Business

HAS COVID CONVERTED CUSTOMERS TO CONVERSATIONAL AI?

Published

3 years ago

October 4, 2020

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By Cathal McGloin, CEO of ServisBOT

A recent webinar hosted by Insurance Post asked customer service experts working at leading insurance companies whether social distancing would cause the demise of the traditional contact centre.

It was noted that, at the start of lockdown, insurers had anything between 15% and 50% of their contact centre employees working from home, which created a particular challenge for offshore operations. The assembled experts discussed whether they had seen any impact on customer satisfaction levels as their customer service colleagues adjusted to working from home.

Shorter queues

Andrew Jones, Head of Express and Retail Claims at Zurich insurance, noted customers’ willingness to use online portals because they assumed they would be waiting in a queue if they called the contact centre.

David Thompson, Director of Claims at Tesco Underwriting, noted that at the start of the UK’s lockdown in March, customers showed a great deal of empathy for the difficulties facing insurance contact centre staff as they adjusted to working from home. However, he noted that this early goodwill appeared to be returning to pre-COVID levels towards the end of September.

The webinar participants were asked to what extent technology such as chatbots and digital AI assistants had been used to assist with triaging insurance claims, to alleviate some of the pressure on contact centre staff, while still looking after customers.

David Thompson, Director of Claims at Tesco Underwriting, emphasised that when customers have been involved in traumatic events, they need to speak to a contact centre agent and value the empathy that can be provided during these conversations, adding “but certainly, there will be others who will be happier going through a digital journey.”

Paul Ridge, Banking and Insurance Specialist at SAS UK & Ireland, observed that consumers are becoming more accustomed to a range of channels available to them and a number of insurers are exploring how this shift could be used to introduce lower cost channels.

Distance Drives Digitisation

Ridge noted the value of using automation to take simple, routine, lower value tasks away from contact centre staff, so that they can focus on those customers who need more support. He believes that a good blend of technology and human skills can be used to benefit both employee experience and customer experience.

David Thompson agreed that automation and digitalisation had been ‘turbo-charged’ by the national lockdown. He underlined the importance of using technology to support the wellbeing of contact centre staff, noting that employee experience feeds directly into customer experience.

As Deloitte’s MD of Applied AI practice, Sherry Comes has observed, “While robotic process automation (RPA) and one-touch ordering buttons are transforming many of these tasks, they don’t always provide the most customer or worker-centric experience. However, finely-tuned conversational systems can.”

By employing conversational AI to understand the customer’s intent and automate the tasks and responses involved in issue resolution, or escalate their query to the right contact centre agent, resolution and CSAT levels remain high, while also reducing the number of routine issues that agents handle.

Digital AI assistants are key to driving down the cost to serve through either fully or partially automating routine customer interactions. AI assistants can also use these interactions to prioritise the correct course of action if they cannot fully automate to completion. By gathering pertinent information that can be passed on to agents, digital AI assistants save customers from having to repeat themselves once they get through to a customer service agent, helping the agent to resolve the issue more swiftly.

The personal touch

Conversational AI applies natural language processing so that customer intent can be understood, appropriate responses can be provided, and actions can be automated to solve their issues, while still providing the option to speak to a human. The advantage is that consistent, high-quality responses can be provided at scale, without overwhelming contact centre employees when human resources are stretched because some employees are self-isolating, or when the organisation is handling a sudden spike in customer enquiries.

While enabling financial organisations to automate repetitive transactions, conversational AI also allows them to be personalised by referring back to elements of previous interactions. The benefit for customers is that they don’t have to repeat themselves and conversations can be picked up where they left off, at the customers’ convenience.

Designed for Digital Natives

According to Deloitte, “younger generations seem to be gravitating toward accessing information through chatbots, with 70 percent of millennials reporting a positive experience after using them.”

Whatever their age, when applying conversational AI, brands must make it clear to customers that they are engaging with a digital assistant and not a human. It’s equally important to avoid trapping customers in a ‘bot loop’ and to provide a clear route to speak to a human if a query can’t be resolved by the digital assistant.

PWC believes that organisations that get their branding and persona development right within their conversational AI can create a customer experience that is akin to engaging with a human agent. However, getting it wrong will alienate users. EY emphasises the need to use the right data when ‘training’ conversational AI, explaining that “The bot uses logic to determine user inquiries and connect with enterprise systems to get the desired results”. Therefore, the bot is only as smart as the data used to build it.

This is why The AA Ireland invested time in studying genuine livechat conversations when developing its successful chatbot project, the Quote Helper Bot, so that they understood what people asked, how they asked, and what the intent was behind their questions. As a result, the Quote Helper Bot provided consistent on-brand responses that were based on live chat conversations with previous customers who had the same requirements. When social distancing measures were introduced in March, The AA Ireland was able to re-apply what it had learned and quickly spin up a call deflection bot, within 48 hours, to ease pressure on contact centre staff as they adjusted to working from home.

The future:

Paul Ridge observed, “The pandemic has given us the chance to glimpse into the future and see what role the contact centre will serve.”

Andrew Jones, Head of Express & Retail Claims, Zurich, commented, “A lot of claims are settled without using voice. In the future, rather than big contact centres, we’ll see more smaller collaboration centres, with people coming in one or two days a week and for training”.

