DATA MINING: A SEGMENTATION ANALYSIS OF U.S. GROCERY SHOPPERS

Consumers make choices about where to shop based on their preferences for a shopping environment and experience as well as the selection of products at a particular store. This study illustrates how retail firms and marketing analysts can utilize data mining techniques to better understand customer profiles and behavior. Among the key areas where data mining can produce new knowledge is the segmentation of customer data bases according to demographics, buying patterns, geographics, attitudes, and other variables. This paper builds profiles of grocery shoppers based on their preferences for 33 retail grocery store characteristics. The data are from a representative, nationwide sample of 900 supermarket shoppers collected in 1999. Six customer profiles are found to exist, including (1) "Time Pressed Meat Eaters", (2) "Back to Nature Shoppers", (3) "Discriminating Leisure Shoppers", (4) "No Nonsense Shoppers", (5) "The One Stop Socialites", and (6) "Middle of the Road Shoppers". Each of the customer profiles is described with respect to the underlying demographics and income. Consumer shopping segments cut across most demographic groups but are somewhat correlated with income. Hierarchical lists of preferences reveal that low price is not among the top five most important store characteristics. Experience and preferences for internet shopping shows that of the 44% who have access to the internet, only 3% had used it to order food.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

Measuring Core Inflation for Turkey - Trimmed Means Approach

This paper is one of the the pioneers in measuring the core inflation for Turkey and uses the methodology developed by Bryan, Cecchetti and Wiggins II (1997). As the price change distributions are not normally distributed, weighted sample means are not the efficient estimators of inflation. In such leptokurtic distributions trimmed means provide statistically more efficient estimators of inflation. For the consumer prices, using historical data, the optimal trim is found to be 19 percent from the each tail of the cross sectional distribution and for the wholesale prices it is found to be 12 percent (percentage that minimizes MAD). Trimmed mean estimators of inflation move in line with the headline inflation in the long run, implying a potential use for future inflation forecasting.Core Inflation, Trimmed Mean Estimators, Turkey

Correlation, hierarchies, and networks in financial markets

We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Specifically, we discuss how to define and obtain hierarchical trees, correlation based trees and networks from a correlation matrix. The hierarchical clustering and other procedures performed on the correlation matrix to detect statistically reliable aspects of the correlation matrix are seen as filtering procedures of the correlation matrix. We also discuss a method to associate a hierarchically nested factor model to a hierarchical tree obtained from a correlation matrix. The information retained in filtering procedures and its stability with respect to statistical fluctuations is quantified by using the Kullback-Leibler distance.Comment: 37 pages, 9 figures, 3 table

The effect of household consumption patterns on energy use and greenhouse gas emissions: comparison between Spain and Sweden

The purpose of this study is to provide a better understanding of the effect of increasing income on energy use and greenhouse gas (GHG) emissions by analyzing Spanish household consumption patterns and afterwards, comparing them with Swedish household consumption patterns (Nässén et al, 2009). In order to carry out this goal, the relationship between household expenditure and both energy use and CO2-eq emissions are calculated with the help of input-output methodology. Furthermore, a regression analysis is used to evaluate how energy use and CO2-eq emissions change when there is an increase in household expenditure on a certain commodity. Additionally, this study also provides an empirical contribution to the literature focused on understanding consumer behavior and options to change towards more sustainable consumer practices. In this research, three analyses have been performed. In the first one, the Spanish case is analyzed and it shows that energy use and CO2-eq emission are strongly linked to household expenditure. Subsequently, the Spanish consumption patterns are investigated with respect to the Swedish intensity factors (i.e. energy and GHG emissions). As an outcome, energy use linked to these consumption patterns is similar to the first study whereas GHG emissions would decrease by more than half if Spain had the Swedish production system. Finally, the Spanish and the Swedish cases are compared. Both countries have similar consumption patterns on average and on the margin; the former are dominated by housing and food products while the latter are dominated by mobility, luxury goods and leisure services. These patterns shift implies an increase by almost 0.9% in energy use and 0.85% in GHG emissions when income is increased by 1% for both countries. However, there are some small differences in the composition of consumption patterns in both countries that influence the total energy use: Swedish households use 27% more energy than Spanish households implying 15% more GHG emissions

Export Dynamism and Market Access

trade flows, trade liberalization, international production networks, development policy