A conceptual framework for the direct marketing process using business intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Direct marketing is becoming a key strategy for organisations to develop and maintain strong customer relationships. This method targets specific customers with personalised advertising and promotional campaigns in order to help organisations increase campaign responses and to get a higher return on their investments. There are, however, many issues related to direct marketing, ranging from the highly technical to the more organisational and managerial aspects. This research focuses on the organisational and managerial issues of the direct marketing process and investigates the stages, activities and technologies required to effectively execute direct marketing. The direct marketing process integrates a complex collection of marketing concepts and business analytics principles, which form an entirely ‘self-contained’ choice for organisations. This makes direct marketing a significantly difficult process to perform. As a result, many scholars have attempted to tackle the complexity of executing the direct marketing process. However, most of their research efforts did not consider an integrated information system platform capable of effectively supporting the direct marketing process. This research attempts to address the above issues by developing a conceptual framework for the Direct Marketing Process with Business Intelligence (DMP-BI). The conceptual framework is developed using the identified marketing concepts and business analytics principles for the direct marketing process. It also proposes Business Intelligence (BI) as an integrated information system platform to effectively execute the direct marketing process. In order to evaluate and illustrate the practicality and impact of the DMP-BI framework, this thesis adopts a case study approach. Three case studies have been carried out in different industries including retailing, telecommunication and higher education. The aim of the case studies is also to demonstrate the usage of the DMP-BI framework within an organisational context. Based on the case studies’ findings, this thesis compares the DMP-BI framework with existing rival methodologies. The comparisons provide clear indications of the DMP-BI framework’s benefits over existing rival methodologies

Atrio – attribution model orchestrator

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementIn Digital Advertising, Attribution Modelling is used to assess the contribution of media touchpoints to the campaign outcome, by analyzing each person’s sequence of contacts and interactions with these touchpoints, designated as the Consumer Journey. The ability to acquire, model and analyze campaign data to derive meaningful insights, usually involves proprietary tools, provided by campaign delivery platforms. ATRIO is proposed as an open-sourced framework for Attribution Modelling, orchestrating the data pipeline through transformation, integration, and delivery, to provide Attribution Modelling capabilities for digital media agencies with proprietary data, who need control over the Attribution Modeling process. From a tabular dataset, ATRIO can produce simple heuristics such as last-click analysis, but also data-driven attribution models, based on Shapley’s Game Theory and Markov Chains. As opposed to the black-boxed tools offered by campaign delivery platforms, which are focused in their media channels performance, ATRIO empowers digital media agencies to customize and apply different Attribution Models for each campaign, providing an agnostic, open-source based, holistic and multi-channel analysis

Goal-driven Command Recommendations for Analysts

Recent times have seen data analytics software applications become an integral part of the decision-making process of analysts. The users of these software applications generate a vast amount of unstructured log data. These logs contain clues to the user's goals, which traditional recommender systems may find difficult to model implicitly from the log data. With this assumption, we would like to assist the analytics process of a user through command recommendations. We categorize the commands into software and data categories based on their purpose to fulfill the task at hand. On the premise that the sequence of commands leading up to a data command is a good predictor of the latter, we design, develop, and validate various sequence modeling techniques. In this paper, we propose a framework to provide goal-driven data command recommendations to the user by leveraging unstructured logs. We use the log data of a web-based analytics software to train our neural network models and quantify their performance, in comparison to relevant and competitive baselines. We propose a custom loss function to tailor the recommended data commands according to the goal information provided exogenously. We also propose an evaluation metric that captures the degree of goal orientation of the recommendations. We demonstrate the promise of our approach by evaluating the models with the proposed metric and showcasing the robustness of our models in the case of adversarial examples, where the user activity is misaligned with selected goal, through offline evaluation.Comment: 14th ACM Conference on Recommender Systems (RecSys 2020