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------=_NextPart_01C4C34D.8879FA90 Content-Location: file:///C:/2669C634/MSRTalkOctober2004_files/pres.xml Content-Transfer-Encoding: quoted-printable Content-Type: text/xml; charset="utf-8" ------=_NextPart_01C4C34D.8879FA90 Content-Location: file:///C:/2669C634/MSRTalkOctober2004_files/slide0001.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="windows-1252" Data Mining for Business Strategy
1
Using Data Mining= to Empower Business Strategy and
The Nonnegative Boltzmann Machine
Oliver B. Downs, Chief Scientist
Analytical Insights, Inc.
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BAA7 ------=_NextPart_01C4C34D.8879FA90 Content-Location: file:///C:/2669C634/MSRTalkOctober2004_files/slide0005.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="windows-1252" Data Mining for Business Strategy
1
Overview=
•= What I’m not going to talk about
–= Quantum Tunneling Optimization <= /div>
•= I spoke at MSR on this work on 09/04/02
•= Since then:
–= It works (small factoring and TSPs), but with wors= t-case exponential complexity
–<= /span>I have proven that there is no equivalent to this algorithm, using classical equations
•= I’m happy to give the details during interviews= 3;
•= What I am<= /i> going to talk about
–= Data Mining for Business Strategy
•= MSN IA Customer Behavior
•= Viewing the data the ‘right way’:  The Standardized Lifecycle =
•= Predicting Customer Behavior
•= Using Predictive Models to develop business strategy
•= Is Measuring Customer Satisfaction Useful?
–= (Some v= ery simple work with very valuable insight)
–= 3;
–= Machine Learning – the Nonnegative Boltzmann Machine
•
•
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1
How this relates to Systems Management
•= Building a Self-managing, Self-healing, Distributed System requires that you can: = ;
–Identify in advance patterns of registry changes, and system behavior= that are likely to lead to a crash
•Need to structure and view accumulating data in a way which reveals t= he underlying common patterns in failing systems from= a multiplicity of indivi= dual system configurations
•Identify commonality with known working and failing configurations ‘P= eer Pressure’
•Events are causal, but not time-dependent – this suggests looking for= the ‘crash lifecycle’ in a trace analysis
•Data Mining
–Determine which specific system changes and events are most likely at= fault, and repair them recoverably
•Determining what is most likely at fault lends itself directly to Bayesian probabilistic analysis
•Generalizing from limited, specific examples to identify all problems= of the same class is ab= out learning a generative model for problems
•Machine Learning
•
‘recov= erable’ – a transactional procedure
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1
Overview=
•What I’m not going to talk about =
–Quantum Tunneling Optimization
•I have already spoken at MSR on this work= 3;
•Since then:
–It works (small factoring and TSPs), but with worst-case exponential complexity
–I have proven that there is no equivalent to this algorithm, using classical equations
•I’m happy to give the details during interviews
•= What I am<= /i> going to talk about
–= Data Mining for Business Strategy
•= MSN IA Customer Behavior
•= Viewing the data the ‘right way’:  The Standardized Lifecycle =
•= Predictive Modeling with SQL Server 2005
•= Using Predictive Models to develop business strategy
•= Is Measuring Customer Satisfaction Useful?
