Electronics Retail e Commerce Predicting Profitability and Customer
Electronics Retail & e. Commerce Predicting Profitability and Customer Preferences Diana Amador
Data Mining Activities • Profitability prediction • Brand preference
Criteria: Accuracy vs. Precision • Hit the bull’s eye ▫ If I hit the bull’s eye , I am accurate ▫ If all my shots land together, I have good precision ▫ If all my shot land together and hit the bull’s eye , I am accurate and precise • It is possible to hit the bull’s eye purely by chance…
Criteria Accuracy vs. Precision
Criteria: Accuracy vs. Precision
What questions is DM answering? • Profitability of new potential products based on similar existent products. • Brand Preference
Criteria: Performance Indicators • • # of Correctly Classified Instances (= #) # of Incorrectly Classified Instances (= 0) Correlation Coeficient (= 1) Kappa Statistic (= 1) Mean Absolute Error ( = 0) Root Mean Absolute Error (= 0) Relative Absolute Error ( = 1) Root Relative Absolute Error (= 1)
How we predict profitability of potential products? • Using Similarity Analysis ▫ Calculation of Euclidean distance ▫ Application of Weighting Schemes • Using Regression Analysis ▫ IBK nearest neighbor ▫ Support Vector Machine (SMOreg)
Performance Graph ROOT RELATIVE SQ ERROR (%) RELATIVE ABSOLUTE ERROR (%) SMOreg(C=1, E=2) ROOT MEAN SQ ERROR IBK (K=2) MEAN ABSOLUTE ERROR CORRELATION COEFFICIENT 0 200 400 600 800 1000 1200 1400 1600 1800
PREDICTED PROFIT SCENARIOS • SCENARIO 1: 63% of total Profit (Game Console 199 Sony and Laptop 176 Razer) • SCENARIO 2: 74% of total Profit (Game Console 199, Sony Laptop 176 Razer and Tablet 187 Amazon) • SCENARIO 3: 73% of total profit (Tablet 187 Amazon, Game Console 199, Sony Laptop 176 Razer and Tablet 186 Apple)
PREDICTED PROFIT CONCENTRATION $600. 00 $500. 00 $400. 00 TOTAL PROFIT (K) $300. 00 CONCENTRATION (%) $200. 00 $100. 00 $0. 00 SCENARIO 1 SCENARIO 2 SCENARIO 3
Recommendations • Allocate marketing/sales resources to most profitable products • Create special marketing for less/ more profitable products • Prepare to maximize profitability using alternative scenarios • Prepare for trade-offs
How to predict brand preference? • K-nearest neighbor (IBk) • J 48 Tree Classifier
Criteria: Performance Indicators • • # of Correctly Classified Instances (= #) # of Incorrectly Classified Instances (= 0) Correlation Coeficient (= 1) Kappa Statistic (= 1) Mean Absolute Error ( = 0) Root Mean Absolute Error (= 0) Relative Absolute Error ( = 1) Root Relative Absolute Error (= 1)
Performance Criteria:
Results 7000 Brand preference percentage differ by approximately 5% between surveyed and predicted values. 6000 5000 4000 Acer 3000 Sony 2000 1000 0 Surveyed preference % Predicted preference
Recommendations • Run Brand Preference Predictions for products to: ▫ Update inventory ▫ Create or modify marketing and branding plans/campaigns ▫ Ask for vendor’s collaboration and support • Run Affinity Analysis to leverage on collaborative marketing
WHAT IS YOUR PLAN?
How to use Data Analytics to support decisions? • We can make more inferences using the survey data (Preference factors related) that we didn’t do in this first analysis • We can gain leverage on factors we oversee (i. e. shipping impact) • Find more relationships and understand how to leverage on them to create marketing and sales plans • Find affinity products or factors
Data-based Decision Making • To reduce cost of opportunity ▫ Get the best ROI • To assess trade-offs: ▫ Higher margin ▫ Faster growth • To create a cross-cutting corporate culture: ▫ Linking traditionally separate or independent parties or interests to achieve a goal
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