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# Churn Predictive Modelling

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• CHAID: Basado en Chi Cuadrado, tiene debilidades
Regresión Logística: Mejor
Redes Neurales: Puede ser muy exacto, pero poco transparente
• ### Churn Predictive Modelling

1. 1. Churn is about dealing with risk The risk of a customer to Churn to another company Hugo Cisternas Director innovandis
2. 2. Hugo Cisternas Director innovandis hcisternas@innovandis.org Risk • The customer has made a Promise to continue using the service – But the future is not predictable with certainty – Not all the customers will continue using the service as promised • Conclusion: Make an imperfect prediction – Estimate the degree of risk involved in every individual case – Define a risk level that is “acceptable”
3. 3. Hugo Cisternas Director innovandis hcisternas@innovandis.org WHAT IS RISK? Usually we have a bad understanding of risk...
4. 4. Hugo Cisternas Director innovandis hcisternas@innovandis.org Example 1 • In a casino, John and Mary are gambling with dices... – Mary is about to draw a dice of 6 sides. If he gets a 6 he wins \$5,000 in any other case he doesn’t get anything – John is about to draw a dice of 10 sides. If he gets a 10 he wins \$5,000 in any other case he doesn’t get anything • Who is facing more risk?
5. 5. Hugo Cisternas Director innovandis hcisternas@innovandis.org Answer • Mary is facing more risk… because she has more uncertainty • John has more certainty to loose • Confusing? – The more the uncertainty, the more the risk
6. 6. Hugo Cisternas Director innovandis hcisternas@innovandis.org So, what is risk? Risk is exposition to uncertainty • If there is certainty, – there is no risk • If there is uncertainty, but you are not exposed to the results, – there is no risk Note: in colloquial terms, risk is used to refer to the possibility of occurrence of a hazardous event, so if a bad event looks more possible to happen, people say the event is more risky… this use of risk makes it more difficult to understand the technical definition of risk above.
7. 7. Hugo Cisternas Director innovandis hcisternas@innovandis.org What is risk? • Example: A person jumps on a parachute. – Will the parachute open? If the parachute fails, this person will suffer the consequences (probably he will die), then, he is assuming a risk – But a spectator in the ground is subject to the same uncertainty abut the parachute, but he will not suffer the consequences of a failure, then he/she is not assuming risks ** ** Unless the jumper owes money to the spectator, or is a relative. In those cases, this spectators will suffer the consequences, emotionally or financially if the parachute does not open, then they are assuming some risk.
8. 8. Hugo Cisternas Director innovandis hcisternas@innovandis.org Summary Every risk has two components:  Uncertainty  Exposure to this uncertainty
9. 9. Risk en the case of service contracts
10. 10. Hugo Cisternas Director innovandis hcisternas@innovandis.org Risk in service contracts Risk in service contracts: • There is uncertainty in the capacity or willingness to pay the service in the future (involuntary churn). • There is uncertainty in the capacity or willingness to continue with the service in the future (voluntary churn) • The exposition is the debt left by a defaulting customer or the lose of the future flows of incomes from a voluntarily churned customer if they have continued with the service. • Note that serious and continuous internal operating problems of the company, like service problems, network capacity, invoicing problems, etc. are not risk factors! It is almost certain that customers will switch to a better operator. This are “hygienic factors” to be controlled. And if those factors are in place for long time, the churn model based on this data will have a very short term life.
