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Uploaded byShuai Zhu
HighPower
The document discusses using external data sources and machine learning models to improve customer experience when searching for vehicles online. It analyzes factors like household income, gas prices, vehicle fuel efficiency, and state incentive laws to predict which customers are more likely to buy fuel efficient cars. Random forest and KNN models are tested, with random forest achieving 79% accuracy in predicting fuel efficiency preferences based on political demographics and other variables. The results recommend using state laws/incentives as well as web behavior data to optimize search rankings and suggestions for more relevant vehicle options.
HighPower
- 1. Jiurui Tang Sharon Xu ShuaiZhu Qianyi Zhao Ze Jia
- 2. External Sources Politics Gas Price HouseholdIncome Incentive Laws Fuel Economy Motivation -- “You must know what your customers are really buying before you can sell it.” Response Predictors Shopping Visitor Leads • Vehicle Safety • Fuel Economy • MPG • Calculator for Fuel Cost • Used Consideration Total Time Spent on Site Credit Worthiness Total Page View Count Fuel Type of Cars Viewd Fuel Type Goal: Improve the customer experience. Optimize search results and suggestions/ads to show cars that are most relevant to the individual consumer based on web behavior and demographics by location Sources: US Department of Energy; www.afdc.energy.gov US Energy International Administration www.eia.gov fueleconomy.gov • Gas • Diesel Fuel Type Random Forest K-Nearest Neighbor Gas Price and Number of Efficient Cars Median Household Income Question - Who is more likely to buy fuel efficient cars? (Customer Segmentation) Methods MPG of Cars Viewed
- 3. Insights Fuel Efficiency byPolitical Demographic Knn.pre d Regular Efficient Fuel Pred .Regular 0.75 0.73 Pred . Eff.Fuel 0.25 0.27 Random Forest Accuracy Rate = 79% K-Nearest Accuracy Rate = 40% Chi-Squared – Political Party Test Statistic = 206.658 P-value < 2.2e^-16 Recommendation: Use number of fuel-efficient car laws/incentives in each state, as well as web behavior for site improvement.