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Improving profitability of campaigns through data science

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Improving profitability of campaigns through data science

Analyze the campaign results and provide insights and recommendations on :

Which type of customers responded positively to the campaign ?
What can the customer be doing for better future campaign performance ?
How much can be the financial gains of the improved campaign strategies ?

Analyze the campaign results and provide insights and recommendations on :

Which type of customers responded positively to the campaign ?
What can the customer be doing for better future campaign performance ?
How much can be the financial gains of the improved campaign strategies ?

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Improving profitability of campaigns through data science

  1. 1. IMPROVING RETAIL SALES CAMPAIGN PROFITABILITY FINAL CAPSTONE PROJECT : DATA ANALYSIS , FINDINGS AND RECOMMENDATIONS SHOUNAK MONDAL POSTGRADUATE DIPLOMA IN DATA SCIENCE, EMERITUS AND COLUMBIA UNIVERSITY
  2. 2. CONTENTS 1 BACKGROUND 2 OBJECTIVES 3 APPROACH 4 ANALYSIS RESULTS 5 RECOMMENDATIONS
  3. 3. 1. The Customer is a B2B retailer for office supplies, office electronics and office furniture 2. A marketing campaign was executed to ~16k of its customers 3. Detailed campaign target data and results are available including key data per customer : a) Resulting Sales b) Historical sales c) Type of previous purchase d) Communication channel preferences e) Size of target company f) Language Background
  4. 4. Analyze the campaign results and provide insights and recommendations on : 1. Which type of customers responded positively to the campaign ? 2. What can the customer be doing for better future campaign performance ? 3. How much can be the financial gains of the improved campaign strategies ? Objective
  5. 5. Approach Exploratory Data Analysis • Removed outliers in Number of Year Prior Transaction • Drop 10 rows which has null values for No of Employees and type of previous purchase • Impute 442 rows with Last Transaction channel as Unknown • Impute 4470 rows with language as unknown • Remove negative sales and Historical sales volume rows Data Transformation & Analsysis • Transform categorical values to binary coded values • Observe correlation between features and remove highly corelated features Model Building • Build a robust classification model with most influential features to predict sales or no sales • Build a regression model to predict sales with the most influential features and predict sales • Using the probability of sales and the amount of sales predicted calculate profitability given, gross margin , the marketing and transaction costs Gains / Lift Chart • Classify the customers into deciles and show profits per decile • Show profitable deciles and expected gains over random targeting • Inner Join based on customer ID, the probabilities from classifier and predicted sales sales from linear regressor model to formula to find profits. • Sort profits and split by deciles Recommendations • Show Which type of customers responded positively to the campaign ? • Show What can XYZ Ltd be doing for better future campaign performance ? • Show How much can be the financial gains of the improved campaign strategies ?
  6. 6. 1. Those who have purchased before ( particularly 16-22 Transactions in prior year ) 2. Have had historical sales of up to $ 720,000+ ( 75% of the purchases ) 3. Made purchases in the year 1993 and 1994 – these 2 years seems to have created long term loyal customers Typical Purchasers are… 0 100 200 300 400 500 600 700 1926 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018 Year of First Purchase Coefficients–DegreeofInfluence Numberof purchaserecords Year of Purchase
  7. 7. Classification Model was built and tested on ~8000 records test data. The model can be now used to identify, predict and target positive sales candidates for future campaigns To build a robust model, Random Forest Classifier was built by training it on ~8000 campaign data which yielded the following : 1. Prediction accuracy score : 85% - Ability of the classifier to make correct predictions 2. Precision score: 77% - The ability of the classifier to predict true sales accurately. Cost of low score is wasted marketing cost. 3. Recall score:65% - The ability of the classifier to find all the true sales. Cost of low score is missed revenue opportunities. 4. f1 Score 70% - balance between the precision and the recall 5. Confusion Matrix NO YES NO 5400 438 YES 788 1453 Predicted Sale ( Yes or No ) Actual Sale
  8. 8. Now that we know targeting which customers will result in sales, next we built a model to predict “Amount of Sales” for purchasing customers and tested it on ~4384 records positive sales test data Linear Regression was used which yielded the following : 1. Linear Regression Fit score Training data : 77% 2. Linear Regression Fit score Test data : 75% 3. Root Mean Squared Error of prediction : 559 4. R squared score 0.75 ( degree to which the model captures and explains the variance of the data ) Findings 1. Size of the company has the largest influence on the sales amount : larger the company, larger the sales amounts 2. Previous purchase of office furniture and computer equipment has next significant influence in amount of sales. Coefficients–DegreeofInfluence
  9. 9. Gains Chart for ~1120 records test data representing a future campaign Total Actual Profit per customer of the campaign that was executed ( 16,000 records ) The lift chart is built from about 1120 records from test data for linear regression and same 1120 records from the classifier by using "inner" join on customer number i.e common records between the two dataframes in the linear reg test and classifier test Deciles Number of customers per decile Actual Profitability per customer Lift over average Total Profit % of Profit Incr Proj Profit 100k Customer base Total Proj Profit 100k Customer base Cuml Incr Profit 100k Customer base Cuml Total Profit 100k Customer base (1009.0, 1121.0] 112 504 497 55,630 76% 4,967 5,037 4,967 5,037 (897.0, 1009.0] 112 271 264 29,535 40% 2,637 2,707 7,604 7,744 (785.0, 897.0] 112 75 68 7,607 10% 679 749 8,283 8,493 (673.0, 785.0] 112 21 14 1,518 2% 135 205 8,419 8,699 (561.0, 673.0] 112 -1 -8 (947) -1% (85) (15) 8,334 8,684 (449.0, 561.0] 112 -12 -19 (2,113) -3% (189) (119) 8,145 8,565 (337.0, 449.0] 112 -20 -27 (2,994) -4% (267) (197) 7,878 8,368 (225.0, 337.0] 112 -28 -35 (3,959) -5% (354) (284) 7,525 8,085 (113.0, 225.0] 112 -37 -44 (4,944) -7% (441) (371) 7,083 7,713 (0.999, 113.0] 112 -49 -56 (6,268) -9% (560) (490) 6,523 7,223 Total 1120 7.00 73,063 100%
  10. 10. Recommendations 1. Instead of random targeting of customer base, use the prediction model to target only first 4 deciles type customers for maximum profitability for future campaigns 1120 record test data. 2. Maximize profitability further by using lower cost channels that reach the above target of customers effectively since marketing channels showed little or no influence on sales 3. Replicate what was done in the year 1993 and 1994, as it seems to have created long loyal customers 4. Use the model to predict sales, profitability, and expected Return on Investment and leverage it for a more fact based budget requirements for decision by management / budget approver for next campaigns
  11. 11. THANK YOU

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