•= 3;
–Machine Learning – the Nonnegative Boltzmann Machine
•= 3;
•
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1
Data Mi= ning for Business Strategy
–Understanding drivers of Dial-Up Churn at MSN, through exploratory analysis of Customer Support, Usage, and Subscript= ion databases
– (Several Mil= lions of Customers)
•Pain Points – eg. dialup connection problems, software installation&#= 13;
•Lack of engagement with ISP’s client software
•Lack of usage
–Identify Customer behaviors associated with different phases of their= subscription lifecycle
•Customer behavior in non-term-commit subscription plans can be viewed= via the Standardized Lifecycle= 3;
•Mapping behavioral triggers to points on the Standardized Lifecycle allows identification of incidents preceding churn/defection, or cross-sell/up-sell <= span style=3D'position:absolute;top:62.16%;left:2.9%;width:87.13%;height:37.83%= '>opportunities
–Enable improvements in ROI by better targeting customer retention investment
•Provide customer-level, look-ahead predictions of <= /div>
–Churn Propensity
–Broadband Upgrade Propensity
–Expected Tenure/Long-Term Value after being ‘Saved’ when attempting cancel
•Empower the development of business strategy based on these models= 3;
–Combinations of Churn and Broadband Upgrade Propensity determine marketing communications to customer
–Expected Tenure/LTV allows call centers to decide who to attempt to retain and who to al= low to cancel
Joint work wi= th:
Jeff Snedden, Angus Cunningham, Nitin Gautam, Paul Smolikov & <= span style=3D'font-size:89%'>Lori Stock; MSN Data Sciences & MSN Acquisi= tion & Retention
Jim M= eyer, Sarah Welch & Phil Auerbach; Novantas LLC
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1
Key Ins= ight – Standardized Lifecycle
=
<= span style=3D'font-size:63%'>•Viewing Customer behavior relative <= span style=3D'position:absolute;top:33.96%;left:6.64%;width:91.36%;height:33.96= %'>to signup date gives a commingled view of the data
Data View:  CS & TS interactions from date of = signup
•Behavior relative to cancel date suggests predictors of churn •Yet provides little insight for life-long customer engagement
<= b>Data View:  CS & TS interactions by number of months before cancellation of subscription
New us= ers, users soon to attrite and users in the middle of their lifecycle are all commin= gled in this view of the data
As exp= ected, cancel calls increase as a proportion of total calls, the closer to the cancel d= ate
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file:///C:/2669C634/MSRTalkOctober2004_files/slide0010_image006.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhKwAlAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAIAAgAm ACEAgAAAAAAAAAJnBIKmy+2OYnq0mqmk3Qv3yG0eo4VUYIEmhKbj+rLtms2uDeNnLPKNSvOxaBdd BWgS/pTDJHOZM7qC0mP18SRdm7koEXn7Zg9b7HisdRKLZXJbxkEvy3KtsD4/ruGQvXn0FiURKJZQ AAA7 ------=_NextPart_01C4C34D.8879FA90 Content-Location: file:///C:/2669C634/MSRTalkOctober2004_files/slide0011.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="windows-1252" Data Mining for Business Strategy
1
•End-to-end view of common lifecycle for customers = with varying tenure lengths •Map all tenures onto a standardized lifecycle thro= ugh time-warping
0
1
04/2000
09/2000
11/1999
11/2003
User B:  “Olly”
Key Ins= ight – Standardized Lifecycle
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User A:  “Jeff”
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Strategy
1
CS/TS Interactions viewed with Standardized Lifecycle
•When a User Contacts CS or TS their relationship is essentially over!
Initial burst of installation related contacts = = at the beginning of the relationship
75% of incidents occur during = the last 10% of the users life<= /span>
Connection calls as a % of total calls climbs dramatically at= the end of the relationship; representing 48% of all contacts in the last period
Initia= l burst of installation related contacts at the beginning of the relationship=
Connec= tion calls as a % of total calls climbs dramatically at the end of the relationship; representing 48% of all contacts in the last period
75% of= incidents occur during the last 10% of the users life
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1
Key Insights - Customer Behavior can be accurately predicted
•For MSN, using S= QL Server Yukon, we developed and deployed decision tree predictive algorithms for:
•
•
•
•=
•=
•=
•
•
•
