Statistical Modelling for Improving Efficiency of Online Advertising

TL;DR

This paper presents statistical and machine learning models to optimize online advertising strategies, leading to increased efficiency, better targeting, and higher conversion rates in real-time bidding environments.

Contribution

It introduces novel algorithms combining statistical modelling and machine learning for ad selection and bidding strategies, validated with real campaign data.

Findings

Improved client conversion rates due to targeted advertising

Significant efficiency gains and cost savings for Crimtan

Effective modelling of repeat-buying behavior using mixed Poisson processes

Abstract

Real-time bidding has transformed the digital advertising landscape, allowing companies to buy website advertising space in a matter of milliseconds in the time it takes a webpage to load. Joint research between Cardiff University and Crimtan has employed statistical modelling in conjunction with machine-learning techniques on big data to develop computer algorithms that can select the most appropriate person to which an ad should be shown. These algorithms have been used to identify suitable bidding strategies for that particular advert in order to make the whole process as profitable as possible for businesses. Crimtan's use of the algorithms have enabled them to improve the service that they offer to clients, save money, make significant efficiency gains and attract new business. This has had a knock-on effect with the clients themselves, who have reported an increase in conversion rates as a result of more targeted, accurate and informed advertising. We have also used mixed Poisson processes for modelling for analysing repeat-buying behaviour of online customers. To make numerical comparisons, we use real data collected by Crimtan in the process of running several recent ad campaigns.