Angus Rogers believes that the traditional service model will change, saying, “There will always be a need for people to work together, but I think that the model of the contact centre we see today will change.”

What we have seen is that organisations are using a blend of conversational AI and highly skilled customer service agents to automate routine enquiries, swiftly adapt working practices to abide by social distancing rules and deliver the right experience for customers and employees alike.”

Business

In-platform solutions are only a short-term enhancement, but bespoke AI is the future

Published

14 hours ago

September 27, 2023

editorial

By Damien Bennett, Global Director, Principal Consultant, Incubeta

If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.

But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.

In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.

It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.

Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.

The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.

Pitfalls of in-platform solutions

In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.

However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.

This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.

Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.

Why customized and adapted AI is the key

The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).

That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.

The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.

The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.

Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.

Managing finances with AI

It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.

But we already know that…and so does everyone else.

So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.

Business

Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management

Published

2 days ago

September 26, 2023

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Nuno Godinho, Group CEO of Industrial Thought Group

In recent years, the advent of AI has sparked both excitement and scrutiny within the Wealth Management industry. The technology’s capabilities, including but certainly not limited to generative AI algorithms like ChatGPT, offer a new dimension to data analysis, market prediction, and portfolio management. However, while it presents a promising avenue for enhancing decision-making and elevating client interaction, AI also carries inherent challenges that demand careful consideration.

Benefits of AI in Wealth Management:

In a world where CX is key, AI enables wealth managers to provide personalised advice, improved portfolio performance, real-time insights, and convenient access to information and support. Previously it has been impossible for advisors to deliver hyper-personalisation at scale; now, AI-driven customisation lets them tailor investment strategies and recommendations to their clients’ unique financial goals, risk tolerance, and investment horizon.

AI algorithms can also analyse vast amounts of data to identify trends and opportunities, resulting in potentially higher returns on investments. And, more widespread use of automation will gradually reduce the cost of wealth management services, meaning higher-quality investment advice at a lower price. This is critical as firms fight to stay relevant for modern investors disillusioned by traditional advisory firms and private banks.

Relationship-wise, there are many other advantages. AI-driven data analytics make it easier to gain a deeper understanding of an investor’s needs, preferences, and behaviours, all of which help to build long-term relationships. Through predictive analytics, firms can differentiate their service and proactively identify new investment opportunities, such as emerging market trends or underperforming assets. At the same time, chatbots and virtual assistants facilitate constant communication to answer queries and increase engagement. By strategically integrating AI technology into their operations, firms have the power to optimise top and bottom lines, strengthen client connections and position themselves for long-term growth.

Navigating the Ethical and Practical Challenges:

While AI holds remarkable potential, major obstacles must be overcome. With AI’s reliance on large amounts of data, ensuring client data confidentiality, managing consent, and complying with global data protection regulations like GDPR are significant challenges. Another issue is algorithmic bias – as AI learns from data, it may inadvertently perpetuate inequalities or biases present in the training datasets used. Vigilance is necessary to ensure that AI systems don’t amplify these issues. A key concern is the absence of standard governance, leading to a lack of accountability and transparency. Black-box algorithms can make decisions without providing clear explanations for their reasoning, making it difficult for clients and regulators to understand and trust AI-driven outcomes. Overall, the responsibility for AI-generated recommendations remains complex, requiring collaborative efforts to establish robust regulatory frameworks.

Striving for Data Integrity and Reliability:

The efficacy of AI-driven solutions hinges on the quality of training dataset they are supplied with and rely upon. Therefore, ensuring accurate, unbiased, and comprehensive datasets is paramount to generating trustworthy insights. The absence of standardised data sharing can lead to skewed results, ultimately impacting the quality of AI-generated advice. Transparency in data usage, validation, and generation reasoning will be pivotal to cultivating client trust and minimising systemic risks, which ties back to the absence of standard governance, as the output from AI-generated advice will only be as good as the data sets provided. We need to understand the “lineage” of all data used and generated by the algorithms. Until the industry can come to some accord on how we plan to use all of our respective data, it will be prone to various biases and fragmented advice, which will lead to liability and reliability issues down the line. It’s worthwhile wondering whether we can see the industry opening up in an age of data equals value.

The Role of Collaborative Partnerships:

Amidst these challenges, collaborative partnerships emerge as a potent avenue. Established wealth management firms can harness the expertise of FinTech AI companies to augment their capabilities while mitigating the risks associated with AI adoption. A symbiotic relationship, where innovative AI solutions are developed by trusted partners, helps safeguard against potential pitfalls and aligns with the pursuit of ethical, data-driven decision-making.

Looking Ahead: Striking a Balance for Sustainable Progress:

As we journey into the AI-powered future of wealth management, it’s evident that a balanced approach is essential. The integration of AI has the potential to expedite the transition to wealth management 4.0, revolutionising personalised client experiences and advisory services. However, this progress must be underpinned by clear ethical guidelines, data integrity, and collaborative partnerships. Striking this equilibrium promises not only a more informed, efficient, and personalised industry but also one that upholds the principles of transparency, accountability, and client trust.

In conclusion, AI’s impact on the wealth and asset management landscape is profound, offering unparalleled insights and opportunities. While navigating challenges will be crucial, a collective effort to harness AI’s power while ensuring its responsible application will pave the way for a resilient, future-forward industry.