Airline CRM big data opportunities
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the pptx of http://journalofbigdata.springeropen.com/articles/10.1186/2196-1115-1-3
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Airline CRM big data opportunities
- 1. Berengueres Opportunities in Etihad miles program Review of findings UAEU study on loyalty program data February 2013 EB2013.02.28 CONFIDENTIAL AND PROPRIETARY Any use of this material without specific permission of Jose Berengueres is strictly prohibited Jose Berengueres
- 2. Berengueres | Abstract Project Background โ CRM data from Etihad Airways was analyzed. The data is three tables that comprise anonimized information about passengers who enrolled the Etihad miles program between 2006 and 2012. The tables contain data such as: (1) Age and Nationality, etc. (2) Loyalty program purchases etc. (Not used in this model) (3) Flight activity, miles earned etc. The following pages explain what can be predicted by using the data. 2
- 3. Berengueres | Predicting if a new customer will become silver (high value) or not can help concentrate the (limited) CRM resources on high potential customers. To do this effectively accuracy is needed. Using extrapolation is 50 % accuracy in predictions. Using new data mining techniques 82% can be achieved. Gold% Silver% Basic% 30k% 50k% Accumulated%% miles% 7me% Tier% ?" 3
- 4. Berengueres | The D/S model can predict who will be high value with very high accuracy. I am flying Etihad for first time! D"days" S"days" I can tell you if she will become Silver with 82% accuracy Silver"-er" before""now" I can tell you if she will become Silver with 50% accuracy 4
- 5. Berengueres | Example of an application that enhances the value of miles program: optimal real-time upgrade allocation Passenger Name Tier Status Probability they will become Silver in X months Wolfgang Amadeus Motzart Basic 0.99 Ludvig Van Beetoven Basic 0.97 Giuseppe Verdi Basic 0.89 Jean-Michel Jarre Basic 0.77 Yasuharu Konishi Basic 0.77 Maki Nomiya Basic 0.45 Teresa Teng Basic 0.42 Carl Philip Emanuel Bach Basic 0.41 Enric Granados Basic 0.00 Leonard Bernstein Basic 0.00 Carl Loewe Basic 0.00 Johan Strauss Basic 0.00 Isaac Albeniz Basic 0.00 George Gerschwin Basic 0.00 John Lenon Basic 0.00 John Williams Basic 0.00 5
- 6. Berengueres | Evaluation of predictive power of D/S model (1/3) Prediction*Model*with*3*months*of*data*&*lookout*1*months Discovery*rate 82% of#future#Silver#where#identified#after#observ False*Positive 3% 97%#of#predictions#are#correct# Count*of*prediction prediction Sivler*attain 0 1 Grand*Total 0 54752 37 54789 1 293 1309 1602 Grand*Total 55045 1346 56391 notes:#start#period#jul#1st#2012 of future Silver identified after observing them 3 months 97% of predictions are correct 6
