Streamlining AI Intervention for Customer Service Experience

The goal of providing Customer Service (CS) is customer satisfaction. A satisfied customer brings a brand its reputation, goodwill and ensures customers’ loyalty.

Traditionally, a customer’s loyalty towards a physical store depended on the employees in-store, followed by the product quality. The relationship building dictated the frequency of the customer’s visits. With the advent of online business, customer service agents were available over a call or mails to address any issue that may arise pre/post-purchase. The demand for quick-response Customer Service experience has increased with the dynamic online business landscape and an ever-increasing customer base. Omnichannel can create a customer led experience.

Adding to the in-app mails/messages, commenting issues on social media handles, selecting tabs to raise a relevant concern, automation can make its way. Direct calls to customer care executives are the last resort in most cases.

Current Scenario of CS

Customer Service automation, thanks to Artificial Intelligence, has evolved as a budget-friendly strategy to scale support service sans compromising quality. It is like an outsourced omnichannel Customer Service management that works independently of human intervention until a complex situation arises.

Basic to-dos information is now given via Frequently Asked Questions (FAQs) and automated workflows. Interactive Voice Response (IVR) and automated mail system, has taken over to manage repetitive tasks like sharing the progress of purchase/complaint. A chatbot can cater to almost all customer questions, direct them to answers or the respective channel for support. A CS executive takes over in complicated issues. As the issue gets resolved, AI takes over again for review and follow-ups.

However, the process, as seamless as it seems, has some friction issues.

Where’s the Gap?

When you ask a reader why they prefer a physical book over an E-reader, they talk about the tactile nature of the book. Similarly, many people prefer human interactions as CS over AI-powered ones.

The AI models have better response time, avail multiple languages, work 24x7, cutting costs and time alike. However, the concerned consumer seeks a conversation where one is heard and understood. According to the PWC analysis, 59% of the customers polled felt that organisations today lack the human element of customer experience. A customer support representative is preferred by 71% of respondents over a chatbot. Values like empathy, cultural sensitivity and the conversational experience are appreciated by many. Demographic irregularities are also observed as millennials are more comfortable using self-service methods. The older generations, may lose track of tabs, face issues uploading relevant information/pictures and may not comprehend virtual assistance.

Moreover, being an input-based system, AI-run models thrive on the options selected by the consumer. In many cases, the inputs get messy as people get lost in the loop of selecting multiple tabs. Sometimes the issue is beyond an order number, a recent purchase or difficult to explain without sharing the context. This adds to the confusion for a machine, leading to an unsuccessful CS experience and longer durations to resolve the issue.

A smooth CS experience is noted by many researchers and businesses as a key factor in maintaining customer loyalty. With new businesses coming at lightspeed, everyone aspires to reach minimal Average Handling Time (AHT) and efficient resolution methods. AI can be leveraged more than the current conventional methods to ensure a harmonious customer relationship.

AI that Compliments CS

By all means, AI/ML and Natural Language Processing are effective tools for CS. However, their involvement in various aspects must be customised. Starting from areas where complete automation may be successful, a CS experience shall be orchestrated where hybrid mechanisms give space and choice for complete human interaction when required.

Complete automation is practical where options are mostly binary. When a person logs in to the application, they can be provided with a CS preference form. While sharing other regular information such as age, gender, they can be asked if they prefer CS interaction. This gives them the power to choose. Personalised database is created for the touchpoint, helping businesses to curate and invest in relevant outreach methods. Moreover, automation can help manage odd hours of work, with the CS executive taking over at the earliest business hour.  Another step is to educate the user and facilitate the acceptance of AI in CS. A knowledge base can be created, updated regularly and shared by the user to understand the know-how of self-service CS mechanisms. The banking sector can leverage the same to ensure individuals’ privacy is maintained. Automation can equally help in walking the customer through the ongoing process or maintain follow-ups. However, human-AI collaboration in such conversations can be beneficial in the future.

Automated live chats and email responses shall be open to customising by the CS executive. Eventually, this generates a pool of more personalised responses. It equally entails the human- feel that the customers look for. As for customers’ average 6-minute call, the amount spent by executives to find the solution can be reduced if AI troubleshoots the problem and lists the probable solutions. Instead of deploying AI initially, human connection is ensured, while AI still assists at the backend. AI integrated system captures infinite data that can be repurposed to identify customer pain points. Intelligent business decisions and resolutions can then be strategised and implemented.

As for human interactions, the data collected at the time of login shall help prioritise customer calls. For customers who prefer solutions on call, the wait time can be reduced. On the other hand, the issues that require context or immediate solution can be flagged at the time of purchase. Cab-servicing and gifting are two examples of time-sensitive discourses that may bother one if the issue is not resolved timely.

To reduce customer abandonment rates and complaints, human-machine relationships are to be realigned. The constant evolution of CS is required to achieve a CS experience that is rewarding first to the customer, then to the brand. Definitely, AI has the potential to serve them right, but it's agility is to be driven by human intervention.

By Kunal Kislay, CEO & CoFounder, Integration Wizards Solutions 

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