The process includes identification of marketing and campaigning targets and optimizing predictive analysis. Following are the broadly discussed aspects of customer modeling:

• Response modeling - Modeling enhances the organization’s knowledge on each individual customer and identify if the customers under specific segment are good and effective for marketing campaigns and promotion. This process includes validation and testing of collected customer response data and information. After analyzing and computing this data, scores or ranks are assigned to customers that represent their willingness to respond to a specific program or promotion. The approach is to divide the customers into modules or sub groups and then assign probability of response to each sub group. Marketing professional and decision making personals then decide the exact number of customers to be included in that particular promotion or program.
• Predicting customer Behavior - All the organizations are interested in determining the future value of all their existing customers. Modeling techniques are used to predict life time value of customers and profit impacting customer behavior like probability of product purchase, frequency of product purchase, spending capabilities, loyalty, usage of support and services. These predictive models support various kinds of processes like marketing campaigns, forecasting of financial and developmental aspects, customer budget management and asset management.
• Return on investment (ROI) optimization - Modeling emphasizes on optimizing following marketing activities like pricing, channeling and response medium determination. Organization usually gets highest return on investment from their marketing promotions by modeling the price elasticity of customers so that a valid offer can be given to each customer. By this the profit margin of product increases with low cost to the organization.
• Measuring market impact - In today’s scenario, organizations have to come up with efficient and attractive marketing programs to communicate with customers and convey their message because customers are exposed to the open market where marketing competition is inevitable. Due to this market stimulation on customers are properly accounted which brings confusions in customers and they become biased. Due to this biased behavior the predictions and analysis could defect from actual implementation. Modeling being multidimensional in nature helps to measure and sustain this impact of marketing on customers’ behavior in a controlled and efficient manner.

Modeling and profiling are mostly same but the basic difference between them is the factor of time involved in modeling processes; as the modeling is not a static process. Modeling is quite more sophisticatedly implemented and thus making it powerful technique to predict customer behavior. Modeling process is action oriented and is not at all static throughout the customer life cycle. Profiling on the other hand is static and no action is taken apart from just recording the actual information and doing analysis on that information. Modeling on other hand involves action to be taken over times. Modeling also increases the return on investment and enhances business perspectives by fetching out good profit. Being more powerful and effective technique, marketing professionals prefer customer modeling in place of customer profiling because they have to deal with actual customer data.