Airline CRM big data opportunities

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<ul><li><p>Berengueres</p><p>Opportunities in Etihad miles program </p><p>Review of findings UAEU study on loyalty program data </p><p>February 2013 EB2013.02.28 </p><p>CONFIDENTIAL AND PROPRIETARY Any use of this material without specific permission of Jose Berengueres is strictly prohibited </p><p>Jose Berengueres </p></li><li><p>Berengueres | </p><p>Abstract </p><p>Project Background 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. </p><p>2 </p></li><li><p>Berengueres | </p><p>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. </p><p>Using extrapolation is 50 % accuracy in predictions. Using new data mining techniques 82% can be achieved. </p><p>Gold%</p><p>Silver%</p><p>Basic%</p><p>30k%</p><p>50k%</p><p>Accumulated%%miles%</p><p>7me%</p><p>Tier%</p><p>?"</p><p>3 </p></li><li><p>Berengueres | </p><p>The D/S model can predict who will be high value with very high accuracy. </p><p>I am flying Etihad for first time! </p><p>D"days" S"days"</p><p>I can tell you if she will become Silver with 82% </p><p>accuracy </p><p>Silver"-er"</p><p>before""now"</p><p>I can tell you if she will become Silver with 50% </p><p>accuracy </p><p>4 </p></li><li><p>Berengueres | </p><p>Example of an application that enhances the value of miles program: optimal real-time upgrade allocation </p><p>Passenger Name Tier StatusProbability they </p><p>will become Silver in X months</p><p>Wolfgang Amadeus Motzart Basic 0.99Ludvig Van Beetoven Basic 0.97Giuseppe Verdi Basic 0.89Jean-Michel Jarre Basic 0.77Yasuharu Konishi Basic 0.77Maki Nomiya Basic 0.45Teresa Teng Basic 0.42Carl Philip Emanuel Bach Basic 0.41Enric Granados Basic 0.00Leonard Bernstein Basic 0.00Carl Loewe Basic 0.00Johan Strauss Basic 0.00Isaac Albeniz Basic 0.00George Gerschwin Basic 0.00John Lenon Basic 0.00John Williams Basic 0.00</p><p>5 </p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model (1/3) </p><p>Prediction*Model*with*3*months*of*data*&amp;*lookout*1*monthsDiscovery*rate 82% of#future#Silver#where#identified#after#observing#them#for#three#monthsFalse*Positive 3% 97%#of#predictions#are#correct#</p><p>Count*of*prediction predictionSivler*attain 0 1 Grand*Total0 54752 37 547891 293 1309 1602Grand*Total 55045 1346 56391</p><p>notes:#start#period#jul#1st#2012</p><p>of future Silver identified after observing them 3 months 97% of predictions are correct </p><p>6 </p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model (2/3) </p><p>id silver(attain prediction confidence miles d_miles s_miles</p><p>Prediction*Model*with*3*months*of*data*&amp;*lookout*1*monthsoptimized</p><p>0011F8ED0AC8C8BB01508088620164121 1 95.8% 31727 31727 00043ADCD0AC8C8BB015080889C9B3D8C1 1 93.2% 26844 18419 8425004E72260AC8C8BA00924088DED48EA21 1 95.4% 28154 24132 4022005C87E60AC8C8BA009240888212E6551 1 93.5% 25818 6454 