Nonresponse is a considerable challenge in the Retailer Survey on the Cost of Payment Methods conducted by the Bank of Canada in 2015. There are two types of nonresponse in this survey: unit nonresponse, in which a business does not reply to the entire survey, and item nonresponse, in which a business does not respond to particular questions within the survey. Both types may create a bias when computing statistics such as means and weighted totals for different variables. This technical report analyzes solutions to fix the problem of nonresponse in the survey data. Unit nonresponse is addressed through response probability adjustment, in which response probabilities are modelled using logistic regression (a clustering approach for the unit response probabilities is also considered) and are used in the construction of a set of survey weights. Item nonresponse is addressed through imputation, in which the gradient boosting machine (GBM) and extreme gradient boosting (XGBoost) algorithms are used to predict missing values for variables of interest.