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1
What Does a PhD Mathematician do in the
= Commercial World?
Oliver B. Downs, PhD
Chief Scientist, Analytical Insights, Inc.
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Overvie= w
•My background
•&= #8216;Classical’
–How do my customers behave, and how will they behave in the future?
–H= ow does what customers say correspond with what they do?
–H= ow can I beat the airlines at their own game?
–Photo-fit = images – generating plausible completions to partial face images
•&= #8216;Quantum’
–C= hanging the future of computing, with advanced mathematics
–G= lobal Optimization and the Tunneling Salesman
–
•
•
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Backgro= und
•<= /span>Grew up in the UK
•<= /span>Went through the public school system <= /span>
•<= /span>BA, MA & MSci. Degrees in Experimental & Theoretical Physics, Univers= ity of Cambridge, UK
•<= /span>MA & PhD in Applied & Computational Mathem= atics, Princeton University, NJ, US= A
•<= /span>Intern: Siemens Corporate Research
•Consultant: Biological Computation Group, Bell Laboratories (Lucent= )
•<= /span>Researcher: Machine Learning & Applied Statist= ics Group, Microsoft Researc= h
•<= /span>President & Chief Scientist, Analytical Insigh= ts, Inc.
–<= /span>MSN
–<= /span>Microsoft Research
–<= /span>Barnes & Noble
–<= /span>American Express
–<= /span>Hamlet
–<= /span>Novantas
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Custom= er Behavior: Data Mining for Business Strategy
•Summary:
–Understanding drivers of Dial-Up Churn at MSN, thr= ough exploratory analy= sis of Customer Support, Usage, and Subscription databases
– (Several Mil= lions of Customers)
•Pain Points – eg. dialup connection problems, software installation
•Lack of engagement with ISP’s client softwar= e
•Lack of usage
–Identify Customer behaviors associated with differ= ent phases of their subsc= ription lifecycle
•Customer behavior in non-term-commit subscription = plans can be viewed via the Standardized Lifecycle
•Mapping behavioral triggers to points on the Standardized Lifecycle allows identification of incidents preceding churn/defection, or cross-sell/up-sell opportunities
–Enable improvements in ROI by better targeting cus= tomer retention investment <= /span>
•Provide customer-level, look-ahead predictions of&= #13;
–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
–Combinations of Churn and Broadband Upgrade Propen= sity determine marketing commu= nications to customer
–Expected Tenure/LTV allows call centers to decide = who to attempt to retain and who to allow to cancel
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Key Ins= ight – Standardized Lifecycle
=
•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 interactio= ns from date of signup
•Behavior relative to cancel date suggests predictors of churn •Yet provides little insight for life-long customer engagement
Data View:  CS & TS interactions by number of months = before cancellation of subscr= iption
New users, users soon to attrite and users in the middle of their lifecycle a= re all commingled in this view of the data
As expected, cancel calls increase as a proportion of total calls, the close= r to the cancel date
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•End-to-end view of common lifecycle for customers = with varying tenure lengths •Map all tenures onto a standardized lifecycle thro= ugh time-warping
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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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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
Connection calls as a % of total calls climbs dramatically at the end of the relationship; representing 48% of all conta= cts in the last period
Initial burst of installation related contacts at the beginning of the relationsh= ip
Connection calls as a % of total calls climbs dramatically at the end of the relationship; representing 48% of all contacts in the last period<= br>
75% of incidents occur during the last 10% of the users life
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Key Insights - Customer Behavior can be accurately predicted
<= span style=3D'font-size:56%'>•For MSN, u= sing we developed and deployed decision tree predictive algorithms for:
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•<= /span>
•<= /span>
