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Predictive Modeling and its Applications

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.

Types of 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:

  • K-nearest Neighbor algorithm
  • Majority Classifier
  • Naïve Bayes
  • Uplift Modeling
  • Group Method Data Handling
  • Logistic Regression

Popular Applications of Predictive Modeling

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:

  • Auto insurance - Predictive modeling can be used to determine the risk of accidents to policy holders
  • Fraud detection systems - Predictive modeling can be used to identify high-risk transactions/customers Pro-active customer retention - Predictive modeling can be used to predict the probability of a customer terminating his/her services. Predictive modeling can also be successfully carried out in live transactions.

The Use of Predictive Modeling in Social Media

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:

  • A predictive model can be used to understand whether a user will buy a product that is advertised online
  • Predictive modeling can help you evaluate the decision influencers for customers (like the opinions of fellow buyers, and so on)
  • Predictive modeling can be used to understand the correlation between Facebook 'likes' and sales. This can help companies allocate an accurate marketing budget

Choose the Right Predictive Model with Outsource2india

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.

Contact us to use predictive modeling to your advantage.

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