Media mix modeling: a case study on optimizing television and digital media spend for a retailer

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceRetailers invest most of their advertising budget in traditional channels, namely Television, even though the percentage of budget allocated towards digital media has been increasing. Since the largest part of sales still happen in physical stores, marketers face the challenge of optimizing their media mix to maximize revenue. To address this challenge, media mix models were developed using the traditional modeling approach, based on linear regressions, with data from a retailer’s advertising campaign, specifically the online and offline investments per channel and online conversion metrics. The models were influenced by the selection bias regarding funnel effects, which was exacerbated by the use of the last-touch attribution model that tends to disproportionately skew marketer investment away from higher funnel channels to lower-funnel. Nonetheless, results from the models suggest that online channels were more effective in explaining the variance of the number of participations, which were a proxy to sales. To managers, this thesis highlights that there are factors specific to their own campaigns that influence the media mix models, which they must consider and, if possible, control for. One factor is the selection biases, such as ad targeting that may arise from using the paid search channel or remarketing tactics, seasonality or the purchase funnel effects bias that undermines the contribution of higher-funnel channels like TV, which generates awareness in the target audience. Therefore, companies should assess which of these biases might have a bigger influence on their results and design their models accordingly. Data limitations are the most common constraint for marketing mix modeling. In this case, we did not have access to sales and media spend historical data. Therefore, it was not possible to understand what the uplift in sales caused by the promotion was, as well as to verify the impact of the promotion on items that were eligible to participate in the promotion, versus the items that were not. Also, we were not able to reduce the bias from the paid search channel because we lacked the search query data necessary to control for it and improve the accuracy of the models. Moreover, this project is not the ultimate solution for the “company’s” marketing measurement challenges but rather informs its next initiatives. It describes the state of the art in marketing mix modeling, reveals the limitations of the models developed and suggests ways to improve future models. In turn, this is expected to provide more accurate marketing measurement, and as a result, a media budget allocation that improves business performance

Multiplicative Bidding in Online Advertising

In this paper, we initiate the study of the multiplicative bidding language adopted by major Internet search companies. In multiplicative bidding, the effective bid on a particular search auction is the product of a base bid and bid adjustments that are dependent on features of the search (for example, the geographic location of the user, or the platform on which the search is conducted). We consider the task faced by the advertiser when setting these bid adjustments, and establish a foundational optimization problem that captures the core difficulty of bidding under this language. We give matching algorithmic and approximation hardness results for this problem; these results are against an information-theoretic bound, and thus have implications on the power of the multiplicative bidding language itself. Inspired by empirical studies of search engine price data, we then codify the relevant restrictions of the problem, and give further algorithmic and hardness results. Our main technical contribution is an $O(\log n)$-approximation for the case of multiplicative prices and monotone values. We also provide empirical validations of our problem restrictions, and test our algorithms on real data against natural benchmarks. Our experiments show that they perform favorably compared with the baseline.Comment: 25 pages; accepted to EC'1

ATTRIBUTION MODELING AND MARKETING RESOURCE ALLOCATION IN AN ONLINE ENVIRONMENT

This dissertation contains one conceptual framework and two essays on the attribution modeling and marketing resource allocation in digital marketing. Chapter II presents the conceptual framework for attribution modeling and hypotheses related to the carryover effects and spillover effects of the information collected during the customer's prior visits through different marketing channels to a firm's website on subsequent visits and purchases. In Chapter III, I propose a method to measure the incremental value of individual marketing channels in an online multi-channel environment. The method includes a three-level measurement model of customers' consideration of online channels, their visits through these channels and subsequent purchase at the firm's website. Based on the analysis of customers' visits and purchases at a hospitality firm's website, I find significant carryover and spillover effects across different marketing channels. According to the estimation results, the relative contributions of each channel are significantly different as compared to the estimates from the widely-used "last-click" metric. A field study was conducted where the firm turned off paid search for a week to validate the ability of the proposed approach in estimating the incremental impact of a channel on conversions. This method can also be applied in targeting customers with different patterns of touches and identifying cases where e-mail retargeting may actually decrease conversion probabilities. Chapter IV analyzes the impact of attribution metric on the overall effectiveness of keyword investments in search campaigns. Different attribution metrics assign different conversion credits to search keywords clicked through the consumers' purchase journey, and the attribution-based credits affect the advertiser's future bidding and budget allocation for keywords, and in turn affect the overall return-on-investment (ROI) of future search campaigns. Using a six-month panel data of 476 keywords from an online jewelry retailer, I empirically model the relationship among the advertiser's bidding decision, the search engine's ranking decision, and the click-through rate and conversion rate, and analyze the impact of the attribution metric on the overall ROI of search campaigns. The focal advertiser changed the attribution metric from last-click to first-click half-way through the data window. This allows me to estimate the impact of the two attribution metrics on budget allocation, which in turn influences the realized ROI under different attribution regimes. Given the mix of the keywords bid by the advertiser, the results show that first-click leads to lower overall revenues and this impact is stronger for the more specific keywords. The policy simulation shows that the advertiser would be able to improve their overall revenue by more than 5% by appropriately changing the attribution metric for individual keywords to account for their actual contribution

