Difference between revisions of "Kiva Project Overview"

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Logistics has always been a huge industry, and even more so with rapid technological advancements since the 21st century.  
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Our objective is to understand the different factors affecting important variables of Kiva’s loans, such as the loan amount and quantity, the rate of funding of the loan, the duration of the loan term and the repayment period. Our project objective is to understand the different features of each loan, such as the sectors and activity type for the loan, the gender breakdown, how the trends differ over time and how these variables vary across different countries and regions.  
  
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Given XXX being one of the largest companies in the logistics and transportation industry, it faces stiff competition from its competitors from other major companies such as Fedex and UPS, as well as local and regional companies who are more price-competitive in their region of expertise. Contracts with corporate companies in particular, take into account several factors when choosing their shipping partner, such as efficiency of delivery in terms of time taken, and this in turn includes other factors such as the ports which XXX will use for the delivery and the mode of transportation, such as air, ocean and roads.
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Being students in the Analytics track, this project provides us the opportunity to utilise the skills learnt from the theory taught in school and practice them from an actual dataset out in the industry. We hope to understand and gain insights into how XXX maintains its competitiveness, with factors such as service, competitive pricing and brand power affecting the bid price of its contracts, which in turn affect its revenue and profit.
 
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The main objective of the project is to help XXX better understand the various reasons behind the final bidding result for their contracts. We aim to explore and determine trends behind the bidding prices at different times of the year, and identify factors behind the differences in pricing, by analyzing into factors such as seasonality, different bidding habits of companies in different industries, differences between customers’ expected prices and actual bid prices, differences in expected and actual shipping times and so on.
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Our team will attempt to use geospatial analysis to find out the how the characteristics of borrowing activities in different geographical locations differ from each other, and analyze how the different attributes of the loan vary across time for each geographical region. Geospatial analysis will allow us to build maps and make the relationships between the other attributes and geolocation data understandable and insightful. From there, we will be able to obtain more accurate trend analysis to our objectives, such as the duration of loan term and the repayment period.
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Some of the salient questions we have include:
 
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# What determines XXX's winner ability in the market? Apart from competitive pricing, to what extent do factors like service reliability (in terms of speed of delivery), expertise (varying in different countries and territories) affect the overall revenue?
 
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# Many different sectors of customers are involved, such as Technology, Life Sciences, Retail and more. How do the different sectors decide on their buying criteria? What is the difference in price sensitivity across the different sectors?
 
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# To what extent external, uncontrollable factors such as seasonality, natural disasters or big events affect the sales?
 
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# Does the location which the company resides in allow them to exhibit stronger brand influence in the region? (Differences between XXX Singapore, XXX China, XXX USA and so on)
 
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Our main objectives are:
 
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# Identify possible clusters based on sectors of customers or requirement of customers in terms of speed of delivery or special services.
 
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# Identify possible seasonal patterns of customer target price and bidding results using time series analysis.
 
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# Identify possible relationships between customer’s sector, speed of delivery and other factors which affect the bidding results.
 
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# Identify patterns of the difference between customer target price and bidding results, and patterns of the difference between customer required transit time and XXX provided transit time.
 
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# Provide meaningful visualizations which explain and account for the bidding habits of different customer profile groups
 
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# Develop a predictive model for XXX to be better prepared for future contract bids and optimize their pricing strategy for greater profit.
 
 
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<h3>Exploratory Analysis</h3>
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There are 4 main data files we received for our exploration and analysis. The primary file we used for analysis is kiva_loans.csv, which contains the main important variables of each loan, such as:
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An exploratory analysis will be conducted to help us understand the shipping patterns and bidding results of different customers.
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#Funded amount of the loan
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# Identify the major inbound and outbound region, country and city for each customer
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# Loan amount of the loan
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# Understand which transportation mode customers prefer
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# Sector which the loan is used for, such as agriculture, education
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# Identify patterns of the difference between customer target price and bidding result
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# Activity which the loan is being used to fund
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# Identify patterns of the difference between customer requested transit time and XXX provided transit time
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# Country and Region where the loan is being used in
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# Study the relationships between different factors and the bidding results.
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# Currency which the loan is being disbursed in
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# Time which the loan was posted, funded and disbursed
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# The term/duration of the loan in months before repayment
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# Tags associated with the loan
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# The repayment interval type, such as whether repayment was done weekly, monthly, irregularly or in bullet
  
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<h3>Customer Analysis</h3>
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The remaining files loan_theme_ids, loan_themes_by_region and kiva_mpi_region_locations provide secondary information. Those which are of use to us include:
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We plan to categorize customers into different groups based on the relevant variables identified in exploratory analysis (referring to exploratory analysis point 5). Each customer will be assigned to a cluster based on this analysis and we attempt to have a better understanding of the buying behaviour of the customers.
 
  
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<h3>Time Series Data Analysis</h3>
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# World region/continent which the country resides in
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Because each excel file is only valid for a certain time period, time series data analysis could be conducted to discover the underlying seasonal trend in customer target price and bidding result.
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# Latitude and longitude of the region (we are using the GADM map to obtain more in-depth geographical information, and obtain a more precise latitude and longitude)
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# Loan theme type of the loan
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# Percentage of borrowers that are in rural areas for particular field partners
  
 
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Revision as of 19:28, 13 March 2018


 

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Project Objectives

Our objective is to understand the different factors affecting important variables of Kiva’s loans, such as the loan amount and quantity, the rate of funding of the loan, the duration of the loan term and the repayment period. Our project objective is to understand the different features of each loan, such as the sectors and activity type for the loan, the gender breakdown, how the trends differ over time and how these variables vary across different countries and regions.

Project Methodology

Our team will attempt to use geospatial analysis to find out the how the characteristics of borrowing activities in different geographical locations differ from each other, and analyze how the different attributes of the loan vary across time for each geographical region. Geospatial analysis will allow us to build maps and make the relationships between the other attributes and geolocation data understandable and insightful. From there, we will be able to obtain more accurate trend analysis to our objectives, such as the duration of loan term and the repayment period.

Data

There are 4 main data files we received for our exploration and analysis. The primary file we used for analysis is kiva_loans.csv, which contains the main important variables of each loan, such as:

  1. Funded amount of the loan
  2. Loan amount of the loan
  3. Sector which the loan is used for, such as agriculture, education
  4. Activity which the loan is being used to fund
  5. Country and Region where the loan is being used in
  6. Currency which the loan is being disbursed in
  7. Time which the loan was posted, funded and disbursed
  8. The term/duration of the loan in months before repayment
  9. Tags associated with the loan
  10. The repayment interval type, such as whether repayment was done weekly, monthly, irregularly or in bullet

The remaining files loan_theme_ids, loan_themes_by_region and kiva_mpi_region_locations provide secondary information. Those which are of use to us include:

  1. World region/continent which the country resides in
  2. Latitude and longitude of the region (we are using the GADM map to obtain more in-depth geographical information, and obtain a more precise latitude and longitude)
  3. Loan theme type of the loan
  4. Percentage of borrowers that are in rural areas for particular field partners
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