Machine learning incorporates various sub-fields of AI and Big Data onto products of daily use. Though many consumers still remain apathetic, machine learning has sprouted roots beyond tech industries. Once perceived to be complicated and out of reach to the everyday person, the implications at present influence the entire globe. Take your smart phone for example: your phone studies and inputs records you feed onto the device. With time, it effectively copies your behavior pattern. Then, it further moves on to develop suggestions providing ability based on your input history.
Similarly, businesses rely on machine learning software to process the data they collect through various sources such as consumer patterns and trends, which are predicted based on data mining and advanced analytics. Therefore, companies strategize on customer attraction tactics by generating probable engagement activities.
Implications of Machine Learning Beyond Tech
From handheld devices to complicated rocket-science processes, machine learning algorithms assist humans to enhance work efficiency. Below are some areas that use machine learning processes.
Kapersky Lab reportedly detected 325,000 new malware files every day. The existence of the immense number of malware makes it an unanticipated problem. However, the similarities in codes in most malwares enable accurate prediction of such bugs. DeepInstinct reports that only 2% and 10% of the files change from iteration to iteration. Machine learning can run through the algorithms and analyze the data pattern and report irregularities.
Financial trade constitutes of risky endeavors. It is beyond human capability to predict uncertain transactions based on compilation of huge data. Hence, machine learning has been introduced to measure financial deviations. Machine learning algorithms are getting close to accurately navigating businesses towards safer investment probabilities. Compared to human analysis, machine learning algorithms provide more accurate profitable trade possibilities at a greater speed.
Examples of machines learning algorithms surpassing human capacity in disease diagnosis have surfaced several times. One such example being the use of computer assisted diagnosis (CAD) to review the early mammography scans of women who later developed breast cancer. The computer spotted 52% of the cancer as much as a year before they were officially diagnosed.
Machine learning has come handy when detecting health risk in densely populated areas. Medicision , with the algorithm it developed, was successful in identifying eight variables to predict avoidable hospitalizations in diabetes patients.
Machine learning is proving to be efficient at handling predictive tasks such as suggesting businesses the highest propensity to drive sales and marketing outcomes. Businesses keen on competing and winning more customers are applying machine learning sales and marketing challenges first. In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover, Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning.”
In a Forbes article by Louis Columbus, he points the key take away of the MIT Slogan Management Review article as:
(1) 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
(2) At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
(3) 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
Haven’t we, time and again, been lured onto a gleaming advertisement placed on a corner of a social site? Have you ever wondered how the ad precisely pops up on your screen? Machine learning is the answer for these utterly relevant products and services displayed onto your screens out of the blue. Machine learning algorithms analyze your internet activities and match them with other users to determine your interests and preferences.
Recent developments are making machine learning relevant outside the tech industry as well. From the mobile hand held cellular devices to home appliances, machine leaning is incorporated to provide ease to consumers.