- 7. Berengueres | Evaluation of predictive power of D/S model (2/3) id silver(attain prediction confidence miles d_miles s_miles Prediction*Model*with*3*months*of*data*&*lookout*1*months optimized 0011F8ED0AC8C8BB01508088620164121 1 95.8% 31727 31727 0 0043ADCD0AC8C8BB015080889C9B3D8C1 1 93.2% 26844 18419 8425 004E72260AC8C8BA00924088DED48EA21 1 95.4% 28154 24132 4022 005C87E60AC8C8BA009240888212E6551 1 93.5% 25818 6454 9682 0083BDAD0AC8C8BB015080889239B1A51 1 90.6% 44760 33570 0 00A8DBB00AC8C8B900A1808887E868951 1 96.6% 41008 41008 0 00FEEDB90AC8C8BB00820088B5AC162F1 0 9.8% 7261 7261 0 01037D1D0AC8C8BB015080888D445D2B1 0 2.9% 18420 7368 11052 0103DA960AC8C8BA00924088CBB007EC1 1 94.2% 29044 7261 7261 016DAB8D0AC8C8BA00924088D0CA81E21 1 93.3% 25422 25422 0 018A74C50AC8C8B900EE8088ABC59E931 0 44.2% 11830 1232 8134 018EF04D0AC8C8BA040B208536F2F5D41 1 94.9% 32870 21597 11273 01A0AA700AC8C8B900EE8088CB46716F1 1 96.1% 42616 21308 21308 01AE88820AC8C8BA040FC08553DEE5701 1 97.0% 42616 21308 21308 01B36BFB0AC8C8BB01508088F95FE4B81 0 4.5% 24314 7261 10201 01D040ED0AC8C8BB00820088113040551 1 94.4% 28716 28716 0 01F5A8220AC8C8BA00924088D75E0D1D1 0 3.9% 18626 9313 9313 0218F5CF0AC8C8BA01BE6088DF8970A21 1 95.1% 27790 27790 0 021C50910AC8C8BA01BE6088B1AF66B41 1 95.6% 27790 27790 0 7
- 8. Berengueres | Evaluation of predictive power of D/S model (3/3) id silver(attain prediction confidence miles d_miles s_miles Prediction*Model*with*3*months*of*data*&*lookout*1*months optimized 1D5478290AC8C884020C2C150404CC2F1 0 40.3% 0 0 0 2385D21B0AC8C8BB01E0A088BBC38CE01 0 7.6% 1855 0 0 297415D70AC8C8BB007D8088F5B405331 0 23.6% 7368 0 0 2A0781C60AC8C8BB007D8088A48FCD751 0 5.9% 3409 0 0 2C562D700AC8C8BB00ABA0883772D40C1 0 20.4% 3908 0 3908 3168DF040AC8C8BB00CC00883B12612B1 1 57.1% 0 0 0 3259FD070AC8C8B901C80088EF0F15681 0 0.6% 1500 0 750 571203D10AC8C8BB01F2608838F1235A1 0 0.2% 0 0 0 6FD9A3290AC8C8BB01D66088822420661 0 5.5% 0 0 0 75FBD22E0AC8C8BB00D87F95DCE5D7FD1 0 10.6% 1000 0 1000 76A280880AC8C8BB00D87F95A29BA62D1 0 0.3% 13925 0 13925 797B15940AC8C8BB005FA0321C1659B01 0 8.4% 1594 0 1594 7B58F52F0AC8C884045DF16DB2E7E8951 0 0.2% 2783 0 0 8893D36B0AC8C8BA00DA80888D7CC84C1 1 95.0% 27408 0 0 8E25ADB90AC8C8BB0036A088E65758641 0 5.9% 5756 0 5756 8E2F15A70AC8C8BB0036A0880E60BF591 0 43.2% 6946 0 6946 91B080AC0AC8C8BA03CE0085983E7A871 0 2.3% 21702 0 21702 94C3111B0AC8C8BB01438088FA43C1F51 0 12.0% 4660 0 0 case hard to predict for humans (ord miles desc) 8
- 9. Berengueres | Evaluation of predictive power of D/S model not optimized case (1/4) Prediction*Model*with*2*weeks*of*data*&*lookout*3*months Discovery*rate 22% of#future#Silver#where#identified#after#observing#them#for#just#two#weeks False*Positive 11% 87%#of#predictions#are#correct#<<>#new#marketing#opportunities Count*of*prediction Prediction Silver#Attained 0 1 Grand#Total 0 10682 8 10690 1 217 62 279 Grand*Total 10899 70 10969 thereshold 75% Notes:#the#model#is#not#opJmized#for#this# period#of#Jme.#Only#Flight#acJvity#and# demographics#was#used.#No#data#from# acJvity#table#was#used.#Values#depend#on# thereshold.Chang#eand#refresh.#Will#update# all#sheets.#Model#is#Not# opJmized.Thereshold#vlaue#set#at#75%#to# minimize#False#posiJves# 9
