Attribute based perceptual mapping by using discriminant analysis for different brands of baby stores in UAE

Dr. Rajashekhar Karjagi and Dr. Joson Jose, Research Analysts Landmark Research Centre, Landmark Group, Dubai

Imagine walking with your family into Baby store on a weekend to pick a toy for your kid. There are 5 top brands of stores fighting for your attention; however you made your choice and walked towards BabyShop. Your family is happy. Thank you!

Now..., that is precisely where my problem starts! Why did you enter that particular store? Was it so jingle, was it the smile of the sales staff, recommended by your colleague, or the store which you went has a better quality of products (simple economics)!

I and my colleague Joson tried to crack the problem. The traditional approach taken to solve the problem were multidimensional scaling or correspondence analysis but we have used discriminant analysis and the results are as discussed below in detail.

Well, we chose to do things differently. Why? We felt that there is another dimension to the decision making which if overlooked can give misleading results! Your sales projections if you are managing any of the brands can go way off the mark.

Here is what we did, we call it the 'Attribute based perceptual mapping by using discriminant analysis'

Methodology

Study area - All the emirates of UAE

Sample size - 1300

Method of data collection - personel interview at the Babyshop stores

Interpretation for Attributes and Dimensions
•It could be observed from the perceptual map in the above figure that, Adams, Mothercare, Toys R us, BabyShop and Others, these five brands (Or Groups) have their unique positions on the map.
• On the same map values of attributes have been plotted on two dimensions (Each discriminant function represents a dimension).
• Dimension 1 (DF1) comprise Product Display, Ambience, Signage, Store Layout, Staff Courtesy, Window Display and Music since the vectors of these attributes are closer to dimension1 – These store attributes contribute more to the dimension 1 and hence this dimension is named as Store Features dimension
• Dimension 2 comprise of only Product Quality which can be evident from the long arrow closer to vertical axis (DF2) and hence its named as Product Quality dimension

Interpretation for Brands and their associations
• BabyShop seem to be stronger in Dimension 1 (Product Display, Ambience, Signage, Store Layout, Staff Courtesy, Window Display and Music), Mothercare and Toys R Us seems to be strong in Dimension 2 (Product Quality). Whereas Adams and Others seems to be weak in both the dimensions as compared to its competitors
• It could be noted that the length of a vector pointing towards a particular brand indicate the association of that attribute with a particular brand and the vectors pointing towards opposite direction from a given brand represent lower association with a particular brand

Significance of the model

•The model was found to be significant at 1 per cent level as could be evident from Box’s F statistic for testing the significance of discriminant analysis.
•The discriminating power (Variation) of Dimension 1 and 2 were found to be 82.9 per cent and 14.3 per cent respectively. It means that Dimension 1 contributes highest to BabyShop to be different from other brands whereas Dimension 2 contributes only 14.3 per cent for discrimination of BabyShop from other brands
•Wilk’s Lambda was found to be significant at 1 per cent level for both the dimensions (DF1 and DF2)
•Although Store Layout and Signage were found to be closer to Dimension 2 they were included under Dimension 1 as these attributes were again in association with BabyShop and no much effect on other brands
•People perceive that BabyShop is known for Store Features rather than product Quality, it means that there is a large gap in the perceived product quality which is very much concern for Babyshop and it can be considered as a weakness as compared with its major competitors (Mothercare and Toys R Us) as the Product Quality is the main strength for them

Conclusion

Although many of the analysts use multidimension scaling technique for analysing the consumer perceptions it is better to go one step ahead by the method of attribute based perceptual mapping by using discriminat analysis or factor analysis but it needs little bit of efforts in data collection and sound knowledge of these techniques.

PRICE RISK MANAGEMENT IN COFFEE – ADVANTAGES OF COMMODITY EXCHANGES

MR. SANDEEP SINGH MAHARA* AND DR. RAJASHEKHAR KARJAGI**
*PROJECT TRAINEE, MANAGE, HYDERABAD AND **ASSISTANT MANAGER – CONSULTANCY, MCX INDIA LTD, MUMBAI

Coffee generates sizable export revenues in the coffee producing countries like Ethiopia, Vietnam and Columbia etc. One of the unique features of coffee trade is that the major coffee consuming countries are not active in coffee production of coffee. The maximum coffee intake is in USA, UK, Brazil, Germany, Italy, and France. Apart from Brazil, other growers are not consuming even 25 per cent of their produce. This disparity gives rise to enormous international exposure to this commodity, hence making it lucrative to trade it on one hand and bringing high volatility on the other hand.

