Applications of Predictive Analytics

In the late 19^th century, Henry Ford made his first attempts towards gaining business insights through analyzing data. Hence, predictive analytics is a concept emerging from a long period of time. Later in the 1960s, computers became part of various industries thus, data analytics gained more importance.

The broad term "business intelligence" gained popularity in the late 1990s. It provided software programs to effectively organize large data, handle it, and have a thorough perspective of the data in an organization. One of such examples is Enterprise Resource Planning (ERP). Such software programs allow managers to make modifications in the processes for the future or make conclusions regarding prior operations.  The idea of business intelligence made use of a variety of data science approaches like data mining, modeling, classification, analytics, visualization and statistics helping organizations in all ways to collect or organize their data.

Predictive Analytics operates on past and current data and mathematical models. If one wants to implement a predictive analytics strategy to a business, certain steps must be taken. Firstly, it is important to identify the problems that need to be solved. This can be done through a thorough check of data and answering several questions as to, “Who will buy our new product?” or “Which target market is the service most suitable for?” Once certain relatable questions are answered, the specific problem will be identified. Secondly, one should have sufficient necessary data in order for the predictive model to work and give efficient output. This data must comprise detailed information over the problem over a period of time like one year, because the analytics model will need to identify the patterns to make the correct conclusions. For example, if one needs to know what type of customers will buy the product, there should be enough information on the potential customers’ buying patterns, what type brands they buy, as well as their personal information.

Thirdly, predictive analytics models should be built and taught. These models need to have necessary past data, be upgraded from time to time according to changing business surroundings to identify and analyze patterns set for predictability. Lastly, one needs efficient managers who will make sure that the insights gained from the predictive analysis are effectively part of the decision-making process.

Examples of Predictive Analysis

There are several examples of predictive analytics that are used in multiple types of industries. Some of them are stated below:

1.      Retail

This industry uses most applications of predictive analysis as it helps organizations to monitor, identify, analyze, and provide quick responses to the changing environment and behaviors of customers with the ever-changing market trends. Thus, retail has multiple uses of these software solutions in order to help the marketers from the start of the customer buying process till the end of it. They use predictive marketing to analyze customers’ personal information, their buying patterns, and market trends. It also helps to apply customer segmentation and market segmentation. Insights can be used to modify and maximize marketing campaigns consequently adding to higher customer retention and higher profits. Furthermore, it uses predictive inventory to forecast product demand helping organizations to keep the inventory on an optimum level hence, minimizing waste or extra costs of overstocking. These industries make use of predictive analytics to even optimize supply chain through predictive supply chain. It can help make use of optimal routes that will be most cost-efficient along with monitoring fuel consumption that allows transportation costs to minimize.    

2.      Internet of Things

Since the Internet of Things collects bundles of information to analyze, predictive analytics and IoT are closely related. Predictive maintenance is currently the key use case in smart manufacturing. IoT sensors that are mounted on machines constantly gather information about how they perform and transmit it over to the processing platform, where predictive algorithms evaluate the data, spot anomalies, and recommend maintaining a particular spare part. Plants and manufacturers can prevent downtime and equipment malfunctions by using such analytics.

3.      Sports

Even in sports, predictive analytics help to track players’ performances and allows managers to select the most profitable and rewarding contract. It can aid to check players’ metrics on-field like tactics, speed, health condition, scoring and so on, and their off-field metrics like the amount of profit each player can draw in for the club they play for. This will eventually help the club to know more about the ticket sales, fan engagement, and predict each player’s value.

4.      Weather

Since the emergence of predictive analytics, weather forecasts have been more accurate. These models are provided with constant upgraded information collected over the history of met observations as well as existing information from the satellites helping in accurate forecasts for the long-term. More importantly, it helps to accurately measure and foresee adverse weather conditions like extremely high temperature, hurricanes, etc. this precision of predictability helps avoid multiple losses and prepare beforehand for the storm ahead to come.  

5.      Social Media Analysis

In today’s world, social media is the most important tool for brands and organizations on the whole, to communicate with their customers. Since there is tons of information to collect and analyze through social media, predictive analysis comes in handy. This information must be used in the appropriate manner to maximize results. Through customers’ posts, their interaction with the brand or rival brands, comments, reviews, polls, discussions, engagement and so on, companies now make most use of what they bring to the market, what they offer to the customers and what are the required arrangements or upgrades towards their business operations.

6.      Human Resources

Since HR departments deal with a lot of data on people, they make use of predictive analytics to improve their workflows. Facts that have been compiled and examined can highlight problems with human resource management and assist managers in assigning personnel to various positions based on these facts. Precise estimates on employee performance, staff attrition, the effect of various actions on employee engagement, and more are available to HR specialists. Workforce data analytics will increase productivity and make employees happy.

7.      Financial Modeling

Financial planning is a crucial component of every organization. To predict risks and income, optimize processes to reduce unanticipated expenses, allocate resources effectively, and other objectives, many financial managers already use predictive analytics or intend to do so. Predictive analytics capabilities are already included in many finance management software programs, showing the widespread use of intelligent algorithms in the financial sector in the years to come.

Given the above discussion, it has been seen that predictive analytics can be used by any sort of industry to reap the benefits of numerous information lying within. These software solutions can lead to beneficial outcomes if used appropriately and managed well. Customers can also benefit from the use of predictive analytics. As far as organizations are concerned, predictive analytics helps them to minimize risks and waste, increase productivity, enhance customer understanding, reduce inefficiencies, and ultimately increase profits.