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Kominers. “To Groupon or Not to Groupon: The Profitability of Deep Discounts.” Harvard Business School NOM Unit Working Paper No
, 2011
"... Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. ..."
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Cited by 23 (0 self)
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Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.
Collective attention and the dynamics of group deals
, 2011
"... We presentastudyofthegroup purchasingbehaviorof daily deals in Groupon and LivingSocial and formulate a predictive dynamic model of collective attention for group buying behavior. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group d ..."
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We presentastudyofthegroup purchasingbehaviorof daily deals in Groupon and LivingSocial and formulate a predictive dynamic model of collective attention for group buying behavior. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time. We find that Groupon deals are easier to predict accurately earlier in the deallifecycle thanLivingSocial dealsduetothetotalnumber of deal purchases saturating quicker. One possible explanation for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial, whereas in Groupon it is based on a collective threshold which is reached very early. Furthermore, the personal benefit of propagating a deal is greater in LivingSocial. Categories andSubject Descriptors
Daily-Deal Selection for Revenue Maximization
"... Daily-Deal Sites (DDS) like Groupon, LivingSocial, Amazon’s Goldbox, and many more, have become particularly popular over the last three years, providing discounted offers to customers for restaurants, ticketed events, services etc. In this paper, we study the following problem: among a set of candi ..."
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Daily-Deal Sites (DDS) like Groupon, LivingSocial, Amazon’s Goldbox, and many more, have become particularly popular over the last three years, providing discounted offers to customers for restaurants, ticketed events, services etc. In this paper, we study the following problem: among a set of candidate deals, which are the ones that a DDS should featureas daily-dealsinorderto maximizeits revenue? Ourfirst contribution lies in providing two combinatorial formulations of this problem. Both formulations take into account factors like the diversification of daily deals and the limited consuming capacity of the userbase. We prove that our problems are NP-hard and devise pseudopolynomial – time approximation algorithms for their solution. We also propose a set of heuristics, and demonstrate their efficiency in our experiments. In the context of deal selection and scheduling, we acknowledge the importance of the ability to estimate the expected revenue of a candidate deal. We explore the nature of this task in thecontextof real data, andpropose a framework for revenue-estimation. We demonstrate the effectiveness of our entire methodology in an experimental evaluation on a large dataset of daily-deals from Groupon.
Towards Reliable Spatial Information in LBSNs
"... The proliferation of Location-based Social Networks (LB-SNs) has been rapid during the last year due to the number of novel services they can support. The main interaction between users in an LBSN is location sharing, which builds the spatial component of the system. The majority of the LBSNs make u ..."
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The proliferation of Location-based Social Networks (LB-SNs) has been rapid during the last year due to the number of novel services they can support. The main interaction between users in an LBSN is location sharing, which builds the spatial component of the system. The majority of the LBSNs make use of the notion of check-in, to enable users to volunteeringly share their whereabouts with their peers and the system. The flow of this spatial information is unidirectional and originates from the users ’ side. Given that currently there is no infrastructure in place for detecting fake checkins, the quality of the spatial information plane of an LBSN is solely based on the honesty of the users. In this paper, we seek to raise the awareness of the community for this problem, by identifying and discussing the effects of the presence of fake location information. We further present a preliminary design of a fake check-in detection scheme, based on location-proofs. Our initial simulation results show that if we do not consider the infrastructural constraints, locationproofs can form a viable technical solution. Author Keywords Location-based social networks, Location proofs, Security.
Does a daily deal promotion signal a distressed business? an empirical investigation of small business survival
- in EWSSN, 2013
"... In the last four years, daily deals have emerged from nowhere to become a multi-billion dollar industry world-wide. Daily deal sites such as Groupon and Livingsocial offer products and services at deep discounts to consumers via email and social networks. As the industry matures, there are many ques ..."