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<= span style=3D'font-size:56%'>•Analysis o= f 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 output= s of these models can be combined to assemble distinct groups or segments of customers
<= span style=3D'font-size:56%'>•The distin= ct groups are treated with different retention, conversion and = marketing strategies, empowered by profiling the g= roups on all available custo= mer data
•<= /span>Tenure to date
•<= /span>Tenure after acquisition incentive
•<= /span>Number CS/TS Incidents
•<= /span>Number of Connectivity Issues
•<= /span>Dialup accelerator installed/used
•<= /span>Software version
•<= /span>Unresolved CS Incidents
•<= /span>Credit Card Expiration
•<= /span>Lack of engagement with service
•<= /span>Up-to-date software version
•<= /span>CS/TS Incidents
•<= /span>Connectivity
Key Drivers
•<= /span>Accuracy: 75%
•<= /span>Lift: 56%/30%
•<= /span>Good  Model
•<= /span>Accuracy: 82%
•<= /span>Lift: 59%/30%
•<= /span>Good Model
•<= /span>Accuracy: 85%
•<= /span>Lift: 80%/30%
•<= /span>One variable Model
•<= /span>Accuracy: 87%
•<= /span>Lift: 88%/30%
•<= /span>Great Model
Summary
Tenure after ‘Save’
Broadband Upgrade
Involuntary Churn
Voluntary Churn
=
------=_NextPart_01C51813.94C6A9E0 Content-Location: file:///C:/CF2A96CD/PSCTM_files/slide0091_image022.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlh2wGWAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAADZ AZQAgAAAAAAAAAL/jI+py+0Po5y02ouz3rz7D4aiA5TmiabqyrZs4MbyTNc2bOf6zqN4DwwKTb+h 8bg7IJc+pvNZKkKnRyn1yrNit1kDN6j9ilfhsTlVPp/T6jZA6aax49g5fWu/T/P6urffwgeIJDi4 VGg4hJhI+Md4sviYJPkVSZljedmVKcapGeP5KRMq+lLqR1p3ypS6iuZ6CAsFd9kKayv7lmuEu/vm SNl7KuxKXGp8CyyJrMks6lzre6j8CL0srYgNZP1Mzcj9rQ0mnkUuRBtsjqk+yV4D3uydCG9IH+4u h58kX68/6p8PoAt7y/gNItgH4UGBAxnKMQhI4R2JCR2asggKYkWM/004qqBIB2REjXpEtjEZB6Ua lSFJTvQICeYrmVFofnQZ0ibLlTZ19YSEM6XOnz5p7nSD7ppRokc7ESUSFOnQn00rPY0S9eTUnlW5 dDWTtNrWsTC/jhmBNq3atWzbun0LN65ct5/MUrGriizXrDyXUuV69RdevLOYAr4a9p5MwncPP008 T29Zx0z5rpHskfETzVcg9/NLeTJiy2Axc+TMKrDnhaBNO0S9h/RZ1xZhE1It22nr3ZlxD6b9OvTe 37xFFzea2+rx08J1JvcKnKHtKr7rRhc4nVf1ZtcBZs82mvhiw92lP8dTXt/3c9ujLce4flx46+9r N0cu3nh6dvGrnP/PO95f+/mzWkQD8nffYv91dqA6/fXwoCILNlZfcA26U+BGASZo4WMT7nEhORG2 42F+vQlYIXwfFpYidhxmtuJmIYozog41bhOjEze+Q16Lr+WYmo8EvngakLEIqR6RKprIHIobVsYk fD0+SZWRtyGJz448zscdlhgqWdtcYo5JZplmnolmmmrSR2WbTZbIpn5emqPlPlHa56Scw8V5IphJ cumem30KquCdHc4oTZ02WkndnA76aZ6h0k2pp3OSupjnoFVe6h2lmu7ZJaFSIioio9o5Siek2JkK nqh4osofq+zBSqOq3skqn6uHVoofn2+SuouimOC6DbC+CAtKe+n/0KoNsg0BuqyukxqLTYYlUZuL s4Eoq5S0mDJbKqdDZvrrpr6Oauuj0HbLa7mfWnruq9gWw61Y4CaarnrEQjjvLflmuW859x77L4YB k9guut7+KO6fBTdbr2IJyztxkQ1n6am78IZa8bQDV3uwjf3SO7Is1r70cbYPOxjyOimbvDKdLd9Q 8jAxhxvvrgtjvK69Ozv8860Xf0muwuZy/G7SFIMaaMffBk3gzFtCjWDNxUgdENXqvhws1jNoS0bG RjMdrdOdWr3KyTlxTTLbJnv9j9s2oz0M3MnKfczNNNr9rNap0n0M39vi/YzezQp+kd8iGg7y0FUz 7m/PEis9tsa9syJteeY6b9w05UtrDqPjWwPO7tGdg7555ZefrjrqZ8OJeeuyewxlzrQTzvrqZXue +ue6l+7602ZHarvwilcbcWS4xz67xcW/TrrPtTPfe/PQm7578NfzvuTz40Le9vS5V+97+WUhHvby 45PvPPW3H4+v5MrDj3z08Yj+N/hpJ/8Z/fGrXxf0fcR+0hveqvC3uKKZL3TeAxoAJzccNUlwghSs oAUviMEManCDHOygBz+IwQIAADs= ------=_NextPart_01C51813.94C6A9E0 Content-Location: file:///C:/CF2A96CD/PSCTM_files/slide0092.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PowerPoint Presentation
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Key Insights – Customer Behavior Predictive Models = Increase Value of ‘Save’ and Generate Revenue
•S= electing which cancel-callers to attempt to retain (‘Save’) can = improve gross margin
•
•B= y targe= ting high ‘save’-propensity customers we achieved
–1= month extension in average tenure after ‘Save’
–~= 39% Increase in Gross Incremental Margin
–
•Increase Gross Margin:
•Previous Save Economics
•Avg Months Life = After Save: 2.6
•Incremental Marg= in after CoGs: $28.60 = ;
•Cost of Save Per Incident: <$2.34>= ;
•Incremental Cost= For Incentive: <$2.75>= ;
•Incremental Gross Margin per Save: $23.51=
•
•New Save Economics
•Avg Months Life = After Save: 3.5 <= /div>
•Incremental Marg= in after CoGs: $38.50 = ;
•Cost of Save Per Incident: <$1.56>= ;
•Incremental Cost= For Incentive: <$2.75&g= t;
•Incremental Gross Margin 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'>•
•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'>•
•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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Customer Behavior: Does Customer Satisfaction reflect Business Performance?
