Predictive Analytics: Next driver of growth
Predictive analytics is the use of various statistical techniques like machine learning, big data, predictive modeling and data mining to make predictions the occurrence of any particular event. It aids in decision making by identifying risks and opportunities for a business based on patterns found in historical data. Such technique finds wide application ranging from fields like marketing, retail, financial to intricate areas like healthcare or pharmaceuticals.
The next big thing
Predictive analytics does attract some kind of criticism from experts. They say that extensive data analysis falls terribly short of accounting for all the factors that affect the outcomes of a certain event or phenomenon. Most of them are skeptic of the effectiveness of such models working with human behavior and environmental factors.
Although this disbelief is not completely unfounded, technological advancements from faster hardware to software that analyze increasingly vast quantities of data are making the use of predictive analytics in a more effective and efficient way than ever before. Also, a technique that was once limited to businesses of select nature, now finds application in a wide range of activities helping businesses take better strategic decisions and gain competitive advantage.
Just like any other technological advancement, predictive analytics will take time to mature and evolve into a robust practice before it meets expectations. One must keep in mind that predictive analytics might currently not be in its best shape, but it is the right step in the right direction when it comes to data backed decision making.
One of the most basic examples of application of predictive analytics is that of credit scoring used widely in the BFSI industry. Banks and financial institutions rate the creditworthiness of their clients, individuals or institutions, based on their credit history and other information such as income sources, current financial health, etc. This score helps the financial institutions to take better decision whether to lend to a particular entity or not. This in turn will help banks to avoid potential bad loan accounts.
Predictive analytics also finds applications in marketing. It helps businesses increase customer retention by helping them identify cross-selling opportunities. Such model helps them attract, retain and grow the most profitable customers and optimize their marketing budgets.
Predictive analytics technique could help in optimizing factory resources like manpower, factory timings, etc. based on the inventory and demand estimations made by advanced predictive models. Such models can take into consideration a much wider set of factors which affect inventory and demand levels.
Your company, GlobCon Technologies, can assist you to better predict company’s growth prospects, thus helping in tactical decision making.
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