Spatial Models in Marketing

Bradlow, Eric T.; Bronnenberg, Bart J.; Russell, Gary J.; Arora, Neeraj; Bell, David; Duvvuri, Sri Devi; Hofstede, Frankel Ter; Sismeiro, Catarina; Thomadsen, Raphael; Yang, Sha

doi:10.1007/s11002-005-5891-3

Cited by 92 publications

(64 citation statements)

References 31 publications

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“…This contrasts our work with the extensive literature on slotting allowances/fees 6 . Proponents of these fees emphasize its power as a signaling tool when manufacturers have better demand information than retailers 7 . However, there is a tradeo¤ when delegating this decision because slotting fees can increase the channel power of retailers relative to smaller manufacturers (Sha¤er, 1991) and bias demand distributions in favor of large manufacturers (Bloom, et al, 2000).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Recommendation Networks and the Long Tail of Electronic Commerce

Oestreicher-Singer

Sundararajan

2010

SSRN Journal

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Summary: It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or "blockbusters" to less popular or "niche" products, and that electronic markets will therefore be characterized by a "long tail" of demand and revenue. In this paper, we develop a novel method to test this conjecture and we report on results contrasting the demand distributions of books in over 200 distinct categories on Amazon.com. Viewing each product as having a unique position in a hyperlinked network of recommendations between products that is analogous to shelf position in traditional commerce, we quantify the extent to which a product is in ‡uenced by its recommendation network position by using a variant of Google's PageRank measure of centrality. We then associate the average level of network in ‡uence on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coe¢ cient derived from the category's Lorenz curve. We establish that categories whose products are in ‡uenced more by recommendations have signi…cantly ‡atter demand distributions, even after controlling for variations in average category demand, the category's size and measures of price dispersion. Our empirical …ndings indicate that doubling the average in ‡uence of recommendations on a category is associated with an average increase in the relative demand for the least popular 20% of products by about 50%, and a average reduction in the relative demand for the most popular 20% by about 12%. We also show that this e¤ect is enhanced when there is assortative mixing in the recommendation network, and in categories whose products are more evenly in ‡uenced by recommendations. The direction of these results persist across time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work o¤ers new ideas for assessing the in ‡uence of networks on demand and revenue patterns in electronic commerce, and provides new empirical evidence supporting the impact of visible recommendations on the long tail of electronic commerce.

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Section: Introduction and Related Workmentioning

confidence: 99%

Recommendation Networks and the Long Tail of Electronic Commerce

Oestreicher-Singer

Sundararajan

2010

SSRN Journal

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“…Spatial models specify a particular type of interdependence across cross-sectional observations which is two (or more) dimensional and bi-directional. This makes calibration in spatial models considerably more complex than in traditional models which assume statistical independence across units, or even time-series models which assume uni-directional relations between time dependent variables (Cressie, 1993;Anselin, 2001;Bradlow et al, 2005).…”

Section: Spatial Association In Marketing Outcomesmentioning

confidence: 99%

“…Whereas the natural sciences primarily use geographic maps for spatial location and Euclidean distance for the distance metric, marketing studies have tended to generalize the notion of a map to include demographic and psychometric representations (Bradlow et al, 2005 In order to achieve a reasonably accurate partitioning of SKU level revenues into those related to brand equity and those that would accrue to the SKU's unbranded equivalent (UE), it is necessary to identify and use a variety of measures to reflect the impact of the UE Revenue Components outlined in equation (1.4). These data are outlined in Table 1 and detailed forthwith.…”

Section: Spatial Association In Marketing Outcomesmentioning

confidence: 99%

Estimating Brand Equity from Aggregate Data

Voleti

2009

SSRN Journal

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“…Many private and public enterprises manage large volumes of data generated in urban environments where the positional component, that is, the ability to geo-locate data, is crucial to obtain valuable information for strategic decision making ( [Fischer and Wang(2011), Haining(2003), Oyana and Margai(2015)]). Therefore, spatial data become important geo-marketing tools aimed at enhancing land management processes and business through the integration and exploitation of the geographical position of some human activity [Bradlow et al(2005), Gliquet(2002)]. …”

Section: Introductionmentioning

confidence: 99%

A centrality measure for urban networks based on the eigenvector centrality concept

Agryzkov

Tortosa

Vicent

et al. 2017

Environment and Planning B: Urban Analytics and City Science

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A massive amount of information as geo-referenced data is now emerging from the digitization of contemporary cities.Urban streets networks are characterized by a fairly uniform degree distribution and a low degree range. Therefore, the analysis of the graph constructed from the topology of the urban layout does not provide significant information when studying topology-based centrality. On the other hand, we have collected geo-located data about the use of various buildings and facilities within the city. This does provide a rich source of information about the importance of various areas. Despite this, we still need to consider the influence of topology, as this determines the interaction between different areas. In this paper, we propose a new model of centrality for urban networks based on the concept of Eigenvector Centrality for urban street networks which incorporates information from both topology and data residing on the nodes. So, the centrality proposed is able to measure the influence of two factors, the topology of the network and the geo-referenced data extracted from the network and associated to the nodes. We detail how to compute the centrality measure and provide the rational behind it. Some numerical examples with small networks are performed to analyse the characteristics of the model. Finally, a detailed example of a real urban street network is discussed, taking a real set of data obtained from a fieldwork, regarding the commercial activity developed in the city.

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Spatial Models in Marketing

Cited by 92 publications

References 31 publications

Recommendation Networks and the Long Tail of Electronic Commerce

Recommendation Networks and the Long Tail of Electronic Commerce

Estimating Brand Equity from Aggregate Data

A centrality measure for urban networks based on the eigenvector centrality concept

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