<= span style=3D'font-size:56%'>•Analysis of the variables driving these models has yielded insight into the = root causes of Churn, Broadband Upgrade and Tenure= after ‘Save’
<= span style=3D'font-size:56%'>•The outputs of t= hese models can be combined to assemble distinct groups or segments of customers
<= span style=3D'font-size:56%'>•The distinct gro= ups are treated with different retention, conversion and marketing strategies, empowered by profiling the g= roups on all available custo= mer data
•= Tenure to date
•= Tenure after acquisition incentive
•= Number CS/TS Incidents
•= Number of Connectivity Issues
•= Dialup accelerator installed/used
•= Software version
•= Unresolved CS Incidents
•= Credit Card Expiration
•= Lack of engagement with service
•= Up-to-date software version
•= CS/TS Incidents
•= Connectivity
Key Drivers
•= Accuracy: 75%
•= Lift: 56%/30%
•= Good  Mode= l
•= Accuracy: 82%
•= Lift: 59%/30%
•= Good Model
•= Accuracy: 85%
•= Lift: 80%/30%
•= One variable Model
•= Accuracy: 87%
•= Lift: 88%/30%
•= Great Model
Summary
Tenure after ‘Save’
Broadband Upgrade
Involuntary Churn
Voluntary Churn
=
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1
Key Insights – Customer Behavior Predictive Models = Increase Value of ‘Save’ and Generate Revenue
•Selecti= ng which cancel-callers to attempt to retain (‘Save’) can improve gross margin
•
•By targe= ting high ‘save’-propensity customers we achieved
–1 month extension in average tenure after ‘Save’
–~39% In= crease in Gross Incremental Margin
–
•Increase Gross Margin:
•Previous Save Econo= mics
•Avg Months Life After = Save: 2.6
•Incremental Margin aft= er CoGs: $28.60
•Cost of Save Per Incid= ent: <$2.34>
•Incremental Cost For Incentive: <$2.75>= ;
•Incremental Gross Marg= in per Save: $23.51
•
•New Save Economics&= #13;
•Avg Months Life After = Save: 3.5
•Incremental Margin aft= er CoGs: $38.50
•Cost of Save Per Incid= ent: <$1.56>
•Incremental Cost For Incentive: <$2.75&g= t;
•Incremental Gross Marg= in per Save: $34.19
•
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•MSN is evaluating monetizing the segment of Likely Churners with High Broadband Propensity through its Broadband partner relationships
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
•For high churn propensity customers with Low BB Propensity, a contact strategy is being developed to drive engagement with the MSN features
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
•Large MSN initiative around updating Credit Card I= nfo. to reduce involuntary churn<= /span>
Key Insights – Customer Behavior Predictive Models = Increase Value of ‘Save’ and Generate Revenue
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VERY SIMPLE – Does Customer Satisfacti= on <= span style=3D'font-size:64%'>reflect Business Performance?
•Setting:
–SteveB has defined reporting of Customer Satisfact= ion metrics as a requir= ement company-wide
–MSN conducts regular Portal satisfaction surveys a= cross brands using comSco= re
–Presently the relationship between stated satisfac= tion and customer behavi= or is poorly understood
<= span style=3D'font-size:64%'>–Using comScore, we are able to append online behav= ioral data for each survey respondent, and investigate the relationship between stated = satisfaction and present and future behavior
Joint work with:
Mick McWilliams, Deepak Agrawal & Bob Foulon, MSN Market Research
Scott Finer, comScore Networks
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<= span style=3D'font-size:44%'>•MSN asks 3 stand= ard ‘Satisfaction’ questions:
–Overall, how satisfied are you with your ‘Primary Portal’ based on yo= ur experience in the past 30 days?
–<= span style=3D'font-size:43%'> 4 Very Satisfied <= /div>
–<= span style=3D'font-size:43%'> 3 Somewhat Satisfied
–<= span style=3D'font-size:43%'> 2 Somewhat Dissatisfied <= /span>
–<= span style=3D'font-size:43%'> 1 Very Dissatisfied
–Based on your experiences in the past 30 days, how likely would you b= e to recommend your ‘Primary Portal’ to a friend or colleague?
–<= span style=3D'font-size:43%'> 5 Definitely would recommend&= #13;
–<= span style=3D'font-size:43%'> 4 Probably would recommend= 3;
–<= span style=3D'font-size:43%'> 3 Might or might not recommen= d
–<= span style=3D'font-size:43%'> 2 Probably would not recommen= d
–<= span style=3D'font-size:43%'> 1 Definitely would not recommend
–How likely is it that you will still be using your ‘Primary Portal’ s= ix months from now?
