Customers expect 24 hour service.

Websites

Email

Not a well suited channel for use from some devices. Has a static interaction interface.

Mobile Apps

Marketing emails are often treated as spam. Users expect more rapid interaction than what emails can provide.

People are reluctant to having too many mobile applications.

Other channels are often more convenient for a user. Tracability and storage of conversations is difficult.

Websites cannot extract information based on interpretation of a user’s intent.

Mass emails do not utilize advances in technology to customize content to individual recipients.

Mobile apps benefit from the same technical advances that drive the chatbot trend. Still, apps meet a growing competition from such substitutes.

Call centers are costly to maintain. Verbal communication is susceptible to misinterpretations.

Information is only pull-based (accessible if a user purposely seeks it).

Communication by email requires manual labor. Messaging platforms tailored for businesses are more efficient for communicating in a workplace.

Mobile apps are costly to develop. The information that can be acquired through mobile apps is limited.

Personell can spend more time handling more complex issues. Higher service availability, more often and available instantaneously.

Chatbots are both push- and pull based. A more effective and dynamic way of acquiring information.

Chatbots are both push- and pull based. Communication is more similar to preferred human interaction.*

Chatbots are instantly accessible, and do not have to be downloaded.

Simulating a real person and understanding sentiments. Handling a wide range of tasks.

Websites presents information in a detailed manner. Chabots can misinterpret user intents.

Information distribution is relatively more confined to a conversational interface. Chatbots may have less functionality than apps.

Chatbots are limited to certain chat interfaces. Even though messaging apps are widely spread, almost everyone has an email.

Chatbots do not have to fully replace current solutions, but can be a beneficial supplement.

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01

Introduction

02

Key trends

03

Market potential

04

Case Study 1: WASTY

05

Case Study 2: Cuoca Rita

06

Why EY


Chatbots have been applied to a range of different business areas Banking and insurance

Accounting

MasterCard has launched a bot on Facebook Messenger that allows users to complete transactions, manage finances and complete payments.

Accounting chatbot Pegg can “make light work of your expenses, record your receipts, track your spending, and access your accounts in a simple, fun way”, and aims to help small businesses with accounting tasks.

Enterprise

News and Media

The American tech startup Talla has developed a bot that works as a workplace assistant. Chatbots are already performing administrative duties like organizing meetings between different parties and room scheduling.

Retail

Toy industry

Clothing giant H&M has made available a chatbot on the messaging app Kik that acts as a “personal stylist”, giving clothing advice. *

Cognitoy’s Dino is both a chatbot with speech recognition and a toy. It can answer questions and tell bedtime stories. Dino’s features include age appropriate vocabulary and math quizzes, as well as personalized interaction.

Travel

Law

Food and restaurant

Health care

Taco Bell launched a chatbot on Slack that easily lets users see menus and order food.

The “Personal nurse” bot Florence reminds people to take their medicine, whereas Your.md helps people understand their symptoms, to seek medical help and avoid unhealthy habits.

Education

Movie industry

KLM has introduced a chatbot that interacts with customers. Its tasks include sending itineraries, boarding passes, check-in information and delay notifications to customers.

Jill Watson, based on IBM’s Watson, is a chatbot that acts as a teaching assistant, answering student questions and urging them to complete their assignments on time, amongst other things.

* See appendix for more details

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CNN, The Guardian and The Washington Post are some of the news channels that are utilizing chatbots. Through Messenger, users easily navigate through articles and topics, read and watch content and subscribe to newsletters.

Jordan Browder’s chatbot DoNotPay helps people with simple legal procedures, such as fighting parking tickets and complaining to airlines. Four months after launch it had helped people save $2 million. *

Disney launched the chatbot Judy Hopps, based on the character from Disney movie “Zootropolis”. The bot acted as a game, where users could solve mysteries with Judy Hopps.


…successfully helping users with the following tasks

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Customer service and support

“What kind of insurances do you offer?” “How do I change my password?” “My computer isn’t working as it should”

E-commerce

“I’d like to order a flower delivery for Valentine’s day” “I’d like to book a flight to New York for next Wednesday”

Personal helper

“I think I mistakenly received a parking ticket” “Remind me to take my medicine at 2pm every day”

Productivity

“Put milk on the shopping list” “What is my schedule for tomorrow?”

