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How does Natural Language Processing reduce costs and improve business productivity?
Data has become the bedrock of 21st century business operations. Business leaders are in unison that digital
transformation demands an efficient data transformation. Consequently, organizations must invest in
disruptive data-oriented technologies such as artificial intelligence (AI) to attain a leading-edge over
their closest competitors. Among the various sub-fields of AI, Natural Language Processing (NLP), as a
technology, has proved effective in reducing costs and increasing productivity for businesses of any size
and domain. How? That is the question we would like to answer through this article.
According to Forbes Magazine, 84 percent of businesses still depend on some type of manual data processing
every day. However, many of these manual tasks could be automated with the efficient use of NLP. Bearing
this in mind, let us take a look at how NLP can help organizations automate data processing across their
important functions such as marketing, customer service, sales, accounting, and HR.
Marketing: There is widespread adoption of NLP and other AI solutions in Marketing
practices. Chatbots have proved to be an effective alternative for email newsletter by delivering
marketing messages instantly to prospective customers. As an instant messaging tool, Chatbots engage
with the audiences better than an email newsletter, enabling the marketing team to segment audiences
(based on intent) and send customized messages for better outcomes. Similarly, instant messages have a
higher probability of being read through than the same information sent in an email. Logically, Chatbots
offer significantly higher click-through-rates (between 15-60%) as compared to email newsletters (with a
maximum of 5%). Chatbots have also provided an ideal alternative to website forms. According to a
Conversational Marketing study, 81 percent of tech buyers don’t bother to fill out website forms.
Chatbots, on the other hand, offer faster responses and removes the tedious process of filling out
forms. This increases the chances of a sale.
Customer Service: Chatbots can be a huge timesaver for customer service. They have been
extensively used to answer the common questions asked by the customers and redirect other technical
questions to humans. The intervention of chatbots has proved to reduce customer service tasks by 40
percent while enhancing the users' expectations by 99 percent.
Sales: Software runs all the necessary statistical tests these days, but a data
scientist still needs to possess the statistical sensibility to know which test to run and under what
circumstances. A good understanding of multivariable calculus and linear algebra, which form the basis
of many data analysis techniques, is likely to allow a data scientist to build in-house implementations
of analysis routines as needed. An understanding of statistical theorems helps data scientists to
understand the capabilities and limitations or assumptions of these techniques. A data scientist should
understand the assumptions that need to be met for each statistical test.
HR: Recommendation engines can also be used in the recruitment process to rank resumes
based on the job description. This can reduce the time taken to go through all the received
applications. Additionally, NLP can also automatically extract information from the resumes (such as
names, experience, education, specific skills, etc.) and store them in a database for reference.
Accounting: There is a lot of data entry involved in this function. Entering the data
can eat up a bulk share of the employee's time. Automation of such tasks can save a lot of time and
money. One estimate suggests that employees can save about 240 hours through the efficient automation of
bookkeeping and accounting tasks and this can provide a return of $9,240 in value to the employers.
As you can see, the business benefits of automating manual data processes are far-fetched. They allow
various key departments within an organization to save time and help the business save money in the process.
If you want to implement NLP to achieve your organizational growth goals, then