Hugo E. Cisternas, profile picture
Uploaded byHugo E. Cisternas
58,715 views

Churn Predictive Modelling

The document discusses customer churn risk and how to develop predictive churn models. It defines risk as having two components: uncertainty and exposure to that uncertainty. When building a churn model, the key steps are: defining active vs churned customers, selecting relevant customer data, analyzing characteristics to identify predictors, developing a predictive score using methods like logistic regression, and evaluating the model's ability to identify customers likely to churn. The goal of a churn model is to provide insights for preventing churn, not just statistical precision.

Embed presentation

Churn is about dealing with risk The risk of a customer to Churn to another company Hugo Cisternas Director innovandis
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”
Hugo Cisternas Director innovandis hcisternas@innovandis.org WHAT IS RISK? Usually we have a bad understanding of risk...
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?
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
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.
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.
Hugo Cisternas Director innovandis hcisternas@innovandis.org Summary Every risk has two components:  Uncertainty  Exposure to this uncertainty
Risk en the case of service contracts
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.
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)
Hugo Cisternas Director innovandis hcisternas@innovandis.org PREDICTIONS
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org CAUTION! Regarding predictive modelling
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
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org BUILDING A PREDICTIVE MODEL
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”
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
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
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
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%
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
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org DEVELOPING OF A CHURN MODEL
Hugo Cisternas Director innovandis hcisternas@innovandis.org First: The team • Sales management • Operations management • IT • Legal department • Marketing
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.
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
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
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org Sixth: Quality control • Make quality assurance for the data
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.
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
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
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org DEVELOPING OF THE SCORE
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
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.
Hugo Cisternas Director innovandis hcisternas@innovandis.org LOGISTIC REGRESSION
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org AFTER BUILDING THE MODEL... Analize gains chart
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
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
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org SCORES
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
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
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
Hugo Cisternas Director innovandis hcisternas@innovandis.org The real distribution • Because the quantity or churning customers is less than the active customers:
Churn is about dealing with risk The risk of a customer to Churn to another company Hugo Cisternas Director innovandis
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.
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…
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.
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

More Related Content

PPTX
Customer Churn Analysis and Prediction
bySOUMIT KAR
 
PDF
Predicting the e-commerce churn
byLviv Data Science Summer School
 
PDF
Customer Churn Prevention Powerpoint Presentation Slides
bySlideTeam
 
PDF
Churn prediction data modeling
byPierre Gutierrez
 
PPTX
Recommender systems for E-commerce
byAlexander Konduforov
 
PPTX
Churn Analysis in Telecom Industry
bySatyam Barsaiyan
 
PPTX
Data mining and analysis of customer churn dataset
byRohan Choksi
 
PPTX
Churn modelling
byYogesh Khandelwal
 
Customer Churn Analysis and Prediction
bySOUMIT KAR
 
Predicting the e-commerce churn
byLviv Data Science Summer School
 
Customer Churn Prevention Powerpoint Presentation Slides
bySlideTeam
 
Churn prediction data modeling
byPierre Gutierrez
 
Recommender systems for E-commerce
byAlexander Konduforov
 
Churn Analysis in Telecom Industry
bySatyam Barsaiyan
 
Data mining and analysis of customer churn dataset
byRohan Choksi
 
Churn modelling
byYogesh Khandelwal
 

What's hot

PDF
Churn in the Telecommunications Industry
byskewdlogix
 
PDF
Telecom Churn Prediction
byAnurag Mukhopadhyay
 
PPTX
Incrementality
byStitch Fix Algorithms
 
PDF
Fraud detection ML
byMaatougSelim
 
PDF
Churn Prediction in Practice
byBigData Republic
 
PDF
Customer attrition and churn modeling
byMariya Korsakova
 
PDF
RFM Segmentation
byKamil Bartocha
 
PPTX
Fraud Analytics
byBig Data Colombia
 
PPTX
Telecom Churn Prediction Presentation
byPinintiHarishReddy
 
PPT
Churn prediction
byGigi Lino
 
PPTX
Telcom churn .pptx
byResearchproGlobal
 
PPTX
Social media analytics powered by data science
byNavin Manaswi
 
PPTX
Prediction of customer propensity to churn - Telecom Industry
byPranov Mishra
 
