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

Digital Marketing Attribution: Understanding the User Path

This article belongs to the Section Computer Science & EngineeringDigital marketing is a profitable business generating annual revenue over USD 200B and an inter-annual growth over 20%. The definition of efficient marketing investment strategies across different types of channels and campaigns is a key task in digital marketing. Attribution models are an instrument used to assess the return of investment of different channels and campaigns so that they can assist in the decision-making process. A new generation of more powerful data-driven attribution models has irrupted in the market in the last years. Unfortunately, its adoption is slower than expected. One of the main reasons is that the industry lacks a proper understanding of these models and how to configure them. To solve this issue, in this paper, we present an empirical study to better understand the key properties of user-paths and their impact on attribution models. Our analysis is based on a large-scale dataset including more than 95M user-paths from real advertising campaigns of an international hoteling group. The main contribution of the paper is a set of recommendation to build accurate, interpretable and computationally efficient attribution models such as: (i) the use of linear regression, an interpretable machine learning algorithm, to build accurate attribution models; (ii) user-paths including around 12 events are enough to produce accurate models; (iii) the recency of events considered in the user-paths is important for the accuracy of the model.The research leading to these results has received funding from: the European Union’s Horizon 2020 innovation action programme under grant agreement No 786741 (SMOOTH project) and the gran agreement No 871370 (PIMCITY project); the Ministerio de Economía, Industria y Competitividad, Spain, and the European Social Fund(EU), under the Ramón y Cajal programme (grant RyC-2015-17732);the Ministerio de Ciencia e Innovación under the project ACHILLES (Grant PID2019-104207RB-I00); the Community of Madrid synergic project EMPATIA-CM (Grant Y2018/TCS-5046)

Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising

In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patterns among the touch points from a user's behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touch-point regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art

ПРИНОС НА ДИГИТАЛНИ РЕКЛАМНИ КАНАЛИ В ПРИВЛИЧАНЕТО НА СТУДЕНТИ ОНЛАЙН

Промяната в картината на българското висше образование през последните години доведе до стремеж на университетите да си служат с типично маркетингови инструменти, за да достигнат до потенциални кандидат-студенти. Същевременно дигиталните маркетингови канали категорично могат да се определят като предпочитани източници на информация за младите хора у нас. Честото прибягване до платена комуникация в интернет от страна на висшите училища поражда необходимост от ефективно управление на използваните дигитални канали. Целта на настоящата студия е да демонстрира резултатите от прилагането на модел за определяне приноса на различни дигитални маркетингови канали в привличането на кандидат-студенти онлайн, който отчита спецификата на образователните продукти. Приложеният атрибутивен модел отчита интереса на потенциалните кандидат-студенти към различните дигитални рекламни канали и степента, в която намират информацията в тях за полезна, както и действителния им опит и ефектите на преливане и пренасяне. Направен е обзор на дигиталните канали, чрез които университетите могат да достигнат до целевата си аудитория, както и ретроспекция на предложените до момента в научната литература атрибутивни модели. Изведени са препоръки за използване на конкретни дигитални канали за целите на образователния маркетинг, съобразно приноса им в подтикване на целевата аудитория към желаното поведение. Установено е, че най-голям принос в подтикване на целевата аудитория да извърши конверсия имат: (1) дисплейната реклама; (2) рекламата в социалната мрежа Фейсбук; (3) платеното търсене