11. 11. Hugo Cisternas Director innovandis hcisternas@innovandis.org Churn modeling • A set of tools to Lower (or control) the risk – At the moment of application (application scoring) – At renewal time – When upgrading the service – At collections (if the customer is defaulting, will he/she repay de amount due?) – To prevent voluntary churn or attrition (behavior scoring)
12. 12. Hugo Cisternas Director innovandis hcisternas@innovandis.org PREDICTIONS
13. 13. Hugo Cisternas Director innovandis hcisternas@innovandis.org Basis of human experience • The near future will be similar the the near past • The future behavior of a person will be similar the his/her behavior in the past
14. 14. Hugo Cisternas Director innovandis hcisternas@innovandis.org CAUTION! Regarding predictive modelling
15. 15. Hugo Cisternas Director innovandis hcisternas@innovandis.org What is a good predictive model? • For marketers and management, a predictive model is not the objective, it is a medium to reach an objective • The objective in this case is to reduce churn – To make customers to stay longer (and continue paying) • To reduce churn, you have to know the actionable factors related to churn, and act to prevent or change those factors. • If you make a good job acting on the factors related to churn, the churn prediction model will become obsolete. • The best churn model will include this actionable factors as components of the model, to be able to manage the churn prevention programs. • Summary: – The best churn model is not the one with best statistical precision. – The best churn model is the one that provide best insights to further prevent churn behavior
16. 16. First law or prediction in marketing: “A good churn model should have in its development , the seed of it own obsolescence” Hugo Cisternas, 2001 Corollary: A successful churn prevention program will require to constantly rebuild the predictive model finding new factors that drive churn behavior
17. 17. Hugo Cisternas Director innovandis hcisternas@innovandis.org BUILDING A PREDICTIVE MODEL
18. 18. Hugo Cisternas Director innovandis hcisternas@innovandis.org First step • Setup the data sets: – Get a set of customers with known outcome Left Active Contracts Dataset “active” “churned”
19. 19. Hugo Cisternas Director innovandis hcisternas@innovandis.org Dataset for analysis • Establish a baseline • Get knowing data at baseline time and back • Flag the outcome at the end of the predicting frame Baseline Predictingframe Still active Churned 6 month 9 month 12 month ¿? Back
20. 20. Hugo Cisternas Director innovandis hcisternas@innovandis.org Which data is relevant? • Every data may be relevant • The analysis should omit prejudices about a data item • Not all data items will become equally important • There are interactions between some data – Older people will, generally, have long time employment – Singles will, in general, not own a house • At the beginning the degree of interaction is unknown
21. 21. Hugo Cisternas Director innovandis hcisternas@innovandis.org First: Just count • For each data item – In each category • How many churners are? • How many active are? – Example: • How many home owners are churners? Active? • How many singles are churners? Active? • How many married are churners? Active? – Better… • How many frequent callers are churners? Active? – Even better… • How many frequent callers from previous 6 months who have increased calls in the next quarter are churners? Active? Please note: examples are simplified and variables are chosen for exposition purposes, no real data s being used
22. 22. Hugo Cisternas Director innovandis hcisternas@innovandis.org Example: Home ownership • sample of 1.000 active and 1.000 churners Please note: examples are simplified and variables are chosen for exposition purposes, no real data s being used Active Churners # % # % Owners 600 60% 300 30% Rent 300 30% 600 60% Other 100 10% 100 10%
23. 23. Hugo Cisternas Director innovandis hcisternas@innovandis.org Example: Home ownership 0 10 20 30 40 50 60 70 Rent Own Other Active Churners Please note: examples are simplified and variables are chosen for exposition purposes, no real data s being used
24. 24. Hugo Cisternas Director innovandis hcisternas@innovandis.org Example: Home ownership • Calculate the “odds” of being active or churner – Odds are 2 to 1 that an owner will be active – Odds are 1/2 to 1 that a renter will churn Please note: examples are simplified and variables are chosen for exposition purposes, no real data s being used Active Churner Odds # % # % Of being active Owner 600 60% 300 30% 2:1 Rent 300 30% 600 60% 1:2 or .5:1 Other 100 10% 100 10% 1:1
25. 25. Hugo Cisternas Director innovandis hcisternas@innovandis.org DEVELOPING OF A CHURN MODEL
26. 26. Hugo Cisternas Director innovandis hcisternas@innovandis.org First: The team • Sales management • Operations management • IT • Legal department • Marketing
27. 27. Hugo Cisternas Director innovandis hcisternas@innovandis.org Second: Define the samples For which population will the churn model be build ? • Have enough history • Stability ( are there significant changes in economy, service, sales promotions, competitors we have to take into account?) • What will be predicted – Involuntary churn (default, delinquent patterns) – Voluntary churn (hard, soft) – On application – From behavior • Have enough data Note: When you look at your data, a lot of fields will be missing, data may look like garbage (and it may be)… but you will always find useful data.
28. 28. Hugo Cisternas Director innovandis hcisternas@innovandis.org Third: Define active and churn • Define with no ambiguity which should be an active customer and a churning (or churned) customer • This definition should be operative, it will be used to select and classify the data records • Organize and align cohorts
29. 29. Hugo Cisternas Director innovandis hcisternas@innovandis.org Fourth: Timeframe of prediction • With how much time in advance will be made the prediction? – At the time of the prediction, al customers should still be active – At the predicted time, some will remain active and some will default to churn
30. 30. Hugo Cisternas Director innovandis hcisternas@innovandis.org Fifth: Get the data • Get history behavior records – Traffic – Payment – Complaints – Contract changes (upgrades, downgrades, etc) – Anything available… don’t’ overlook any data. • Get demographic, psychographic data • Get data from application
31. 31. Hugo Cisternas Director innovandis hcisternas@innovandis.org Sixth: Quality control • Make quality assurance for the data
32. 32. Hugo Cisternas Director innovandis hcisternas@innovandis.org Seventh: Data transformations • Some data will require transformation • Dates: – Birth date becomes age – Application date become tenure – etc • Amounts – Is it necessary to correct inflation? Note: take care of cohorts.. Time frame alignment is critical for transformations.
