How NLP сan Increase Efficiency by Reducing Time Spent on Emails
Natural language processing (NLP) has various useful everyday applications to improve efficiency. One application is email routing. Email routing redirects incoming emails to proper receivers within an organization. It has proved to be an effective tool to increase productivity and remarkably reduce the time spent on sorting emails within organizations.
Emailing has become such an integral part of our professional life. Worldwide, we are receiving and sending more than 124.5 billion work-related emails every day. According to McKinsey, an average worker spends around 28 % of his working hours reading and answering emails, which roughly corresponds to 11 hours a week. That is surprisingly a lot of time, without even taking into account the time it takes to refocus after having answered unnecessary emails. Therefore, reducing the time spent on sorting emails manually, can have a great impact on productivity.
CLIENT CASE: How a government regulatory authority will save 80% of its employees’ time on a routine process by using AI
There are different simple guidelines on how to cut down the time spent on emails. However, these depend heavily on employees’ ability to stick to the guidelines, thus before you know it, you might be back in the same mess again.
But business departments can benefit from email-routing AI models, such as customer service, service desk, and other various departments.
Email routing is a more sophisticated and modern solution obtained by the means of NLP, where a document classification AI model learns how to classify emails into different groups – just like a spam filter.
FIGURE 1. Document classification – The input text document, the Acme article on the left, is first transformed into a numerical representation that is then fed into a machine learning algorithm that learns to predict (shown as arrows) the class of each document (the subarticles with labels on the right).
Fundamentals of NLP
One important aspect to understand about NLP is that the underlying mathematical algorithm do not understand human language, but learn the structure or relationship of a text that is represented in a numerical format.
The initial step of any NLP workflow is to clean the linguistically messy raw text using different methods. The most frequently used are:
TABLE 1. NLP preprocessing – The raw text must be preprocessed to discard uninformative words thereby only preserving words and sentences that are informative to the document classes we are trying to learn.
All the above-mentioned methods are essential to remove linguistic noise and reduce the features of the text corpus, thereby preserving only useful information in a document/email.
The last step is to transform this cleaned text data into a numeric feature matrix that can be used by the classification AI model to learn the underlying statistical characteristics of the target groups that we are trying to predict.
FIGURE 2. Bag-of-words representation – The input text is simplified to a count occurrences representation called “bag-of-words”. This is the numerical representation of the words that are treated as features for the classification algorithm.
Once we find a suitable and optimized AI model that is able to discriminate between our target groups with acceptable accuracy, we can put it into production and measure different positive effects of email routing. Some of those measurable benefits include increased productivity and a remarkable reduction in the time an average person spends on irrelevant emails. Email sorting leads to faster case response times, automatic tagging and increases the quality of customer service just to mention a few.
We spend a lot of working hours on reading, writing and tagging emails in a very time-consuming process. This problem can be overcome via NLP AI models. Implementing NLP AI models can significantly improve several areas of an organization’s workflow by increasing efficiency and productivity, improving the quality of hours spent on reading and writing emails, reducing response times and improve customer service, and last but not least, saving money by having your employees spend their time on tasks that really brings value to the organization.
About the author
Data Scientist, 2021.AI
Ahmed Zewain is a Data Scientist at 2021.AI with an MA in mathematical modeling and computing, and extensive knowledge of several data engineering tools. Ahmed’s skills include building ML POC projects and taking them further into production for a wide variety of clients.
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