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

Profit maximization through budget allocation in display advertising

Online display advertising provides advertisers a unique opportunity to calculate real-time return on investment for advertising campaigns. Based on the target audiences, each advertising campaign is divided into sub campaigns, called ad sets, which all have their individual returns. Consequently, the advertiser faces an optimization problem of how to allocate the advertising budget across ad sets so that the total return on investment is maximized. Performance of each ad set is unknown to the advertiser beforehand. Thus the advertiser risks choosing a suboptimal ad set if allocating budget to the one assumed to be the optimal. On the other hand, the advertiser wastes money when exploring the returns and not allocating budget to the optimal ad set. This exploration vs. exploitation dilemma is known from so called multi-armed bandit problem. Standard multi-armed bandit problem consists of a gambler and multiple gambling-slot machines i.e. bandits. The gambler needs to balance between exploring which of the bandits has the highest rewards and simultaneously maximising the reward by playing the bandit having the highest return. I formalize the budget allocation problem faced by the online advertiser as a batched bandit problem where the bandits have to be played in batches instead of one by one. Based on the previous literature, I propose several allocation policies to solve the budget allocation problem. In addition, I use an extensive real world dataset from over 200 Facebook advertising campaigns to test the performance impact of different allocation policies. My empirical results give evidence that the return on investment of online advertising campaigns can be improved by dynamically allocating budget. So called greedy algorithms, allocating more of the budget to the ad set having the best historical average, seem to perform notable well. I show that the performance can further be improved by dynamically decreasing the exploration budget by time. Another well performing policy is Thompson sampling which allocates budget by sampling return estimates from a prior distribution formed based on historical returns. Upper confidence and probability policies, often proposed in the machine learning literature, don’t seem to apply that well to the real world resource allocation problem. I also contribute to the previous literature by providing evidence that the advertiser should base the budget allocation on observations of the real revenue generating event (e.g. product purchase) instead of using observations of more general events (e.g. clicks of ads). In addition, my research gives evidence that the performance of the allocation policies is dependent on the number of observations the policy has to make the decision based on. This may be an issue in real world applications if the number of available observations is scarce. I believe this issue is not unique to display advertising and consequently propose a future research topic of developing more robust batched bandit algorithms for resource allocation decisions where the rate of return is small

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

Retailer and Manufacturer Advertising Scheduling in a Marketing Channel

Producción CientíficaDespite the fact that the use of sporadic advertising schedules is well established in both the advertising literature and market place, the marketing channel literature that focuses on vertical interactions has consistently prescribed continuous advertising strategies over time. This paper investigates, in a bilateral monopoly context a situation in which a manufacturer and a retailer control their pricing and advertising decisions, the optimal scheduling of advertising in a planning horizon of three periods. We found that, consistent with the advertising literature, the integrated channel adopts pulsing to benefit from advertising positive carryover effects. Conversely, when pricing and advertising decisions are uncoordinated, channel members can optimally implement each of the following three advertising schedules. The full continuous schedule where channel members advertise in the three periods. %The full pulsing schedule in which the two channel members advertise only in the first and third periods. The mix schedule where the retailer advertises in the three periods and the manufacturer advertises exclusively in the first and third periods. Depending on the magnitude of the long-term effects of retailer and manufacturer advertising, each of the three schedules can be implemented.The first author's research is partially supported by MEC under project ECO2014-52343-P, co-financed by FEDER funds and the COST Action IS1104 "The EU in the new economic complexgeography: models, tools and policy evaluation