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Learning Goal: I’m working on a r case study and need guidance to help me learn.On a sunny Monday afternoon in early spring 2013, David Powell entered his new office and took a deepbreath. He pondered his first few days as the new data analyst for Scholastic Travel Company (STC), aneducational tourism firm. Powell had filled his first week of employment meeting the firm’s departmentalleadership and attending a company-wide new-employee-orientation program, and he was eager to get startedon his first project.Just a few hours earlier, at the weekly marketing strategy meeting, Powell’s new supervisor, StephenBlackford, stressed the urgency of a new data initiative centered on customer retention. As Blackford outlined,in less than two weeks, contract renewal opportunities would begin for customers who had gone on an STCtrip in 2012. During the meeting, he presented a dataset with all of the known information about the previousyear’s client base (see Exhibits 1 and 2). From his past experience, Blackford was confident that models couldbe constructed to predict whether or not a customer would book again in 2013. With such a model, he hopedto design a more nuanced marketing strategy that would target certain subsets of the client population to savecost and improve yield. With multiple plausible methodologies in mind, Powell knew he needed to get to workimmediately so he could give Blackford an accurate prediction model before the end of the week.Company BackgroundSTC was not a particularly young company. Founded in the 1960s, it grew from a single-person operationto a multi-million-dollar business, was bought and sold twice (first to the management when the founder retired,then to a private equity firm), and survived almost having to declare bankruptcy post-9/11. Yet as of 2013, itwas one of the premium providers of cultural and educational trips: history and science trips to middle- andhigh-school students, exchange trips for university students, cultural immersion, artistic destination trips, andother tours worldwide.Customers chose STC because of its superb ability to coordinate the numerous details associated withtaking a large group of primarily young people on a far-away journey. These included the procedures related toobtaining proper documents and permits (e.g., visas); logistical details (bus, plane, train, and other tickets); meetand-greets at the transfer points and destinations; hotel, meal, and entertainment bookings; taking care of safetyconcerns (chaperones and accompanying security guards to ensure physical safety and eliminate the possibilityof sexual, emotional, and substance abuse); insurance; and accident “resolution” (e.g., searching for missingtravelers1 or replacing a lost passport). All were critical elements of a successful trip, which the school teachers,1 As a company executive put it, “We never lost a student…permanently.”Page 2 UV7579university administrators, parents, and students themselves were glad to outsource to trusted professionals, andSTC was a prime example.The majority of the trips STC managed were of the “teacher organized, parent paid” type. This meant thatthe teacher (or university administrator) determined the itinerary, desired duration, and activity schedule, butthe parents (or students) paid for the trip. For that purpose, it was not uncommon for theteachers/administrators to hold meetings with parents/students prior to the trip. STC typically kept track ofthose meetings, as they often revealed important information about the upcoming trips. STC representativeswould often attend such meetings, either in person or virtually.STC also collected and carefully tracked multiple types of data about the travel group and the organizingteacher/administrator, and it sought feedback after the trip. This was all recorded in the STC cloud-baseddatabase and was easily accessible to Powell.Prediction Task and Available DataPowell’s ultimate task was to predict which customers would book with STC in the 2013–14 school year(fall 2013 to spring 2014). He decided to build a model that took the data available as of spring 2013 to makethis prediction. To build such a model, however, Powell would need to replicate the 2013–14 prediction taskon the available data. This meant that for training his model, he would use the data from the 2012–13 schoolyear—which showed whether a certain group had been retained or not—and try to predict based on the clientprofileinformation as of the end of the 2011–12 school year. He was lucky that STC took snapshots ofcustomer-profile data once per year, so this historical data was available (see Exhibit 1).Powell knew from the marketing meeting that the company