Smart Grid is an important cyber-physical infrastructure for power generation, transmission and distribution. As energy becomes a necessity for our modern living, effective management of Smart Grid can have great impact on power services. Recent smart meter technology has provided an opportunity that allows an economic model of utility to be more flexible and optimal. Construction of such a model requires better understanding of consumer behaviors in response to a given pricing. Most existing research uses mathematical models that tend to rely on assumptions that may not be realistically realizable. Majority of empirical work aims to predict or optimize utility loads. To the best of our knowledge, no empirical published work has addressed this issue. Our research aims to find an appropriate Big data analytic approach to learning consumption pattern of each household at appliance level. Particularly, this paper investigates how machine learning can be used to predict the change of customer's consumption habits for lower pricing. The problem is non-trivial due to conflicting dependent factors that are dynamically changing. Our preliminary results, obtained from three popular machine-learning models using synthesized data, show relatively high accuracy ranging from 61.5% to 99.4% for various appliances. Although the results are encouraging, we provide insights towards the validity and improvement of these predictive models.