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In the last four years, daily deals have emerged from nowhere to become a multi-billion dollar industry world-wide. Daily deal sites such as Groupon and Livingsocial offer products and services at deep discounts to consumers via email and social networks. As the industry matures, there are many questions regarding the impact of daily deals on the mar-ketplace. Important questions in this regard concern the reasons why businesses decide to offer daily deals and their longer-term impact on businesses. In the present paper, we investigate whether the unobserved factors that make mar-keters run daily deals are correlated with the unobserved factors that influence the business, In particular, we employ the framework of seemingly unrelated regression to model the correlation between the errors in predicting whether a business uses a daily deal and the errors in predicting the business ’ survival. Our analysis consists of the survival of 985 small businesses that offered daily deals between Jan-uary and July 2011 in the city of Chicago. Our results indicate that there is a statistically significant correlation between the unobserved factors that influence the business’ decision to offer a daily deal and the unobserved factors that impact its survival. Furthermore, our results indicate that the correlation coefficient is significant in certain business categories (e.g. restaurants).
Threshold Effects in Online Group Buying
"... This paper studies two types of threshold-induced effects: a surge of new sign-ups around the time when the thresholds of group-buying deals are reached, and a stronger positive relation between the number of new sign-ups and the cumulative number of sign-ups before the thresholds are reached than ..."
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This paper studies two types of threshold-induced effects: a surge of new sign-ups around the time when the thresholds of group-buying deals are reached, and a stronger positive relation between the number of new sign-ups and the cumulative number of sign-ups before the thresholds are reached than afterwards. This empirical study uses a dataset that records the inter-temporal cumulative number of sign-ups for groupbuying deals in 86 city markets covered by Groupon, during a period of 71 days when Groupon predominantly used "a deal a day" format for each local market and posted the number of sign-ups in real time. We find that the first type of threshold effects is significant in all product categories and in all markets. The second type of threshold effects varies across product categories and markets. Our results underscore the importance of considering product and market characteristics in threshold design decisions for online group buying.
Latent Topic Analysis for Predicting Group Purchasing Behavior on the Social Web
"... Group-deal websites, where customers purchase products or services in groups, are an interesting phenomenon on the Web. Each purchase is kicked off by a group initiator, and other customers can join in. Customers form communities with people with similar interests and preferences (as in a social net ..."
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Group-deal websites, where customers purchase products or services in groups, are an interesting phenomenon on the Web. Each purchase is kicked off by a group initiator, and other customers can join in. Customers form communities with people with similar interests and preferences (as in a social network), and this drives bulk purchasing (similar to online stores, but in larger quantities per order, thus customers get a better deal). In this work, we aim to better understand what factors influence customers ’ purchasing behavior for such social group-deal websites. We propose two probabilistic graphical models, i.e., a product-centric inference model (PCIM) and a group-initiator-centric inference model (GICIM), based on Latent Dirichlet Allocation (LDA). Instead of merely using customers ’ own purchase history to predict purchasing decisions, these two models include other social factors. Using a lift curve analysis, we show that by including social factors in the inference models, PCIM achieves 35 % of the target customers within 5 % of the total number of customers while GICIM is able to reach 85 % of the target customers. Both PCIM and GICIM outperform random guessing and models that do not take social factors into account. 1
The Profitability of Deep Discounts ∗
, 2010
"... Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. To Groupon or Not to Groupon: ..."
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Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. To Groupon or Not to Groupon:
and
"... Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by the daily-deal providers. Unlike the traditional web ads, the advertiser’s profits for group-buying ads depends on the time to expiry and additional customers needed to satisfy the minimum group si ..."
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Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by the daily-deal providers. Unlike the traditional web ads, the advertiser’s profits for group-buying ads depends on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts, and dynamically vary the bids to maximize profits. Further, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real time ad selection, admissibility, and real time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection and admission strategies on a multi-million click stream of 935 ads. The proposed real-time bidding, selection and admissibility show significant profit increases over the existing strategies. Further the experiments illustrate the robustness of the bidding and acceptable computation timings. 1.
