Both literature and practice have investigated how the vast amount of ever increasing customer information can inform marketing strategy and decision making. However, the customer data is often susceptible to modeling bias and misleading findings due to various factors including sample selection and unobservable variables. The available analytics toolkit has continued to develop but in the age of nearly perfect information, the customer decision making has also evolved. The dissertation addresses some of the challenges in deriving valid and useful consumer insights from customer data in the digital age. The first study addresses the limitations of traditional customer purchase measures to account of dynamic temporal variations in the customer purchase history. The study proposes a new approach for representation and summarization of customer purchases to improve promotion forecasts. The method also accounts for sample selection bias that arises due to biased selection of customers for the promotion. The second study investigates the impact of increasing internet penetration on the consumer choices and their response to marketing actions. Using the case study of physician’s drug prescribing, the study identifies how marketers can misallocate resources at the regional level by not accounting for variations in internet penetration. The third paper develops a data driven metric for measuring temporal variations in the brand loyalty. Using a network representation of brand and customer the study also investigates the spillover effects of manufacturer related information shocks on the brand’s loyalty.