Mark Lunt Group Managing Director, JOS

Mark Lunt is Group Managing Director of JOS, one of Asia’s leading IT service and solution providers, with a team of 2,000 professionals serving more than 10,000 customers in Greater China and Southeast Asia. In this role he heads the JOS senior leadership team, driving the company’s vision, strategy and growth.

Key Takeaways
With the rising adoption of conversational AI, we expect a revolution in customer service throughout the interaction of Ask, Answer, Advice and Action.
Ask: Customer enquiry via man-machine interaction
Answer: Reduce friction through better customer experiences
Advice: Enable proactive engagement and actions
Action: Process automation
Conversational AI transforms customer service workforce
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September 26, 2018
Trends
4As in customer services makeover
AI transforms customer services interactions in ask, answer, advice and action

 

More Hong Kong enterprises are turning to AI to enhance their business operations. Many started the journey with transforming customer services. A report from Tractica forecast that, the largest proportion of enterprises’ investment in conversational AI from 2019 to 2025 will go to customer service and marketing, accounting for 44% of the spending in this technology.

With a rapidly growing and larger scale adoption of conversational AI, we can expect a revolution in customer service throughout the interaction of Ask, Answer, Advice and Action. At JOS, we are seeing many local customers starting the conversational AI journey. Although they are still at the initial stage, their investments are building a foundation for a significantly greater transformation in customer service in the near future.

1. Ask: Customer enquiry via man-machine interaction

One major challenge of automating customer service is the system’s ability to understand customers’ enquiries. This causes a lot of frustration to the customers, compromising the quality of customer service and the enterprises’ operation efficiency. 

Natural language processing (NLP) technologies like speech recognition and optical character recognition (OCR), help machines to understand customers through simple human-like interactions. These technologies can recognise analogue information like hand written characters and speech, then turn it into digital data for processing. 

Through machine learning, the system can also be trained to better understand customers’ queries from analysing the context, their intent and sentiments, in order to provide accurate, useful and meaningful responses. With the maturity of NLP technologies, enterprises can process a large volume of personal and complex customer enquiries 24x7.
 

2. Answer: Reduce friction through better customer experiences

Another frustration customer often face is the confusing and unclear answers to their enquiries. Very often, this is a result of a lack of training of customer service representatives, but the limited medium of communication—text only or voice only—also restricted the ability for customer service representative to provide comprehensive answers. Conversational AI is perfect tool to fill this gap.

Studies how the human brains process images 60,000 times faster than text, and 93% of all human communication is visual-based. To answer customers’ enquiries, it is likely that chatbots will no longer rely only on text or voice communications. Organisations are increasing visual elements, including graphics to provide specific instructions or video to explain complex concepts.

A Gartner report stated that by 2020, augmented reality, virtual reality and mixed reality immersive solutions will be evaluated and adopted in 20% of large enterprises as part of their digital transformation strategy. We believe part of that adoption will be to enhance customer services.

AI-enabled chatbots are perfect for visual communications, as they can identify and extract the right multimedia content from an enormous resource archive, incorporating them instantly with text and voice in conversations, offering a more engaging customer experience and reducing the friction during customer engagement.

3. Advice: Enable proactive engagement and actions

Apart from enabling natural and effective conversations, AI allows chatbots to anticipate needs based on context, preferences, and prior queries. It enables chatbots to not only answer enquiries, but deliver advice, resolutions, alerts, and offers.

One major reason that conversational AI is able to provide personalised advice is the system’s ability in search and knowledge discovery. The integration of NLP and text analytics allow the system to extract topics and automatically classify ingested content to understand the intent behind customers’ queries. Machine learning also optimises the organisation of data and search for results based on customer profile, history and context.

AI-enabled chatbots can make personalised cross-sells and upsells recommendations instantly, based on their interaction and purchase records. One example of AI-enabled customer service is being an “online personal shopper” recommending products to customers based on their previous purchases and their surfing behaviours. Another example is an insurer chatbot can remind customers to renew their policies or to recommend a change in policy based on customers’ behaviours and needs. These services enable customers to make quicker and better purchase decisions, boosting engagement and revenue for enterprises.

4. Action: Process automation

AI-enabled chatbots not only understand and answer customers’ enquiries, but also support fast and proactive actions. 

Technologies like robotic process automation (RPA) automate routine business processes to speed up the delivery of customer services. One example is handling document-intensive processes such as filing an application for bank loans. RPA can automatically search from the system for previously provided personalised information (like date of birth or ID numbers) upon customers’ approval, speeding up the process to file these applications.

In addition to speeding up document-intensive process, AI integrated with IoT can also provide proactive actions. IoT sensors collect data about the environment or a person, while machine learning system interprets large data sets to define normalities and abnormalities. Proactive actions can be taken upon the detection of abnormalities. One example is chatbots from insurance providers integrated with smart watches. The chatbot can provide proactive actions by offering lower life insurance policy premium when the smart watch detects a healthier lifestyle.
 

Conversational AI transforms customer service workforce

As AI reshapes different stages across the customer service journey, a transformation in workforce is inevitable. Human customer service representatives are no longer the only party to receive and deliver information to the customers. With the integration of different technologies, like IoT and analytics, more accurate and real-time information can be delivered automatically through chatbots.

But this does not mean human customer representatives will be replaced. This only means human interactions will become more valuable in customer services. There will be demand for highly skilled professionals to oversee the development of AI systems, ensuring their integrity, security, objectivity and proper use. As businesses investing in AI, they should also equip their customer service team to embrace AI by being the forefront of man-machine interactions and training the system.

Nothing compares to human interactions when building relationships, offering premium services or discussing emotions and feelings. Traditional roles for human customer representatives to provide information and answer enquiries may no longer exist, but their roles will change towards handling emotional engagement. Human customer representatives will be valuable in providing judgement and creative thinking, ensuring enterprises to offer unique services and differentiate in the market.