Difference between revisions of "ANLY482 AY2016-17 T2 Group20 Project Overview"
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| + | == Motivation and Business Problem == | ||
| + | === Background Information === | ||
| + | On average, car dealers spend between $4000 and $20,000 on SaaS products, and spend thousands to find out more about their competitors. The industry itself is highly competitive with franchise dealers (Legacy, Jim Ellis) dominating the market followed by smaller individual dealers. | ||
| − | == | + | === Business Problem === |
| + | The resale market makes up about 80% of the total automobile market<sup>1</sup>. The main problem we are addressing is that resale dealers do not know the best way to price their used cars. Even though other dealers’ pricing information can be found online, it is difficult for retailers to compare prices, mileage, make, customizations along with other factors to come up with the most competitive price. There is also a highly competitive market of resale dealers which shows the seriousness of this problem. We think that by using existing data, modelling and visualizing it can help these resale dealers make better pricing decisions. | ||
== Project Objectives == | == Project Objectives == | ||
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== Risks & Limitations == | == Risks & Limitations == | ||
| + | |||
| + | == References == | ||
| + | <sup>1</sup>http://www.niada.com/uploads/dynamic_areas/tRRlH6fX2WoqiCcaonlq/33/2015ManheimUsedCarMarketReport.pdf | ||
Revision as of 18:12, 8 January 2017
Contents
Motivation and Business Problem
Background Information
On average, car dealers spend between $4000 and $20,000 on SaaS products, and spend thousands to find out more about their competitors. The industry itself is highly competitive with franchise dealers (Legacy, Jim Ellis) dominating the market followed by smaller individual dealers.
Business Problem
The resale market makes up about 80% of the total automobile market1. The main problem we are addressing is that resale dealers do not know the best way to price their used cars. Even though other dealers’ pricing information can be found online, it is difficult for retailers to compare prices, mileage, make, customizations along with other factors to come up with the most competitive price. There is also a highly competitive market of resale dealers which shows the seriousness of this problem. We think that by using existing data, modelling and visualizing it can help these resale dealers make better pricing decisions.