Sports and entertainment

“I want to play some trivia” “Give me the scores from Man United’s latest soccer match”

Information gathering

“How is the weather outside today?” “Are there any Chinese restaurants in this area?”


…and providing the following benefits Chatbots can produce value for both companies and users…

Availability

Resource allocation

Information utilization

24/7 service Instantly available, yearround, 24 hours a day.

Productivity Chatbots are suited to handle routine tasks, enbling employees to engage in more profit driving activities.

Tracability (non-repudiation) By storing chat logs, companies can obtain a clear view of user inquiries.

Eliminates waiting time Time spent waiting in line and waiting for manual data lookups is eliminated. Scalable Once deployed, the chatbot can easily cater to increasing amounts of users.

A supplement to existing solutions Users can be redirected to e.g. relevant parts of the company websites or to human customer service whenever neccessary.

On the user’s preferred platform Allows users to interact with the company on the platforms they already are on.

Cost reductions Firms can reduce personnel costs if their bot can do tasks previously performed by employees.

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Analytics Chat logs can be analyzed in order to improve service and learn more about users. Consistency Well-trained chatbots will always reply in the same manner.

…helping to realize several positive effects Increased user satisfaction Neat, convenient chatbot services can help increase user or customer satisfaction. Increased user engagement Having an easily available chatbot that performs key tasks well may lead to more interaction with users. Personalized user experience The chatbot can learn about users through e.g. their social media profile or user provided information, allowing it to deliver content that mathches specific user needs. Synergy effects benefitting users With a growing number of chatbots available on each platform and an increasing chatbot awareness, users can fulfill several needs and desires in one place, reducing the need for external apps.


The market potential for chatbots is huge, although limitations do exist Chatbots are versatile, with many application areas Chatbots have already been successfully applied to a wide range of industries. Their applications include answering FAQ questions, providing entertainment, being personal assistants, and a lot more.

They cannot do everything… Chatbots are often best at doing simple, specific tasks, and may struggle if they are asked to deal with too big domains. They do have limitations, and one has to be realistic when determining the chatbot’s scope and purpose.

…but they still have a lot of benefits Even simple chatbots with limited domains can help fulfill business needs, and they can be good supplements to e.g. a firm’s existing customer service. Chatbots are always available, they can help increase employee productivity, and they can provide companies with new customer insights.

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There are many cases in the Italy and Europe as well.. Elen from Enel Energia provides information to questions related to products and activities available

This bot can be used to obtain information related to sports events

eBay is using a bot to drive sales by including purchase tips as well as full process coverage into a Chatbot ToBi from Vodafone provides information about commercial topics as well as support with a smooth conversation with the client

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Fissan bot provides tips and tricks to moms and dads to better care their children

Italian newspaper bot provides relevant news to customers

iGuzzini bot provides pre-sales support to their customers as well as company information Meteo.it bot allows to obtain information about weather conditions by providing him the location


There are already some examples of very successful bots on the internet, either on Customer Care... Vodafone TOBi TOBi tries to answer queries of the customer and if the question is too intensive to deal, the chat will be transferred to a real advisor Used in Italy by 1.5 million users each month, through Web, Mobile App and Facebook Messenger Vodafone goal was to develop the best conversational experience in the Vodafone world, to taking care of the customer in every aspect TOBi learns by interacting with the customer during the conversation, and improves the neural networks supporterd by an Artificial Intelligence team. Page 21


But also to drive purchases from customers directly on the conversational interface eBay Shopbot Personal shopping assistant available through Facebook Messenger Create a new pathway to get customers directly to all the best parts of eBay, helping the shopping experience become a little more tailored. Powerful engine that drives the customer inside eBay products providing them specific filters based on item category Capabilities to retain information from user (eg. Favorites) and ask questions to improve the experience and products shown Page 22


The key topics of this report can be summarized with these four questions How can a chatbot improve business processes?