PPTX
Bank churn with Data Science
byCarolyn Knight
 
PDF
churn prediction in telecom
byHong Bui Van
 
PDF
IRJET - Customer Churn Analysis in Telecom Industry
byIRJET Journal
 
PPTX
Data Driven Marketing
byAlexei Cherenkov
 
PPTX
Linear Regression and Logistic Regression in ML
byKumud Arora
 
PPTX
Role of data analytics in educational industry
byRuthVanlalremruati
 
PPTX
Big data
byAmi Redwan Haq
 
Churn in the Telecommunications Industry
byskewdlogix
 
Telecom Churn Prediction
byAnurag Mukhopadhyay
 
Incrementality
byStitch Fix Algorithms
 
Fraud detection ML
byMaatougSelim
 
Churn Prediction in Practice
byBigData Republic
 
Customer attrition and churn modeling
byMariya Korsakova
 
RFM Segmentation
byKamil Bartocha
 
Fraud Analytics
byBig Data Colombia
 
Telecom Churn Prediction Presentation
byPinintiHarishReddy
 
Churn prediction
byGigi Lino
 
Telcom churn .pptx
byResearchproGlobal
 
Social media analytics powered by data science
byNavin Manaswi
 
Prediction of customer propensity to churn - Telecom Industry
byPranov Mishra
 
Bank churn with Data Science
byCarolyn Knight
 
churn prediction in telecom
byHong Bui Van
 
IRJET - Customer Churn Analysis in Telecom Industry
byIRJET Journal
 
Data Driven Marketing
byAlexei Cherenkov
 
Linear Regression and Logistic Regression in ML
byKumud Arora
 
Role of data analytics in educational industry
byRuthVanlalremruati
 
Big data
byAmi Redwan Haq
 

Similar to Churn Predictive Modelling

PDF
Customer churn prediction in banking
byBU - PG Master Computing Conference
 
PPT
How to apply CRM using data mining techniques.
bycustomersforever
 
PDF
Churn analysis
byNaveen Kumar
 
PPTX
Customer_Churn_prediction.pptx
bypatilaniket2418
 
PPTX
Customer_Churn_prediction.pptx
byAniket Patil
 
PDF
A Proposed Churn Prediction Model
byMona Nasr
 
PPTX
Copy of BUAN357 - Final Presentation.pptx
bydhrunalbelani
 
PPTX
Scoring Customer Churn Risk
byIsrael Kloss, MBA
 
PPTX
Scoring Customer Churn Risk
byrisraelkloss
 
PDF
QWE Inc Report_Group 2
byXinyu Liu
 
PPTX
Insurance Churn Prediction Data Analysis Project
byBoston Institute of Analytics
 
PPTX
Fintech is money for paltforms learning baout bank churn
byIBrahimNehad
 
PPTX
Maximizing Retention with Minimal Effort
byvijaykumardevalla199
 
PDF
CHURN ANALYSIS AND PLAN RECOMMENDATION FOR TELECOM OPERATORS
byJournal For Research
 
PPTX
Predictive Analytics And Churn
byMartin Hill-Wilson
 
PDF
Customer Churn Prediction Using Machine Learning Techniques: the case of Lion...
byIIJSRJournal
 
PPTX
PYTHON (IETE).pptxmanjunanr75pythonproject
byManjunathNR1
 
PDF
Customer 360 brochure
byUber Space Solutions
 
PPT
Harris Interactive Src Risk, Churn, Win Back Workshop
byMichael Lowenstein
 
DOCX
ANALYTICS PLAN TO REDUCE CUSTOMER CHURN AT YORE BLENDS Himabin.docx
bydaniahendric
 
Customer churn prediction in banking
byBU - PG Master Computing Conference
 
How to apply CRM using data mining techniques.
bycustomersforever
 
Churn analysis
byNaveen Kumar
 
Customer_Churn_prediction.pptx
bypatilaniket2418
 
Customer_Churn_prediction.pptx
byAniket Patil
 
A Proposed Churn Prediction Model
byMona Nasr
 
Copy of BUAN357 - Final Presentation.pptx
bydhrunalbelani
 
Scoring Customer Churn Risk
byIsrael Kloss, MBA
 
Scoring Customer Churn Risk
byrisraelkloss
 
QWE Inc Report_Group 2
byXinyu Liu
 
Insurance Churn Prediction Data Analysis Project
byBoston Institute of Analytics
 
Fintech is money for paltforms learning baout bank churn
byIBrahimNehad
 
Maximizing Retention with Minimal Effort
byvijaykumardevalla199
 
CHURN ANALYSIS AND PLAN RECOMMENDATION FOR TELECOM OPERATORS
byJournal For Research
 
