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Customer Relationship Management (CRM) solutions often require the creation of customer level models that accurately predict buying patterns of customers, based on historical and current data. Or, you may want to determine the probability of a customer purchasing a product based on the price points. Analyzing such historical and current data and generating a model to predict the future outcomes of a product/service is termed as Predictive Modeling.
Predictive Modeling is a statistical technique that is widely used in research and analysis. There are many types of predictive models to choose from, a few of them being:
Predictive analytics is widely used in underwriting, risk management, collection analytics, direct marketing and CRM, to name a few. Here are some examples of how predictive modeling can be used:
Predictive Modeling is also extremely useful in Big Data scenarios where the data is large, unstructured, and complex, and cannot be managed by using a normal database management system. For example, social networks and web logs are sources of Big Data, which if studied and analyzed carefully, can provide you with significant insights into user behavioral patterns.
Such data available from social media is useful for companies to study and understand customer behaviour. Social media data types include those from blogs (Blogger, WordPress) social networks (Facebook, LinkedIn) micro blogs (Twitter) and social news websites (Digg).
The data from social media is characterized by rich user interaction and can be extracted, analyzed, and used in a variety of ways. Companies can benefit from understanding customer behavior on social networks. For example:
While there are many predictive models, choosing the apt one is always a challenge. Outsource2india has a well-defined process that can help you choose the right predictive model for your business. Our expert analysts will first use a data audit to sanitize the data. This will be followed by data analysis, which will establish correlations and build linear/non-linear mathematical models. We will then use simulation models to help you understand the outcomes of the data analysis.
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