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Why are product prices in online markets not converging?

Mizuno T, Watanabe T - PLoS ONE (2013)

Bottom Line: Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close.This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price.Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers' preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Informatics, Tokyo, Japan ; Graduate School of Economics, University of Tokyo, Tokyo, Japan ; The Canon Institute for Global Studies, Tokyo, Japan.

ABSTRACT
Why are product prices in online markets dispersed in spite of very small search costs? To address this question, we construct a unique dataset from a Japanese price comparison site, which records price quotes offered by e-retailers as well as customers' clicks on products, which occur when they proceed to purchase the product. The novelty of our approach is that we seek to extract useful information on the source of price dispersion from the shape of price distributions rather than focusing merely on the standard deviation or the coefficient of variation of prices, as previous studies have done. We find that the distribution of prices retailers quote for a particular product at a particular point in time (divided by the lowest price) follows an exponential distribution, showing the presence of substantial price dispersion. For example, 20 percent of all retailers quote prices that are more than 50 percent higher than the lowest price. Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close. This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price. Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers' preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking.

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The estimated exponents  for different products.We split the entire sample of observed purchase prices into groups with different lowest prices at the time when clicks occurred, and then estimate  for each group.
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pone-0072211-g004: The estimated exponents for different products.We split the entire sample of observed purchase prices into groups with different lowest prices at the time when clicks occurred, and then estimate for each group.

Mentions: Next, we propose a hypothesis to explain the observed relationship between price rank and click probability. We focus on the difference in customer preferences regarding various retailer attributes. For instance, a customer who wants to pay by credit card will choose a shop that accepts credit card payment. We assume that a customer first chooses a set of retailers which satisfy a certain set of criteria determined by the customer, and then purchases the product from the retailer offering the lowest price among them. Importantly, customers are assumed to be heterogeneous in terms of their preferences over shop attributes. That is, some customers may prefer shops that accept credit cards, while others may not prefer such shops. Given these assumptions, the probability that a retailer with rank in terms of price is clicked is given by(4)where represents the probability that a particular retailer belongs to the set of favorite retailers for a customer. Equation 4 simply states that a retailer with rank will be clicked only when none of the retailers offering a lower price are included in the set of favorite retailers. Comparing equations 2 and 4, we obtain . From this, we estimate that coefficient is 0.115. That is, when 100 retailers sell this product, the number of favorite retailers is only . In other words, customers on average ignore 88.5 percent of retailers, including some or many that offer a lower price on the product the customer is interested in. Note that the coefficient may differ across products. Figure 4 shows how the coefficient for each product depends on the lowest price quoted for that product. The figure indicates that there exists a convex relationship, with coefficient highest for prices in the range of 10,000 yen (or about 100 US dollars), implying that customers do not pay much attention to shop attributes when they purchase products in this price range and price competition therefore is fiercer for such products.


Why are product prices in online markets not converging?

Mizuno T, Watanabe T - PLoS ONE (2013)

The estimated exponents  for different products.We split the entire sample of observed purchase prices into groups with different lowest prices at the time when clicks occurred, and then estimate  for each group.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3756077&req=5

pone-0072211-g004: The estimated exponents for different products.We split the entire sample of observed purchase prices into groups with different lowest prices at the time when clicks occurred, and then estimate for each group.
Mentions: Next, we propose a hypothesis to explain the observed relationship between price rank and click probability. We focus on the difference in customer preferences regarding various retailer attributes. For instance, a customer who wants to pay by credit card will choose a shop that accepts credit card payment. We assume that a customer first chooses a set of retailers which satisfy a certain set of criteria determined by the customer, and then purchases the product from the retailer offering the lowest price among them. Importantly, customers are assumed to be heterogeneous in terms of their preferences over shop attributes. That is, some customers may prefer shops that accept credit cards, while others may not prefer such shops. Given these assumptions, the probability that a retailer with rank in terms of price is clicked is given by(4)where represents the probability that a particular retailer belongs to the set of favorite retailers for a customer. Equation 4 simply states that a retailer with rank will be clicked only when none of the retailers offering a lower price are included in the set of favorite retailers. Comparing equations 2 and 4, we obtain . From this, we estimate that coefficient is 0.115. That is, when 100 retailers sell this product, the number of favorite retailers is only . In other words, customers on average ignore 88.5 percent of retailers, including some or many that offer a lower price on the product the customer is interested in. Note that the coefficient may differ across products. Figure 4 shows how the coefficient for each product depends on the lowest price quoted for that product. The figure indicates that there exists a convex relationship, with coefficient highest for prices in the range of 10,000 yen (or about 100 US dollars), implying that customers do not pay much attention to shop attributes when they purchase products in this price range and price competition therefore is fiercer for such products.

Bottom Line: Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close.This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price.Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers' preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Informatics, Tokyo, Japan ; Graduate School of Economics, University of Tokyo, Tokyo, Japan ; The Canon Institute for Global Studies, Tokyo, Japan.

ABSTRACT
Why are product prices in online markets dispersed in spite of very small search costs? To address this question, we construct a unique dataset from a Japanese price comparison site, which records price quotes offered by e-retailers as well as customers' clicks on products, which occur when they proceed to purchase the product. The novelty of our approach is that we seek to extract useful information on the source of price dispersion from the shape of price distributions rather than focusing merely on the standard deviation or the coefficient of variation of prices, as previous studies have done. We find that the distribution of prices retailers quote for a particular product at a particular point in time (divided by the lowest price) follows an exponential distribution, showing the presence of substantial price dispersion. For example, 20 percent of all retailers quote prices that are more than 50 percent higher than the lowest price. Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close. This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price. Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers' preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking.

Show MeSH