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27.08.2009,[object Object],CRISP-DM,[object Object]
27.08.2009,[object Object],Agenda,[object Object],Business Understanding,[object Object],1.1 		Business Objectives,[object Object],1.2 		Assess the Situation,[object Object],Data Understanding,[object Object],Data Preparation,[object Object],3.1 Filters,[object Object],3.2 Population,[object Object],3.3 Flow,[object Object],Modeling,[object Object],Evaluation,[object Object],Deployment,[object Object],v,[object Object]
27.08.2009,[object Object],1.1 Business Understanding –       Business Objectives,[object Object],	Yıldız portföyde yer alan müşterilerden terketme ve uyarıya geçme eğiliminde olan müşterilerin önceden tahmin edilerek müşterinin kalmasını sağlamak amacıyla aksiyon alınması,[object Object],xxxx ile yapılan görüşme,[object Object]
27.08.2009,[object Object],1.1 Business Understanding – Business Objectives,[object Object],İş Hedefi,[object Object],Yıldız Müşterileri Tutundurma,[object Object],“Bireysel Müşteri” ye dönüşen veya “Uyarı” statüsüne geçen Yıldız müşteri sayısını azaltmak,[object Object],Model Hedefi,[object Object],Mevcut datayı kullanarak “Açık” statüsünden “Uyarı” statüsüne ve “Uyarı” statüsünden “Bireysele” dönüşen müşterileri kullanarak, Yıldız müşteriler arasından gitmeye eğilimli müşterileri yüksek güven düzeyinde tahmin etmek amacıyla Retention Modeli geliştirmek,[object Object]
27.08.2009,[object Object],1.2 Business Understanding – Mevcut Durum Değerlendirmesi,[object Object],YILDIZ Müşteri Statüleri:,[object Object],Açık = Çalışma Büyüklüğü Ortalaması>100,000,[object Object],Uyarı = Çalışma Büyüklüğü Ortalaması 3 ay <100,000,[object Object],Bireysel = Uyarı ve Çalışma Büyüklüğü Ortalaması 3 ay <100,000,[object Object],Açık Yıldız,[object Object],Uyarı Yıldız,[object Object],Bireysel,[object Object],[object Object],[object Object]
27.08.2009,[object Object],3. Veri Hazırlama,[object Object],Popülasyon,[object Object],Filtreler,[object Object],Veri Seti ,[object Object],Datamart,[object Object],Değişkenler,[object Object],Exclude target dependent variables,[object Object], Değişken Seçimi,[object Object],Hipotez testleri (ANOVA),[object Object],Korelasyon ( yüksek olanlar çıkarılacak),[object Object],Multiplot, İstatistikler,[object Object],Ay1, Çeyrek1,[object Object],Karar Ağacı (Largest) (EM),[object Object],Değişken Seçimi (EM),[object Object]
27.08.2009,[object Object],3.1 Data Hazırlama - Filtreler,[object Object]
27.08.2009,[object Object],3.2 Data Preparation - Population,[object Object],2008 Q3,[object Object],2008 Q4,[object Object],Model Veri Seti Periyodu,[object Object],Hedef Belirleme Periyodu,[object Object],Q3’de Otomatik Ödemeye sahip olmayan Q4’de Otomatik Ödemeye sahip müşteriler hedef:1 olarak tanımlanmıştır,[object Object],(Kasım’da sahip olmayan Aralık’da sahip olanlar ile daha küçük bir hedef listesi olşuyor 2794  kişi),[object Object],Model kitlesinin (860,634  müşteri);,[object Object],% 99.12 ’si (853,036 müşteri) hedef:0 	,[object Object],% 0.88 ’i (7,598  müşteri)hedef:1,[object Object],Oversampling?,[object Object]
27.08.2009,[object Object],3.3 Data Preparation - Flow,[object Object],HEDEF SETI,[object Object],MODEL_DATA_SET,[object Object],VERI SETI,[object Object],Haciz Kaydı Yok,[object Object],Takip Kaydı Yok,[object Object],Yaşayan Müşteri,[object Object],KK statüsü “K”, “I” olmayan,[object Object],filtreleri uygulanıyor ,[object Object],Dönemi Verileri,[object Object],Türetilen değişkenler,[object Object]
27.08.2009,[object Object],4. Modeling,[object Object],Modelleme Tekniğinin Seçilmesi,[object Object],Lojistik Regresyon,[object Object],Karar Ağacı,[object Object],Generate Test Design ,[object Object],Train, Validation, Test Sets,[object Object],Use 80%, 10%,10 % distribution,[object Object],Build Model,[object Object],SAS EM,[object Object],Assess Model,[object Object]
27.08.2009,[object Object],5. Evaluation,[object Object],Compare Models,[object Object],Choose at least two model,[object Object],Prepare Analysis based on models,[object Object],Extract Rules, Variables,[object Object],Summarize Model Performance,[object Object],Define Cutoffs,[object Object],Evaluate whether model achieves business objectives,[object Object],Apply Model Score to available data( according to your target deifnition),[object Object],Select Model,[object Object]
27.08.2009,[object Object],6. Deployment,[object Object],Score Customers (with model filters),[object Object],Integration with Oracle, BO,[object Object]
27.08.2009,[object Object],Monitoring,[object Object],When to renew,[object Object]
27.08.2009,[object Object],APPENDIX,[object Object]
27.08.2009,[object Object],Zaman Planı ?,[object Object]
27.08.2009,[object Object],bacs Direct Debit Case,[object Object]
27.08.2009,[object Object],Resource: bacsR:ireysel pazarlamaustomer InsightropensityTOMATIK ODEMEesourcesustomer profiles.htm,[object Object],Different customers have different reasons to buy in to Direct Debit. And they fall into four clear groups: preferers, selectives, reluctants and will nots/cannots. ,[object Object],The first three groups are well worth targeting. Using the right motivational messages can change their mindset and behaviour, and they can be converted to Direct Debit. For example, preferrers are defined as likely to be aged between 25 and 44, ABC1C2s with younger children or older ones who have left home. For them, convenience is the best feature of Direct Debit, so that’s the best message to use to convince them to sign up to Direct Debit. Simple!,[object Object],As the name suggests, will nots are ardent cash and cheque payers and will not convert. Cannots do not have appropriate bank accounts, so your marketing materials will be wasted on them,[object Object]