–<= span style=3D'font-size:43%'> 5 Definitely will still be us= ing six months from now
–<= span style=3D'font-size:43%'> 4 Probably will still be usin= g six months from now
–<= span style=3D'font-size:43%'> 3 Might or might not be using= six months from now
–<= span style=3D'font-size:43%'> 2 Probably will not be using = six months from now
–<= span style=3D'font-size:43%'> 1 Definitely will not be usin= g six months from now
•
•From these questions several ‘satisfaction metrics’ can be constructed
•The metric found to be most closely related to business performance is based on the ‘likelihood to recommend’ question, and is termed ‘NetPromoter’:
–<= span style=3D'font-size:43%'> WHEN (PortalWTR=3D'1' OR PortalWTR=3D'2') THEN 'Detractor'
–<= span style=3D'font-size:43%'> WHEN (PortalWTR=3D'3' OR PortalWTR=3D'4') THEN 'Neutral'
–<= span style=3D'font-size:43%'> WHEN (PortalWTR=3D'5') THEN 'Promoter'
Key Insights – Does Customer Satisfaction reflect <= span style=3D'font-size:64%'>Business Performance?
Key Business Questions:
<= span style=3D'mso-special-format:"numbullet3\,1";position:absolute;left:-4.4%'>= 1.Do we invest in making Detractors happier?
<= span style=3D'mso-special-format:"numbullet3\,1";position:absolute;left:-4.4%'>= 2.How brand-loyal are Neutrals to MSN?  =
3.Should we spend money keeping the Promoters happy or making the Neutrals happier?
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•Use Survey data from November ’03 joined with mont= hly behavioral data for Nov ’03 through April ’04 (Months 47 – 52) •Using Factor Analysis on monthly share of page vie= ws data by portal, we identified a 6-factor solution through which we could identify changes in brand-loyalty:<= /span>
Key Insights – Does Customer Satisfaction reflect Business = Performance?
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quoted-printable Content-Type: text/html; charset="windows-1252" Data Mining for Business Strategy
1
•Looking= at the factor loadings for the ‘throughout factors’ for MSN by NetPromoter score
–Detractors make significantly higher use of the Yahoo Portal, than the MSN Portal, throughout Nov’03 – Apr ’04
–In some sense, Detractors are already l= ost, in terms of their loyalty to MSN at the time of = the Survey
–Promote= rs, and Neutrals make substantially more use of MSN than the other 2 portals
Key Findings – Customer Satisfaction is predictive of present and future brand loyalty
Detrac= tors account for only 5.5% of the customer base for the MSN Portal
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1
Key Findings – Customer Satisfaction is predictive of present and future brand loyalty
•Looking= at the factor loadings for the ‘late months factors’ for MSN by NetPromoter score
–Neutral= s make significantly increased use of the competing Portals in later months, while Promoters do not
–Detract= ors already make more use of another Portal throughout the period <= /span>
–In summary, Detractors are already lost to MSN, and Neutrals ar= e at risk of being lost to competing Portals in the future<= /div>
<= span style=3D'font-size:44%'>•Neutrals are an important group for MSN
–T= hey account for 52% of unique visitors to the MSN Portal
–A = 5% Decline in MSN’s share of PVs among this group yields a 10% decline in mon= thly revenue
–A= loss of ~$24 million per year
•Since Detractors are already lost to MSN, there is no value in attempting to eng= age them
–
•T= hese insights suggest MSN needs to drive to convert Neutrals into Promoters=
Neutra= ls account for 51.9% of visitors to the MSN Portal
A 5% d= ecline in MSN’s share of PVs among this group can produce a 10% decline in monthly revenue - ~$24 million per year
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1
Overview=
•What I’m not going to talk about =
–Quantum Tunneling Optimization
•I have already spoken at MSR on this work= 3;
•Since then:
–It works (small factoring and TSPs), but with worst-case exponential complexity
–I have proven that there is no equivalent to this algorithm, using classical equations
•I’m happy to give the details during interviews
•= What I am<= /i> going to talk about
–Data Mining for Business Strategy
•MSN IA Customer Behavior <= /div>
•Viewing the data the ‘right way’:  The Standardized Lifecycle =
•Predictive Modeling with SQL Server 2005 = ;
•Using Predictive Models to develop business strategy
•Is Measuring Customer Satisfaction Useful?&#= 13;
–
–= Machine Learning – the Nonnegative Boltzmann Machine
•= 3;
•
------=_NextPart_01C4C34D.8879FA90 Content-Location: file:///C:/2669C634/MSRTalkOctober2004_files/slide0015.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="windows-1252" Data Mining for Business Strategy
1
Maximum Entropy Data Modeling
•Consider the abstract problem of selecting a natural probability model to capture different classes of data. •Consider the ‘least-committal’ model, the maximum entropy model, and constrain it to match certain statistics of the data.