used post-trip surveys to track performanceand get feedback from the teachers. These responses, coupled with trip data such as trip revenue, trip length,and school size were what he would use to construct his retention model. A comprehensive list of data fieldswas included with the spreadsheet that Blackford sent to Powell (see Exhibit 2). With a sample size of nearly2,400 groups, Powell was hopeful he could have a model that would make reasonably accurate predictions forBlackford before the end of the week, so that the new marketing strategy could be deployed before the salesseason started later in the spring.Page 3 UV7579Exhibit 1Retention Modeling at Scholastic Travel Company (A)Snapshot of the Data(First five and last five rows of data shown; to fit the snapshot on a single page, several data fields are hidden)IDProgram.CodeFrom.GradeTo.GradeGroup.StateIs.Non.AnnualDaysTravel.TypeDeparture.DateReturn.DateDeposit.DateSpecial.PayTuition.Retained.in.20121 HS 4 4 CA 0 1 A 14/01/2011 14/01/2011 30/08/2010 NA 424 . 12 HC 8 8 AZ 0 7 A 14/01/2011 21/01/2011 15/11/2009 CP 2350 . 13 HD 8 8 FL 0 3 A 15/01/2011 17/01/2011 15/10/2010 NA 1181 . 14 HN 9 12 VA 1 3 B 15/01/2011 17/01/2011 07/01/2011 NA 376 . 05 HD 6 8 FL 0 6 T 16/01/2011 21/01/2011 30/09/2010 NA 865 . 0. . . . . . . . . . . . . . .2385 HC 7 8 CA 0 5 A 28/06/2011 02/07/2011 15/12/2010 NA 1892 . 02386 HD 8 8 CA 0 5 A 29/06/2011 03/07/2011 15/10/2010 FR 1699 . 12387 HD 10 12 CA 0 6 A 29/06/2011 05/07/2011 18/01/2011 SA 2149 . 12388 HS 4 4 CA 0 1 A 30/06/2011 30/06/2011 17/12/2010 NA 449 . 12389 HD 8 8 WA 0 6 A 30/06/2011 05/07/2011 29/10/2010 NA 2135 . 1Note: The full dataset is available in the accompanying student spreadsheet, UVA-QA-0864X.Data source: Company data adjusted by author using unspecified constants.Page 4 UV7579Exhibit 2Retention Modeling at Scholastic Travel Company (A)Data DictionaryData Field Name Example DescriptionID 1 Self-explanatory.Program.Code HD This is a very granular code that describes where the trip went and what it did. HN, forinstance, is a history program that runs in New York.From.Grade 8 This is the lowest grade in school of a participant on that program.To.Grade 8 This is the highest grade in school of a participant on that program.Group.State IN This is the two-letter designator for the state in which the originating school is located.OTHER stands for rare geographies that appear in the data only once.Is.Non.Annual. 1 1/0 indicating if the group from this school typically skips a year in between programs. Thesewill rarely repeat the very next year.Days 3 The number of days the group was on the program and with one of the instructors.Travel.Type A Mode of travel from the originating school location to the starting location of the program(A = Air, B = Bus, T = Train).Departure.Date 19/02/2011 The date that the group left its originating school.Return.Date 21/02/2011 The date the group returned to its originating school.Deposit.Date 20/10/2010The date by which registrants are supposed to have at least an initial deposit in prior todeparture. The time in the school year when certain events occur can be important; forinstance, there are no deposit dates in the summer since no one would be around to act onthem.Special.Pay NAThe most important of these are school accounts (SA). That means that, contrary to the usualpractice, the teacher collects all of the money and then remits it in bulk to STC. The normalarrangement is STC handling all of the cash collection from parents/students.Tuition 1174 This is the price it costs each full-paying participant (FPP) to go on the program. West-coastair trips are more expensive per person than midwestern bus groups.FRP.Active 72 FRP is the full refund program. This is the number of FPPs on the trip who bought tripcancellationinsurance.For the exclusive use of j. siko, 2022.This document is authorized for use only by jona siko in Data Mining for Business – Spring 2022 taught by NEGAR SOHEILI AZAD, University of Illinois at Chicago from Jan 2022 to May 2022.Page 5 UV7579Exhibit 2 (continued)Data DictionaryFRP.Cancelled 13 This is the number of FPPs on the trip who bought trip-cancellation insurance, but thencancelled it.FRP.Take.up.percent. 