27.08.2009,[object Object],Resources-  Customer profile - Preferers R:ireysel pazarlamaustomer InsightropensityTOMATIK ODEMEesourcesreferer.htm,[object Object],Definition : Choose to pay the majority of their regular commitments by Direct Debit. ,[object Object],Generalised portrait : Equal male/female split , Aged 25-44 , ABC1C2s ,The better off the more likely they are to be preferers than people on lower incomes ,Tend to be home owners with a mortgage, followed closely by those owning a home outright and ones being bought/part rented ,Less likely to live in the South East with a relatively even split over other UK regions ,More likely to be in full time employment than self employed or retired.,[object Object],Assumptions :Actively preferring to pay by this method and generally opt to do so if it is offered as an option. ,[object Object],Reasons for preferring Direct Debit:Find it a convenient, quick and hassle free form of payment ,Have the time to be very well organised financially. ,[object Object],Payment attitudes :Prefer regular, convenient methods for paying bills.,[object Object]
27.08.2009,[object Object],Resources-  Customer profile - Selectives R:ireysel pazarlamaustomer InsightropensityTOMATIK ODEMEesourceselectives.htm,[object Object],Definition :Pay some of their regular commitments by Direct Debit, but are selective which ones. ,[object Object],Generalised portrait :Equal male/female split ,Tend to be slightly older, 45+ with highest proportion being 65+ , They are more likely to be ABC1s who have older children that no longer live at home , A slightly higher percentage come from lower income households this could be due to the high proportion of 65+ people who may be retired,Living in privately rented properties or homes they own outright, Higher proportion live in East Anglia, London, South East and Wales than other UK regions, They are less likely to be students with a relatively equally split across other employment status classifications.,[object Object],Assumptions :Their decision is usually influenced by their level of trust in the organisation collecting the payment or where there is no option, all payments have to be made by Direct Debit.,[object Object],Reasons for being selective: Concerns about the safety aspect of automated payments, Don’t trust some organisations to administer Direct Debit correctly , Have time to manage financial matters and may be stuck in their ways in terms of how they make their payments. For example, they like paying bills in full where possible ,Fear losing control of their finances when too many bills are paid by Direct Debit.,[object Object],Payment attitudes :Like to pay some regular, necessary payments by Direct Debit but have concerns over security and safety of automated payments. So they like to remain in control and will opt to pay by other methods such as cash or cheque.,[object Object]
27.08.2009,[object Object],Resources- Customer profiles – ReluctantsR:ireysel pazarlamaustomer InsightropensityTOMATIK ODEMEesourceseluctants.htm,[object Object],Definition: Will only use Direct Debit if there is no other option, reticent to use an automated payment for financial commitments.,[object Object],Generalised portrait: Equal male/female split, Predominately falling into two age brackets 16-24 and 55-64 , With a high proportion being lower social grades (D and E) , From lower income households , More likely to live in Eastern areas of the UK, from the North East down to the South East , Mainly living in being bought /part rented houses ,Students are likely to be reluctants with house sharing and low income levels most likely influencing their current reluctance to Direct Debit.,[object Object],Assumptions; General lack of education and understanding about automated payments is the key issue. Loss of control is their main fear. Irregular and limited income means this audience may believe Direct Debit is not for them. Direct Debit can appeal if it seen to enhance control of finances not threaten this and show that they are in control of the situation – not the bank or the biller.,[object Object],Reasons for reluctance: The fear of losing control of their bank account/balance , Concerns about banks and organisations collecting Direct Debit payments making mistakes , Assumption that companies can dip into their account and take money whenever they want , Don’t trust themselves to save enough money or have the required funds when the Direct Debit is collected ,Concerns over bank charges, which could cause havoc with their budgeting, if they miss a Direct Debit payment.,[object Object],Payment attitudes: They feel the ‘pay as you go’ approach suits their needs and behaviour better than Direct Debit. They tend to opt for payment cards, cash and cheque, preferring to pay for bills over the counter so they know they have been paid.,[object Object]
27.08.2009,[object Object],Models developed by bacs,[object Object],The first set of models predicts an individual’s propensity to pay a particular bill type by Direct Debit.,[object Object],Separate models have been created for all major bill types including Council Tax, Utility, Credit Card and TV Licensing bills.,[object Object],The second set calculates an individual’s reasons for using Direct Debit and their main drivers, for example,[object Object],saving time, helping them manage their finances more effectively, or capitalising on financial discounts.,[object Object],Our Data mining goal : is to develop one model (regardless  of ,[object Object],bill type),[object Object]
27.08.2009,[object Object],CRISP - DM,[object Object]
27.08.2009,[object Object]

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