Analytical Techniques created for the offline world can they yield benefits online?

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1 Analytical Techniques created fr the ffline wrld can they yield benefits nline? Dr. Barry Leventhal BarryAnalytics Limited Transfrming Data

2 Abut BarryAnalytics Advanced Analytics Cnsultancy funded in 2009 Specialises in: Targeting and Segmentatin analysis Develping analytics plans and radmaps Training and mentring marketing analysts Mre than 20 years experience in Marketing Analytics See barryanalytics.cm fr inf + dwnlads Transfrming Data

3 Agenda Intrductin t data mining and analytical mdelling Predictive and descriptive analytical mdels Predictive case study targeting bank custmers Predictive case study - lifetimes f mbile custmers Descriptive case study financial segmentatin Further pints t cnsider Data integratin Imprtance f a team apprach Mdel deplyment Cntact ptimisatin Pssible applicatins t nline marketing Transfrming Data

4 What is Data Mining? A prcess f discvering and interpreting patterns in data t slve business prblems Data Pattern Infrmatin Actin Cnverts Data int Infrmatin Transfrming Data

5 And What s Analytical Mdelling? Discvering meaningful relatinships in data, in rder t increase knwledge + understanding Leverages relatinships between many variables t predict r describe patterns Outcme is a set f rules which assign each individual t a predictive r descriptive segment Querying data t btain answers t questins OLAP and ther frms f reprting Ad hc business reprts - althugh these tls can all help t increase ur understanding and supprt Analytical Mdelling Transfrming Data

6 Discvery and Deplyment f Data Mining Analyse Analyze data Build mdel Analytic Analyst mdeler Mdel Discvered Pattern Apply mdel t data Act n predicted utcme Business users Discvery: Finding the the relatinships hidden patterns that cnvert that transfrm data int data infrmatin int infrmatin Deplyment: Applying discvered mdel fr a useful purpse - e.g. predictin Transfrming Data

7 The Data Mining Prcess (CRISP) Data Crss Industry Standard Prcess fr Data Mining Reference: Step-by-step data mining guide CRISP-DM 1.0 Transfrming Data

8 Agenda Intrductin t data mining and analytical mdelling Predictive and descriptive analytical mdels Predictive case study targeting bank custmers Predictive case study - lifetimes f mbile custmers Descriptive case study financial segmentatin Further pints t cnsider Data integratin Imprtance f a team apprach Mdel deplyment Cntact ptimisatin Pssible applicatins t nline marketing Transfrming Data

9 Tw main types f Analytical Mdel Type 1: Mdels driven by a Target Variable e.g. Which custmers t crss sell? - Implies building a Predictive Mdel - Directed Data Mining Techniques Type 2: Mdels with n Target Variable e.g. What are ur mst imprtant custmer segments? - Implies a Descriptive Mdel - Undirected Data Mining Techniques Transfrming Data

10 The rle f a predictive mdel KEY = respnders = nn-respnders CUSTOMERS SCORING FORMULA Open/Clsed prducts Length f relatinship Transactinal behaviur etc. Predictive mdels cntain many attributes and s utperfrm single variable mdels HIGH SCORES LOW SCORES DISCRIMINATION PERFORMANCE Transfrming Data

11 Case Study Example - Targeting Bank Custmers Objectives: T help a financial institutin t leverage its new data warehuse fr custmer management Analysis: Set f 6 prduct prpensity mdels were develped cvering the cre financial prducts Fr tw prducts, alternative mdelling techniques were cmpared: Decisin Tree Lgistic Regressin Neural Netwrk Actin/Return: Mdels were used t generate prspects fr direct marketing and help develp a cntact planning strategy Transfrming Data

12 Results f Alternative Mdelling Techniques Cmparing Lgistic Regressin, Neural Netwrks and CHAID fr Hme Lans 220 Cumulative index Neural Netwrk CHAID Lgistic Regressin % f ppulatin Sample base : Develpment Sample Predictive pwer f data is ften mre imprtant than chice f technique Transfrming Data

13 Targeting custmers made a difference Mdels were part f the cmpany s initiative t leverage the Data Warehuse fr effective custmer management Enabling imprved custmer cntact planning The mdels were used fr custmer selectins, 5 ut f 6 wrked well and were still being emplyed sme 3 years later Transfrming Data