•Setting:
–Reporting of Customer Satisfaction metrics as a requirement company-wide&#= 13;
–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
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<= span style=3D'font-size:44%'>•MSN asks 3 standard ‘Satisfaction’ questions:
–Overall, how satisfied are you with your ‘Pr= imary Portal’ based on your experience in the past 30 days?
– 4 Very Satisfied <= /div>
– 3 Somewhat Satisfied
– 2 Somewhat Dissatisfied <= /span>
– 1 Very Dissatisfied
–Based on your experiences in the past 30 days, how likely would you be to recommend your ‘Primary Portal’ = to a friend or colleague?
– 5 Definitely would recommend&= #13;
– 4 Probably would recommend= 3;
– 3 Might or might not recommen= d
– 2 Probably would not recommen= d
– 1 Definitely would not recommend
–How likely is it that you will still be using your ‘Primary Portal’ six months from now?
– 5 Definitely will still be us= ing six months from now
– 4 Probably will still be usin= g six months from now
– 3 Might or might not be using= six months from now
– 2 Probably will not be using = six months from now
– 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 bus= iness performance is based on the ‘likelihood to recommend’ question, and is termed ‘NetPromoter’:
– WHEN (PortalWTR=3D'1' OR PortalWTR=3D'2') THEN 'Detractor'
– WHEN (PortalWTR=3D'3' OR PortalWTR=3D'4') THEN 'Neutral'
– 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'font-family:Arial;font-size:75%'>1.Do we invest in making Detractors happier?
<= span style=3D'font-family:Arial;font-size:75%'>2.How brand-loyal are Neutrals to MSN? 
3.Should we spend money keeping the Promoters happy or making= the Neutrals ha= ppier?
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•Use Survey data from November ’03 joined with monthly 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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•L= ooking at the factor loadings for the ‘throughout factors’ for MSN by NetPromoter score
–Detractors make significantly higher use of the Ya= hoo Portal, than the MSN Portal, throughout Nov’03 – Apr ’04
–I= n some sense, Detractors are already l= ost, in terms of their loyalty to MSN at the time of = the Survey
–P= romoters, 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
Detractors account for only 5.5% of the customer base for the MSN Portal
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Key Findings – Customer Satisfaction is predictive of present and future brand loyalty
•L= ooking at the factor loadings for the ‘late months factors’ for MSN by NetPromoter score
–N= eutrals make significantly increased use of the competing Portals in later months, while Promoters do not
–D= etractors already make more use of another Portal throughout the period <= /span>
–In summary, Detractors are already lost to M= SN, and Neutrals are 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
–<= b>They 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% decli= ne in monthly revenue
–<= b>A loss of ~$24 million per year
•S= ince Detractors are already lost to MSN, there is no value in attempting to eng= age them
–
•<= b>These insights suggest MSN needs to drive to convert Neutrals into Promoters=
Neutrals account for 51.9% of visitors to the MSN Portal
A 5% decline 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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Classic= al – Beating the Airlines
•Hamlet, Inc. is a local startup company, founded b= y Oren Etzioni a CS profess= or at UW •The company was founded about 18 months ago, around initial research conduct= ed in Etzioni’s lab, and featured on NBC
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Summary=
•<= /span>Using a huge database of airline ticket pricing da= ta from January through= May 2004, we have explored predictive modeling of the = magnitude and timing of variations in the price of= an airline ticket
•<= /span>Top 10 fares 5 times a day for 60 US domestic city pairs, from 45 days be= fore departure, to date of departure
•<= /span>Basic approach:
–To create dependent variables and training data, f= or each unique flight id (id + departure date):