Coffee cultivation in India is confined to the southern states of India such as, Karnataka, Kerala and Tamil Nadu. Karnataka accounts for a little more than 50 per cent of the total country’s production. In 1990”s coffee entered into a new phase of free market in India. Quality and price became the factors driving Coffee in the free traded commodity unlike that of wheat, rice etc. Around 7 million tones of coffee is being shipped to various destination worldwide annually. The estimated value of annual exports is 10 billion dollars.

Indian coffee production during 2005-06 was 274000 MT, out of which contribution Arabica is 94000 MT and that of Robusta is 180000 MT. India’s contribution to the total exports was 213143 MT with a value of Rs.115845 lakh. Italy, Russia and Germany are the major export destination. Such is the dependence on the exports, that more than 75 per cent of the domestic produce is shipped to the foreign land. India is a price taker in coffee, since Indian traders are referring to the prices of international exchanges like NYBOT and LIFFE for Arabica and Robusta respectively. The Indian players have to hedge their price risk on foreign exchanges in order to deal with the fluctuations in international prices. But in order to do that they are exposed to currency risk, which in the recent past have shown a lot of volatility. Hence there is a need to manage the price risk where in commodity exchanges play a vital role in minimizing the price risk.

We acknowledge that coffee trade is associated with many types of risk e.g. Production risk, price risk, credit risk etc. To manage the production risk the coffee farmers have to take the insurance or to trade in weather derivative. Of these instruments insurance the fact that information regarding the land records, cropping patterns are not in tune to actual field data, thus the insurance companies are circumspect to enter into this field. The weather derivatives are yet to be launched in the commodity exchanges in India. So there is less likelihood of farmers managing the production risk at this stage.

Another risk which the coffee producers and traders face is the price risk. India is among the top six coffee producing countries, yet it does not have any influence in setting the coffee prices. India have to take the prices which set on LIFFE and NYBOT for Robusta and Arabica respectively. The price risk can be covered by taking the suitable hedge position on the Future Exchanges. This market based instrument (future contract) allows the hedgers to protect themselves from adverse movement o prices, thus reducing their price exposure in the volatile market. Indian exchanges have another added advantage over NYBOT and LIFFE is that of trading in Indian currency. This mitigates the currency exchange rate risk, which in recent times become a headache for the exporters. Moreover the proximity of Indian exchange makes the delivery process easy for domestic traders. The delivery locations are in production hubs of coffee.

The grades of coffee traded on exchange are universally accepted by the rosters and traders. Further the quality standards which the exchange adheres to, is acceptable worldwide. Any grade which is accepted by the future exchanges carries the quality certification from the national level certification agencies. Thus the exchange traded coffee is geared to give appreciable returns the stakeholders by minimizing the price risk.

MULTIVARIATE MODEL FOR FORECASTING WHEAT PRICES IN INDIA

DR. RAJASHEKHAR KARJAGI, ASST MANAGER – CONSULTANCY; DR. RAOSAHEB MOHITE, ASSISTANT VICE PRESIDENT – CONSULTANCY; AND DR CHIRAGRA CHAKRABARTY, VICE PRESIDENT – TRAINING AND CONSULTANCY, FT Knowledge Management Company Ltd., MUMBAI

Abstract
The study was carried out by FT Knowledge Management Company Ltd to assess the long-term trends in the prices of wheat and factors affecting the wheat prices in India. The data on area, yield, production, beginning stocks, imports, exports, and domestic consumption was collected for the period from 1970-71 to 2005-06 from USDA Production, Supply, and Distribution database. The data on CBOT daily prices have been extracted from MCX Meta stock software and the average annual price of wheat worked out and used for the analysis. Depending upon the suitability of the data the regression equations were fitted and forecasted individually and then the long-term forecasting was made by fitting a multiple regression model. The results of the multiple regression indicated that Demand, MSP, and CBOT were affecting the prices of domestic wheat significantly. One year lagged response model was also used to predict the short-term trends in prices of wheat. The predicted line is coinciding with the actual line and hence the model is good.

Introduction
The present study attempts to develop an econometric model for both long-term and short-term forecasting of wheat prices in India. The detailed methodology involved in developing the model and forecasting process can be understood in this paper. The short-term forecasting was studied by using lagged response model, where as the long-term forecasting was made with the help of a multivariate model.

Methodology
The study was carried out to assess the long-term trends in the prices of wheat and factors affecting the wheat prices in India. The investigation calls for data on different variables which affect the prices of wheat in Indian markets. The data on area, yield, production, beginning stocks, imports, exports, and domestic consumption was collected for the period from 1970-71 to 2005-06 from USDA Production, Supply, and Distribution database. The total consumption data includes both the quantity consumed for the purposes of food and feed. The per capita food consumption was taken into account while estimating the total quantity of wheat consumed for food purpose. However, the yearly data on average prices of wheat for India was available only for the period from 1979-80 to 2005-06 and hence the previous prices were interpolated and included in the analysis. The data on CBOT daily prices have been extracted from MCX Meta stock software and the average annual price of wheat worked out and used for the analysis.

