Who will reply to my promotion

simone_gilardonsimone_gilardon Member Posts: 1 Contributor I
edited November 2018 in Help
Hello everybody,
I'm new in the forum and in datamining also  :-[

I'm working with our CRM department to identify a list of customer that will replay to a promotion

starting from DWH and CRM data (no external data at the moment) I built a csv file where I've:

CUST_ID CUST_GENDER_ID AGE_RNG TRANS_RNG FRQ_RNG OTH_PROMO REC_RNG TARGET_SS15

1009860 F 1 1 1 1 1 0
1009931 F 3 1 1 1 1 1
1015277 F 2 2 3 0 2 0
108887 F 2 1 1 1 1 0
108898 F 2 1 1 1 2 1
108930 F 1 2 2 0 1 0
108931 F 1 1 1 0 3 0
108958 F 3 1 1 1 1 0
121794 F 3 1 1 1 2 0
121841 F 2 2 1 1 2 0
121853 F 2 1 1 1 1 0
121902 F 3 1 1 0 1 0
121906 F 1 1 1 0 1 0
121945 M 2 1 1 1 1 1
121955 F 1 1 1 1 1 0
122044 M 0 1 1 1 1 0
122109 F 2 1 1 1 1 0

CUST_ID                              represent the customer ID
CUST_GENDER_ID             Male / Female / -  (at the moment I decided to keep also the non classified gender)
AGE_RNG                     The age has been divided in 4 classes
TRANS_RNG                     The number of transactions has been divided in 4 classes
FRQ_RNG                     The frequency the Customer visit a store has been divided in 4 classes
OTH_PROMO                     0 means never replied to any promotion - 1 the customer replied in past to at least one promo
REC_RNG                     The recency has been divided in 4 classes
TARGET_SS15                    This is the target I guess to identify... who'll reply on the next promotion based on the other data

I'm wondering which ways could I use to get the result, I was thinking to use Naive Bayesian but really I don't know if it could work and also how to use it.

any help or suggestion will be appreciated
thanks in advance
Simone
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Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 1,952  RM Data Scientist
    Hello Simone,

    Am i right that you have historic data and that the attribute TARGET_SS15 codes whether the person replays or not? If so, you have the perfect data for supervised learning. Naive Bayes is definitly an option for this and a good baseline model. However for better results i would recommend more sophisticated methods like SVM or Random Forest.

    If you have no experience with data mining at all i would recommend to have a look at this free ebok: https://rapidminer.com/wp-content/uploads/2013/10/DataMiningForTheMasses.pdf

    For further more detailed studies i would recommend this book: http://www.amazon.com/Predictive-Analytics-Data-Mining-RapidMiner/dp/0128014601/ref=sr_1_1?ie=UTF8&;qid=1435305346&sr=8-1

    Cheers,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
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