Altering life styles urge consumers to diversify their behaviours towards an ever increasing demand for online market use. This current trend makes market players to seek for contemporary business models. In that respect, understanding their target customers deeply has become prominent for the companies that has been in online market for a while and also the companies that are planning to enter the market in terms of developing effective marketing strategies. In this study, we apply an online survey with the goal of exploring the consumer profile in online market. We investigate the answers with statistical inference techniques and identify key demographic determinants of the consumers who opt for online grocery shopping. Then, we use machine learning techniques, particularly classification techniques, to estimate ‘buy’ or ‘do not buy’ decisions of the consumers over the predetermined demographic factors and we compare performance of different estimation methodologies according to different performance measures. Among others, the random forest technique has yielded the three best scores out of four metrics on the test set. Furthermore, we provide current and potential companies in online grocery market with some insights regarding the influential factors on customer satisfaction. We have found that user friendly mobile applications and efficient after-sales services had a positive impact on the ‘buy’ decision of a customer, while setting a minimum amount to checkout and designing a restricted delivery area had negative impact on the ‘buy’ decision of a customer.