33. 33. Hugo Cisternas Director innovandis hcisternas@innovandis.org Initial enumeration Count active and churners for each category for each variable Active Churn Not available 4 5 Rent 1106 1467 Own 806 443 From parents 27 29 From relatives 16 20 Government 16 12 Other 25 24
34. 34. Hugo Cisternas Director innovandis hcisternas@innovandis.org Characteristic analysis • Calculate percentages and odds Active Churn % Active % Churn Odds 4 5 0.2% 0.3% 0.8/1 1106 1467 55.3% 73.4% 0.75/1 806 443 40.3% 22.2% 1.81/1 27 29 1.4% 1.5% 0.93/1 16 20 0.8% 1.0% 0.8/1 16 12 0.8% 0.6% 1.33/1 25 24 1.3% 1.2% 1.04/1 Not available Rent Own From parents From relatives Government Other
35. 35. Hugo Cisternas Director innovandis hcisternas@innovandis.org Re categorization • Some categories may have not enough cases. – Join cases in one category. Rent 1106 1467 55.3% 73.4% 0.75/1 Own 806 451 40.3% 22.6% 1.78/1 All other 84 85 4.2% 4.3% 0.98/1 Not available 4 5 0.2% 0.3% 0.8/1 Active Churn % Active % Churn Odds
36. 36. Hugo Cisternas Director innovandis hcisternas@innovandis.org DEVELOPING OF THE SCORE
37. 37. Hugo Cisternas Director innovandis hcisternas@innovandis.org Predictive modeling P = probability of getting the condition of “churn” P ~~ 0: customer will remain active P ~~1: customer will churn
38. 38. Hugo Cisternas Director innovandis hcisternas@innovandis.org Getting the Score • CHAID, CART, etc. • Logistic regression • Neural networks ** ** Neural networks are a fascinating tool, but have an operative problem for marketers: they are black boxes, there is very difficult (if not impossible) to understand the underlying factors that explain the churn. If you cannot know the factors, you have no insight to build your churning prevention strategy… and you don’t want your model to change or adapt constantly without your involvement. Neural networks are excellent for reactive strategies to control imminent churning customers.
39. 39. Hugo Cisternas Director innovandis hcisternas@innovandis.org LOGISTIC REGRESSION
40. 40. Hugo Cisternas Director innovandis hcisternas@innovandis.org Logistic regression • Odds to churn: • Logistic regression: ( ) ∑=       − ⋅+== n i ii p p acZ 11 ln µ ( )p p −1 ( ) i n i iac p p Zln µ∑= +==      − 11
41. 41. Hugo Cisternas Director innovandis hcisternas@innovandis.org AFTER BUILDING THE MODEL... Analize gains chart
42. 42. Hugo Cisternas Director innovandis hcisternas@innovandis.org 0 10 20 30 40 50 60 70 80 Churn rate (%) 1 2 3 4 5 6 7 8 9 10 Deciles Illustrative Gains Chart
43. 43. Hugo Cisternas Director innovandis hcisternas@innovandis.org Score • The score can be calculated as: • The probability can be calculated as: ( )     − = 001.0 X Z SCORE ( ) ( )e Z p − + = 1 1
44. 44. Hugo Cisternas Director innovandis hcisternas@innovandis.org Churn % ( % ) 0 5 10 15 20 25 30 35 40 45 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 Score Example: Score
45. 45. Hugo Cisternas Director innovandis hcisternas@innovandis.org SCORES
46. 46. Hugo Cisternas Director innovandis hcisternas@innovandis.org Distribution of scores • Percentage of active and Churn:0 100 200 300 400 500 600 700 800 900 Churn Active
47. 47. Hugo Cisternas Director innovandis hcisternas@innovandis.org 0 100 200 300 400 500 600 700 800 Distribution of scores • Distribution (smoothed) of active and churns by score: – Average churn: 300 points – Average active: 500 points ActiveChurn
48. 48. Hugo Cisternas Director innovandis hcisternas@innovandis.org Break point • Breakpoint is the score below which a customer will be flagged as potential churn . Example: Break point in 150 0 100 200 300 400 500 600 700 800
49. 49. Hugo Cisternas Director innovandis hcisternas@innovandis.org The real distribution • Because the quantity or churning customers is less than the active customers:
50. 50. Churn is about dealing with risk The risk of a customer to Churn to another company Hugo Cisternas Director innovandis
51. 51. Hugo Cisternas Director innovandis hcisternas@innovandis.org Hugo Cisternas DIRECTOR INNOVANDIS Database Marketing / Strategic Planning / Market Research Contact: hcisternas@innovandis.org With more than 25 years experience in Database, Information Architecture and Statistical Analysis, had the risponsability of Database Marketing and Planning for Wunderman clients between 1999 and 2010. Wile at Wunderman, he led the team of Database Marketing and Planning of the agency, both in the areas of direct marketing, internal marketing, sales, marketing B-to-B and branding, including consulting, design, implementation and campaign management, database marketing and CRM. Currently developing specialized consulting work, applying technology and innovation to the demanding business and marketing needs that the companies have today. It also makes classes and lectures.