14 Case Study Example Predicting Lifetimes f Mbile Phne Custmers Objectives T increase life f a mbile phne custmer by intrducing analytical apprach t managing their lifecycle Analysis Mdels fr predicting custmer lifetime were develped fr pre-pay and pst-pay mbile custmers Actin/Return The mdels were validated by tracking subsequent churn Ptential imprvement in pst-pay mdel was demnstrated, using variables built frm mst granular data The mdels were refined and implemented by the client Transfrming Data

15 The mdels strngly discriminated between churners and nn-churners predicted future survival prbabilities Pre-pay Pst-pay Prb. Survival > 'n' mnths Mnths Active Nn-churner Survival Prb TEST Nn-churner Survival Prb HOLDOUT Nn-Churners - Dev Nn-Churners - Val Churners - Dev Churners - Val Churner Survival Prb TEST Churner Survival Prb HOLDOUT Develpment and Validatin results were almst identical fr each mdel Transfrming Data

16 Alternative Pst-pay Mdels were built, based n variables calculated frm calling recrds Mdel 0 - Existing mdel standard mnthly variables Mdel 1 - uses best variables frm existing mdel and mnthly variables built frm detailed data Mdel 2 - uses best variables frm existing mdel and weekly variables built frm detailed data Mdel 3 - uses best variables frm existing mdel and daily variables built frm detailed data Churn Index* Cmparisn f Mdel Gains Survival Prbability Decile (1 = lw, 10 = high) Mdel 0 -Existing Mdel Mdel 1 - Mnthly Data Mdel 2 - Weekly Data Mdel 3 - Daily Data Mdel 4 - Best Inputs Mdel 4 - uses best inputs frm all mdels Mdels based n detailed data were up t twice as pwerful Transfrming Data

17 There are many ways t segment... Yur segmentatin will wrk best n the criteria used t build it - S, select apprpriate criteria fr yur intended applicatin Custmer Lifestage RFM Custmer Behaviur Custmer Mbility Custmer Ptential Custmer Demgraphics Segmentatin Strategy Custmer Attitudes Ge- Demgraphics Custmer Lyalty Custmer Prfitability Market-wide Ptential Transfrming Data

18 Case Study Example Cnsumer Segmentatin fr Financial Services Objective T segment financial services custmers n their ptential Analysis A market-wide segmentatin was develped using financial research survey data The segments were verlaid nt varius custmer databases And nt the UK Electral Rll Actin/Return Segments were tested and taken up by a number f UK cmpanies banks, building scieties, insurance Transfrming Data

19 The Segmentatin was called FRuitS! Each f the 8 segments was named after an apprpriate fruit The first 4 segments The segments were defined by cnsumer s lifestage, affluence and prduct hldings Transfrming Data

20 The last 4 segments The segments were identifiable and culd be verlaid nt custmers, prspects & market research data Transfrming Data

21 Example 1: An insurance cmpany used FRuitS t target investment prducts Index f Invested Per Marketing Spend Incme PEP Grwth PEP Area type Prpensity mdel Pears Plums Market-wide segments identified insurance custmers interested in investment prducts Transfrming Data

22 Example 2: A retailer used FRuitS t identify prfitable stre card custmers Incidence f Prfitable Custmers by FRuitS Index Plums Pears Cherries Apples Dates Oranges Grapes Lemns Segment The segments were designed t discriminate n financial behaviur Transfrming Data

23 Agenda Intrductin t data mining and analytical mdelling Predictive and descriptive analytical mdels Predictive case study targeting bank custmers Predictive case study - lifetimes f mbile custmers Descriptive case study financial segmentatin Further pints t cnsider Data integratin Imprtance f a team apprach Mdel deplyment Cntact ptimisatin Pssible applicatins t nline marketing Transfrming Data

24 The Imprtance f Data Integratin The business value f yur data increases thrugh integrating and leveraging cmplementary surces New insights may be created by integrating, fr example... Custmer Transactins Demgraphics & Lifestyles Integratin Market Research Online Behaviur There are many applicatins f data integratin... Predictive mdels t target behaviurs identified by research, e.g. churn by reasn Deplying cmmn custmer segmentatin nline and ffline Tracking acrss channels, e.g. fllw-up abandned baskets Transfrming Data

25 Data Integratin can be a challenge due t differing data structures Offline data Online data Name & address Ckie & sessin Husehld/persn Transactin behaviur Segmentatins e.g. RFM Persn/ckie/sessin Brwsing + transactins Segmentatins e.g. RFM Can cmpare RFM segments between ffline and nline channels Transfrming Data

