A branch of advanced analytics, it is used to make predictions about unknown events in the future. Predictive analytics solutions involve extracting information from existing sources of data, and determining patterns, and predicting future trends and results. It uses different techniques to make such predictions, like artificial intelligence, statistical modelling, machine learning, etc. Predictive Analytics solutions are a reliable method of forecasting, since it also focuses on risk management and takes what-if scenarios into account. Moreover, it helps organizations adapt to the needs of the industry and innovate on the go.
When applied to business, predictive analytics solutions analyze historical and current data to understand products, customers and partners, and identify potential opportunities and risks. This results in analyzing buying patterns and providing meaningful insights, helping organizations in adding more value to their offerings and ensuring that customers enjoy a better buying experience.
Predictive analytics solutions offer decision makers the insights required to foresee developments, respond to challenges proactively, and capitalize on future trends. It aids industry experts uncover relationships between unstructured and structured data, draw relevant inferences and plan accordingly. It also helps businesses harness big data to their advantage, which in turn helps them in pricing products, identifying trends, maintaining inventory, etc.
Predictive analytics solutions reduces risk, improves efficiency and increases profits through data analysis and systematic reasoning. Businesses can use this analysis to understand customer preferences and respond accordingly. Leveraging big data to collect information from social streams will help companies get a holistic report based on behavioral data, which in turn helps them initiate marketing campaigns that target certain segments of the market, depending on their interests.
Predictive analytics solutions help businesses analyze the entire customer lifecycle and devise better marketing strategies for better customer service. Organizations can use in-house data on customer preferences to improve their offerings and study the data collected by partners to know what more the customers shop for outside their stores. Not only is it key to understanding customer behaviors and attitudes, it can also be used to identify critical factors- like the type of customer base the company is catering to, i.e. price-driven or brand-driven, and stock products accordingly. Also, in the service industry, predictive analytics solutions can help organizations pre-empt if customers (categorized based on certain attributes) would prefer a specific kind of service or deal in certain situations.
Functional Analytics: Predictive analytics solutions can be applied to various functions too. Since employees generate a lot of information, HR can tap that data to gain useful insights. Marketers can use analytics generated by predictive analytics solutions to uncover deficiencies/deviations/issues in specific channels in the marketing mix, while making accurate forecasts. Moreover, executives can study various factors affecting business operations and analyze them for patterns/trends, impact on business performance or costs. This can help them restructure the business model and address operational challenges effectively.
Industry Analytics: Predictive analytics solutions has varied applications in other sectors like banking, retail, healthcare, insurance, telecommunications, pharmaceuticals, etc. Through proper charting of reports, it can be used to identify patients who are at a risk of developing certain health conditions like diabetes, etc. For companies providing financial services, predictive analytics solutions can help find inaccurate credit applications and fraudulent transactions done offline or online and identify thefts and false claims. It can optimize resource allocation by effective identification of contact strategies, collection agencies and legal actions to increase recovery and reduce costs. Manufacturing firms can use it to keep a tab on changing consumer sentiment, which can help them improve the quality of their offerings. In the retail sector, organizations can analyze the information collected from various online and offline data streams for understanding trends and customer behavior and preferences better.