Insurance Chatbot

Success KitHow to transform your digital customer

experience

The 3 Pillars of an Insurance Chatbot

Part 2

Here's how to accelerate the design of successful chatbot conversation, working alongside compliance

and regulation.

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Pillar 1

What value are you bringing to your customer?

Let's imagine you need to build a chatbot to help people buy travel insurance. In this case, your value lies in the end travel insurance product. So, how can you deliver it most effectively?

To start mapping out your current customer journey, you'll need a desk and post-it notes. Write down each step on separate post-it notes and lay them out. Where does the journey start and where does it end? Consider every possible scenario. This will provide an overview of problems you can turn into opportunities to add value to your services.

First, look at the problems your customers are facing. Then look at how you can solve these problems.

Value

Challenges

The Value, Meaning, Engagement Framework (VME)

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Each use-case will mean something different for your customer, and may vary person to person. 

Let's take Sue as an example. 

Situation A: Sue is looking to file a claim on life insurance after her husband passes away. The priority is to connect her with a compassionate customer service handler as quickly and easily as possible. The last thing she needs is to search for a phone number online or wait for hours on hold.

Situation B: Sue is moving house and needs to update the home address on her insurance policy. This simple, day-to-day task could be completed in seconds through an online chatbot. Instead of needing to call while at work, she can do it when she gets home at 9:30 pm.

It's important to carefully evaluate the meaning of each interaction when designing your chatbot. In Situation A, the situation will likely be very emotional and need an immediate human switchover. Meanwhile, in Situation B, a simple, seamless service is required to take Sue from A to B. 

How do you want your customer to feel? List the emotions. For instance, you could write:

Meaning

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Engagement

Delight. Surprised by the ease and simplicity.Trust. Feel that the insurer is giving you all the information you need to make a decision.Laughter. Perhaps as part of your branding, you would like to create a bot to charm your customer like a charismatic broker. 

One of the ways a chatbot can engage a user is through gamification. However, you must apply this with caution. Gamification is used in technology and social media to create addictive habits for the user. For instance, an Instagram user might keep returning to the app to get more likes and follows. Each like, follow and positive interaction is registered as a reward, together with a release of dopamine.

If you're selling a product, gamification is potentially unethical. Yet, if you're looking to encourage good financial habits such as saving money each month, regularly checking a bank balance or checking an insurance cover, it might be useful to get customers better protected.

One example of gamification is PNC's Virtual Wallet, and its 'Punch the Pig' game.

And it worked! One reviewer called it "one of the coolest features ... when I was spending money like crazy, I used to justify things by saying "it's only a dollar, it's only ten dollars". Well, now I do the same thing with saving money! I see the pig and can't help clicking."

What would your customer users say? The next pillar explores the fundamentals of user testing.

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"The only thing more rewarding than saving money is having a little fun doing it. That's the idea with Punch the Pig.

When you're banking online, you just "punch" the piggy bank and money (the amount of your choice) will transfer from your

Spend account to your Growth account. (That "punch' is a "shake" for smartphone users.)"1

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1 2

https://www.pnc.com/en/personal-banking/virtual-wallet-overview.html http://www.sooverthis.com/pnc-virtual-wallet-love-of-my-life/

Pillar 2

This is perhaps the most important step of the entire process. It's easy to forget how much work goes into every product behind the scenes.

To make it seamless, you must test every potential outcome thoroughly. Ensure you have as diverse a customer group as possible. To make insurance accessible for everyone, and for a world that's 100% insured, we must recognise the needs of customers vary widely.

Questions to consider:

Instead of relying on gut feeling or instinct, it's key to test with people outside of the insurance industry. You might be surprised what feedback you receive!

User testing is an integral step to the Spixii chatbot, which informs the analytics. The analytics dashboard shows you where customers drop out or stop engaging. If you're able to get this information, it is often helpful to see whether this is an issue due to the chatbot itself, or the information being asked. Do they have enough background or context to answer? Is the language unclear?

User testing

Conversation scripting

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How would someone with a visual impairment use the chatbot?Does the language suit a bilingual tester, who speaks English as their second language?If you use colloquialisms, are any culturally sensitive?Does someone with limited digital skills intuitively know where to click to continue the conversation?

Another important feature of usability and customer-centric design is its length. How long is your conversation? If you see a lot of customers dropping out towards the end, try reducing the number of questions. Tools such as the Spixii CXD allow you to visualise your conversation and see where you can simplify or shorten the customer journey.

Signpost your customer throughout. For instance, tell them when they're halfway through or how many questions they have to go. On a form, you can scroll down to see the number of questions. In a human conversation, there may be spoken signposts or visual body cues that build trust. Design your chatbot to mediate between the two.

Dialogue management

Signpost your user

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Matteo Schgor Spixii UX & UI designer 

Pillar 3

Once you have optimised your workflow, you can add Natural Language Understanding (NLU). 

In chatbots like Replika, an app that "learns from you, gets to know you, and keeps your memories"   using artificial intelligence, NLU is frequently used. This means you can type freely and give detailed answers, just like you would a close friend. The interaction feels personal. Indeed, it may even start to feel like a friendship if used regularly.

