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

Models for Budget Constrained Auctions: An Application to Sponsored Search & Other Auctions

The last decade has seen the emergence of auction mechanisms for pricing and allocating goods on the Internet. A successful application area for auctions has been sponsored search. Search firms like Google, Bing and Yahoo have shown stellar revenue growths due to their ability to run large number of auctions in a computationally efficient manner. The online advertisement market in the U.S. is estimated to be around $41 billion in 2010 and expected to grow to$50 billion by 2011 (http://www.marketingcharts.com/interactive/us-online-advertising-market-to-reach-50b-in-2011-3128/). The paid search component is estimated to account for nearly 50% of online advertising spend. This dissertation considers two problems in the sponsored search auction domain. In sponsored search, the search operator solves a multi-unit allocation and pricing problem with the specified bidder values and budgets. The advertisers, on the other hand, regularly solve a bid determination problem for the different keywords, given their budget and other business constraints. We develop a model for the auctioneer that allows the bidders to place differing bids for different advertisement slots for any keyword combination. Despite the increased complexity, our model is solved in polynomial time. Next, we develop a column-generation procedure for large advertisers to bid optimally in the sponsored search auctions. Our focus is on solving large-scale versions of the problem. Multi-unit auctions have also found a number of applications in other areas that include supply chain coordination, wireless spectrum allocation and transportation. Current research in the multi-unit auction domain ignores the budget constraint faced by participants. We address the computational issues faced by the auctioneer when dealing with budget constraints in a multi-unit auction. We propose an optimization model and solution approach to ensure that the allocation and prices are in the core. We develop an algorithm to determine an allocation and Walrasian equilibrium prices (when they exist) under additive bidder valuations where the auctioneer's goal is social welfare maximization and extend the approach to address general package auctions. We, also, demonstrate the applicability of the Benders decomposition technique to model and solve the revenue maximization problem from an auctioneer's standpoint

Optimization Models for Applications in Portfolio Management and Advertising Industry

Optimization problems in two different application fields are investigated: the first one is the popular portfolio optimization problem and the second one is the newly developed online display advertising problem. The portfolio optimization problem has two main concerns: an appropriate statistical input data, which is improved with the use of factor model and, the inclusion of the transaction cost function into the original objective function. Two methods are applied to solve the optimization problem, namely,the conditional value at risk (CVaR) method and the reliability based (RB) method. Asset allocation problem in finance continues to be of practical interest because decisions as to where to invest must be made to maximize the total return and minimizing the risk of not attaining the target return. However, the commonly used Markowitz method, also known as the mean-variance approach, uses historic stock prices data and has been facing problems of parameter estimation and short sample errors. An alternative method that attempts to overcome this problem is the use of factor models. This thesis will explain this model in addition to explaining the basic portfolio optimization problem. Conditional value at risk and the reliability based optimization method are applied to solve the portfolio optimization problem with the consideration of transaction costs in the objective function.They are applied and evaluated by simulation in terms of their convergence, efficiency and results. The online display advertising problem extends a normal deterministic revenue optimization model to a stochastic allocation model. The incorporation of randomness makes it more realistic for the estimation of demand, supply and market price. Revenues are considered as a combination of gains from guaranteed contracts and unguaranteed spot market. The objective is not only to maximize the revenue but also to consider the quality of ads, so that the whole market obtains long-term benefits and stability. The thesis accomplishes in solving the online display advertising allocation problem in a stochastic case with the measure of conditional value at risk algorithm

Demand Estimation at Manufacturer-Retailer Duo: A Macro-Micro Approach

This dissertation is divided into two phases. The main objective of this phase is to use Bayesian MCMC technique, to attain (1) estimates, (2) predictions and (3) posterior probability of sales greater than certain amount for sampled regions and any random region selected from the population or sample. These regions are served by a single product manufacturer who is considered to be similar to newsvendor. The optimal estimates, predictions and posterior probabilities are obtained in presence of advertising expenditure set by the manufacturer, past historical sales data that contains both censored and exact observations and finally stochastic regional effects that cannot be quantified but are believed to strongly influence future demand. Knowledge of these optimal values is useful in eliminating stock-out and excess inventory holding situations while increasing the profitability across the entire supply chain. Subsequently, the second phase, examines the impact of Cournot and Stackelberg games in a supply-chain on shelf space allocation and pricing decisions. In particular, we consider two scenarios: (1) two manufacturers competing for shelf space allocation at a single retailer, and (2) two manufacturers competing for shelf space allocation at two competing retailers, whose pricing decisions influence their demand which in turn influences their shelf-space allocation. We obtain the optimal pricing and shelf-space allocation in these two scenarios by optimizing the profit functions for each of the players in the game. Our numerical results indicate that (1) Cournot games to be the most profitable along the whole supply chain whereas Stackelberg games and mixed games turn out to be least profitable, and (2) higher the shelf space elasticity, lower the wholesale price of the product; conversely, lower the retail price of the product, greater the shelf space allocated for that product