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Which benefits can a chatbot help realize?

What are the technical considerations I should know of?

Which considerations affect whether making a good chatbot is possible?


Do’s and don’ts on the way to success Development ►

► ►

► ► ► ►

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Define a clear purpose for introducing a chatbot – make sure the chatbot is the best solution to a real problem Keep in mind the limitations that exist with today’s technology – most chatbots today rely on decision trees Focus on doing one or a few tasks really well Make sure the bot can scale to fit your needs

Think that a chatbot will solve all your business’ problems Overestimate what can be accomplished with a chatbot at present Create a bot that does a lot of things badly rather than a few things well Release your bot prematurely

Deployment ► ► ►

► ►

Use pre-launch user testing Make sure people know they are talking to a chatbot Have a clear strategy for what to do when the bot fails to help, such as letting humans take over Make the bot adapt language and way of speaking to different users

Make your bot appear to be human – this may lead to a loss of user trust Frustrate the user by having him/her repeatedly try to unsuccessfully do the same thing Mimic user too heavily – it may make people feel under surveillance


01

Introduction

02

Key trends

03

Market potential

04

Case Study 1: WASTY

05

Case Study 2: Cuoca Rita

06

Why EY


From simple "out of the box" chatbots to an advanced and integrated platform Semantic Engine

Machine Learning

Write what I understand

I understand what you write

The chatbot works by keywords, following a predetermined conversational flow and providing identified answers within the perimeter

The chatbot includes the phrase, learning concepts and expanding its knowledge base, and responding based on the input provided by the user

Wasty 1.0

Wasty 2.0

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Omni channel l understand what you write .. what you show me .. what you tell me ... and how you say it The chatbot incorporates user input through multiple modes (text, voice, visual ..) and responds accordingly Wasty 3.0


Advanced Semantic Engine

Ciao

Database

Chatbot

Telegram

«Happy flow» without exceptions

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Ciao, sono Wasty. Inserisci Tipo Richiesta Smaltimento Rifiuti

In quale indirizzo vuoi conferire il tuo Rifiuto?

Io abito in via Stalingrad o numero 27

Prodotto

Prodotto

Indirizzo

Indirizzo

Cliente

Cliente

Ricerca Indirizzo

Che tipo di cliente sei? Azienda

Che tipo di prodotto vorresti smaltire?

Vorrei smaltire della carta per fotocopie

Puoi effettuare lo smaltimento nella Stazione Ecologica più vicina: Bologna CAAB nei seguenti Orari di Raccolta: …..

Famiglia

Prodotto Via Stalingrado, 27

Indirizzo Cliente

Via Stalingrado 27 Famiglia

Prodotto

Carta e cartone

Indirizzo

Via Stalingrado,27

Cliente

Ricerca Prodotto

Famiglia

Generazione Risposta Finale


Advanced Semantic Engine

The applied logics: the research of the product INPUT

Product Research

1

Stop-words removal

2

Stemming of the sentence

3

Search for the synonyms in the sentence

Vorrei smaltire della carta per fotocopie

Sinonimi Prodotto Vorrei smaltire della carta per fotocopie vorre smalt cart fotocop

vorre smalt cart fotocop

Carta e cartone

OUTPUT

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439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 … 664

agend bicc cart cart carton giornal rivist block notes libr quotidian quadern fumett cart fotocop scatol scarp scatol farmac scatolon … plastic

Prodotto 1 Agende 2 Bicchieri di plastica 3 Bicchieri in materiale cartaceo 4 Bottiglie in plastica vuote e pulite 5 Carta e cartone 6 Cartone per pizza (pulito) … … 33 Plastica


Advanced Semantic Engine

The applied logics: the address search INPUT Io abito in via Stalingrafo numero 27 Ricerca Indirizzo

1 2

Search for stopwords +Civic Matrix calculation of distances with Street map

Io abito in via Stalingrafo numero 27

via Stalingrafo numero

Via Stalingrado

3

Extraction of addresses more similar to the one entered

Via Stalingrado, 27

OUTPUT

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Distanza di Damerau Levenshtein + Distanza di Jaro Winkler via Stalingrado numero