0.6857 This is the percentage of the FPPs who bought the FRP and ended up paying for it.Early.RPL 02/03/2010 This is the date that the first communication went out to the group. Often this can be 12 to18 months before the trip actually departed.Latest.RPL 10/08/2010 This is the date that the last communication inviting people to join the group went out. Oftenthis can be 6 to 9 months before the trip actually departed.Cancelled.Pax 15 This is the number of passengers who signed up with a $100 deposit but then cancelledbefore the group departed.Total.Discount.Pax 7 This is the total number of extra passengers who went along without paying full price (ortypically anything). These would be the chaperones and the teachers.Initial.System.Date 02/03/2010 This is the date when the teacher first agreed to get this trip organized. It is typically theearliest of the dates relative to group activities.Poverty.Code APoverty code for the area in which the originating school (and by extension, most of theparents who will be paying for the trip) resides based on estimated percentage below thepoverty line. A is 0 to 5.9, B is 6 to 15.9, C is 16 to 30.9, D is 31 or more, E is unclassified,Space if DISTCLASS = U (Supervisory Union).Region Other This is a larger aggregation of state areas. Some large states, like California, are their ownregion. Others are combined.CRM.Segment 1This is a type of school code used in the customer-relationship-management (CRM) system todescribe the school. The codes are numbered 1–11 but are in no particular order; proprietary,but it is a designation of a customer type that may be helpful.School.Type PUBLIC Public or private.Parent.Meeting.Flag 11/0 indicating whether a parent meeting was held. These are typically strong indicators ofparent engagement and of a teacher who understands that these can be important tosuccessfully organizing one of these out-of-school programs.MDR.Low.Grade 7 This is the lowest grade in the originating school.MDR.High.Grade 8 This is the highest grade in the originating school.Total.School.Enrollment 955 This is the total enrollment of the school (to differentiate big schools from little ones).Page 6 UV7579Exhibit 2 (continued)Data DictionaryIncome.Level P Like poverty code, an indication of ability of parents to pay for these programs. A is lowest, Qis highest, Z is unclassified.EZ.Pay.Take.Up.Rate 0.2286 This is a % of the FPPs that sign up for an automatic bank draft installment plan.School.Sponsor 0This is an indication (1/0) of whether or not the school is officially sponsoring the trip.Mostly, though these programs draw from the same school, they are typically runindependently.SPR.Product.Type East Coast A high level of aggregation of the very granular tour types.SPR.New.Existing EXISTING EXISTING means that the group has traveled with STC before—most often the year before.NEW, with few exceptions, means that the school has never traveled before with STC.FPP 105 This is the actual number of FPPs who went on the trip.Total.Pax 112 This is the actual number of total passengers (including chaperones and teachers) who wenton the trip.SPR.Group.Revenue 125735.4 This is the total amount paid for all of the participants to go on the program from that group.NumberOfMeetingswithParents 0 Number of meetings with parents prior to the trip.FirstMeeting 18/11/2010 The date of the first meeting with parents (NA if none held).LastMeeting 28/11/2010 The date of the last meeting with parents (NA if none held, may be same as the first meetingif only one meeting was held).DifferenceTraveltoFirstMeeting 93 The number of days from the first parent meeting to travel date.DifferenceTraveltoLastMeeting 103 The number of days from the last parent meeting to travel date.SchoolGradeTypeLow Elementary The lowest grade type in the school.SchoolGradeTypeHigh Elementary The highest grade type in the school.SchoolGradeType Elementary->Elementary Combination of the above denoting the type of school.DepartureMonth January Month of departure.GroupGradeTypeLow K The lowest grade type in the group that travels.GroupGradeTypeHigh Elementary The highest grade type in the group that travels.GroupGradeType K->Elementary Combination of the above denoting the type of the group that travels.Page 7 UV7579Exhibit 2 (continued)Data DictionaryMajorProgramCode H Aggregation of the granular program code; the first letter of the program code.SingleGradeTripFlag 1 Indicator for the trip taken by a group comprising students from the same grade.FPP.to.School.enrollment 0.06364617 The ratio of FPP to school enrollment.FPP.to.PAX 0.93650794 The ratio of FPP to total PAX on the trip.Num.of.Non_FPP.PAX 4 The number of PAX who are not FPP.SchoolSizeIndicator L A label for the size of the school (S, M, L, S-M, M-L), by quintiles of sizes.Retained.in.2012. 1 THIS IS THE 1/0 SUCCESS METRIC WE ARE TRYING TO PREDICT—DID THEGROUP ACTUALLY RETURN THE NEXT YEAR?Data source: Company data, adjusted by author.For the exclusive use of j. siko, 2022.This document is authorized for use only by jona siko in Data Mining for Business – Spring 2022 taught by NEGAR SOHEILI AZAD, University of Illinois at Chicago from Jan 2022 to May 2022
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