Predictive Analytics And Churn
byMartin Hill-Wilson
 
Customer Churn Prediction Using Machine Learning Techniques: the case of Lion...
byIIJSRJournal
 
PYTHON (IETE).pptxmanjunanr75pythonproject
byManjunathNR1
 
Customer 360 brochure
byUber Space Solutions
 
Harris Interactive Src Risk, Churn, Win Back Workshop
byMichael Lowenstein
 
ANALYTICS PLAN TO REDUCE CUSTOMER CHURN AT YORE BLENDS Himabin.docx
bydaniahendric
 

Recently uploaded

PDF
Open a Bank of America Featured CD Account Online Today A new of it_.pdf
bymarketing
 
PDF
Trade Facilitation Monitoring in Ukraine № 96
byІнститут економічних досліджень та політичних консультацій
 
DOCX
Best Platforms to Buy Verified Wise Accounts in the US.docx
by#SEO, #usaallhub, #socialmedia, #USAaccounts, #seoservice, #Digitalmarketer
 
DOCX
Case Study: The Industrialized Negative SEO & Extortion Model of TraderKnows
byQingShieldCenter
 
PDF
How To Convert Sidra Tokens To SDA - New Method
byTRADERS / CRYPTO SPACE 💱
 
PDF
Buying Verified Cash App Accounts_ Step-by-Step Guide for 2026 .pdf
by#SEO, #usaallhub, #socialmedia, #USAaccounts, #seoservice, #Digitalmarketer
 
PDF
7 Trading Themes for 2026 - Special report for Deriv traders by veteran trade...
byVince Stanzione
 
DOCX
6 Steps to Buy Verified Stripe Accounts – in 2025.docx
byusatrustacc
 
PDF
How to Buy Verified Binance Account at .A new of it_.pdf
byusatrustacc
 
PDF
Asstkaft_Seeman_Newsletter_December_2025.pdf
byAshis Kumar Dey
 
PDF
Lundin Gold Corporate Presentation December 2025
byAdnet Communications
 
PDF
How to Buy Verified Chime Accounts in 2025.pdf
bymarketing
 
DOCX
Understanding Twitter Accounts_ A Deep Exploration of Digital Voice, Identity...
bymarketing
 
PDF
_How to Buy////// TikTok Seller Account.pdf
byhi1885171
 
PPTX
Income_Tax_Master_Presentation_All_Modules.pptx
byRohitGupta920212
 
PPTX
Reinventing Procurement Through Big Data (1).pptx
byamitshovon2
 
PDF
_how to buy Buy/// Self Bank Account.pdf
bymarketing
 
PPTX
Beacon Kit slides tech framework for world game (s) pptx
bySteven McGee
 
PDF
Buy LinkedIn Accounts_Best Step-by-Step Guide for 2025.pdf
bymarketing
 
DOCX
CNCPW Market Report: Silk Road Bitcoin Transfers and Liquidity Analysis
byCNCPW Sobrenome
 
Open a Bank of America Featured CD Account Online Today A new of it_.pdf
bymarketing
 
Trade Facilitation Monitoring in Ukraine № 96
byІнститут економічних досліджень та політичних консультацій
 
Best Platforms to Buy Verified Wise Accounts in the US.docx
by#SEO, #usaallhub, #socialmedia, #USAaccounts, #seoservice, #Digitalmarketer
 
Case Study: The Industrialized Negative SEO & Extortion Model of TraderKnows
byQingShieldCenter
 
How To Convert Sidra Tokens To SDA - New Method
byTRADERS / CRYPTO SPACE 💱
 
Buying Verified Cash App Accounts_ Step-by-Step Guide for 2026 .pdf
by#SEO, #usaallhub, #socialmedia, #USAaccounts, #seoservice, #Digitalmarketer
 
