Evaluating Advertisement Appeals' Effectiveness on Top of Mind Awareness Using AI and Machine Learning Techniques

The purpose of this research is to evaluate the effectiveness of a variety of marketing strategies in influencing consumers' Top of Mind Awareness (TOMA) by utilising AI and machine learning technology. TOMA is a valuable indicator for brand managers to employ, as it represents the brand that people predominantly connect with a particular category. For an extended period, surveys and focus groups have been frequently employed to evaluate the efficacy of advertisements, despite their potential for bias and time-consuming nature. This research employs sophisticated artificial intelligence and machine learning algorithms to analyse digital footprints, including search engine queries and social media participation, in order to determine the most effective advertising concepts for enhancing TOMA (top-of-mind awareness). The study utilises natural language processing (NLP) to analyse customer sentiment and the relationship between keywords, utilising machine learning algorithms to predict and evaluate the efficacy of various advertising strategies. Marketers can derive significant value from the findings, as they can determine which appeals are most likely to increase top-of-mind awareness (TOMA) among their target audience. This, in turn, allows them to create advertising campaigns that are more effective.