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In the world of internet marketing, search engine optimization is a popular term. Getting higher rankings on the search engine and thereby getting more views to advertiser’s site is basically what it is. However, those views will not mean a lot if they do not lead to sales or conversion. Search advertisers join an online auction in order to get a slot in the search engine results pages. This means, in Pay per Click online model, advertisers have to pay to the search engine for the number of clicks on the advertisement they posted. Therefore, predicting conversion likelihood for advertisers is highly crucial for the sake of the revenue. Besides, being able to understand and track the conversion rates not only allows advertisers to measure the performance of the web pages but also to identify areas for improvement. In this study, machine learning is used for predicting the conversion rate using search term queries in google shopping ads. The purpose is to analyse if a pattern on search term queries to predict conversion rate can be observed. Search term queries with a high probability of better conversion rate can be advertised more. While the bids on low-conversion segments can be lowered to reduce the costs. For this purpose, extracting features from the text to represent it in a way that can be understood by the machine like term frequency and paragraph vector are tested on machine learning and deep learning model. The results show that different patterns of search term queries do not lead to a predictable conversion rate in the specific use case. Thus, the search term itself is no indicator for good or bad conversion rate.