Machines are exceptionally good students. We can see that in the rapid and widespread adoption of machine learning – also known as auto-learning – to automate and improve the collection and processing of large amounts of data. A version of artificial intelligence, 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 to continuously build a knowledge base and to analyze data and identify patterns is particularly useful in industries that generate large volumes of documents, such as healthcare and finance. But it also has important applications in any business situation that requires a company to accurately process sales orders.

“ML will ensure orders are processed correctly and free up staff to do what they do best – delivering on promises and fulfilling customer needs.”

The risk of human error
A good way to illustrate the challenge of relying on people to accurately process sales orders is the childhood game of telephone. In the game, each player whispers a phrase or piece of information from one person to another in a circle. The last player then says the phrase out loud. Inevitably the original phrase has been significantly altered.

While telephone may make for a fun game, it doesn’t elicit many laughs in the business world. Indeed, sales orders with illegible information, errors or missing details eventually require someone to check and correct any inaccuracies, a time-consuming, expensive and not always error-free process itself.

In far too many instances, error-riddled sales orders lead to lost sales or unhappy customers. In fact, according to a recent Aspect survey, 49 percent of consumers have stopped doing business with at least one company in the last year because of poor customer experience.

Removing the guesswork
Here’s how the flip side of the telephone game might look with auto-learning technology in a sales order setting. Let’s say the company taking the order is a pharmaceutical manufacturer. Traditionally, the customer buys two to five units of a medication. But in this particular instance, the customer opts to order 50 units.

Even the most diligent sales agent – if they are exceptionally busy or distracted by other duties – could miss just how different this particular order is from the customer’s past transactions. But an auto-learning-enabled document process automation system would recognize and flag this anomaly. It may very well turn out that the order is correct, but by identifying and flagging the sale, the automation system can trigger a follow up by a customer service representative.

And that is exactly what customer service representatives should be doing. Instead of examining the minutiae 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.

Much has been written about how automation and machine learning will impact future employment. In the best-case scenario, emerging technologies don’t actually replace jobs but rather enhance the ability of employees to perform them by taking over duties that are time consuming, error-prone or of low value. That is exactly the case in the order-taking realm of sales.

The application of auto-learning to document process automation builds on past, albeit inadequate, efforts to improve accuracy and efficiency. Document process automation once relied on the construction of knowledge databases that would track users’ habits. As the database gained knowledge and experience of users’ habits, it would be able to make corrections to common mistakes – much the same way word processing software fixes spelling and grammar mistakes.

But this approach to document process automation was hardly ideal. Document quality had to be high and certain fonts – not to mention all handwriting – simply couldn’t be processed. 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 on the true promise of document process automation.

A continuous improvement model
In sharp contrast to this static approach, cloud technology-enabled auto-learning gets 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 recognise which keywords are commonly used in specific fields. As the system learns more about which keywords are associated with specific fields, it becomes adept at filling in the fields itself.

What makes this different and better from past efforts at document process automation? Put simply, it’s because the system has a tremendous capacity to learn from past mistakes – it never makes the same mistake twice. 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 is a system that will only get better in the future, as improved algorithms help it work faster and more accurately.

It’s hard enough to land sales in the first place, yet the challenge doesn’t end there. It’s imperative to 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 you receive will be processed correctly and free up customer service representatives to do what they do best – delivering on promises and fulfilling customer needs.

ABOUT ERIC BUSSY//

Eric Bussy is is Worldwide Product Management Director at Esker.