96820083BDAD0AC8C8BB015080889239B1A51 1 90.6% 44760 33570 000A8DBB00AC8C8B900A1808887E868951 1 96.6% 41008 41008 000FEEDB90AC8C8BB00820088B5AC162F1 0 9.8% 7261 7261 001037D1D0AC8C8BB015080888D445D2B1 0 2.9% 18420 7368 110520103DA960AC8C8BA00924088CBB007EC1 1 94.2% 29044 7261 7261016DAB8D0AC8C8BA00924088D0CA81E21 1 93.3% 25422 25422 0018A74C50AC8C8B900EE8088ABC59E931 0 44.2% 11830 1232 8134018EF04D0AC8C8BA040B208536F2F5D41 1 94.9% 32870 21597 1127301A0AA700AC8C8B900EE8088CB46716F1 1 96.1% 42616 21308 2130801AE88820AC8C8BA040FC08553DEE5701 1 97.0% 42616 21308 2130801B36BFB0AC8C8BB01508088F95FE4B81 0 4.5% 24314 7261 1020101D040ED0AC8C8BB00820088113040551 1 94.4% 28716 28716 001F5A8220AC8C8BA00924088D75E0D1D1 0 3.9% 18626 9313 93130218F5CF0AC8C8BA01BE6088DF8970A21 1 95.1% 27790 27790 0021C50910AC8C8BA01BE6088B1AF66B41 1 95.6% 27790 27790 0</p><p>7 </p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model (3/3) </p><p>id silver(attain prediction confidence miles d_miles s_miles</p><p>Prediction*Model*with*3*months*of*data*&amp;*lookout*1*monthsoptimized</p><p>1D5478290AC8C884020C2C150404CC2F1 0 40.3% 0 0 02385D21B0AC8C8BB01E0A088BBC38CE01 0 7.6% 1855 0 0297415D70AC8C8BB007D8088F5B405331 0 23.6% 7368 0 02A0781C60AC8C8BB007D8088A48FCD751 0 5.9% 3409 0 02C562D700AC8C8BB00ABA0883772D40C1 0 20.4% 3908 0 39083168DF040AC8C8BB00CC00883B12612B1 1 57.1% 0 0 03259FD070AC8C8B901C80088EF0F15681 0 0.6% 1500 0 750571203D10AC8C8BB01F2608838F1235A1 0 0.2% 0 0 06FD9A3290AC8C8BB01D66088822420661 0 5.5% 0 0 075FBD22E0AC8C8BB00D87F95DCE5D7FD1 0 10.6% 1000 0 100076A280880AC8C8BB00D87F95A29BA62D1 0 0.3% 13925 0 13925797B15940AC8C8BB005FA0321C1659B01 0 8.4% 1594 0 15947B58F52F0AC8C884045DF16DB2E7E8951 0 0.2% 2783 0 08893D36B0AC8C8BA00DA80888D7CC84C1 1 95.0% 27408 0 08E25ADB90AC8C8BB0036A088E65758641 0 5.9% 5756 0 57568E2F15A70AC8C8BB0036A0880E60BF591 0 43.2% 6946 0 694691B080AC0AC8C8BA03CE0085983E7A871 0 2.3% 21702 0 2170294C3111B0AC8C8BB01438088FA43C1F51 0 12.0% 4660 0 0 case hard to </p><p>predict for humans </p><p>(ord miles desc) </p><p>8 </p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model not optimized case (1/4) </p><p>Prediction*Model*with*2*weeks*of*data*&amp;*lookout*3*monthsDiscovery*rate 22% of#future#Silver#where#identified#after#observing#them#for#just#two#weeksFalse*Positive 11% 87%#of#predictions#are#correct#</p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model not optimized case (2/4) </p><p>id silver_attained predictionconfidence miles d_miles s_miles09CABFBA0AC8C8B900D8C08863F7EA3C 0 1 0.985 27653 27653 1639456C9887C0AC8C8BA005FE088ED47FFCF 0 0 0.015 24896 24896 0296469DC0AC8C8BA007D00885EB18956 0 0 0.135 23696 23696 03771ED480AC8C8BB00CC0088BDE4C478 0 0 0.037 23370 23370 03D3137C10AC8C8B90076A088BB6EBB89 0 0 0.047 23034 23034 0F1343CCE0AC8C8B900C0808816BD44C5 0 0 0.060 22857 22857 03F0378660AC8C8B90076A088CAE8C374 0 0 0.026 22768 