11 July 2019

Greatest and latest of AI in Customer Service in 2019: Chatbots, Machine Learning and more.

By: Gustavo E Perez-Lopez

 

How will AI impact Customer Service in 2019?

According to “The Three Customer Service Mega-trends for 2019” Forrester report[i], automation and Artificial Intelligence (AI) are predicted to deepen their penetration into customer service and to lead its transformation into a more efficient operation while enhancing the customer experience.

Robotic Process Automation (RPA), prescriptive AI, chatbots and customer service robots are among the top AI listed by Forrester. In this blog, I briefly describe some of the most popular AI applications in Customer Service grouped by the main channel of interaction where it is being used.

 

Selected Customer Service AI Applications by Channel

communication channel

Voice

Voice is still the most used customer interaction channel, especially when it comes to complex issues that require human intervention. However, the high complexity of human language means it is difficult for speech and voice interactions to follow the widespread application of AI, until recently.  Thanks to the latest advancement in speech and language processing, we have been witnessing the deployment of more and more AI applications in the voice channel.

 

Voice-enabled Assistants

Many call centres are replacing much-hated old-school DTMF based IVR menus with customer-friendly voice-enabled assistants, enabled by major technological advances in natural language processing including speech recognition and speech synthesis, primarily thanks to the use of neural network models.

Nuance’s Nina or IPsoft’s Amelia are regarded amongst the best intelligent virtual assistants in the market today.  However, major AI vendors like Amazon, Google and Microsoft have made massive improvements in their speech technology (both speech-to-text and text-to-speech) and are replacing previous robotic versions with substantially improved human-like natural voices in an increasing number of languages.

 

Behavioural pairing

Ensuring that each customer call results in a great customer experience and a positive business outcome is a key objective of every call centre. This is critical especially in the case of high-value customers and top-rated agents.

Behavioural pairing technology uses artificial intelligence to analyse human interaction patterns and predict the interpersonal behaviours leading to a successful interaction. This model is then applied to the contact centre to find and pair the best matching customers and agents’ profiles. 

One such vendor we have come across is Afiniti which has joined forces with Avaya to provide a native integrated solution using behavioural pairing[ii]. It is worth noting that Geomant Contact Expert also comes with a built-in connector for Afiniti integration.

 

Speech analytics

Speech analytics is another popular AI-powered tool offered by vendors such as Verint used by many contact centres to extract insights and critical business information from customer conversations. Emotions drive our decisions, therefore the ability to accurately detect emotions while speaking to a customer and take appropriate actions can give a business significant competitive advantage.

Real-time speech analytics can be used to predict the customer’s propensity to buy a product and guide the agent to offer the right promotion or product mix leading to increased sales, or to identify a dissatisfied customer and prompt the supervisor to take immediate action to prevent churn.

Offline analysis (post-interaction analytics) of recorded calls is used to understand overall customer behaviour, product trends, etc. This technology is also used on the agent side to perform automated performance evaluation on various aspects of call handling, such as greetings, closure, appropriate use of language, compliance, etc.

 

Email

email communication

Email remains the most commonly used digital customer service channel. The “Email Statistics Report 2019-2023” from the Radicati Group, reveals that the worldwide daily email traffic – including all business and personal emails sent and received per day - will exceed 293 billion in 2019[iii] and will continue growing at a 4-5% annual rate.

To manage the ever-growing number of email customer interactions, customer service teams usually need to hire additional staff, incurring high costs. AI technology offers a more economical alternative to contact centre managers with tight budgets to cope with increasing email interaction volumes.

 

Email classification and routing

In many cases, customer emails are first read and analysed by an email pre-processing team, and manually classified and sorted according to the relevant topic or service requested, then they are routed to the appropriate department (Ordering, Accounting, Refunds, etc) for further action and response. 

AI can help automate a large part of the process, in particular determining the topic of the email (labelling the request), routing it to the appropriate queue or department, and even sending automated responses without human intervention.

Note: we wrote a separate blog on this subject if you're interested in finding out more. 

 

Support ticket assignment and prioritisation

Another application scenario for AI is the prioritisation and assignment of customer support tickets received via email. It is not untypical to have a support Service Level Agreement (SLA) requiring the supplier to begin high severity fault resolution within 30 minutes of having received a fault report. In such cases it is critical to correctly identify the faulty system component and assign the ticket to the adequate technical team. 

Having an AI-powered, self-learning, automated ticket assignment mechanism can help reduce the human error and maintain SLA compliance while ensuring that the problem is handed to the proper technical expert team and fast resolution is provided to the customer. 

 

Auto answering and knowledge management

AI is also used to provide automatic answers to repetitive questions from customers. For instance, Microsoft QnA Maker service enables the processing of Frequently Asked Question (FAQ) lists, product documents, support manuals, etc. to build a knowledge base that can be used to intelligently analyse incoming email inquiries and auto-reply with the best suitable answer with high confidence.

 

Chat and Instant Messaging

Use of webchat and other instant messaging platforms shows a continuous increase, especially amongst Millennials and the Z generation. Recent usage data shows WhatsApp as the preferred messaging app worldwide[iv], followed by Facebook Messenger. The rise of WhatsApp as the top messaging app has become particularly important for companies, since WhatsApp launched its "WhatsApp for Business" platform in 2018 – an API for enterprises to send automated messages to consumers.

 

Customer chatbots

Chatbots continue to be deployed by more and more companies and across an increasing number of industries and use cases, and have become a key AI tool in customer service. Chatbots can take the burden of repetitive tasks from customer services representatives allowing them to focus on higher-value, complex activities.

Geomant offers an ecosystem of intelligent chatbots with capabilities ranging from basic conversational bots that will answer frequently asked questions, right through to more advanced predictive bots possessing up, and cross-selling abilities. Support for webchat and the most popular messaging platforms such as WhatsApp, Messenger and Twitter is included.

 

Agent chatbots

Some companies may not want to use chatbots as a direct customer facing tool, but are using them behind the scene to aid agents engaged in a customer chat to find the best possible response to the customer’s enquiries in real-time.

Geomant’s Buzzeasy Chat solution comes with such built-in AI capabilities. It works as an internal FAQ chatbot and provides the agent with one or more suggested answers that the agent can chose from or alternatively write his/her own answer. The bot learns from the agent choices and becomes more “intelligent”, providing better suggestions over time.

 

Employee chatbots

Modern workplace and collaboration tools such as Microsoft Teams are also driving the deployment of “employee chatbots”, used internally within a company to drive and enhance employee engagement and communication.

Typical examples of employee chatbots are IT Helpdesk bots, HR and recruiting assistants, and also Sales assistant bots.

 

Summary

In this blog, I have reviewed some of the most popular uses and applications of AI in the customer service across multiple customer interaction channels. I believe it is safe to say that AI is here to stay and is continuing to make its way deeper and deeper into customer service. Companies who have not done so yet, should start looking at their customer services operations and identify those sweet spots where applying AI could represent an immediate gain in efficiency, but more importantly, in customer experience.  

The AI opportunities are endless, and it is not so difficult to get started as you may think.

 

[i] https://go.forrester.com/blogs/the-3-customer-service-megatrends-in-2019/

[ii] https://www.intelligentcio.com/me/2018/04/27/avaya-and-afiniti-partner-to-bring-ai-driven-behavioural-pairing-to-the-contact-centre/

[iii]https://www.radicati.com/wp/wp-content/uploads/2016/01/Email_Statistics_Report_2016-2020_Executive_Summary.pdf  

[iv] https://www.statista.com/statistics/258749/most-popular-global-mobile-messenger-apps/

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