Automobile industry has been undergoing key transformations recently. There are very many complexities arise with respect to these transformations. The automobile data and its exponential growth along with its high dimensionality issues, distributional patterns apart from the normal distribution and its sparse attributes added to its complexities. These complexities possess a great challenge when it comes to dealing with flood of personalized automobile data available about the customer preferences, as well as general business and economic data in making informed decisions. On one hand, the science of machine learning comprises of a family of models has been known to have very many applications such as detection, classification and prediction amongst many. The application of the advanced machine learning techniques helps to leverage the existing automobile data in informed decision making. On the other hand, econometric techniques created their own niche in predictive and exploratory analysis domain. Econometric techniques leverage on the main advantage of capturing time-series and panel data types. The importance of machine learning in automobile analysis has been prominent and quintessential in the contemporary world as it can capture numeric and text data. In the science of machine learning, artificial intelligence models that mimics the functionality of biological neuron plays an inevitable role. Artificial neural networks train the available automobile data based on the realization and thus adapts the dynamics in the inputs by approximating an arbitrary function. Support vector machines have been widely popular in machine learning domain and are expected to have wide application in automobile domain. These algorithms can be widely used for and prediction in the automobile domain. Hence we aim to compare the performance and implication of this advanced machine learning algorithm in comparison to its econometric techniques such as ARIMA, Multinomial Logit and Panel Models in customer analytics domain in automobile industry. This research is of utmost importance as the application of machine learning and econometric techniques in automobile industry needs more focus and it would improve the efficiency and effectiveness of business operations as well as customer service.