•<= /span>Find the minimum observed price =
•<= /span>Find the number of days between each query of the = flight pricing/fare inform= ation and the minimum observed price
•<= /span>For queries where the minimum price occurred befor= e the query, compute the minimum price of the ticket on or after the query date
–<= /span>For each query, create derived variables:
•<= /span>Trip duration
•<= /span>Days to departure
•<= /span>Saturday Return
–<= /span>Produce optimal, pruned decision tree models for Categorical Variables descri= bing future price change, and time to that change
•<= /span>Decision Trees allow us to probabilistically descr= ibe our confidence in our predic= tion
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Categori= cal Dependent Variables
•Via a combination of univariate analysis of the Maximum Price Change, and Days to Future minimum price variables, and input = on important categories we have defined the following discrete categories on these 2 variables:
WHEN (PriceChange>=3D-10) THEN 'BuyToday'
WHEN (PriceChange<-10 AND PriceChange>-50) THEN 'Up To $50'
WHEN (PriceChange<=3D-50 AND PriceChange>-100) THEN 'Up to $100'
WHEN (PriceChange<=3D-100 AND PriceChange>-200) THEN 'Up to $200'
WHEN PriceChange<=3D-200 THEN= 'More than $200'
WHEN (days_to_minprice<=3D0) = THEN 'BuyToday'
WHEN (days_to_minprice>0 AND days_to_minprice<=3D7) THEN 'Within a Week'
WHEN (days_to_minprice>7 AND days_to_minprice<=3D14) THEN '1 to 2 Weeks'
WHEN (days_to_minprice>14 AND days_to_minprice<=3D30) THEN '2 to 4 Weeks'
WHEN (days_to_minprice>30) TH= EN 'More than 4 Weeks'
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1
Model Performance
<= span style=3D'font-size:50%'>•Price Change Model:
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:50%'>•Price Change Timing Model:
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:50%;visibility:hidden'>•
<= span style=3D'font-size:31%;visibility:hidden'>•
<= span style=3D'font-size:50%'>•Model Combination:
•Using the following rules for the predictions asso= ciated with each flight query, combined with the overall average values of the 5 price change categ= ories, acting on the combinations of the 2 models results in an average savings of $32.03 per ticket
•  WHEN (PriceChangeCategory=3DPredictedPriceChangeCat AND PriceChangeCategory=3D'= Buy Today') THEN 0
• =
•  WHEN (PriceChangeCategory=3DPredictedPriceChangeCat AND DaystoMinPriceCategory=3DPredictedDaysCat AND PriceChangeCategory!=3D'Buy Today') THEN AvgPredictedPriceChange
• =
•  WHEN (PriceChangeCategory!=3DPredictedPriceChangeCat AND DaystoMinPriceCategory=3DPredictedDaysCat) THEN -1*AvgPriceChange
• =
•  WHEN (DaystoMinPriceCategory!=3DPredictedDaysCat AND PriceChangeCategory=3DPredictedPriceChangeCat <= span style=3D'font-family:Arial;font-size:33%'>AND PriceChangeCategory!=3D'Buy = Today') THEN -1*AvgPriceChange
• =
•  WHEN (DaystoMinPriceCategory!=3DPredictedDaysCat AND PriceChangeCategory!=3DPredictedPriceChangeCat) <= span style=3D'font-family:Arial;font-size:33%'>THEN -1*AvgPriceChange
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Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PowerPoint Presentation
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Explorin= g the Fare Basis Code
<= span style=3D'font-size:56%'>•The last 3 digits of the Fare Basis Code, can be as predictive as the first 2
<= span style=3D'font-size:56%'>•Examples:
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•There are some predictively powerful combinations = of First2 and Last3 digits:=
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Class= ical: Why make probabilistic models of data?
•H= ow do we use a probabilistic generative model?
–S= ampling from the model generates new prototype data
–C= an perform probabilistic completion of partial data
x2<= /span>
x1<= /span>
x2<= /span>
x̵= 7;1<= /span>
•We can also use the model for recognition, by considering the ‘= log-likelihood’
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1
The Nonnegative Boltzmann Distribution
Surface plot
Contour plot
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x2<= /span>
x1<= /span>
x2<= /span>
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