Predicting Average Basket Value

Transcription

1 Predicting Basket Value Business Analytics Using Data Mining Group 9A Aashish Sharma: Abhishek Agrawal: Saurabh Malhotra: Madhav Pathak: Madhur Chadha:

2 Table of Contents I. Executive Summary... 3 A. Problem Description... 3 B. Brief description of the data, its source, key characteristics, and chart(s)... 3 C. High level Description (Prediction Methodology)... 4 D. Technical Summary... 5 E. Performance Metrics... 5 F. Limitations... 6 G. Recommendations... 6 II. APPENDIX... 7 A. CART Model... 7 B. Multiple Regression Model... 8 C. KNN Model... 9 D. Naïve Model... 10

3 I. Executive Summary A. Problem Description Retailers spend a considerable amount of time, effort, and money to acquire a new customer. However once a customer has been acquired, the maximum value can only be derived if the customer becomes a repetitive buyer and his/her purchase amounts increase with time. Identifying which customer will qualify for a promotion is a key to this problem and our study makes an attempt to solve this issue. Our model predicts the future shopping basket value of a customer. Basis this prediction, every week top 10% customers will be identified and the store will the promotional discount coupons to these customers. Accuracy of model will be another deciding factor in the overall scheme of business strategy. A miss out on a probable loyal customer could impact the long term customer life time value associated. As the hypermarket, we are interested in predicting the average basket value of the next customer who walks in based on his/her demographic data as well as his previous purchase pattern prior to this visit B. Brief description of the data, its source, key characteristics, and chart(s) Transaction data for ABC that include information at SKU level customer ID, purchase date, extended price, quantity sold, item description, department, sub-department, class and subclass. Data are provided on a period of 13 months from Aug 2011 to Aug Customer demographic information has also been provided which includes enrolment date, date of birth, sex, marital status and customer IDs. Source: Data collected by Hansa Cequity Solutions Key Characteristics: The available data was at the SKU and customer level. However, we needed basket level data for our analysis. In order to efficiently filter and sort the data to make it suitable for basket level analysis, we used BASE SAS v9.1. Compiled data at the Basket level for each customer Merged customer information with the transactions data to get customer_id and transaction_data level data Computed the lagged (i.e. till previous purchase) Basket Price & Purchase Quantity for each customer at transaction level using all purchases prior to current transaction date Included 4 dummy variables in data set - Sex, Married, and Flag

4 Variables : Customer_No, Transaction_Date, Day of Week, Quantity_Sold, Extended_Price, Avg_Quantity_Sold_Lag, Avg_Ext_Price_Lag, Count_Footfall, Clean_ _Flag, Total_qty_sold_tilldate, Total_cart_value_tilldate, Sex_M, Mobile_Status, Married, Enrollment_Age,, Missing Values Handling: At summary level, approx ~4000 rows had SEX and MARITAL status missing. This subset of was removed for our analysis. This is done deliberately as we want to create a robust model for only those customers for which we have all the information captured (it will also prompt customers to provide information next time in order to avail discounts) C. High level Description (Prediction Methodology) Following three methods were used to analyze the data and to create the predictive model on the driver file (created above) Multiple Linear Regression: Y = *Day of week *Avg Ext Price (Lag) *Count (Footfall) *AGE *SEX_M *ENROLLMENT AGE *MARRIED *CLEAN_ _FLAG Naïve Forecast: (Benchmark): Y = * Avg. Ext Price_Lag k-nn Model: Input Variables: Day of Week, Avg_Ext_Price_Lag, Count_Footfall, AGE, SEX_M, ENROLLMENT_AGE, MARRIED (k=20) CART: Input Variables: Day of Week, Avg_Ext_Price_Lag, Count_Footfall, AGE, SEX_M, ENROLLMENT_AGE, MARRIED (best pruned one) The Appendix shows the model working and other technical details. Residuals Histogram for the above models-

5 D. Technical Summary The lift chart and RMSE were used to evaluate the performance of the models above. The lift charts are shown below followed by comparison of RMSE - Best Final CART Model (Pruned) E. Performance Metrics The cost of offering a discount to a customer whom the model predicted but actually is misclassified, is very negligible. The cost of not identifying a person through the model could be very high because the hypermarket might lose this customer s average basket value sales less the discount that would have been offered. Underlying assumption to this evaluation is that because of not offering the discount promotion to the correct customer, we have lost the opportunity to make the customer visit the store next time. Also, we are assuming that during the next purchase, this customer s basket size would approximately be equal to his average last purchase value (Naïve). This cost turns out to be ~Rs.500 per customer for each misclassification (weekly prediction Vs actual data analysis on misidentified customer set).