Selection of Model
In the beginning, all the selected variables were included in the model, but due to interrelationship among the independent variables few variables have been clubbed and used for the analysis. The variables included in the model are: total supply, total demand, minimum support price, CBOT prices and area under wheat cultivation for the whole study period. The total demand includes consumption for food and feed and export, whereas total supply includes imports, total production, and stocks held by both government and private agencies.

To assess the trends in different variables which affect the price of wheat, different forms of equations were used, depending upon their suitability. The equations used are as below:

y=a+bt-ct2+dt3+ut - For trends in Prices ----------- (1)

y=a+bt+ut - For Supply ------------------------ (2)

y=a+bt+ut - For Demand ----------------------- (3)

y=a+bt-ct2+dt3-et4+ut - For MSP --------- (4)

y=a+bt-ct2+dt3+ut - For Area ---------- (5)

Where,

y = Price, supply, demand, MSP and area for respective equations
a= Intercept
bt, ct2, dt3, et4 = Coefficients
ut= Stochastic term

Results and Discussion
The coefficients, standard errors of the corresponding coefficients, coefficient of determination, and significance of the model are presented in Table 1. The predicted variables from the above equations were used to forecast the prices of wheat by using a multivariate model of the following type:

logY= log(a)+(b1)logX1+(b2)logX2+(b3)logX3+(b4)logX4+(b5)logX5+ui -------- (6)
Where,
Y = Price of wheat
b1, b2, b3, b4, b5 = Coefficients
X1, X2, X3, X4, X5 = Independent variables
ui = Stochastic term

The regression coefficients, standard errors of coefficients, coefficient of multiple determination, and significance of the model can be seen from Table 2. It could be seen from Table 2 that the multiple regression model developed for forecasting the wheat prices was found to be highly significant for the factors like demand, MSP, and CBOT prices. The total effect explained by these variables together was found to be 89%, which seems to be highly significant for price forecasting validated by regression analysis. Model explanatory power is 89% which is good enough to address fundamental factors like demand, MSP, and CBOT prices and rest 11% is explained by other exogenous variables, which we have not included in the model due to lack of data and inability of quantifying some of the variables like climatic factors. The other fundamental factors like area and total supply have negative and non-significant effect. For forecasting the prices of wheat for the years 2007, 2008, 2009, and 2010, the forecasted values of independent variables affecting the prices were taken into account and the predicted and actual trends in the prices of wheat can be seen in Figure 1.

Lagged Response Model
Similarly, by using the available short-term data on Delhi spot prices and CBOT wheat price, the lagged response model of the following type was used by using the daily time series data for the period from September 2004 to December 2006 and August 1973 to August 2007 respectively for Delhi spot prices and CBOT. The results were significant at first lag with high r2 value of 99 per cent and low Mean Sum of square for error was found to be 120 and 135.5, respectively, which is low, and best fit to address the forecasting ability of the model. The results of actual and predicted prices for Delhi spot price and CBOT respectively can be seen from Figures 4 and 5. The following type of the model was used for forecasting wheat prices:
Pt=f(pt-1) ------------------------- (7)
Where,
Pt = Price of current year
f(pt-1)= Price of previous year





Conclusions
The FTKMC team developed a Multivariate Price Forecasting Model for Wheat with Area, Supply, Demand, Minimum Support Price (MSP), and Global Wheat Prices as variables. Forecast models, both the structural model as well as lagged time series model, predicted the prices fairly efficiently. Forecasts for the year 2006-07, 2007-08, 2008-09 and 2009-10 were Rs 9,964, Rs 10,820, Rs 11,752 and Rs 12,764 per ton, respectively. This assumes an average yearly increase in acreage and MSP of 3 percent and 4 to 6 percent, respectively (as predicted by the model). However, if there is a less than desired increase in acreage/level of MSP, then there could be shortfall in supply which would have to be met with imports from abroad. The above forecast models are amenable to fine-tuning the predicted prices by incorporation of new and additional information as and when they become available.

References
FAO reports
CBOT website
http://www.mcxindia.com/
Meta stock software of MCX


DISCLAIMER: The research paper is solely for information purpose only and should not be regarded as a recommendation by FTKMC. All information in the note is obtained from the sources believed to be reliable and FTKMC or any of the associate entities make no representation as to its completeness or accuracy. FTKMC accepts no obligation to correct or update the information or opinion.