Decision Making on Optimal Selection of Advertising Agencies using Machine Learning

The decision makers of every business firm take strenuous efforts to achieve the target of sales promotion using different marketing strategies. In recent times, advertising is becoming one of the most preferred and adopted marketing techniques to reach customers at larger magnitude. The business firms irrespective of their production sizes and scales associate with various advertising agencies to familiarize their products to the customers. The advertising strategic approach of marketing also gives rise to a decision-making problem on the optimal choice of advertising agencies as the wrong selection of the agencies harms the sales. This research paper intends to propose a decision making (DM) model integrating both multi–criteria decision methods and machine learning algorithms to make optimal selection of advertising agencies. The proposed DM model considers 25 alternatives of advertising agencies, six criteria, linguistic analytic hierarchy process (LAHP) and random forest algorithm for ranking the advertising agencies. The machine learning (ML) algorithm identifies the feasible alternatives with the help of criterion weights obtained using the method of LAHP. This method is more compatible as it minimizes the risks of decision making by grouping the alternatives into two groups of acceptance and rejection based on their likelihood. The computations are done using Python programming language especially in Google Colabs.