Via 1.0 0.13 0.33

Solferino 0.22 0.46 0.49

via Stalingrafo numero

Via 1.0 0.13 0.33

Stalingrado 0.19 0.89 0.52

Street map 1626 Via Solferino 1627 Via Spartaco 1628 Via Speranza 1629 Via Spina 1630 Via Stalingrado 1631 Via Stanislao Mattei 1632 Via Stazione Roveri … … 1827 Vicolo Viazzolo

44,48784 44,49132 44,50612 44,48745 44,5162 44,49175 44,49728 … 44,48592

11,34255 11,37741 11,29621 11,38134 11,35972 11,32342 11,41064 … 11,34887


Advanced Semantic Engine

The applied logics: the generation of final answer Scenario

Prodotto

Carta e cartone

Indirizzo

Via Stalingrado, 27

Cliente

Famiglia

Output Scenario

1

Search Door to Door for Scenario

2

Existing door to door?

3

Via Stalingrado, 27 (id:1630)

Door to door information extraction

id civico id_via id_zona 6612 27 1630 10 6613 35 1630 10 … … … … 6615 37 1630 10

Calendario PaP

Calendario di raccolta più Orari relativi al prodotto indicato

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Anagrafica SE

Porta a Porta

1

Identifying the nearest Ecological centre

2

Extraction of the access times to the station

Stradario

Casistiche Orari

Stazione Ecologica più vicina e relativi Orari di Conferimento


Machine Learning Engine

Extrapolate the conversation variables from a sentence «Abito in Piazza Maggiore e dovrei disfarmi il contenitore PET dell’acqua che usiamo in casa. Quando c’è la raccolta?»

«Abito in Piazza Maggiore e dovrei disfarmi il contenitore PET dell’acqua che usiamo in casa. Quando c’è la raccolta?»

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Indirizzo

Tipo Rifiuto

Tipo Richiesta

Tipo Cliente

Piazza Maggiore

Plastica

Smaltimento

Famiglia


Machine Learning Engine

Training is the basis of the solution Training (1) Wasty is trained by inserting sentences and "labeling" the "entities" in the sentences

Attualmente abito in Piazza Maggiore Abito in Via Berti Pichat Vivo a Viale Mazzini

Entity Family

Sto in Piazza Liberty Ciao, risiedo in Via Ugo Bassi

New input From interaction Si mi trovo in via Irnerio

(2) The moment that new inputs arrive, these are processed by Wasty

(3) Wasty extracts phrases from the input and identifies the "entities" that with a certain "confidence" he believes are present

Extractions Wasty

Adesso ho traslocato in via Centotrecento

Si mi trovo in via Irnerio Adesso ho traslocato in via Centotrecento La mia casa si trova in via Avesella

La mia casa si trova in via Avesella

(4) In the event that Wasty is not sure that what is written is â&#x20AC;&#x153;associated withâ&#x20AC;? an entity, it will be necessary to retrain Wasty on the unrecognized phrases

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Evolution of conversational systems

New functionalities: Recognition of images Training Instagram

Pinterest

(1) "Sample" images are collected from different sources and the most common "tags" are extracted using Google's "Image Recognition" tool

TAG

#Bicchiere di Plastica Image recognition

New image from integration

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(2) When a new image arrives, it is processed using the same Google tool

(3) On the basis of what Wasty has "learnedâ&#x20AC;&#x153;, it identifies which is the category to which the image belongs

Wasty 3.0


Evolution of conversational systems

New functionalities: Voice recognition and management MS Speech API True Text

MS Speech API

Wasty 3.0

Dovrei smaltire della plastica

Synthesized voices MS* vs Google* Eng

Dovrei smaltire nella plastica

Eng

Google TTS Ita

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Ita

*Rilevazioni effettuate a Marzo 2018

Dove devi smaltire?


Evolution of conversational systems

New functionalities: sentiment detection Sent phrases from the user Si vorrei smaltire della plastica Mi stai scocciando: ti ho detto che vorrei smaltire della plastica ma non mi capisci!

