analytical factory in a crm context - sas institute · 2019. 6. 7. · analytical factory in a crm...
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![Page 1: Analytical factory in a CRM context - Sas Institute · 2019. 6. 7. · Analytical factory in a CRM context. Hans de Wit, Senior Data Scientist, Telenor Norway](https://reader033.vdocuments.mx/reader033/viewer/2022051607/602c9b3a5ee9c16a431848da/html5/thumbnails/1.jpg)
Analytical factory in a CRM contextHans de Wit, Senior Data Scientist, Telenor Norway
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My passion: Making the unreal happen
My key goal:
Hans de Wit
• Telenor Mobile Norway (since 2013)• Advanced Analytics & Data Science
Manager• ING Bank, The Netherlands
• Senior member 'model‘/Innovation-team ING Retail Customer Intelligence
• Member analytical campaign management ING Bank Customer Intelligence department, 1997-2005
• ING Card, 2005-2008• Direct Marketing, Credit Risk, Fraud
• Master of Marketing (SRM) and bachelor of Commercial economics and Direct Marketing.
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Sample path to traditionalcampaigns wih typically averageresponse rates
From one-offs to real-time: What makes the difference?
Dep
th o
fMar
ketin
gIn
sigh
t
One_off(Manual
Degree of Marketing Automation
Repeatable(Manual)
Scheduled(Automated)
Event-based(automated)
Real time(automated))
Ad HocLists
Profiling &Segmentation
PreditiveModelling
Detect Changes in Behavioral pattern
ContactOptimalization
Niche AutomatedCustomer Relevancy
First generation Spam
Sample path to optimizedrelevancy and timeliness Sample path to failed marketing3
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An overview of what is “under the hood”
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Life Cycle of a Model = Model FactoryIdentity
business problem
Data preparation
Data exploration
Transform & select
Analyticalmodeling
Validatemodels
Deploymodels
Evaluate/monitor results
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Evolution of Data Mining Processes
Old Data Mining Process• run a simple process
• One person responsible for all.
• build more sophisticated and powerful models.
New Data Mining Process = Model Factory• speed up the computation speed
• and administer the entire process
6 6
Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Reduce time-to-market
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Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
![Page 8: Analytical factory in a CRM context - Sas Institute · 2019. 6. 7. · Analytical factory in a CRM context. Hans de Wit, Senior Data Scientist, Telenor Norway](https://reader033.vdocuments.mx/reader033/viewer/2022051607/602c9b3a5ee9c16a431848da/html5/thumbnails/8.jpg)
Identify business & problem
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• New campaign or New product.• I have problems that need solving…
• I don’t know which are my good customers!• Many of my customers are leaving!• I don’t know what I can say to them to avoid it!
• Business• Inbound = AST Controller• Outbound = Campaign manager or direct to marketing manager.
Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Often 80% of time spent is on data preparation. In the new process it is reduced to 5%!
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Useful Notions
• ADM=Analytical Data Mart• ABT=Analytical Base table• Input variable=variables, which explain the
target.• Sandbox= experimental input variables• Target variable=if a customer buy specific
product in a timeperiod• Metadata driven (macro)= add a new product
is just filling a excelsheet.
Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
Defining Rules
• Identify the target.• Identify Target group
• Nse=New sale existing customers• Nsp=New sale prospect• Nss=New sale suspect• Uds=Up/down sale• Ups=Up sale
• Upsale one step up.• Extra filters, 18 years and older, etc.
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Mbb_<…>
Mpr_<…>
Mpp_<…>
CuCu_<…>
ABT
Fix_<…>
Dsl_<…>
Cu_<...> Cu – level for NSP, NSE, NSS Models
Mbb_<…> Cu_<...>
Targ
TargMBB
MPP
MPR
DSL
FIX
Mpp_<…> Cu_<...>Targ
Mpr_<…> Cu_<...>Targ
Dsl_<…> Cu_<...>Targ
Fix_<…> Cu_<...>Targ
Abt_Master_Cu
Abt_Master_Mbb
Abt_Master_Mpp
Abt_Master_Mpr
Abt_Master_Dsl
Abt_Master_Fix
Cu_<…>Sandbox Cu_<...>
ADM
Sub level – for UDS, UPS• The target variables
of potential modelsare calculated everymonth(abtmaster.sas).
• To select the right abtfor a specific model is easy (abtmodelling.sas).
• Last month• All months• Selection of a
month
Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Data Exploration in SAS Visual Analytics to get a first feeling
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Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Transform & Select the Right Input Variables withMaximum Predictive Power• Numeric encoding for high-cardinality nominal variables such as zip code.• Normalizing, binning, log transformation for interval variables.• Transformations based on missingness patterns.• Dimension reduction techniques such as autoencoders, principal component analysis (PCA), t-Distributed
Stochastic Neighbor Embedding (t-SNE), and singular value decomposition (SVD).
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Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Analytical Modeling
• Many different algoritms in Sas Enterprise Miner available• Decision tree• Regression• Neural Network• Gradien Boosting• Random Forest
• Model comparison node for comparing whichmodel is the best.
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Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Validate models
• Is the initial model better than the champion model (old model)• Validation and approval of the champion model
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Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Deploy (scoring) a model is easy!
• Models are available for many Sas application• Sas CI Studio• Sas Enterprise Guide• Sas DI studio• Sas Model manager• Sas RTDM• Sas Esp (A-store)
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Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
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Easy to monitor the model, so we can react fast.
Monitoring• Variable distribution• Lift• Gini (ROC)• Kolmogorov-Smirnov (KS)
Threshold• AUC decay• Lift decay
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Results/decisions• Recalibrate a model• Retire a model (new)
Identity business problem
Data preparati
on
Data explorati
on
Transform &
selectAnalytic
almodelin
g
Validatemodels
Deploymodels
Evaluate/monitor results
![Page 17: Analytical factory in a CRM context - Sas Institute · 2019. 6. 7. · Analytical factory in a CRM context. Hans de Wit, Senior Data Scientist, Telenor Norway](https://reader033.vdocuments.mx/reader033/viewer/2022051607/602c9b3a5ee9c16a431848da/html5/thumbnails/17.jpg)
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Next challenge #2: Fully AI enabled customer journeyoptimization
Telenor Research:
Developing deepreinforcement modelto optimize customerjourney, based on all the interactions of the
customer.
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Thank youHans de Wit, Telenor Mobile Norway, +47 48 29 1399