“Prediction is difficult especially when it involves the future”- said well-renowned American novelist Mark Twain. Predicting and viewing the future through a mirror is not possible- at least for the time being- due to uncertainty and unforeseen events. Impossible word doesn’t work in the business realm, so organizations came up with predictive analytics, an option that gives them insights about events that are likely to happen in the future.
An organization can forecast the possibility of an event by utilizing modern technologies and approaches such as machine learning and artificial intelligence. Data enables stakeholders to find probabilities and possibilities about the future for taking informed actions at the right time. Insights generated by predictive analytics are mainly used to solve business challenges, uncover potential risks, and forecast market opportunities.
What is predictive analytics?
Predictive analytics is the use of AI, ML and data mining to predict the likelihood of an event even before that event occurs. Predictive analytics can be used to predict short-term events such as the probability of winning a market campaign or long-term events such as predicting the revenue for the next year. It utilizes historic, current and real-time data to identify and study patterns that can provide vital information about the future. The future of predictive analytics is promising with numerous industries acknowledging the importance of predictive analytics for success.
Almost all industries use predictive analytics for creating a sustainable business model. Let us take a look at some of the prominent real-world use cases of predictive analytics.
Predictive analytics in the healthcare industry is extremely prominent, considering the uncertainty which lies in the industry. It is gaining traction with the growing demand for personalized healthcare and earlier detection of diseases. Since prevention is better than cure is the mantra of the healthcare domain, developing a preventive mechanism for every possible event is inevitable in the healthcare industry. Predictive analytics helps in identifying patterns from past and current data to detect disease at the earliest and reduce the impact of the event.
The sudden arrival of the pandemic and its prolonged existence reclaimed the importance of predictive analytics in healthcare. With the Covid cases increasing in a few countries while declining in others, the healthcare experts utilized data to give alerts to countries. With predictive analytics, experts could identify the preventive measures for covid and helped countries in designing a lockdown. Predictive analytics helped the global health care industry to predict the Covid surge and take preventive actions to tackle the situation.
Accuracy and time take the front seat in the finance industry, considering the fact that it deals with the financial resources of an organization. Predictive analytics in finance helps in accurate forecasting using statistical models for making promising investment decisions. The finance industry can utilize predictive analytics to forecast revenue for the coming years to make strategic decisions and effective planning.
For instance, in the banking industry, predictive analytics helps to predict customers’ payments using data such as the payment history of the client, economic conditions, interest rate, etc. It helps the financial organizations answer the questions such as,
- Will the customer pay the money on time?
- When will a customer pay the money?
- In what frequency will the customer pay?
These questions help banks to evaluate a customer before they make an investment. Predictive analytics helps banks to eliminate credit risk and non-performing loans to a great extent.
Fast-moving consumer goods (FMCG)
With FMCG reshaping the economy and our lifestyle, the industry is customer and market-centric. The manufacturing industry swims on valid data to provide exactly what the customer wants and to create a gap for their product. From identifying a product idea to eliminating manufacturing bottlenecks, data is the fuel they need for every decision they make. Predictive analytics helps FMCG companies to save costs by improving operational efficiency.
For instance, the term predictive maintenance has become a part of the manufacturing industry in recent years. Equipment maintenance and effective operations are critical in the FMCG sector. Predictive analytics evaluate the efficiency and condition of the manufacturing equipment and tools to gauge when maintenance is required. Predictive maintenance helps in predicting the maintenance need and time beforehand so that the organizations can reduce the downtime.
The proliferation of internet users and the widespread usage of mobile phones has hastened the expansion of the telecom industry. The industry is fiercely competitive, with everyone striving for market dominance. Almost all of the telecom industry’s business strategies rely heavily on data. Data is useful for everything from designing offerings that appeal to the target audience to building a market-based pricing strategy. Companies in the telecom business may use predictive analytics to understand more about their consumers’ preferences and requirements, which will help them succeed in the market.
For example, adhering to the competition in the market, retaining customers is important for long-term survival. Customer review data and their behavior pattern enable telecom players to identify how a client feels about their service. It alerts telecom companies on when a client is likely to abandon their service by analyzing their behavior. Predictive analytics in the telecom industry helps companies address the issue at the right time and satisfy the customers to improve retention.
Predictive analytics is the future and the benefits are ever-growing. It may help you address problems you’re already aware of and find problems you’re not yet aware of. You can gain a competitive advantage by having a better understanding of the market and how it will perform in the future.
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