Simply put, machine learning (ML) is a process a software application uses to actively learn from imported data, using it in a way humans would use past experiences as a part of their learning process. Business intelligence (BI), on the other hand, is a complex field representing a process that depends on technology to acquire, store, and analyze business-related data. The goal of BI is to reach optimal courses of action in as short time as possible, so the process includes several different aspects, such as analytics, predictive modeling, performance management, data mining, etc.
MIT Sloan reports that, according to their survey questioning executives from 168 large companies, two out of five companies have already included ML in their sales and marketing efforts. This information comes as no surprise, as the processes behind machine learning have close ties to those of data mining and predictive modeling. When it comes to processing large amounts of data, there simply is no comparison between what a human and a machine can do, so ML naturally appears on stage as a potent tool BI can greatly benefit from.
Boosting Sales and Marketing
There is a lot of information a business can harvest from (potential) customers’ purchasing behavior online. ML brings a significant improvement in understanding a target audience and its needs, providing businesses with valuable information that can be used in marketing in order to skyrocket sales. Data collected from personal profiles (realized purchases, browsing searches, personal details) are irreplaceable, powerful information a company can use to predict, for example, how a new product will be accepted on the market, or which qualities should be included when a new product is made, according to what consumers want and look for.
Improving Employee Safety
ML brings significant improvements to the field of employee safety, providing optimized protection for operators working in high-risk environments. Superior monitoring combined with predictive analysis can prevent malfunctions or system failures that could endanger human lives, avoiding accidents before they can even happen. ML can also use the data in order to comprehend and “remember” the causes that have led to malfunctions and in the past. Identifying the potential threats and risks timely benefits companies in the long run, as both human lives and costly, complex systems used for the operation are kept safe.
Enhancing Customer Experience and Loyalty
Another business-related field ML leaves a meaningful impact on is a field of customer experience. In a never-ending race to reach more people and ensure their purchasing loyalty, many large corporations use ML as a significant help in the process. For example, information Facebook users leave on their profiles are collected and analyzed with the help of ML. Based on a user’s age, gender, location, and previous behavior on the platform, Facebook creates personally customized sponsored posts and ads suggestions.
Predictions made by ML, focused on customer service, are also used by hospitals and clinics: analyzing information about ER layout, staff information, department charts, and patient data, the wait for emergency rooms can be predicted more precisely.
Streamlining Operational Processes
According to the Harvard Business Review, there are several business processes that have already been significantly enhanced by ML:
- Managing customer service,
- Managing risks and compliance,
- Managing financial resources,
- Developing and managing business capabilities, and
- Marketing and selling products and service.
As ML is able to store and use data collected from every business aspect, it can lead to creating successful automation of many processes and workflows. This process is commonly referred to as intelligent IT automation.
The ML ability to streamline operational processes is probably its most important one. With the help of intelligent IT automation, productivity boosts can be massive, and this is why this aspect of ML is attracting a lot of attention.
Artificial intelligence and machine learning are no longer vague futuristic, Sci-fi-like concepts, but the fast-developing reality present in numerous processes we encounter on a daily basis. From playing a major part in Facebook’s People You May Know and Face Recognition, to helping refine search engine results, suggesting product recommendations and filtering emails, machine learning is already very actively present in human lives. ML is already improving many BI-related processes, and it’s expected to become even more potent and useful in the years to come.