However, NLU also makes conversations difficult to audit for compliance, legal or marketing purposes. If the technology makes a mistake and misreads a customer's answer, the chatbot could switch a customer into an inappropriate workflow.

For instance, let's imagine your chatbot is designed to sell life insurance. If the chatbot misinterprets the size of your customer's family, it might switch to a workflow designed to sell life insurance for a large family instead of a small one. The differences would be so subtle, the customer might not realise and buy an ill-suited product.

In this situation, who's liable? The state of today's machine learning can never successfully predict situations with 100% accuracy. This would suggest overfitting: the practice of training on a model of data until it can successfully predict outcomes within that model. However, when presented with a new set of data, it will not be able to predict with the same level of accuracy. 

Liability

AI and natural language understanding (NLU)

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3 https://replika.ai/

In use-cases where 1% of error is acceptable, NLU can be used. For example, if a webpage receives an average of 60k visitors per month and a third (20k) interact with the chatbot, this 1% would equate to 200 people per month. This means 7 people every day. Here, evaluate whether your business can accept the risk of potentially them. Until NLU is 100% accurate, a chatbot with one-click buttons can be fully audited, thus meeting your legal and regulatory demands.

NLU should be seen beyond being a simple reply to a message. It should fit the context of the conversation.

AI has become a buzzword of the last few years, and not only in the world of fintech and insurtech startups. Indeed, in 2016, Andrew Ng, Stanford professor and former Google scientist, called it "the new electricity" at the WSJD Live Asia conference:

Arguably, one of the challenges with AI is in its 'black-box' tendency and a misunderstanding of its potential. In a recent poll, Spixii asked its Twitter followers what they expected to be the biggest benefit from using AI. The answers?

'Black-box' artifical intelligence (AI)

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"Just as 100 years ago electricity transformed industry after industry - transport, communications, healthcare

- today, we see a surprisingly clear path for AI to transform all of these industries."

Andrew Ng

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4 https://news.stanford.edu/thedish/2017/03/14/andrew-ng-why-ai-is-the-new-electricity/

43% automated processes 36% increased personalisation14% more customer insights

The application of AI to chatbot technology will certainly enable insurers to optimise their conversation flow and use data to choose the most effective question. With AI, there's no reason why you can't personalise every conversation you have with customers, and continuously iterate based on your customer data (as demonstrated by the value-adding wheel below).

Should we worry about AI and its application to insurance?

It's important to consider that we have already used AI, at least in a rudimental form, for decades. Joel Bassani, Insurance Consulting Manager, explains how AI can be seen in email management.

Concerns

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"For 20 to 25 years, your inbox filters incoming mail by determining whether it's spam or not. This filtering is based on two things: first, the repetition of words that you, or the filter, considers spam. Each time you mark an email as spam, the filter learns which words to look out for," Bassani points out.

"Today's artificial intelligence is a consequence of that technology. You can design more rules and improve them in this decision-tree style automation. You move towards individualisation, and the more individualisation you get, the more data. For insurance companies, this means focusing on precise requests from customers and using deep learning technologies to understand and learn from that content."

Undoubtedly, the application of AI and machine learning is not without its ethical challenges. An innovation of any kind must occur alongside careful discussions on its potential impact on society.

For instance, machine learning might enable a chatbot to personalise a conversation so efficiently, the customer buys a product that they don't need. Similarly, an overreliance on technology risks discriminating against those without digital skills.

The 3 As to consider when considering AI and machine learning?

Having these three things in place means one step towards better protecting customers.

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AccountabilityAuditabilityAI that's transparent

The idea for Spixii first took wings in 2012. The three co-founders, Alberto Chierici, Renaud Million and Alberto Pasqualotto, shared a belief in the social and economic value of insurance. Alberto C. and Renaud first met at a tutorial preparing for the actuarial qualification. Renaud was a French engineer by training, while Italian-born Alberto C. was a data scientist. Both were insurance professionals in London, frustrated by the way insurance was sold but passionate about its social and economic value.

This value was illustrated when Alberto C. delayed buying home insurance for his new flat for three years. Even while working in insurance, he struggled to find a product that was flexible enough. Meanwhile, Renaud's apartment was burgled and wasn't adequately protected to file a claim. Alberto P. also found himself unprotected when he returned to his car with its windows smashed in.

Insurance is important to everyone. So let's make it accessible.

In 2015, the co-founders left their offices and talked to people about their insurance stories. Inside, outside, in coffee shop queues, on work commutes - everywhere and anywhere, thousands of light-bulb moments happened across London. People called insurance a nightmare, or they had no idea where to start. 

For many, their first reaction was to text a friend or family member for advice. The idea for Spixii was born: a trusted chatbot that could take people under its wing and guide them through the process. 

Our story

Spixii, the beginnings

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Every year, Spixii runs a number of workshops offering in-depth consulting on these chatbot principles.

If you're determined to transform your digital customer experience, please email team@spixii.com or find out more here:

Ready to take the next step?

https://www.spixii.com/workshops/

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Spixiifiers at Plexal, the new Spixii nest