Automate Your Boring Tasks with Machine Learning

Last updated on Aug 11, 2021

A 2017 study by McKinsey & Company, a global management consulting firm, estimates that 30% of business tasks done by 60% of occupations can be automated using artificial intelligence, machine learning, and/or robotics. Professional services firm PricewaterhouseCoopers (PwC) conducted a similar analysis covering twenty-nine countries in a report titled, “Will robots really steal our jobs?” In their report, PwC predicts that automation could replace around 30% of jobs by the mid-2030s. Today, machine learning can be used to automate activities that would take longer and be more expensive if done by humans. If you’re a business owner or executive, now is a great time to ask yourself: What activities can be automated in my business? Could automation help my employees eliminate repetitive—and often boring—tasks?

This article lists seven categories of less-than-exciting tasks that are ideal for automation through machine learning (and may even be handed off willingly by your employees).

 

1. General and Administrative

a. Finance and Accounting

Your finance and accounting employees likely work with plenty of spreadsheets. Luckily for them, spreadsheet tasks can typically be easily automated. Check out our case study with ANZ Bank for some inspiration on how to automate financial workflows using Python.

b. Legal and Compliance, Records Maintenance, and General Operations

Many tasks in these areas can be repetitive, yet detailed. By automating them, they can be done more quickly—and likely more accurately—by machines.

 

2. Human Resources and Talent Acquisition

a. Matching Candidates to Positions

Machines can be trained on data collected from previous successful applicants to help recruiters make a hire.

b. Career Planning and Retention Risk Analysis

Machine learning can model your employees’ ideal career paths. Using employee profile data, machine learning can match an employee’s goals, experience, and interests with opportunities within an organization to build teams designed for success. Machine learning can also use predictive analysis to mitigate the risk of losing employees and ease HR concerns about employee retention.

 

3. Business Intelligence and Analytics

Businesses in any sector can use machine learning to take large quantities of data and use it to help maximize performance. Not only can machine learning be used to analyze your data and identify potentially profitable relationships, it can do so faster than your fastest human employee.

 

4. Software Development

Writing repetitive code can try the attention span of even the most enthusiastic developer. Because code is modular, machine learning can be used to independently generate it. Many apps have already been built that can automatically produce portions of code for a variety of uses, one common example being web design. Intelligent programming assistants are another example of software development machine learning tools that are already used to decrease the time spent on reading documentation or debugging code. Some examples of intelligent programming assistants include Kite for Python and Codota for Java. Additionally, machine learning is also used in automatic analytics and error handling, where machine learning-based software tools can automatically flag errors within a system.

 

5. Marketing

a. Digital Ad Optimization

Machine learning is already used by many businesses to make pricing, placement, and creative decisions in digital advertising.

b. Recommendations and Personalizations

Applying recommendations and personalizations to products, emails, and marketing touch points can be automated using machine learning.

c. Revenue Attribution

Accurately attributing revenue to advertising can be a challenging task, especially for marketing and sales teams. Eliminate human bias in revenue analysis and attribution by using machine learning models.

 

6. Sales

a. Customer Segmentation

Customer segmentation is used by marketing teams to divide customers into groups with the goal to better target ad campaigns at the right people. It is also used by sales teams to divide prospects and customers for the purposes of targeting sales promotions, incentives, and account division within a sales team. Previously, segmentation was a time-consuming task. But now, machine learning models can be used to process customer data and find customer segments that would otherwise be complicated for a human to spot.

b. Lead Qualification and Scoring

Machine learning can make accurate predictions about potential and current customers.

c. Sales Development

Machine learning can be used to boost your sales by executing customer segmentation of your current and prospective clients, which in turn can help increase the efficiency and productivity of your sales development reps.

d. Sales Analytics

Sales analytics are often done using an Excel file. Instead of having employees input data into Excel, machine learning-based tools can use your data to generate sales predictions so that you can plan for the future of your sales.

 

7. Customer Support

a. Conversational Agents

You’ve probably already encountered conversational agents in your day-to-day life. Examples of them include digital assistants, chatbots, and autocomplete.

b. Social Listening

Machine learning can be used for sentiment analysis and biometrics from audio-visual data.

c. Customer Churn

Unsatisfied customers can churn and abandon your brand, and your churn rate is a significant indicator of the health of your business. Machine learning can be trained on data to identify potential churners so that you can take action and gain them back.

d. Lifetime Value

Knowing your clients’ lifetime value can help you retain your most profitable clients. Machine learning can help you predict lifetime values and identify your key customers.

Automating tasks using machine learning in your business can be a challenging, but rewarding, decision. Many industries are already using automation to simplify employee jobs, and in the face of potential economic slowdowns, automation can help you increase your efficiency and stay competitive. Interested in automation for your business? Find out more about how you can automate any task with machine learning in our other blog posts.

About the author

Claudia is a data scientist, consultant and trainer. She is the CEO of Edlitera, a data science and machine learning training and consulting company helping teams and businesses futureproof themselves and turn their data into profits.

Before Edlitera, Claudia taught Computer Science at Harvard, and worked in biotech (Qiagen), marketing tech (ZoomInfo), and ecommerce (Wayfair). Claudia earned her degree in Economics from Yale, with a focus on Statistics and Computer Science.