Predictive analytics is a highly sophisticated form of advanced analytics that utilizes current and historical data to forecast future activity, behavior, and trends. It is a complex yet effective methodology that involves applying statistical analysis techniques, data queries, and cutting-edge machine learning algorithms to data sets that facilitate the creation of accurate predictive models.Â
These predictive models are designed to assign a numerical score, or probability, to the likelihood of a particular action or event happening in the future. This numerical score is calculated using advanced mathematical and statistical techniques that take into account numerous variables and factors.
 Predictive analytics has become a critical tool in business, allowing organisations to identify emerging trends and patterns, make informed business decisions, and stay ahead of the curve in today’s fast-paced and ever-changing business landscape.
There are three major types of business analytics.
 descriptive analytics
which gives an account of what has happened in a business. Predictive analytics, the subject of this guide, helps businesses predict what will likely happen. It looks for patterns in data and projects them forward to help businesses mitigate risks and capitalize on opportunities
 prescriptive analytics
prescribes or automatically takes a next best course of action based on intelligence generated by the other two kinds of analytics. Two additional modes of analytics sometimes figure into the business analytics continuum:Â diagnostic analytics, which explores why something happened
real-time analytics
 By harnessing the power of data analysis, we can understand our customers better, drive product innovation and improve operational efficiency. With the help of cutting-edge tools and techniques, we can analyze data as it’s generated, collected or updated.Â
Predictive analytics industry use cases
Predictive analytics can be deployed in across various industries for different business problems. Below are a few industry use cases to illustrate how predictive analytics can inform decision-making within real-world situations.
Banking: As we know, the financial industry is rapidly evolving with the help of new technologies. One such powerful technology is machine learning which is being extensively used by financial service providers to predict credit risk and detect fraud. A perfect example of such a company is BondIT which specializes in providing fixed-income asset management services through predictive analytics. This technology allows BondIT to not only support dynamic market changes in real time but also overcome static market constraints by making use of powerful quantitative tools. BondIT’s reliance on technology enables it to customize personal services for clients and minimize risk, creating a cohesive and streamlined experience for all its customers. With this kind of cutting-edge technology, financial services providers like BondIT can stay ahead of the curve and continue to provide the best possible service to their clients.
Healthcare: Predictive analytics in health care is used to detect and manage the care of chronically ill patients, as well as to track specific infections such as sepsis. Geisinger Health used predictive analytics to mine health records to learn more about how sepsis is diagnosed and treated. Geisinger created a predictive model based on health records for more than 10,000 patients who had been diagnosed with sepsis in the past. The model yielded impressive results, correctly predicting patients with a high rate of survival.
Human resources (HR): HR teams use predictive analytics and employee survey metrics to match prospective job applicants, reduce employee turnover and increase employee engagement. This combination of quantitative and qualitative data allows businesses to reduce their recruiting costs and increase employee satisfaction, which is particularly useful when labor markets are volatile.
Marketing and sales: While marketing and sales teams are very familiar with business intelligence reports to understand historical sales performance, predictive analytics enables companies to be more proactive in the way that they engage with their clients across the customer lifecycle. For example, churn predictions can enable sales teams to identify dissatisfied clients sooner, enabling them to initiate conversations to promote retention. Marketing teams can leverage predictive data analysis for cross-sell strategies, and this commonly manifests itself through a recommendation engine on a brand’s website.
Supply chain: Businesses commonly use predictive analytics to manage product inventory and set pricing strategies. This type of predictive analysis helps companies meet customer demand without overstocking warehouses. It also enables companies to assess the cost and return on their products over time. If one part of a given product becomes more expensive to import, companies can project the long-term impact on revenue if they do or do not pass on additional costs to their customer base. For a deeper look at a case study, you can read more about how FleetPride used this type of data analytics to inform their decision making on their inventory of parts for excavators and tractor trailers. Past shipping orders enabled them to plan more precisely to set appropriate supply thresholds based on demand.
Benefits of predictive modeling
An organization that knows what to expect based on past patterns has a business advantage in managing inventories, workforce, marketing campaigns, and most other facets of operation.
Security- Data protection is crucial in business, as breaches are costly and harmful to reputation. Companies need to use multiple strategies to reduce risk, including automation and predictive analytics. These tools can detect suspicious user activity and respond with security procedures. Machine learning and algorithms can enhance this further. When used alongside traditional security measures, companies can ensure their data is safe and they can handle any potential threats.
Risk reduction- In addition to keeping data secure, most businesses are working to reduce their risk profiles. For example, a company that extends credit can use data analytics to better understand if a customer poses a higher-than-average risk of defaulting. Other companies may use predictive analytics to better understand whether their insurance coverage is adequate.Â
Operational efficiency- More efficient workflows translate to improved profit margins. For example, understanding when a vehicle in a fleet used for delivery is going to need maintenance before it breaks down on the side of the road means deliveries are made on time, without the additional costs of having the vehicle towed and bringing in another employee to complete the delivery.
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Improved decision making- Use predictive analytics to make informed strategic decisions as a business owner or entrepreneur. It offers a competitive edge by analyzing large data sets to identify patterns, trends, and anomalies that traditional analysis methods alone cannot. These insights inform product line additions and expansion decisions, increase growth, and provide a competitive advantage in your industry.