•Simple Lagrange multiplier problem
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•= 3;
•We simply attempt to match 1st and 2nd order stati= stics
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Maximum Entropy Data Modeling Contd.
•For continuous, unbounded data p(x<= /b>) is Gaussian. (Very well known)
•For binary data t= he maximum-entropy distribution is the <= i>Boltzmann Distribution with quadratic energy.
–This is indeed the probability distribution learned and explored by the Boltzmann Machine.
–The Boltzmann Machine learns the maximum entropy probability distribution of binary data based upon 1st and 2nd order statistics.
•For continuous data bounded t= o the nonnegative orthant the distribution is the Nonnegative Boltzmann Distribution
<= span style=3D'font-size:63%;visibility:hidden'>•
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The Nonnegative Boltzmann Distribution
Surface plot
Contour plot=
x1
x2
x1
x2
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1
The Nonnegative Boltzmann Machine
•So how do we learn the NNBD to best represent some= set of nonnegative data {= V}?= 3;•Search for maximum likelihood parameters, given the = data, under the assumption that= 3;
•
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
•We perform Gradient Ascent to maximise this log-likelihood function
<= span style=3D'font-size:63%;visibility:hidden'>•= 3;
<= span style=3D'font-size:63%;visibility:hidden'>•
(Downs, Mackay & Lee, 2000, Downs 2001, Downs 2003)
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1
The NNBM Contd.
•Learning rules, (cf. Boltzmann Machine)
•
•
•
•
•
•Learning is made= hard by which is a multidimensional Gaussian integral over the nonneg= ative orthant.
•Ways ahead
–Integra= te
–Mean-Fi= eld/Variational Approximation
–Markov = Chain Monte-Carlo methods
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Strategy
1
Practi= cal Implementation
•Why not integrate?
<= span style=3D'font-size:57%'>–Consider
–
–
–
–
<= span style=3D'font-size:57%'>–This defines a rectified Gaussian distribution wit= h        modes.
–Hard to integrate efficiently if we must first fin= d the modes in a high-dimensional space.
<= span style=3D'font-size:75%'>•Use of Mean-Field Approximation =
–Allows analytic approximate solutions for A,b
–Fundame= ntal assumption - factorisability <= /span>of the model density
–
–
–Factori= sed fit density is unimodal=
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1
Practical Implementation
•Markov = Chain Monte-Carlo Sampling = ;
–Problem is to gen= erate a set of ‘data’ representative of the topography of the probability density.
–Develop dynamics satisfying d= etailed balance, s.t. evolving random poi= nts asymptotically produces samples characterising the probability density
–
–
–
–For NNB= D, this is simplified by
•Gaussian= ity of conditional densities
•Quadrati= c L(x)
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1
MCMC for= the NNBM
•Reflective Slice Sampl= ing (Neal 2003)
x0
x0
From starting point, x0 pick slice at random = height in [0, p*(x0)]
p*(x)
p
p’
x1
x0
          Connected= Slice
Choose random momentum, p bouncing randomly <= /span>from slice boundarie= s over path length, t
x0
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