5 real-life revolutionary use cases of Predictive Analysis
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5 real-life use cases of Predictive Analysis transforming industries
Big data and data science have emerged to be one of the most potent sources of information and useful insight that's driving a change in how businesses are functioning. Due to the computational power of modern computers combined with cheap storage, we can process a heap of data and predict outcomes, which may take several hours or days for humans to achieve. Retailers are using Predictive Analysis to improve the efficiency of their supply chain network, insurance companies are using predictive algorithms to detect fraudulent behavior and avoid possible fraud before it happens. Predictive Analysis combined with machine learning is revolutionizing every sector, here are some of the very widespread use cases of Predictive Analysis.
Detecting Insurance / Credit Fraud
Insurance and credit fraud costs insurance companies and banks billions of dollars every year causing a hit to their profitability. The cost to prove a particular fraud may turn out to be more expensive than the actual claim. Investigating without prolonging the claim for the customer has always been the challenge, and Predictive Analysis and Machine learning has been the shot in the arm in detecting frauds. Predictive algorithms have been able to identify the potential behavior from the previous cases of fraud and recognize the claims and transactions that need to be audited or closely monitored.
Since now the company has data-driven points of identification for an account that may be fraudulent, this saves an immense amount of cost for the company by preventing fraud, also provides faster claims for accounts that are trustworthy, thus improved customer satisfaction.
Retailers can predict the best price
Every customer has purchasing power but this differs for everyone. The predictive algorithm can help retailers decide the right price for the customer so that a sale opportunity can be converted into a potential sale. It's like you walk into a store in a designer suit and from your appearance the salesman assumes your purchasing power to be high and decides to sell a particular product for a higher price, and the same product he may have sold for a lower price to the previous person thus achieving a sale at both the opportunities of varied purchasing power and at the same time maximizing profit. Retailers can fetch data about a customer from his Glassdoor salary estimates, LinkedIn profile and shopping history to predict the right price for the customer such that he will be compelled to make the purchase.
Whenever a business loses customers, it loses revenue
and only a new customer can make up for that loss. Attracting a new potential customer comes with a cost which is always higher than the cost of customer retention. Predictive analysis can help businesses identify the signs of dissatisfaction among the customers or customer base and pinpoint those customers that are at the highest risk of leaving. Businesses can take the necessary steps to keep their customers happy and engaged and prevent losing them.
Customer Lifetime Value
Customer identification has been the most difficult part of the marketing strategy, and even more difficult is to identify the customer who is going to spend the most money consistently over the longest period of time. Predictive Algorithms can help retailers and businesses identify such customers to optimize their marketing strategy and engage such existing customers to maximize retention.
Predicting why Patients are getting readmitted
Predictive algorithms can help hospitals identify patients who are prone to readmission within a period of discharge. This insight reduces the cost of hospitals and improves the health of people. It also helps in understanding the reason for re-admittance. Not just from the perspective of health, this also saves the medical claims made due to readmission prior to 30 days. Predictive analysis and machine learning can predict a possible case or identify a certain segment of the population the relation between their readmission, demographic, and socioeconomic data points like income.
Predictive analytics brings together assorted sets of data and diverse data modeling techniques and when combined with machine learning it establishes hidden correlation between various aspects of the process which otherwise remains hidden. It turns raw data into clear inputs that
improve decision making,
and makes better utilization of time and resources.
And with gradual fine-tuning of the algorithm, the predictions will become more accurate and improve the overall business efficiency.
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