26 A Tlkit f Data Mining Techniques Traditinal Statistics Crrelatin Analysis Regressin Mdels Principal Cmpnents/Factr Analysis Cluster Analysis Machine Learning Rule Inductin Neural Netwrks Genetic Algrithms Cllabrative Filtering Transfrming Data

27 Driving Business Value frm yur Data Many peple ver-stress imprtance f analytical tls and under-estimate the Business Analysis prcess Best apprach is cnsultative and business-fcused Business Knwledge Business Users Prduct Managers Campaign Team IS Department Data Mining Analysts/Statisticians Technical Knwledge Data Warehuse Data mining Sftware Resurces Transfrming Data

28 Hw are Analytical Mdels built and deplyed? Mdels must be trained befre they are deplyed Training Histrical Data + Knwn Outcmes Mdel Deplying Predictins Recent Data + Mdel Training is ne-ff until mdel requires rebuild Deplyment takes place repeatedly Transfrming Data

29 Typical Mdel Deplyment Architecture Train mdel Deply mdel Data Warehuse Data Mdel Mdel Apply predictins Results Data Predictins Disadvantages: Very time cnsuming n large databases Prduces multiple cpies f same data Cmplex prcess t manage Transfrming Data

30 Imprved Mdel Deplyment Architecture Data Warehuse SQL Cde Apply Data Advantages: Mdel Reduced data migratin saves time and strage Scres can be cntinuusly updated Wrks with large vlumes f data and many mdels Transfrming Data

31 Mdel Deplyment Best Practice If data vlumes are vast, r yu have large number f mdels t be deplyed, cnsider an architecture that brings the algrithm t the data, i.e. scres the mdel in-database Use predictive mdel mark-up language - PMML fr cmmunicating mdels between mdel training and scring systems Or use a data mining tl that utputs scring algrithm as SQL cde fr running in-database Ensure that mdel scring des nt require manual cding time cnsuming and errr prne Transfrming Data

32 Cntact Optimisatin - maximising returns ver time Custmers Time The bjective is t ptimise each individual custmer's value delivered thrugh time by managing the mix f cmmunicatins/ffers Transfrming Data

33 Cntact Optimisatin selects best actin fr each individual Custmer Prpensity scres fr all available actins are calculated fr each custmer Derive expected return frm each actin, select actin with highest ROI Cntact Plan fr One Custmer ROI Actin with highest ROI Sub-ptimal actin may be selected by prduct led apprach + P1 P2 P4 P5 P8 - P3 P6 P7 Cntact Optimisatin Challenge: Optimisatin is subject t multiple cnstraints and business rules And must deliver ptimal cntact plan ver time Transfrming Data

34 Agenda Intrductin t data mining and analytical mdelling Predictive and descriptive analytical mdels Predictive case study targeting bank custmers Predictive case study - lifetimes f mbile custmers Descriptive case study financial segmentatin Further pints t cnsider Data integratin Imprtance f a team apprach Mdel deplyment Cntact ptimisatin Pssible applicatins t nline marketing Transfrming Data

35 Sme pssible applicatins t nline marketing Predictive Mdels Predictive mdels may be applied fr: Deciding hw much t invest in recruiting a custmer, accrding t their predicted lifetime value Targeting campaigns r cntent Selecting which prducts and ffers website shuld display fr each custmer Targeting custmers in rder t save abandned baskets Targeting custmers fr ffline fllw-up Identifying custmers at risk f churn Predicting time duratin till custmer revisits yur website Transfrming Data

36 Sme pssible applicatins t nline marketing Descriptive Mdels Descriptive mdels may be applied fr: Custmer segmentatin fllwing a cmmn segmentatin strategy acrss all channels Identifying shpping missin purpse f each shpping visit Custmising lk and feel f s and website pages t match usage & attitudes f each segment Targeting prduct ffers accrding t segment purchase prfile Understanding purchase affinities between prducts Making crss-sell ffers t annymus site visitrs Transfrming Data

37 Cnclusins Business requirements cme first thse shuld drive yur analytics prcess, data selectin and chice f methds Integrating data surces such as ffline & nline behaviur creates new insights and imprves targeting Emply apprpriate analytical techniques hwever, gd predictive data is ften mre imprtant than cmplex algrithm Design yur analytics prcess t enable seamless mdel discvery and deplyment Analytical mdelling invlves advanced statistics get help frm a trained statistician r data mining analyst! Transfrming Data

38 Thank yu! Barry Leventhal +44 (0) Transfrming Data

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