Predictive Analytics is a branch of advanced analytics that employs a variety of statistical techniques such as data mining and modeling, machine learning, statistics, and artificial intelligence. These techniques analyze the historical and current data in order to make forecasts about future or unknown events.
Predictive models are used in the business field to identify opportunities and risks by observing patterns found in the transactional and historical data. They guide decision-making for transactions by capturing relationships among many factors and allowing assessment of risks or potential, associated with a particular set of conditions. Organizational processes in fraud detection, healthcare, manufacturing, credit risk assessment, marketing, and government operations including law enforcement involves large numbers of individuals. Predictive analytics is utilized by these large organizations to determine a probability for each individual in these organizations which determines, informs, or influences organizational processes.
Businesses can utilize big data for their benefit through application of predictive analytics. By discerning patterns and relationships in the structured and unstructured data, the data mining and text analytics along with statistics produces predictive intelligence for businesses. Age, gender, marital status, income, sales are the structured data that can be analyzed. Social media content, call center notes or other types of open text are unstructured data that is textual. This unstructured data has to be drawn from the text, along with the sentiment, which is then used in the model building process.
Predictive analytics makes organizations more prepared, proactive in expecting behaviors and outcomes based upon the data rather than on just a bunch of assumptions.
Organizations use predictive analytics technology to get a clear understanding of clients’ preference, which in turn provides enhanced and effective post-sales service. Sales can be spurred, if an intelligent search is able to predict what the customers are looking for. Predictive search analyzes past-click through behavior, preferences and history in real-time, capturing a customer’s intent. Thus predictive search runs proprietary algorithms analyzing data based on machine learning to produce results to consumers. Predictive analytics also maximizes revenue and profit, with its capability to determine the right prices at the right time, by analyzing pricing trends in correlation with sales information. For managing pricing, a predictive model is used to look at historical data of sales, customers, products and more.
Moreover, predictive analytics puts an end, to a retailer’s nightmare of fraud management and chargebacks. By analyzing customer behavior and product sales, predictive analytics can reduce the credit card chargeback and minimize fraud, through removal of products vulnerable to fraud. The fraud management predictive models locate a possible fraud before the completion of a customer’s purchase transaction. As a result, there is a reduction in chargebacks along with the decrease in labor and fees needed to process the chargeback.
It helps in better understanding of consumer demand for effective management and overall supply chain process.
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