How Machine Learning can raise the bar in Customer Service?

Feb 9, 2018 | 3561 Views

With the development of artificial intelligence, machines have been human's best friend. We can see that the rapid and widespread adoption of machine learning has automated and improved the collection and processing of large amounts of data. Machine learning refers to the ability of computers to adapt and learn new things without having to be specifically programmed with new software.

This capacity of continuously building up a knowledge base, analyzing data and identifying patterns is useful in industries that generate large volumes of documents, such as healthcare and finance. It also has important applications in any business situation that requires a company to process sales orders accurately.

Machine learning reduces 'the risk of human error'
Business world has no room for any errors. Relying on people for accurately processing sales orders inculcates many risks. The sales orders with illegible information, errors or missing details eventually require someone to check and correct any inaccuracies, time-consuming, expensive and not always error-free process itself.

In many instances, error-riddled sales orders lead to lost sales or unhappy customers. In fact, according to a recent aspect survey, 49% of consumers have stopped doing business with at least one company in the last year because of poor customer experience, which makes it a problem for document process automation that utilizes machine learning algorithms. 

Also, helps to 'remove the guesswork'
Instead of examining the minute of orders looking for errors and anomalies in sales transactions, the best use of employees' time is ensuring that customers are getting what they want and finding new ways to deliver more value to them. 

In the best-case scenario, emerging technologies don't actually replace jobs but rather enhance the employees' ability to perform them by taking over duties that are time consuming, error-prone or of low value. That also occurs in order-taking area of sales.

The application of auto-learning to document process automation builds on past and inadequate efforts to improve accuracy and efficiency. Document process automation once relied on the construction of knowledge databases that would track users' behavior. As the database gained knowledge and experience of users' habits, it would be able to make corrections to common mistakes.

But this approach to document process automation was not considered to be quite ideal. Document quality had to be high and certain fonts, and these aspects had to be considered. These inadequacies translated into characters not being recognized properly, or at all. It also means that people had to review the documents for accuracy, which hardly delivers the true document process automation.
Machine learning consists of 'A Continuous Improvement Model'

The cloud technology-enabled auto-learning started being smarter and better over time. Instead of just correcting data that was entered by an employee, this advance in document process automation is able to examine large numbers of customer orders and recognize which keywords are commonly used in specific fields. As the system learns more about which keywords are associated with specific fields, it becomes maestro at filling in the fields itself.

The system has a tremendous capacity to learn from past mistakes and not repeating. For instance, if an employee reviews sales orders compiled by the system and finds a series of mistakes, the system will adjust how it processes information to ensure that its future processing will be more accurate and efficient. This system will get better in the future, as improved algorithms help it work faster and more accurate.

It's imperative for any company's brand reputation and bottom line to maintain a satisfied and loyal customer base. The power of machine learning will ensure that the orders received will be processed correctly. This will set the customer service representatives free as how can they deliver best on promises and fulfilling customer needs.

Source: HOB