6 Also we cannot offer this promotion to every customer at the hypermarket, because by offering the promotion, the probability of re-visit increases tremendously, thus the discount hit would be much higher. metric for evaluation F. Limitations Macro-economic factors have been ignored for the purpose of building this model. To improve the prediction quality of the model, the hypermarket should further add more predictor variables by collecting information from the customers. Demographic data might be misleading as many customers might have one loyalty card per family that might be getting used by multiple members of the family. All customers will be given a loyalty card while he/she makes a purchase at the hypermarket. Two ways to measure the success of our proposal- % Increase in Sales and % increase in re-purchase rate for existing customers. Prerequisite for this benchmark: % increase in sales should be greater than 5% % increase in profitability: G. Recommendations We would recommend the hypermarket to adopt and deploy our CART model to predict the basket value for the next purchase. Using this model, hypermarket will be better able to identify top 10 percentile customers and incentivize them for more frequent visits as well as reward them for being loyal customers. Hypermarket should run this model weekly update the misclassification cost and take proactive actions in defining the probability of repeat purchase for all the discounts offered. Tracking of the actual increase in customer footfall will help hypermarket evaluate whether the strategy is actually translating into real values. This model should be updated on a real time basis since it extensively uses the prior purchase of these customers as an input variable Ideal measure but since data is not available we are not using this

7 II. APPENDIX A. CART Model Full Tree Rules (Using Training Data) #Decision Nodes 6 #Terminal Nodes 7 Level NodeID ParentID SplitVar SplitValue Cases LeftChild RightChild PredVal Node Type 0 0 N/A Ext_Price_Lag Decision Count_Footfall Decision N/A N/A 1102 N/A N/A Terminal 2 3 1OLLMENT_AGE Decision Ext_Price_Lag Decision Ext_Price_Lag Decision N/A N/A 1296 N/A N/A Terminal AGE Decision N/A N/A 1091 N/A N/A Terminal N/A N/A 1806 N/A N/A Terminal N/A N/A 1551 N/A N/A Terminal N/A N/A 1489 N/A N/A Terminal N/A N/A 1664 N/A N/A Terminal Pruned Tree Rules (Using Validation Data) #Decision Nodes 4 #Terminal Nodes 5 Level NodeID ParentID SplitVar SplitValue Cases LeftChild RightChild PredVal Node Type 0 0 N/A Ext_Price_Lag Decision Count_Footfall Decision N/A N/A 2945 N/A N/A Terminal 2 3 1OLLMENT_AGE Decision Ext_Price_Lag Decision N/A N/A 8518 N/A N/A Terminal N/A N/A 4322 N/A N/A Terminal N/A N/A 5390 N/A N/A Terminal N/A N/A 345 N/A N/A Terminal Training Data scoring - Summary Report (Using Full Tree) E E-07 Validation Data scoring - Summary Report (Using Prune Tree) E Test Data scoring - Summary Report (Using Prune Tree) E

8 We have checked multiple box plots to see how this model is doing with respect to various predictors. One of them Predictor value vs day of week is shown here B. Multiple Regression Model The Regression Model Input variables Constant term Day of Week Avg_Ext_Price_Lag Count_Footfall AGE SEX_M ENROLLMENT_AGE MARRIED CLEAN_ _FLAG Coefficient Std. p-value SS E+11 Residual df Multiple R Std. Dev. estimate Residual SS E Training Data scoring - Summary Report E Validation Data scoring - Summary Report E

9 Box plot: Predictor value vs day of week is shown here C. KNN Model Validation error log for different k Value of k Training Validation <--- Best k Training Data scoring - Summary Report (for k=20) Validation Data scoring - Summary Report (for k=20) E Test Data scoring - Summary Report (for k=20) E

10 Similar to MLR, the box plot of predicted value vs day of week is shown below- D. Naïve Model The Regression Model Input variables Constant term Avg_Ext_Price_Lag Training Data scoring - Summary Report Coefficient Std. p-value SS Residual df Multiple R- Std. Dev. estimate Residual SS 1.362E E-06 Validation Data scoring - Summary Report