7 Trading Themes for 2026 - Special report for Deriv traders by veteran trade...
byVince Stanzione
 
6 Steps to Buy Verified Stripe Accounts – in 2025.docx
byusatrustacc
 
How to Buy Verified Binance Account at .A new of it_.pdf
byusatrustacc
 
Asstkaft_Seeman_Newsletter_December_2025.pdf
byAshis Kumar Dey
 
Lundin Gold Corporate Presentation December 2025
byAdnet Communications
 
How to Buy Verified Chime Accounts in 2025.pdf
bymarketing
 
Understanding Twitter Accounts_ A Deep Exploration of Digital Voice, Identity...
bymarketing
 
_How to Buy////// TikTok Seller Account.pdf
byhi1885171
 
Income_Tax_Master_Presentation_All_Modules.pptx
byRohitGupta920212
 
Reinventing Procurement Through Big Data (1).pptx
byamitshovon2
 
_how to buy Buy/// Self Bank Account.pdf
bymarketing
 
Beacon Kit slides tech framework for world game (s) pptx
bySteven McGee
 
Buy LinkedIn Accounts_Best Step-by-Step Guide for 2025.pdf
bymarketing
 
CNCPW Market Report: Silk Road Bitcoin Transfers and Liquidity Analysis
byCNCPW Sobrenome
 
In this document
Powered by AI

Definition of churn and associated risks. Understanding uncertainty versus exposure in churn.

Analyzes risks in service contracts including involuntary and voluntary churn factors.

Methodologies to mitigate churn risk using modeling techniques during various customer phases.

Importance of past behavior in predicting future churn; caution in predictive modeling approaches.

Steps to set up data sets and establish baselines for churn prediction using historical data.

Emphasizes importance of diverse data and pattern analysis in understanding churn behaviors.

Procedural steps in developing a churn model including team formation and data collection.

Quality assurance measures and data transformation processes prior to analyzing churn.

Introduction to logistic regression as a tool in developing churn predictive models.

How to analyze gains charts and calculate probability scores to assess churn risk.

Understanding score distributions and identifying breakpoints for potential churn.

Recap of churn risk and solutions; introduction of Hugo Cisternas's experience and consulting background.

Churn Predictive Modelling

  • 1.
    Churn is aboutdealing with risk The risk of a customer to Churn to another company Hugo Cisternas Director innovandis
  • 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org WHATIS RISK? Usually we have a bad understanding of risk...
  • 4.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Example1 • 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.
    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.
    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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Whatis 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Summary Everyrisk has two components:  Uncertainty  Exposure to this uncertainty
  • 9.
    Risk en thecase of service contracts
  • 10.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Riskin 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Churnmodeling • 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.
  • 13.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Basisof 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.
  • 15.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Whatis 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.
    First law orprediction 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.
  • 18.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Firststep • Setup the data sets: – Get a set of customers with known outcome Left Active Contracts Dataset “active” “churned”
  • 19.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Datasetfor 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Whichdata 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.
    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.
    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.
    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.
    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.
  • 26.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org First:The team • Sales management • Operations management • IT • Legal department • Marketing
  • 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.
    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.
    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.
    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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Sixth:Quality control • Make quality assurance for the data
  • 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Initialenumeration 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Characteristicanalysis • 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Recategorization • 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.
  • 37.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Predictivemodeling P = probability of getting the condition of “churn” P ~~ 0: customer will remain active P ~~1: customer will churn
  • 38.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Gettingthe 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.
  • 40.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Logisticregression • 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.
  • 42.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org 0 10 20 30 40 50 60 70 80 Churnrate (%) 1 2 3 4 5 6 7 8 9 10 Deciles Illustrative Gains Chart
  • 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.
    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.
  • 46.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Distributionof scores • Percentage of active and Churn:0 100 200 300 400 500 600 700 800 900 Churn Active
  • 47.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org 0100 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Breakpoint • 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org Thereal distribution • Because the quantity or churning customers is less than the active customers:
  • 50.
    Churn is aboutdealing with risk The risk of a customer to Churn to another company Hugo Cisternas Director innovandis
  • 51.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org HugoCisternas 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org HUGOCISTERNAS 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org HugoCisternas 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.
    Hugo Cisternas Director innovandis hcisternas@innovandis.org HUGOCISTERNAS 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

Editor's Notes

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