Lift-Based Bidding in Ad Selection

Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering an account or placing a purchase order) to the advertiser. However, this industry standard approach to decide the bid price does not consider the actual effect of the ad shown to the user, which should be measured based on the performance lift among users who have been or have not been exposed to a certain treatment of ads. In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. We describe the modeling methodology to predict the performance lift and demonstrate the actual performance gain through blind A/B test with real ad campaigns in an industry-leading Demand-Side Platform (DSP). We also discuss the relationship between attribution models and bidding strategies. We prove that, to move the DSPs to bid based on performance lift, they should be rewarded according to the relative performance lift they contribute.Comment: AAAI 201

Persistence Modeling for Assessing Marketing Strategy Performance

The question of long-run market response lies at the heart of any marketing strategy that tries to create a sustainable competitive advantage for the firm or brand. A key challenge, however, is that only short-run results of marketing actions are readily observable. Persistence modeling addresses the problem of long-run market-response quantification by combining into one measure of “net long-run impact†the chain reaction of consumer response, firm feedback and competitor response that emerges following the initial marketing action. In this paper, we (i) summarize recent marketing-strategic insights that have been accumulated through various persistence modeling applications, (ii) provide an introduction to some of the most frequently used persistence modeling techniques, and (iii) identify some other strategic research questions where persistence modeling may prove to be particularly valuable.long-run effectiveness;marketing strategy;time-series analysis

Persistence models and marketing strategy.

Marketing; Persistence; Models; Model; Strategy;

Pseudo-social network targeting from consumer transaction data

This design science paper presents a method for targeting consumers based on a 'pseudo-social network' (PSN): consumers are linked if they transfer money to the same entities. A marketer can target those individuals that are strongly connected to key individuals. We present the PSN design and a large-scale empirical study using data from a major bank. For two different product offerings, consumers that are close to existing customers in the PSN have significantly higher take rates than the 'most likely' candidates identified by state-of-the-art socio-demographic (SD) predictive modeling. Interestingly, the PSN targeting only does better for the closest neighbors. However, the different models capture different information: combining the two does significantly better than either alone. The results demonstrate that social targeting can be applied broadly, to settings where the network among consumers is unlikely to be a true social network, but nonetheless captures inherent similarity.Faculty of Applied Economics, University of Antwerp, Belgium; Department of Information, Operations and Management Sciences, Stern School of Business, New York Universit

Analyzing and Modeling Special Offer Campaigns in Location-based Social Networks

The proliferation of mobile handheld devices in combination with the technological advancements in mobile computing has led to a number of innovative services that make use of the location information available on such devices. Traditional yellow pages websites have now moved to mobile platforms, giving the opportunity to local businesses and potential, near-by, customers to connect. These platforms can offer an affordable advertisement channel to local businesses. One of the mechanisms offered by location-based social networks (LBSNs) allows businesses to provide special offers to their customers that connect through the platform. We collect a large time-series dataset from approximately 14 million venues on Foursquare and analyze the performance of such campaigns using randomization techniques and (non-parametric) hypothesis testing with statistical bootstrapping. Our main finding indicates that this type of promotions are not as effective as anecdote success stories might suggest. Finally, we design classifiers by extracting three different types of features that are able to provide an educated decision on whether a special offer campaign for a local business will succeed or not both in short and long term.Comment: in The 9th International AAAI Conference on Web and Social Media (ICWSM 2015

Towards More Robust Uplift Modeling for Churn Prevention in the Presence of Negatively Correlated Estimation Errors

The subscription economy is rapidly growing, boosting the importance of churn prevention. However, current true lift models often lead to poor outcomes in churn prevention campaigns. A vital problem seems to lie in instable estimations due to dynamic surrounding parameters such as price increases, product migrations, tariff launches of a competitor, or other events with uncertain consequences. The crucial challenge therefore is to make churn prevention measures more reliable in the presence of game-changing events. In this paper, we assume such events to be spatially finite in feature space, an assumption which leads to particularly bad churn prevention results if the selected customers lump in an affected region of the feature space. We then introduce novel methods which trade off uplift for reduced similarity in feature space when selecting customers for churn prevention campaigns and show that these methods can improve the robustness of uplift modeling

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

Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both