The New Frontier: Improving Customer Experience through AI

There are actually two frontiers when it comes to improving customer experience in an enterprise, namely the voice of your customer and the voice of your employees.

Customer feedback is integral to optimizing service offerings, improving products, and increasing operational efficiency. Yet a lot of feedback is misplaced, irrelevant, or even vicious.

Disgruntled customers with unrealistic expectations do not offer feedback that makes your company any better, more efficient, or more profitable.

Read also: The Importance of Customer Feedback

Employee feedback is probably the most overlooked source of improvement ideas in any corporation. Employees have their own agenda, their own career ideas, and their incentives, dreams, and values may be entirely misaligned with those of the enterprise. Yet employees experiences offer a great source of feedback for anything from marketing decisions to future product design.

The key to utilizing both frontiers is filtering. This is where AI and NLP (natural language processing) comes in. Using these new technologies in a practical way takes away the fear, increasing understanding of processes, feedback motivation, and the value of feedback itself.

The Voice of the Customer

Applying AI in the customer experience improvement process is one of the easiest ways to familiarize yourself with artificial intelligence. You do not need to dismantle existing systems and you don’t need expensive data scientists or machine learning experts to see the results.

The voice of the customer – if listened to correctly and efficiently – can improve other aspects of your business. Complaints processing may lead to insights into compliance issues, allowing future iterations of a service or product to offer a better UX/UI and a higher level of compliance, thus reducing incidents of future complaints.

The main reasons this does not work with artificial intelligence is that without data gathering, analysis, and quantifiable results, any customer feedback remains anecdotal. Handled by differing operators at differing times, management may never get the overall picture. To give you an example from one of our clients: over 90 complaints about problems streaming video through a hand-held projector were handled by 11 different support engineers, all of them trying to solve the technical problem on a case-by-case basis using existing checklists and protocols. Implementing a simple AI was able to trace all of the problems to a licensing issue, which, once solved, effectively stopped new complaints about the streaming issue entirely.

In the hospitality business, AI offers a plethora of insights normal human supervision would simply miss.

A hotel in central Taiwan had complaints about malfunctioning air-conditioners and followed up each of them by a visit from the A/C repair guy. Implementing an AI solution would have shown how all of the complaints happened on rainy days with a particular employee on duty – who habitually changed the settings of the room A/C thinking that high humidity required different operating parameters.

Read also: The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning

In short, AI makes the “anecdotal” quantifiable, thus saving countless hours or repetitive support tasks and avoiding future problems.

This process has a direct impact on a company’s KPIs and spending. If AI analysis can drastically reduce the number of complaints on a specific issue, KPIs improve and costs go down.

But AI is not just about complaints. Getting feedback on new features can align future R&D spending, reducing waste and trial-and-error. Taking a large number of product reviews and employing simple NLP algorithms will give you more meaningful insight than anecdotally reading perhaps 1/10 of them. Solving the problems or adding the most demanded features can significantly increase sales and elevate the brand.

Read also: How to Know When You Are Ready for AI

What about the Employees?

Employees know a lot about the business, its products and how they are perceived by customers. In retail especially, employees on the ground receive a lot more insightful feedback than ever reaches the complaints inbox of HQ. Listening to employees and employing AI/NLP to analyze incident reports can help streamline operations and improve efficiency.

That, of course, means making sure employees have a reason, an incentive, or some kind of motivating factor to share their voice. Very often external factors are far more important to users experience than the service offering itself. Long waiting times make customers edgy, long forms to fill in annoy them. Yet when asked about the service itself, customer and employees may not be able to pinpoint a single factor that made their experience less than desirable.

Taiwan’s Tax Office analyzed the experience of thousands of foreign residents and found that understanding existing reporting forms was the biggest headache. New forms and temporary assistance from students and interns during peak season reduced complaints by over 80%. The AI necessary to identify such problem areas is rudimentary, available, and does not interfere with existing business processes.

As AI evolves, usage cases will become more and more transparent and ubiquitous. Companies who are familiar with AI/NLP will always have a lead over others, and the longer you wait, the farther you will fall behind.

Read also: Employee Advocacy: The Key to Digital Marketing Success

Published by Dr Martin Hiesboeck

Futurist, Marketer, Policy Advisor for Companies and Government Head of Blockchain and Crypto Research at Uphold and CEO of Alpine Blockchain Consultants Zurich - London - New York - Taipei

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