Classification of Predicting Customer Ad Clicks Using Logistic Regression and k-Nearest Neighbors (original) (raw)

JOIV : International Journal on Informatics Visualization

Nowadays, conventional marketing techniques have changed to online (digital) marketing techniques requiring internet access. Online marketing techniques have many advantages, especially in terms of cost efficiency and fast information delivery to the public. Therefore, many companies are interested in online marketing and advertising on social media platforms and websites. However, one of the challenges for companies in online marketing is determining the right target consumers since if they target consumers who are not interested in buying the product, the advertising costs will be high. One use of online advertising is clicks on ads which is a marketing measurement of how many users click on the online ad. Thus, companies need a click prediction system to know the right target consumers. And different types of advertisers and search engines rely on modeling to predict ad clicks accurately. This paper constructs the customer ad clicks prediction model using the machine learning app...

Machine Learning Based Ad-click Prediction System

International Journal of Engineering and Advanced Technology, 2019

Online advertising is a gargantuan commerce and has potential for rapid growth. This paper presents novel approach of solving the advertisement prediction problem. The aim of this research is to predict an ad-click through various machine learning techniques and to compare their accuracy rates. This, would help the advertisers use the appropriate technique to increase their overall revenue through targeted advertising. The combination of features used, makes this research unique.

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Predicting Click-Through Rates using Data Mining Technique on Digital Advertisements

International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2019

In times of increasingly busy use of social media, placing advertisements on social media such as Facebook is an attractive alternative. With the various advantages of advertising on social media, making it very suitable for MSMEs. The use of video as a format for delivering advertising messages is also rife because of the faster internet speed. However, the cost of making ads in the form of videos is relatively more expensive so it needs a lot of consideration when making it be efficient. One thing that advertisers often pay attention to on social media is the click-through rate. This variable becomes one of the measures of the effectiveness of an advertisement. Hence predicting click-through rate is become important nowadays for advertisers, especially to those who have budget constraints. This research tries to predict the click-through rate using data mining techniques. This paper use CRISP method. The dataset was taken from a Facebook advertisement from a small-medium enterprise in Indonesia. Video watches at 25%, 50%, 75%,95% and 100% is use as predictors. The results show that data mining can be used to predict the click-through rate using video watches percentage. Deep learning is the most suitable model for this prediction. The interpretation of the results from data mining is done and managed to find the variables that support the predictions and contradict the predictions.

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Ad click prediction

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013

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A FRAMEWORK FOR PREDICTING ONLINE BEHAVIOUR OF THE USERS USING CLICK STREAM

IAEME PUBLICATION, 2021

Customer Relationship Management systems have been used to allow businesses to attract new customers, develop a long-term relationship with them and improve the retention of customers for greater profitability. CRM systems use machine-learning models to evaluate personal and behavioural data of customers to give company a competitive edge by increasing the retention rate of customers. These models can predict customer's purchases, and their reasons. Predictions are used in the development of targeted marketing plans and service offerings. This paper focuses to develop a framework by applying machine learning techniques to predict the purchases by the customers on e-commerce platform. Through clickstream and additional customer data, frameworks for predicting customer behaviour can indeed be developed. Predicting potential consumer behaviour generates pertinent information for sales and marketing teams to strategically focus on different resources. Such information facilitates inventory planning at the warehouse and at the point of sale, as well as strategic decisions during production processes can be planned accordingly. Next, this research provides insight into the performance differences of the models on sequential clickstream and static customer data by performing a data analysis and training the models separately on the various datasets. Deep Belief network along with auto encoders and Adam optimizer is performed to validate the data and predict the likelihood of purchase and customer conversion probability.

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Click Through Rate Prediction for Contextual Advertisment Using Linear Regression

This research presents an innovative and unique way of solving the advertisement prediction problem which is considered as a learning problem over the past several years. Online advertising is a multi-billion-dollar industry and is growing every year with a rapid pace. The goal of this research is to enhance click through rate of the contextual advertisements using Linear Regression. In order to address this problem, a new technique propose in this paper to predict the CTR which will increase the overall revenue of the system by serving the advertisements more suitable to the viewers with the help of feature extraction and displaying the advertisements based on context of the publishers. The important steps include the data collection, feature extraction, CTR prediction and advertisement serving. The statistical results obtained from the dynamically used technique show an efficient outcome by fitting the data close to perfection for the LR technique using optimized feature selection.

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Advertisement-Click Prediction Based on Mobile Big Data from HyXen AdLocus

2016

The popularity of Internet has made advertisement marketing gone virtualized and location-based mobile advertising successful in recent years. Adlocus, an APP developed by HyXen Technology, is one good example to achieve this. This advertising software can tailor to the campaign needs and target users within a diameter of 1 km. However, the question is that is it possible to predict whether the user is willing to click on the advertisement. This paper adopts many ways to analyze how these relations influence in different kinds of mobile advertisement. A comprehensive performance comparison of different models is provided, and the analysis of different factors is also discussed, including click time, advertisement category, language, and mobile phone manufacturers.

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