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selected at random. A Chi-square distance metric is used as a measure of proximity among OBS pairs and a cluster analysis helps us uncover the presence of three distinct clusters of OBS. OBS within a cluster are used together more when compared to those across clusters. Cluster 1 consists of OBS such as Edmunds, Autobytel, KBB, etc., while Cluster 2 consists of OBS such as Motor Trend Online, Car & Driver, Road & Track Online, etc. Cluster 3 consists of OBS such as Lycos Autos, Netscape Autos, Yahoo! Autos, etc. We further find that using Cluster 1 results in consumers finding more price information, while using Cluster 2 results in consumers finding more product information, and using Cluster 3 results in consumers finding less of both. Given the differences in the type of information found by consumers using these three clusters – the price, product, and portal clusters – we then examine the outcomes - the price paid, vehicle choice, and customer satisfaction – related to the use of these OBS clusters.
We find that the usage of these different clusters is associated with significant differences in consumer outcomes. In particular, we find that the price paid by consumers is systematically related to the type of infomediaries they visit in their search process, implying that all referrals don’t have the same value for dealerships, and pointing to a new basis for consumer segmentation. We also find that the users of the product cluster were more satisfied with the product fit, although they paid a higher price compared to the price cluster users. The users of the portal cluster were the least satisfied with their price as well as product choice outcomes. In addition, we also find that the observed behavioural choices—i.e., consumers’ use of OBS clusters—are related to underlying systematic differences in consumer characteristics and the properties of the information found. These findings have significant implications for traditional firms as well as online intermediaries.
Effective market segmentation, which is vital for price discrimination, can also play a critical role in a firm’s profitabilityand survival. However, although price |
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discrimination can boost a firm’s profits, it is very challenging to identify the “right” consumer segments. Compared to other segmentation and price discrimination strategies such as versioning, product line extensions, or couponing, our findings suggest that segmentation based on OBS-usage can serve as a low-cost, effective, and robust self-selection mechanism. Dealers and manufacturers can design their value proposition and marketing strategies and allocate their resources in an efficient way to deliver the maximum value to these different consumer segments by tracking their online infomediary-usage
Our findings also show that consumers using price and product OBS clusters differ in their psychographic profiles, but are demographically similar. While differences in demographics characteristics are usually more easily observable by sellers, attitudinal differences as revealed by psychographics are less conspicuous. This makes the findings of the effectiveness of using online infomediary clusters for segmentation and price discrimination more compelling.
Our findings also have interesting implications for dealers’ partnerships with online infomediaries. Contrary to conventional wisdom about the optimality of exclusive referral arrangements between an OBS and competing dealers in a given geographical area, our results suggest that a traditional dealer can benefit from using these different categories of infomediaries as complementary referral mechanisms. As for OBS, given that consumers are clearly differentiated on their underlying needs for the different types of information, OBS would benefit by better highlighting their domain of specialisation. Greater specialisation and differentiation of OBSs clusters would help facilitate consumer self-selection and enable OBS and dealers to more effectively understand the value of a referred lead.
(Viswanathan S, Gosain S, Kuruzovich J, Agarwal R. “Online Infomediaries and Price Discrimination: Evidence from the Auto-Retailing Sector,” Journal of Marketing, Vol. 71, No.3, July 2007.)
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