Where is machine learning being leveraged to help solve real business problems nowadays? We see a few main areas where enterprises are looking for business value and business benefits and applying machine learning to their daily operations.
The first category is a top line growth. This is about serving better recommendations to customers, finding the right customers, finding the right opportunities, approaching them in the right way and upselling and cross-selling in order to drive the top line.
Recommended systems is primarily a domain of natural language processing, of sales cycle, sales insight systems, as well as marketing optimizations. The second big area is to help re-imagine business processes with digital intelligence. So this is not just about redoing what we do today with ML insight but about truly rethinking the end-to-end process in a way that this leverages ML capability to deal with the routine and the optimal current cases, and this can have tremendous bottom line benefits.
The third category is about employee engagement and having healthier and more satisfied employees with a more varied set of challenges during their working hours because again they can focus on the top and to the underserved cases that require new thinking and that require unstructured problem solving. This also includes automating away some of the mindless or repetitive tasks that nobody particularly enjoys about their daily activities today.
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ML for Employee Engagement
The third category is about employee engagement and having healthier and more satisfied employees with a more varied set of challenges during their working hours because again they can focus on the top and to the underserved cases that require new thinking and that require unstructured problem solving. This also includes automating away some of the mindless or repetitive tasks that nobody particularly enjoys about their daily activities today.
Imagine a large corporation aiming to enhance employee engagement across its diverse workforce. To achieve this goal, the company decides to leverage machine learning techniques to gain deeper insights into employee sentiment, identify factors influencing engagement, and personalize interventions. One example of using machine learning in this context is sentiment analysis of employee feedback. By applying natural language processing (NLP) algorithms to analyze text data from employee surveys, performance reviews, and communication channels, the company can automatically categorize and quantify the sentiment expressed by employees. This allows HR teams to detect trends, identify areas of concern, and proactively address issues affecting employee engagement.
Another application of machine learning is predictive modeling to forecast employee turnover. By analyzing historical data on employee turnover alongside various demographic, performance, and engagement-related features, machine learning algorithms can learn patterns and identify risk factors associated with attrition. This enables HR departments to anticipate which employees are at higher risk of leaving and take proactive measures to intervene, such as offering personalized career development opportunities, mentorship programs, or retention incentives. By prioritizing resources and interventions based on predictive insights, the company can mitigate turnover, improve retention rates, and foster a more engaged workforce. Furthermore, machine learning can facilitate personalized recommendations and interventions to boost employee engagement. By leveraging data from employee profiles, preferences, and interactions with internal platforms, recommendation systems powered by machine learning algorithms can suggest relevant training programs, learning resources, career paths, or social activities tailored to individual employees' interests and developmental needs.
Additionally, by analyzing real-time data on employee behaviors and engagement metrics, machine learning models can dynamically adjust and optimize interventions over time, ensuring they remain effective and aligned with evolving employee preferences and organizational goals. Overall, by harnessing the power of machine learning, the company can create a more data-driven and proactive approach to improving employee engagement, leading to a happier, more motivated workforce and ultimately driving better business outcomes.
Last but not least machine learning can also be employed very productively to drive customer satisfaction and customer retention by providing superior customer service and being able to serve customers at all hours of the day or night with personalized and rapid fire answers and responses because customers do write email to the customer support department and expect a response faster than two weeks later.
They would like customer service at the speed of social media at any time of week or hour of day. Together these four factors encapsulate much of the help that machine learning and artificial intelligence provide for improving enterprise outcomes.
Which main business areas are impacted most by machine learning or are there multiple business areas? Every business area that works on digital information or that deals with unstructured data processing can benefit from machine learning. So sales, marketing, and customer service are big areas.
Other big areas are the back office in every shared service organization, whether it's finance, whether it's human resources, whether it's procurement, any of the horizontal functions which essentially operate according to defined business processes.
These business processes are usually codified with three ring binders which are nothing but vaguely defined computer programs. Until now we haven't been able to write down the process out of the computer program because the rules were hard to determine. With machine learning and computers learning the data we can also bring computerization to a more vast array of business processes. Computers and humans working hand-in-hand together can deliver superior value.
Last but not least, consider less digital areas like logistics or production. Actually these areas also stand to benefit massively. Just think of the benefits that self-driving vehicles and autonomous craft can provide, both to the logistics operations and to a shop floor which may no longer need conveyor belts, which can rely on self-driving vehicles bringing semi finished goods from station to station and to the actual output product of these industries.
The self-driving car or vehicle is just one of the new products and offerings that's enabled by machine learning. I truly believe that we are standing on the crest of another industrial revolution that will bring us product services and offerings that are hitherto undreamt of.