- 10. Berengueres | Evaluation of predictive power of D/S model not optimized case (2/4) id silver_attained predictionconfidence miles d_miles s_miles 09CABFBA0AC8C8B900D8C08863F7EA3C 0 1 0.985 27653 27653 16394 56C9887C0AC8C8BA005FE088ED47FFCF 0 0 0.015 24896 24896 0 296469DC0AC8C8BA007D00885EB18956 0 0 0.135 23696 23696 0 3771ED480AC8C8BB00CC0088BDE4C478 0 0 0.037 23370 23370 0 3D3137C10AC8C8B90076A088BB6EBB89 0 0 0.047 23034 23034 0 F1343CCE0AC8C8B900C0808816BD44C5 0 0 0.060 22857 22857 0 3F0378660AC8C8B90076A088CAE8C374 0 0 0.026 22768 22768 0 3794410A0AC8C8BA00AE20883E1652C4 0 0 0.017 22216 22216 0 AB0B85FC0AC8C8BB010B4088B6809697 0 0 0.295 21928 21928 0 E3FF02EE0AC8C8BA006E6088E43A076D 0 0 0.259 21784 21784 1855 1653EBF70AC8C8840491FD219EA21F75 0 0 0.103 21784 21784 0 3BF040EA0AC8C8BA0332E0851046F487 0 0 0.005 21554 21554 0 421552F70AC8C8BA012E0088F88B3F4E 0 0 0.503 21537 21537 0 Prediction2Model2with222weeks2of2data2&2lookout232months not5optimized Linear5extrapolaAon5 piBalls:5in5two5weeks5 23k5miles,5in5125more5 weeks5...?5 our5model5seldom5fails5 here.5 Linear extrapolation pitfalls: in two weeks 23k miles, in 12 more weeks ...? our model seldom fails here. 10
- 11. Berengueres | Evaluation of predictive power of D/S model not optimized case (3/4) Threshold Selected id silver_attained predictionconfidence miles d_miles s_miles Prediction5Model5with525weeks5of5data5&5lookout535months not$optimized 6EABD2E40AC8C8BA0322808554F60797 1 1 99% 29369 15448 13921 69354F350AC8C884067B5BE2535913DE 1 1 99% 14042 14042 14042 3CF691C00AC8C8B90076A0884A9F9B23 1 1 99% 30024 30024 0 589B92470AC8C8B9032760850728D40D 1 1 99% 25614 10978 67112 C7F33DC20AC8C8B902F4C086FD407221 1 1 99% 28716 28716 21514 EABC93930AC8C8B900C08088B1962D24 1 1 98% 39208 19604 19604 D78D29E80AC8C8BA010F6088616214AB 1 1 98% 43028 43028 0 716E35F80AC8C8B901A7008844A4FF4B 1 1 98% 31560 15780 31560 32E79DFC0AC8C8BB00CC008857814D19 1 1 98% 26257 15389 15389 6B0AB9000AC8C8B90328C085C3CAE112 1 1 98% 31560 15780 31560 E16627680AC8C8B9012100884C61D367 1 1 97% 43660 21830 65490 243B8BDE0AC8C8BB00ABA088A4D3CEFD 1 1 97% 30978 15489 15489 750A14220AC8C883067781FE4BB3C6F3 1 1 96% 33570 16785 16785 ordered$by$ con๏ฌdence$ 11
- 12. Berengueres | Evaluation of predictive power of D/S model not optimized case (4/4) Threshold Selected id silver_attained predictionconfidence miles d_miles s_miles Prediction5Model5with525weeks5of5data5&5lookout535months not$optimized E16627680AC8C8B9012100884C61D367 1 1 97% 43660 21830 65490 F78416DC0AC8C8BB01056088E52AE59F 1 1 79% 43660 21830 21830 F78BFDB60AC8C8B900FC6088B04ACF26 1 0 70% 43660 21830 21830 D78D29E80AC8C8BA010F6088616214AB 1 1 98% 43028 43028 0 AFD57B700AC8C8BA00868088358FDB7F 1 1 95% 43028 43028 0 EABC93930AC8C8B900C08088B1962D24 1 1 98% 39208 19604 19604 750A14220AC8C883067781FE4BB3C6F3 1 1 96% 33570 16785 16785 2C1B4FF40AC8C8BB00ABA088FF0A6B96 1 1 92% 32746 32746 0 C89F688F0AC8C8B900E460884156C145 1 1 91% 32746 32746 0 8B9896A50AC8C883045631A37038FF09 1 1 85% 32746 16373 16373 CD0C91060AC8C8BB0029E088D94C8A38 1 1 96% 31964 15982 15982 58F87A470AC8C8B904018085369AD47A 1 1 93% 31964 15982 15982 58ECEF6B0AC8C8B904018085F9EAAD0C 1 1 92% 31964 15982 15982 ordered$by$ miles$ accumulated$in$ 2$weeks$ 12