52. 52. Hugo Cisternas Director innovandis hcisternas@innovandis.org HUGO CISTERNAS Involved in projects like:: Database Marketing for Financiera ATLAS of Citibank, CMR Falabella, Johnson’s, Codigas, Enagas, Isapre Consalud, Entel S.A., Entel PCS, Seguros Cruz del Sur, Transbank, LanPass, Soprole, Caja de Compensación Los Héroes, Ripley, Larraín Vial stokbrokers, CRM consultancy for VTR Cable, Euroamérica life insurance, Torre, Larraín Vial Direct Marketing for Citibank and Atlas, CMR Falabella, Multiopción credit card from Johnson’s, Codigas, Enagas, Isapre Consalud, Entel S.A., Entel PCS, Cruz del Sur insurance, Transbank, Caja de Compensación Los Héroes, Metrogas, etc. Branding and Planning: ATLAS Citibank, Johnson’s, Isapre Consalud, Transbank, Caja de Compensación Los Héroes, Ripley, Aguas Andinas, Mademsa, Cousiño Macul, Toblerone , etc Internal marketing: ING, Metrogas, EntelPCS, Aguas Andinas. IT and database projects: Servicio de Impuestos Internos (Internal revenue services), National Library of Congress Telefónica CTC, Mutual de Seguridad, CTC Celular (Movistar), Movistar (Argentina), TelCel (Venezuela), Ministerio de Agricultura, Ministerio de Justicia, Ministerio de Relaciones Exteriores, Canal 13 TV channel…
53. 53. Hugo Cisternas Director innovandis hcisternas@innovandis.org Hugo Cisternas DIRECTOR INNOVANDIS Database Marketing / Planificación Estratégica / Market Research Contacto: hcisternas@innovandis.org Con más de 25 años de experiencia en Bases de Datos, Arquitectura de Información y Análisis Estadísticos, tiene la responsabilidad de los servicios de Database Marketing y Planificación Estratégica de Marketing para los clientes de Wunderman entre 1999 y 2010 Durante este período ha dirigido al equipo de Planning y de Database Marketing en la planificación estratégica requerida por los clientes de la agencia, tanto en las áreas de marketing directo, marketing interno, promociones, marketing B-to-B y posicionamiento de marca, como en la asesoría, diseño, implementación y administración de campañas, database marketing y CRM. Actualmente desarrolla trabajos de consultoría especializada, aplicando tecnología e innovación a las exigentes necesidades comerciales y de marketing que tiene la empresa de hoy. Además hace clases y dicta conferencias.
54. 54. Hugo Cisternas Director innovandis hcisternas@innovandis.org HUGO CISTERNAS Ha participado en proyectos destacados como: Database Marketing para Financiera ATLAS de Citibank, CMR Falabella, Johnson’s, Codigas, Enagas, Isapre Consalud, Entel S.A., Entel PCS, Seguros Cruz del Sur, Transbank, LanPass, Soprole, Caja de Compensación Los Héroes, Ripley, Larraín Vial corredores de bolsa, Consultorías CRM para VTR Cable, Euroamérica Seguros, Torre, Larraín Vial Marketing Directo para Citibank y Atlas, CMR Falabella, Tarjeta Multiopción de Johnson’s, Codigas, Enagas, Isapre Consalud, Entel S.A., Entel PCS, Seguros Cruz del Sur, Transbank, Caja de Compensación Los Héroes, Metrogas, etc. Posicionamiento y gestión estratégica de marcas como: ATLAS Citibank, Johnson’s, Isapre Consalud, Transbank, Caja de Compensación Los Héroes, Ripley, Aguas Andinas, Mademsa, Cousiño Macul, Toblerone , entre otras Planificación y desarrollo de marketing interno para empresas como ING, Metrogas, EntelPCS, Aguas Andinas. Participación en proyectos tecnológicos y de bases de datos de gran envergadura como por ejemplo: Servicio de Impuestos Internos, Biblioteca del Congreso Nacional, Telefónica CTC, Mutual de Seguridad, CTC Celular (Movistar), Movistar (Argentina), TelCel (Venezuela), Ministerio de Agricultura, Ministerio de Justicia, Ministerio de Relaciones Exteriores, Canal 13 de Televisión