22768 03794410A0AC8C8BA00AE20883E1652C4 0 0 0.017 22216 22216 0AB0B85FC0AC8C8BB010B4088B6809697 0 0 0.295 21928 21928 0E3FF02EE0AC8C8BA006E6088E43A076D 0 0 0.259 21784 21784 18551653EBF70AC8C8840491FD219EA21F75 0 0 0.103 21784 21784 03BF040EA0AC8C8BA0332E0851046F487 0 0 0.005 21554 21554 0421552F70AC8C8BA012E0088F88B3F4E 0 0 0.503 21537 21537 0</p><p>Prediction2Model2with222weeks2of2data2&amp;2lookout232months</p><p>not5optimized</p><p>Linear5extrapolaAon5piBalls:5in5two5weeks523k5miles,5in5125more5weeks5...?5our5model5seldom5fails5here.5</p><p>Linear extrapolation pitfalls: in two weeks 23k miles, in 12 more weeks ...? our model seldom fails here. </p><p>10 </p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model not optimized case (3/4) </p><p>Threshold</p><p>Selected</p><p>id silver_attained predictionconfidence miles d_miles s_miles</p><p>Prediction5Model5with525weeks5of5data5&amp;5lookout535months</p><p>not$optimized</p><p>6EABD2E40AC8C8BA0322808554F60797 1 1 99% 29369 15448 1392169354F350AC8C884067B5BE2535913DE 1 1 99% 14042 14042 140423CF691C00AC8C8B90076A0884A9F9B23 1 1 99% 30024 30024 0589B92470AC8C8B9032760850728D40D 1 1 99% 25614 10978 67112C7F33DC20AC8C8B902F4C086FD407221 1 1 99% 28716 28716 21514EABC93930AC8C8B900C08088B1962D24 1 1 98% 39208 19604 19604D78D29E80AC8C8BA010F6088616214AB 1 1 98% 43028 43028 0716E35F80AC8C8B901A7008844A4FF4B 1 1 98% 31560 15780 3156032E79DFC0AC8C8BB00CC008857814D19 1 1 98% 26257 15389 153896B0AB9000AC8C8B90328C085C3CAE112 1 1 98% 31560 15780 31560E16627680AC8C8B9012100884C61D367 1 1 97% 43660 21830 65490243B8BDE0AC8C8BB00ABA088A4D3CEFD 1 1 97% 30978 15489 15489750A14220AC8C883067781FE4BB3C6F3 1 1 96% 33570 16785 16785</p><p>ordered$by$confidence$</p><p>11 </p></li><li><p>Berengueres | </p><p>Evaluation of predictive power of D/S model not optimized case (4/4) </p><p>Threshold</p><p>Selected</p><p>id silver_attained predictionconfidence miles d_miles s_miles</p><p>Prediction5Model5with525weeks5of5data5&amp;5lookout535months</p><p>not$optimized</p><p>E16627680AC8C8B9012100884C61D367 1 1 97% 43660 21830 65490F78416DC0AC8C8BB01056088E52AE59F 1 1 79% 43660 21830 21830F78BFDB60AC8C8B900FC6088B04ACF26 1 0 70% 43660 21830 21830D78D29E80AC8C8BA010F6088616214AB 1 1 98% 43028 43028 0AFD57B700AC8C8BA00868088358FDB7F 1 1 95% 43028 43028 0EABC93930AC8C8B900C08088B1962D24 1 1 98% 39208 19604 19604750A14220AC8C883067781FE4BB3C6F3 1 1 96% 33570 16785 167852C1B4FF40AC8C8BB00ABA088FF0A6B96 1 1 92% 32746 32746 0C89F688F0AC8C8B900E460884156C145 1 1 91% 32746 32746 08B9896A50AC8C883045631A37038FF09 1 1 85% 32746 16373 16373CD0C91060AC8C8BB0029E088D94C8A38 1 1 96% 31964 15982 1598258F87A470AC8C8B904018085369AD47A 1 1 93% 31964 15982 1598258ECEF6B0AC8C8B904018085F9EAAD0C 1 1 92% 31964 15982 15982</p><p>ordered$by$miles$accumulated$in$2$weeks$</p><p>12 </p></li></ul>

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