As a business leader in almost any industry, it helps to have at least some knowledge of analytics to help inform strategic, data-driven decisions.
Predictive analytics, in particular, can help in many areas – including in sectors such as banking, sales and marketing, supply chain management, healthcare, and human resources.
To learn more about how to use predictive analytics in business, completing an online business analytics master’s can help immensely. Alternatively, stay with us as we guide you through the concept in further detail.
Predictive Analytic Pros: The Benefits of Data-Driven Decisions in Business
Virtually any business or organization can benefit massively from using predictive analytics to manage, analyze, and understand company data. By using predictive analytics software and techniques to project future outcomes, the advantages of using such processes to ascertain future profitability and business viability can be great.
In sales and marketing, for instance, predictive analytics can help companies better understand their client base, and also, assist them in making customer-driven decisions to benefit the business long term. This is also true of the banking and healthcare sectors, as well as in supply chain management. In the workplace, predictive analytics can assist with HR practices, also.
Predictive Analytics in Workplace Human Resources (HR)
Human resources is one area of corporate life that can benefit massively from using predictive analytics. From helping with hiring to analyzing what is known as a company’s ‘people data’ to preempt, manage, and ultimately prevent high staff turnover rates, predictive analytics can assist in a big way. Using predictive analytics can also help companies understand their employees better, as well as more accurately understand employee behavior patterns.
Predictive Analytics in Healthcare
In the healthcare industry, the use of predictive analytics can be exceptionally helpful in diagnosing and delivering treatment to patients. For chronically ill patients in particular, predictive analytics can help monitor and track their progress over time.
So how does predictive analytics in healthcare work exactly? Essentially, medical professionals use data collated from past patient records to inform future outcomes for patients experiencing the same symptoms. This can be extremely effective, as current patients who are diagnosed with the same illnesses or diseases as past patients will likely experience similar patient outcomes.
Predictive Analytics in Sales and Marketing
Using predictive analytics can help businesses better understand their clients. This can then help inform their marketing campaign strategies, and also, assist with generating sales.
So, how does it do this? For one, using customer data obtained via a loyalty program system can help companies understand their customers’ buying habits. By applying the principles of predictive analytics to previous customer transactions, businesses can premeditate what future purchases might look like. Do customers purchase specific items at certain times of the year? What triggers their purchases – is it engagement with certain types of ads or marketing campaigns? The answers to these questions can all be uncovered through the use of predictive analytics.
Predictive Analytics in Banking
The financial sector also uses predictive analytics processes such as machine learning and quantitative tools to make future projections about profitability, revenue, and customer spending habits.
In banking and lending, in particular, analyzing past customer habits can help lenders ascertain whether they are reliable recipients of a loan. Will they be able to make timely repayments? Are they living within their means? Looking up a customer’s credit score and using this to predict future outcomes is a solid example of using predictive analytics in banking.
Predictive Analytics in Supply Chain Management
Supply chain management and predictive analytics also go hand in hand. Predictive analytics techniques can help immensely with inventory management and projection, especially in terms of predicting what stock levels will be required in the future. It also assists with preempting future business viability based on ascertaining manufacturing costs versus projected revenue.
Finally, predictive analytics can also help buyers make decisions on what consumer items to stock up on in the future. Preempting upcoming supply and demand issues based on external factors is a big part of this – as is understanding how a business can be impacted by current world events. Including, for example, how international conflicts have historically contributed to logistical complications with product importation and exportation.