(1) The user writes a sentence that is sent to the "sentiment detection" engine

Sentimento positivo

Sentiment Analysis

Sentimento negativo

(2) The engine returns the sentiment of the sentences that the user has written

(3) Depending on the sentiment the chatbot returns more-or-less a formal response

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Extractions

Response of the chatbot

Qual è il tuo indirizzo? Qual è lâ&#x20AC;&#x2122;indirizzo in cui lei si trova in questo momento?


01

Introduction

02

Key trends

03

Market potential

04

Case Study 1: WASTY

05

Case Study 2: Cuoca Rita

06

Why EY


Chatbot for enterprise use

An idea to develop new digital services for employees Develop new digital services to improve the dining experience and transform it from a «place to have lunch» to a «user-centric omnichannel service» SIMPLE EVOLUTION

FOCUS on NUTRITION

COMMITMENT and «CONTROL»

Replacement of PDF with «daily menù» with a Chatbot Service provided through web and able to present meals and provide personalized hints to the end user.

Promote a healthy lifestyle and incentivize it through a reward program to follow a balanced nutrition regimen

Involve employees into using new tools and improve perception and healthness of the company while at the same time gather nutrition data from employees

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Chatbot for enterprise use Preview

Chatbot is configured to interact with the employee and discuss about mediterranean diet in an «educational approach», while still providing daily menù in a «practical approach». By gathering data about food choices and matching them with data provided by the employee, Chatbot is able to provide tailored ideas for new food experiences.

Page 38


Chatbot for enterprise use Cuoca Rita features (1/2)

CHATBOT

DASHBOARD

REWARDS

Provides information about weekly menĂš, suggest meals and leverage preferences to provide customized services

Provides detailed information about meals and supports the mediterranean diet by helping choose the right food

Incentivizing logics enables to gather points that can be converted in prizes and gifts

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Chatbot for enterprise use Cuoca Rita features (2/2)

ADDITIONAL SERVICES

EDUCATION

PROFILO

The tools is a hub that provides collateral services, like business lunch or lunch box booking

Informative contents allow to better understand how to follow the principles of health and well balanced nutrition

Each user can add personal data like height, BMI, etc. to obtain tailored tips

Page 40


01

Introduction

02

Key trends

03

Market potential

04

Case Study 1: WASTY

05

Case Study 2: Cuoca Rita

06

Why EY


Our Methodology

Step 1: Map the user journey, design the experience Adopt a customer-centric mindset Understand the ecosystem in which you are introducing a ChatBot:

Persona analysis

Key interactions

Pain points

Signature points

Key considerations for a great experience Begin with the target customer and their needs and desires Address real issues that will generate positive experiences

End-to-end journey map

Employee and customer insights Persona analysis EY insights Research insights

â&#x20AC;&#x153;Avoid the shiny new toy phenomenon.â&#x20AC;? Page 42


Our Methodology

Step 2: ChatBot concept modeling & interaction flow EY has successfully developed and implemented in-house Natural Language Understanding models and Conversational Design tools for our clients. This suite of NLP analytical accelerators can dramatically increase the effectiveness of resources when designing & reviewing customers interactions. User Input

Concepts/Entity Analysis COMPANY We should meet ABC-Co

in

LOCATION

TIME

Detroit

9:30AM

at

Semantic Understanding

When the user inputs the data

Response

Intent

Final response to the user

Recognition of intent of the user Conversation Flow

An example of a partly generative bot is Microsoft’s Twitter-bot Tay

subject

object

Emotionality/Sentiment “I'm not happy with the terms of my contract and every time I call in to pay my bill I have to wait a long time.” Sentiment: Negative Confidence: 0.8112

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Service Call

Task

Fetching the required data from the external APIs.

Recognition of task

Entities

Dialog

Recognizing entities

Triggering of the dialog


Our Methodology

Step 2B: Conversation design “Hi Brittney, thanks for contacting American Family to initiate an auto-storm claim. For immediate assistance, please click the link below to speak with Amy, our American Family virtual assistant. www.amfam.com/claims/storm-claims” User texts AmFam

“I'm Amy, American Family's virtual assistant Chatbot. I can help you with your auto-storm claim.”

