Chatbot Research Paper_FIL GDW_Edited_v0

GLOBAL WEALTH
FIDELITY CHATBOT
RESEARCH
OCTOBER 2017
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MARKET RESEARCH
Global perception of Chatbot
Chat bots are gaining popularity globally. A 2017 survey by
Liveperson 2 , which surveyed over 5,000 participants across 6 countries,
found 38% of consumers globally already rated their overall
perception of chatbots as positive, despite the technology as relatively
new. Only 11% of those surveyed globally reported a negative
perception of chatbots, while the remaining 51% took neutral stances.
However, it is interesting to note that over half of consumers in Japan
and Germany would prefer to speak with a bot than a human (Table
2).
Table 2: Communication method preference
While overall sentiment toward chatbots outweighed the negative
(Table 1), the majority of consumers still prefer human assistance. 56%
of global consumers still prefer speaking with humans, with 60%
believing that a human would better understand their needs than a
chatbot would 3 .
Table 1: Sentiment toward chatbots
The usage of chatbots continues to gain momentum. Of consumers
who have interacted with bots during the past year, 67% used them for
customer support while 30% chatted for fun and 25% for purchases
(Table 3). In regard to customer support, globally, most consumers
(52%) would not be open to waiting more than 2 minutes to chat with
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customer care agents, considering that to be a sub-par experience.
That said: consumers in Japan and Germany appear to be the most
patient, 25% of respondents from both countries would be willing to
wait 3 minutes and still rate the customer service as excellent.
Interestingly, 25% of UK respondents would wait more than 5 minutes.
Table 3: Bot with name & personality
apt opportunity to harness the strength of both bot and human
channels, use the bot first: 67% want to be transferred directly to a
human when the bot does not understand what they need. 26% are
more forgiving and would be prepared to reword or try their requests
again.
Research highlights that bots with personalities are generally
preferred (Table 4). Many customers, especially in the US, don’t care
about personality, however more than 40% of German and Japanese
consumers believe in giving bots names and personalities. Friendly
bots are generally preferred over formal bots except in Japan, where
a formal personality rules the consensus.
In terms of chatbot implementation, 92% of businesses want to build
chatbot on Facebook Messenger, while 80% want to house their
chatbots on their own company website, followed by Slack and Twitter
respectively, according to another Chatbot Survey by Chatbots
Journal 4 . One of the biggest statistics comes from Facebook, in just a
year, Facebook Messenger has significantly grown to enable more
than 100,000 developers who have made around 100,000 bots.
According to Venturebeat survey, Facebook Messenger now has more
than 11,000 chatbots have been created for users to try. 5
It is expected that chatbot adoption will drastically pick up and
become more intelligent in 2017.
The survey suggests that human support agent is still essential for
businesses that integrate chatbots into their services. This identifies an
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Other key figures on global perspective
Source: Chatbot Survey 2017 by Chatbot Journals, surveying 300+ organisations 7
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1. Chatbot that help with Form Filling:
Hello.Vote
• What: Voter registration in 2 minutes
• Like: Targets people more likely to engage via smartphone than
respond to a mass mailing campaign
SPIXII
• What: Insurance
• Like: Designed to enhance customer experience by replacing form
filling
• Regulated by the FCA and can speak all existing languages
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4. Chatbots in Financial Services
erica, Bank of America Merrill Lynch
• What: make payments; check balances; save money; pay down
debt; look at educational videos; etc.
• Like: Lots of work went into teaching erica to learn conversational
nuances and conversational contexts
• Bot is also being trained to find insights for customers and make
recommendations: e.g. if a customer’s FICO score dropped, for
instance, erica might suggest better money habits, drawing on a
partnership with Khan Academy, a provider of educational tools.
The chatbot may identify ways the customers could save more or
pay down debt. erica could help customers avoid mistakes like
missing a mortgage payment.
• Next steps: BoAML is looking at integrating the technology with
mainstream virtual assistants like Alexa and Siri. Working proof of
concepts already exist 23
Wells Fargo
• What: account balance reporting; finding closest branches;
providing spending breakdown; launched May 2017
• Next steps: the bot is powered by machine learning, i.e. an
engine that learns over time, so eventually its keyword responses
will be replaced by full conversation. Once the bot has mastered
the art of conversation, it will learn emotional comprehension 24
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Chatbots Landscape 2017
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TECHNICAL RESEARCH
Introduction
The chatbot market place is currently flooded with a range of chatbot
solutions from novel implementations to enterprise grade. It is
predicted that by year 2020, 80% of all first-line user-business
interactions would be fielded by a chatbot. A chatbot enables the
human agent to concentrate on higher valued tasks like business
development, customer understanding, tailoring products/ services
and conversations per user, reduce organization costs, among many
benefits. This brings to the fore the key question – “How do I design
and implement an enterprise grade chatbot?” 25
There are three design principles which can be used for this as shown
in diagram below. And technology selection and implementation is the
3rd step in this process. Once the business has a good understanding
of why and if the users would want it, then comes the technology
question – which platform to use, what NLP engine to implement, how
to design conversations, etc?
