A. Primary Objective
Create a model for identifying new towns for creating distribution footprint.
B. Tools used
1. Census Town List
2. Nielsen Hit List
3. Google Maps
4. Local Survey C. Research Methodology
The research was conducted in three phases:
1. Analysis of the company data, i.e. Census Town List and Nielsen Hit List: raw data was provided to us by the Perfetti Van Melle’ MIS team regarding the geographical area we were going to work in. In my case it was Rural Punjab. They gave us two reports, Census Town List and Nielsen Hit List. Census Town List provided us with the population of all the towns in Rural Punjab. This was very helpful since it helped in determining which towns to target. Nielsen Hit List on the other hand gave …show more content…
Collection of Data: After successfully designing a relevant questionnaire it became easier to collect data from the market. For this I had to set out in the market and survey local keryana stores. Based on the variables, information was collected. In this whole process some 44 towns were visited and surveyed. During these visits I was able to successfully open nine new Sub Distributors for Perfetti Van Melle India.
D. Analysis
Population of Punjab according to Census 2011 is approximately 27 million out of which approximately 65% (17 million) resides in rural Punjab. This just shows the potential of FMCG companies in rural India since majority reside in rural.
Below is the table for calculating Population per Dealer-
This table assisted in arriving at the number of retailers in a town since the population was readily available from the Census Town List. This was helpful in arriving at the prospective retail outlets before actual town visits. According to the calculations, there is approximately one retailer per 243 people in rural …show more content…
The Total Credit Rating was calculated by adding all the credits from each variable for a town. We could determine a threshold credit level using historical data as well as the data from the towns I had visited and collected information from. This threshold level would help the company’s DSE to decide whether it was feasible to visit the town in question. While working on the model I came to a successful credit level of 3, above this level I was able to successfully open a new Sub Distributor in a town not covered by Perfetti Van Melle. The Yellow highlighted area denotes the towns opened by me while the Green highlighted area is the historical data used to verify the model’s
Create a model for identifying new towns for creating distribution footprint.
B. Tools used
1. Census Town List
2. Nielsen Hit List
3. Google Maps
4. Local Survey C. Research Methodology
The research was conducted in three phases:
1. Analysis of the company data, i.e. Census Town List and Nielsen Hit List: raw data was provided to us by the Perfetti Van Melle’ MIS team regarding the geographical area we were going to work in. In my case it was Rural Punjab. They gave us two reports, Census Town List and Nielsen Hit List. Census Town List provided us with the population of all the towns in Rural Punjab. This was very helpful since it helped in determining which towns to target. Nielsen Hit List on the other hand gave …show more content…
Collection of Data: After successfully designing a relevant questionnaire it became easier to collect data from the market. For this I had to set out in the market and survey local keryana stores. Based on the variables, information was collected. In this whole process some 44 towns were visited and surveyed. During these visits I was able to successfully open nine new Sub Distributors for Perfetti Van Melle India.
D. Analysis
Population of Punjab according to Census 2011 is approximately 27 million out of which approximately 65% (17 million) resides in rural Punjab. This just shows the potential of FMCG companies in rural India since majority reside in rural.
Below is the table for calculating Population per Dealer-
This table assisted in arriving at the number of retailers in a town since the population was readily available from the Census Town List. This was helpful in arriving at the prospective retail outlets before actual town visits. According to the calculations, there is approximately one retailer per 243 people in rural …show more content…
The Total Credit Rating was calculated by adding all the credits from each variable for a town. We could determine a threshold credit level using historical data as well as the data from the towns I had visited and collected information from. This threshold level would help the company’s DSE to decide whether it was feasible to visit the town in question. While working on the model I came to a successful credit level of 3, above this level I was able to successfully open a new Sub Distributor in a town not covered by Perfetti Van Melle. The Yellow highlighted area denotes the towns opened by me while the Green highlighted area is the historical data used to verify the model’s