Inputs Zip Code

Click on URL Claim Center has the Insured's zip code on file.

Claim Center has the Insured's phone number on file, matches Insured's first name and policy number.

09/22/2017

“Thanks. To further ensure it’s you, please select the year of your insured vehicle.”

“To begin, could you please enter your zip code?”

Claim Center has the Insured's year of vehicle on file and only provides one correct option.

“Can you please provide the date of the hail damage (MM/DD/YYYY)?”

Claim Center is able to provide a claim number based on the required information provided. Additional information will be collected by the American Family handler.

Selects ‘2006’

2006 2009

Chatbot autopopulates vehicle type if only one on file. “To confirm, has there been damage to your 2006 Honda Civic?”

“Sorry to hear there that. What was the cause?” Selects Yes

Selects ‘Hail’ “Thanks, Brittney! Your claim is now being processed by American Family. Please take note of your claim number 00-00-002197 for future reference. A representative will contact you within the next 24 business hours to finalize this claim.”

1999

Hail Other Conversational Interface submits data request to backend system for customer service group to respond to member’s claim.

Claim Center is able to identify the vehicle involved and determine possible causes based on weather, events.

Yes No

Very Satisfied

Very Satisfied “In the meantime, please rate your experience with the auto-storm claim service:”

Satisfied Neutral

“Thank you, I'm glad I was able to help. Please let me know if there is anything else I can help you with today and feel free to share your experience on our Facebook page: https://www.facebook.com/amfam/”

Unsatisfied Very Unsatisfied

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User message

Bot message

User action

Back-end Function

Menu Option


Our Methodology

Step 3: NLP & knowledge engineering

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Our ChatBot Accelerator Offering provides the method and assets to accelerate time to value User research, design & user testing

Conversation design & knowledge base creation

Internal pilot & defect resolution

User pilot & knowledge base refinement

Rollout>>

Bot development, services & integration

Define value case

Define governance & product roadmap

Measure value case

Core systems insight & integration 12 – 16 weeks •

The phase begins with a scoping workshop to define the journey, select use cases and define the value case for the pilot.

The deliverable is a ChatBot deployed to a Pilot set of users (typically, between 50 – 100 in order to get coverage, statistics and feedback on the breadth of the knowledge base – but this is TBD with the client).

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Why EY Our qualifications, expertise and experience Cross Disciplinary Team

Multi-faceted across UX, Advanced Analytics and Insurance Domains. Integrated methodology covering User Journey Mapping, Conversation Design, Natural Language Understanding, Knowledge Engineering and Systems Integration. Each facet brings a unique set of accelerators to efficiently deliver quality output.

Experience across Multiple Industries

We have delivered bots across multiple verticals â&#x20AC;&#x201C; financial services, consumer products, mining & manufacturing, public sector and health. EY has also incorporated bots in its platform products for a variety of use cases, and in internal employee self-service and knowledge management solutions.

Technology Skills & Alliances

While we have platform agnostic subject matter expertise, we are also skilled in the leading bot platforms of today. Microsoftâ&#x20AC;&#x2122;s Azure bot framework is one of those primary platforms - Our strategic alliance with Microsoft has enabled us to rapidly skill up in this technology to build bots for our clients and ourselves.

Insurance Domain Expertise

We have the deepest and broadest subject matter expertise in the Insurance Domain among consulting firms. We incorporate the right level of insurance business and systems analysis expertise to make our insurance projects successful.

Institutional Knowledge

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We know clients, and have been close partners with them on transformation projects; we have acquired institutional knowledge of business processes, technology and the organization that we believe will help us work efficiently and deliver a solution quickly.


Thank You!

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Evento "Digital Transformation: Tecnologie digitali e nuovi paradigmi di business"  

Presentazione dell'evento "Digital Transformation: Tecnologie digitali e nuovi paradigmi di business", tenutosi il 3 dicembre ad Ancona e or...

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