Recognizing the market trend, all of the technology giants have
already made their foray into this area, either for improvement of their
own products/ services or as a commercial offering for other
organizations to implement on their own. Google integrated API.ai into
their cloud offering and has been acquiring additional businesses to
bolster their offering. Microsoft built Language Understanding
Intelligent Services (LUIS) which can plugin into any dialog manager
as well as their own BOT framework. Facebook has recently acquired
wit.ai to enhance their products. IBM has built the most popular
enterprise chatbot infrastructure as part of their Watson conversation
engine 26 (Table 11).
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Sample Architecture
The existing technology providers are rapidly addressing organization needs and security requirements in terms of re-usability of conversations for
community model training, PII, etc. In any chatbot infrastructure or technology stack, there are certain core components of an enterprise grade
chatbot architecture. They are listed below and these are incorporated into the available offerings in varying degrees of flexibility and stability.
At a very high level, as described in a best practice article for building bots, the general areas of a chat bot are as follows.
The figure below shows a sample architecture that is built with a vendor agnostic view of the chatbot solution. The exact architecture will be designed
in the next phase, but the figure below is a step in that direction. The technologies and vendors will be finalised in the design phase. GDW would be
integrating into this architecture via API calls to the CRM systems and any user related data warehouses. One of data content providers would be the
CMS system, providing curated content from FIL. There is no specific data technologies in use, especially for the Engage phase, but once the scope of
the phase is finalized, the exact data technology requirements will be finalised as well.
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Generative vs. Retrieval Based Chatbot Models
There are broadly two types of chatbots, as defined by the way they
respond to user interactions 27 :
Generative models are difficult and complex to build as they rely on
the model to learn automatically from the conversations, but are also
prone to learning abusive, unconventional and un-curated content.
These models are still the lab phase.
Retrieval-based models are those seen implemented commercially as
they provide consistent responses using curated content and
conversations. They rely on the fact that there is a list of responses to
choose from and the model chooses the closest to right response from
the list. In effect, it retrieves a response instead of generating it. This
process of response retrieval can be made as bespoke and as
business rule friendly as possible. See diagram on the right such
version.
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Implementation Options
Our research in chatbot technology stacks and implementations has identified the following breakdown of different components of an enterprise
chatbot implementation (see Appendix 1 for terminology):
Conversation Channel
Messaging
Service
Conversation Providers
Dialog Managers with
Convo. Design
Chatbot Analytics
External Content
Providers
• Facebook
• SF chat add-in
• SMS
• iMessage
• Slack, etc.
• Pubnub
• Twilio
• RabbitMQ,
etc.
• Detects presence of users
• Delivers content to the user via channel of choice
• Real time or otherwise
• Tree based
• Graph based
• Branded
• Unbranded – Built
bespoke
• News articles
• Reports
Internal Content
Providers
Analytics Engine Conversation Data Conversation Engine Data Storage
• News articles
• Fidelity product
Reports
• Fidelity curated
marketing material
• ML Knowledge Base
w/ Insights engine
• Customer profiler
• Product/ Service
recommendation
engine
• Sentiment analyser
• Emotion analyser
• NLP service (optional)
• Data
transformation
service
• Conversation
map
• NLP Engine: Interprets user entered text into machine readable and machine
interpretable content
• Rules/ Transformation engine: Business rules and logic is applied to the logic
• Intent service: Identifies the intention behind a conversation from a predefined
list of intents
• Entity service: Extracts conversation specific data-objects which are passed on
to other services as parameters
• Context service: Captures context and carries forward or relinquishes context
of a sub-section of a conversation, based on the conversation map
• Fulfilment engine: Defines the logical and (or) data end point of a
conversation that defines the end of context and intent
• Emotion service: Identifies the emotion of the user via the conversation text
• Sentiment service: Identifies the sentiment of the user via the conversation text
• User
• Conversations
• Contexts
• Conversational
metadata
• Session
Within Fidelity, there are several instances of a chatbot being implemented for specific purposes with varied degrees of success. One of the pivotal
pieces of work has been in the area of creating a template of an enterprise grade chatbot architecture from FMR. There have been other successful
chatbot implementations in HK, etc using ClaireAI (similar to traditional big players of API.ai or IBM Watson). These would be explored in further
detail in the next phase and a decision made and a solution designed in the next phases of Design and Build.
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