What is Predictive Analytics?
Predictive analytics encloses a diverse set of statistical methods from data mining, modeling, and machine learning that analyze present and past information to make reliable predictions about upcoming or otherwise undetermined events. In business, predictive models take advantage of patterns discovered in previous and transactional information to analyze risks and opportunities. Models use links between many data elements to grant assessment of risk or potential accompanying a particular set of circumstances, guiding decision making for user transactions.
Why is Predictive Analytics Important?
Predictive analytics is unique due to its ability to foretell a predefined pattern of behavior at an individual level. Companies can define specific conditions, which when met would let an analyst determine an individual’s behavior such as a customer’s probability to visit a store again, or the chance of a voter to be persuaded into electing a particular candidate. A predictive score can be generated to each person as to the actions that might be important for an organization to predict. These actions may improve drive operations for a business or simply offer smart insights on upcoming events.
Predictive analytics is not the same as traditional business intelligence frameworks in that it follows a proactive approach to collected information. Traditional business intelligence frameworks utilize data to learn about a customer or to find trends in businesses. Predictive analytics determines how a customer will act in the future and how that customer may behave in response to various touchpoints. The difference stands in the capability to directly identify patterns in data that showcase conflicts and pinpoint opportunities. Predictive analytics strengthens companies to organize for the future, which can transform uncertainty into a usable action with high probability.
The capability to forecast and impact the future is a lucrative opportunity and organizations like IBM and SAP are good examples of companies that rely on this initiative. IBM utilizes predictive analytics software to raise profits, obstruct fraud, and even calculate the social media impact of marketing campaigns. SAP grants customers the ability to act on big data and provide insights into new opportunities and any hidden risks. Predictive analytics also extends beyond these two companies and to many industries.
What are Some Use Cases of Predictive Analytics?
The following are brief examples of some ways that predictive analytics could be used. More detailed examples will be covered in future articles.
Predictive Analytics in Marketing…
The main goal of almost every marketing campaign is to maximize the returns. Predictive analytics has made it possible to acquire real-time information from several customer touch-points, both static and dynamic, to improve the effectiveness of upcoming marketing projects. Predictive techniques can be used to gain insights into most efficient ways to assign budgets to a media mix or understand the likely effectiveness of a potential campaign. Highly sophisticated market strategies, market segmentation, real-time pricing, and contactless conversions have all been possible due to predictive analytics in recent years.
Predictive Analytics in Healthcare…
The healthcare industry has gone through a massive change ever since this sector got introduced to information technologies. Predicting plausible diseases and patients who are at high risk is a main benefit that machine learning and big data have offered, making patient-care a joint task between the healthcare providers and the patients. Professionals can use predictive analytics to analyze a patient’s information and forecast the potential for illness. Healthcare is more about preventing illnesses than about treatment, and the more the healthcare industry works with patients the more stable the sector will be to stop several potential sicknesses in the future.
Predictive Analytics to Identify Fraud…
Fraudulent events cost both organizations and customers billions of dollars every year. To add to the issue, trying to demonstrate that claims are fraudulent can, in turn, further increase expenses incurred. For this reason, many companies have been relying on machine learning and predictive models to identify fraud situations. This helps showcase more claims that should be researched by human auditors. The method doesn’t just reduce the costs of human hours but also increases the opportunity to reclaim stolen dollars from fraudulent claims. Once the algorithm becomes fine-tuned, the accuracy and rate at which a team processes fraudulent claims will dramatically increase.
Predictive Analytics gives an opportunity to assume future trends and allows organizations to act beforehand. Better decision making leads to success. Previously, decisions were based on intuition. As data has become more available, making completely intuitive decisions has become rare. As a result, data-driven decision making has become more prevalent to ensure a reasonable path for success.