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Fast Facts

Chatbots - TechBrief

Introduction

Insure-Pro View

Chatbots have a role to play in today's value chain if used for high volume, low complexity triage style tasks when operating at scale to ensure cost efficiencies are achieved.


Integrated chatbots and platforms would allow greater benefits enabling these tools to share information and trigger workflow actions, but this is a wider technical challenge for the industry.


As natural language processing and artificial intelligence continue to evolve, we will only see these Bot’s ability to converse with us in a more human-like manner increase. The topic of emotional intelligence and artificial intelligence is of particular interest - how we train ‘bots’ to understand our moods, facial expressions (facial coding), speech tone and then respond to us factoring in these inputs.

Hiring - Robots for low complexity, high volume tasks - Must have experience dealing with customers; low complaints ratio a must; personality a Bonus.

The Statistics

  • According to the Business Insider the chatbot market is projected to grow from $2.6 billion in 2019 to $9.4 billion by 2024, a CAGR of 29.7%.

  • Gartner claim over 50% of enterprise companies are predicted to spend more money on chatbot development than on mobile app creation in 2021.

  • Salesforce estimates that 69% of consumers prefer to use chatbots because they deliver quick answers to simple questions.

  • Drift analysis shows that 64% of internet users say 24-hour service is the best feature of chatbots.

  • A 2017 IBM report stated Chatbots could answer 80% of standard questions.

  • Customer Service Chatbots are expected to be the fastest-growing market segment between 2019-2026 with a CAGR of 31.6%.

Impact

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Impact: Low

Potential: Low

Tech Maturity

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Current Maturity : Low

The Market

The ‘chatbot’ market really took off in 2016 as the technology became cheaper, processing power increased and the many no-code platform providers such as Chatfuel, removed the implementation complexities. Chatbots became a conversational topic with the likes of Amazon, Facebook, Microsoft, and Shopify all deploying their own bots.

 

Market sentiment has been negative at times, given the numerous early-stage failures – everyone recognises the terms ‘sorry, I do not understand you’, ‘I didn’t’ get that, can you repeat the request’ - however with integration into existing processes and technology improvements the ‘confused’ bot scenario has almost, died out.

The Techology

What are Chatbots? In essence, they are systems that allow individuals to converse with them using natural language. On websites in text form, website ‘chats’, and in voice form typically via automated calls.

 

The technology is quite multifaceted but, there are basically two types:

 

Rule-Based – These bots are programmed with a predetermined set of rules. Think of it as a conditional if:then process or root and branch structure. As you can imagine it works well for simple clear tasks where input questions and output answers are clearly defined. The ‘bot’ itself is stupid and only as good as the training provided to it.

 

Despite this simplicity, there are still challenges with different languages and question styles. This can keep developers busy as they attempt to program in a multitude of different permutations. For example, a user asking for a bank statement could say ‘Can I have my statement?’ or ‘what is my latest statement?’

 

They also need to be audited to ensure they are processing requests correctly after all it is a computer program, and the rule scripts need to be kept updated to reflect current processes and business rules.

 

AI Bot: Using complex algorithms and training, typically using recordings of real-life client calls to mimic actual conversations, this application is significantly more complex than the rule-based approach but, when done properly, can produce bot’s whose interactions can be much closer to that of a real person. They are often referred to as smart bot’s as they are self-learning versus the earlier rule-based model. The new Emotibot’s push the boundaries even further as they develop an understanding and can respond effectively to human emotions.

These AI Bot’s introduce a whole new set of issues for organisations ranging from - the validity of the training dataset - the ethics of the trained AI's - to the quality of decision making, requiring continual auditing to ensure the AI learning algorithms continue to work effectively.


Examples

Ada Health – Named after Ada Lovelace the first computer programmer, Ada is health-check app powered by an extensive data dictionary of medical knowledge.

 

Babylon – Covid-19 Mobile Care Assistant to help you work out if you are at risk, monitor your health, and connect you with the correct practitioners.

 

Next Insurance – Chatbot guides customers through the insurance buying process.

 

Insurify – Chatbot compares auto quotes from multiple carriers.

 

Zurich – Zara can assist you in the claims process.

 

Lemonade – Maya is there to help you through the sign-up process.


Tech Roadmap

1966: Enter Eliza, the first rule based ChatBot

1971: Parry, the first AI ChatBot

1995: Alice, NLP ChatBot

2010: Apple launches Siri

2014: Amazon's Alexa

2016: Facebook messenger platform introduces ChatBots

Examples

Technology

the market

fast facts

Key Players

Other Market Participants

market participants

Value Chain

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