Mini project1 team5
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Inspire…Educate…Transform.
The best place for students to learn Applied Engineering http://www.insofe.edu.in
Team 5
Mini Project - 1
Loyalty Program
SatyasheelRadhika RaniK Krishna Chaitanya
Saikumar AllakaPrashanth Soma
November 25, 2013
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IntroductionWe shall identify most likely loyal customer for the gaming company based on its customers details and their previous game preferences.
We assume that customers demographic and game-play preferences would help us define loyal customers.
We will use the customers demographic and their past game preferences to discover relationship between the known characteristics of a customer to their reaction to the loyalty program.
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Business use
Gaming company can maximize their games sales and retain customers without compromising on revenues
Provides customer’s gaming pattern insights and helps gaming company in building and promoting right games to the right customers.
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Process• Derive new attributes from the existing data to get a
bigger picture and to learn their characteristics.
• Bin them according to the Recency, Frequency, Monetary Value (RFM) Model.
• Merge them accordingly and keep the most relevant data for discovering relationships
• Fill the missing values, by guessing or ignoring.
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Process [contd.]
• Visualize the processed data to bring better business insights
• Test the data insights accordingly
LOYAL CUSTOMER Predicted Positive Predicted Negative
Actual Positive TP FN
Actual Negative FP TN
Predicting a loyal customer as non Loyal Customer (False Negative)Error metric: Sensitivity/Recall.
TP: True Positive TN: True NegativeFP: False Positive FN: False Negative
Error
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Derived Attributes
Recent Customer+
Frequent Customer+
Monetary Value---------------------------Most likely customer
Derived Attributes
FrequencyLF Transactions From the Website2
FrequencyAppTransactions From the Other application/other websites
FreqGamePlay Number of games played
RecencyApp Last time purchased from the application
RecencyLF Last time purchased from the LF
Recencydown Last time downloaded
Recency
Frequency
minRecencyCum360Min number of days Last time purchased within 180 days
NumHouseChildren Number of children per customer
TotalTimeGamePlay Total gaming time
Monetary Value
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Graphs
Houses with children between the age group of 5 and 10, across all game types, generate maximum Revenue. Target these age group houses.
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Graphs [contd.]
FavoriteSource
app cart lf
0M
5M
10M
15M
20M
25M
30M
TotalRevenueGenerated
Revenue VS FavSource
Sum of TotalRevenueGenerated for each FavoriteSource.
US customers seems to be the ones who buy/play more games. Hence, generate much of the Revenue compared to UK.
Apps are the most favorite source of medium used to play games. Thus, maximum Revenue is generated through app.
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Graphs [contd.]
House with 2 – 4 children, irrespective of their age, seem to play more games. Hence, Families with 2-4 children have to be concentrated up on, as they are the ones who are generating highest revenue.
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Graphs [contd.]
Number of gamers who play games are gender unbiased. Frequency of game play distributions are similar for female and male children. Gender need be major point of focus.
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Graphs [contd.]
Most revenue is generated through customers who use credit card followed by USL.F com Physical Card and US Retail physical card. These customers can be considered for providing offers.
FavoriteChannel
AU/NZ Retail Physical CardBonus code for LeapPad/Ex.. Bonus Code for TRU LeapPa.. Bonus Code for TRU Value C..
Bonus for TRU1 Bonus for TRU2 Bonus for TRU3
Canada Retail Physical Card
Cheerios Bonus Code
Credit Card CS for TRU
Customer Service
Early Bird Bonus Code
Ireland Retail Physical CardLeapfrog.com Online Purcha.. LeapSchool Cart Bonus Code Marketing Promotion - The .. NON SHIPPABLE, LEARNING.. NON SHIPPABLE, LEARNING.. Rest of World Retail Physical..
Retail Hardware
Retail Leaplet CardsTarget Exclusive 2010
TRU Exclusive Book App Card TRU Exclusive Creativity Card
TRU Exclusive Game CardU.S. Bundle $20 Value Card
U.S. LF.com Physical CardU.S. LF.com Virtual Code (e..
U.S. Retail Physical Card UK Retail Physical Card
US Bundle (in-package) - Ta..
0K
50K
100K
150K
200K
TotalRevenueGenerated
Revenue VS FavChannel
TotalRevenueGenerated for each FavoriteChannel.
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Hypothesis TestNull Hypothesis: Average number of children in a house is 6 with 99% accuracy
Alternate Hypothesis: Average number of children in a house can never be 6 with 99% accuracy
Statistic: Z-Statistic, One attribute with more than 30 records
From the above graph, we are 99.99% confident that, average number of children per house can never be 6. Null Hypothesis assumtion is incorrect
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Code snippets• Packages: sqldf, DMwR, reshape
• Filter Data toDate30 <- as.Date('2012-01-26',format='%Y-%m-%d')
widCart_30 <- subset(stg_bgdt_cust_purc_app_updated,
stg_bgdt_cust_purc_app_updated$TRANSACTION_DATE < toDate)
• Missing Values Central Imputation used for filling central tendency values (KNN Imputation is not used as there are 108 dimensions with 1 lakh plus records, we tried it by running 2 days, but no use!!)
• Derive Data maxValueInRow <- data.frame(castedChannel$CONTACT_WID, apply(castedChannel[, -1], 1, which.max), colnames(castedChannel[, -
1]) [apply(castedChannel[, -1], 1, which.max)], rowMeans(withoutWID, na.rm=T))
#computes column name with with max value and maximum value plus mean value for eacsh row in castedChannel removing NA value
. . .
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