AA7 – Customer Choice Model Assignment
Notes for Expert:
1. Please read the Choice Models Case.pdf
2. Watch the Video Lecture.mp4 where the professor walks through examples of this assignment, use the following link to access video: https:
1drv.ms/v/s!AloMtfooflkegqZeq5vevL8qCVh4fQ?e=HEmZV1
3. Utilize the Enginius Predictive Modeling.xlsx to answer the 6 questions below
4. American English spelling – simply answer the 6 questions below with screenshots as instructed in the professor’s tasks below
Task:
· Read the case background, analyze the accompanying data sets and make necessary calculations, then respond to the following writing tasks.
· Use tables and annotated screenshots copy/pasted in your answers below to display the Enginius/Excel information that is useful for the written responses.
Address the following in your write up:
1. (20 pts) List out each statistically significant (i.e. p <.05) predictor of a travelers likelihood to book on AirBnB. Rank them by their impact on the likelihood to book (elasticities in the “1” Column, not “0”). Use your intuition to provide a
ief explanation of what may cause there to be a significant statistical relationship for each of the variables. Use the “AA7 AirBnB Traveler Data.xlsx” to find the relevant information on Enginius.
2. (20 pts) Follow the same process for the prediction of Hosts to have continued to remain as a host listing their property on AirBnB until the city banned it in 2015. Use “AA7 AirBnB Hosts Data.xlsx”.
3. (10 pts) Report on the accuracy of each model (travelers baseline & host baseline) in terms of the model’s ability to accurately predict the outcome (book a trip, remain on the service) and the alternative (not book, leave). See the “confusion matrix” and how many travelers/hosts the model accurately predicted to book/not-book or host/not-host. What are the models good
ad at predicting?
4. (20 pts) What would be your plan of attack to attract travelers to Eugene to book on AirBnB? Who would you target? What email(s) might you send? Etc. Provide details.
5. (20 pts) What would be your plan of attack to entice owners to list their homes on AirBnB for travelers to rent? Who would you target? What characteristics would you look for? What offers might you send them? Etc. Provide details.
6. (10 pts) State any concerns you have with making decisions based on the analysis approach, modeling choices, or the inclusion of any problematic variables in the modeling effort. Be specific as to why you think this might be a problem.
Marketing Strategy: Based on First Principles and Data Analytics
1
Marketing Strategy:
Based on First Principles and Data Analytics
XXXXXXXXXXAA7: AirBnB Choice Model
Marketing Strategy: Based on First Principles and Data Analytics
2
Acquiring New Customers in the Hospitality
Industry1
Conor Henderson, Shrihari Sridhar and Alejandro Lerza Durant
Background
Ai
nb is an Internet firm that helps travelers discover and book unique accommodations offered
y hosts with a room or house available for short term rental around the world.
Ai
nb was founded in the summer of 2008 in San Francisco, CA, and quickly became a disrupter
to the traditional hotel and lodging industry. The company bypassed the
ick and mortar business
model by creating a
okerage service between prospective guests and decentralized lodging
locations. Ai
nb’s service can be accessed via company websites or mobile applications for
Android, iOS, and Apple Watch. System users may search for accommodations by filtering data
such as date, location, lodging type, and price. Prior to booking lodging, users must authenticate
the process by providing their name, valid email address, photo id, telephone number, and payment
information. Ai
nb earns revenue by charging service fees to both guests and hosts.
The cost of Ai
nb lodging and accommodations is determined by the host. By not directly owning
physical locations, Ai
nb has been able to quickly scale its business to accommodate demand.
Cu
ently, the company offers more than 3 million listings in over 65,000 cities and nearly 200
countries.
As of late 2016, Ai
nb was valued at around $30 billion, a similar amount to the Ma
iott hotel
group, even though it does not own any of its properties.
Some local governments have decided Ai
nb is illegal or highly subject to local restrictions.
Imagine a city, which had previously made listing homes on Ai
nb illegal due to concerns that it
was raising home values and making ownership unaffordable, makes a decision to allow the firm
to operate as long as it pays travel taxes. In order to grow the local marketplace, Ai
nb hires you
to come up with a plan and execute it.
Problem Statement
1 Conor Henderson is an Assistant Professor of Marketing at the University of Oregon, Shrihari Sridhar is Center for
Executive Development Professor at Texas A&M University, and Alejandro Lerza Durant is an M.S. Analytics
Student at Texas A&M University.
Marketing Strategy: Based on First Principles and Data Analytics
3
As documented in Chapter 3, customers change over time due to industry fluctuations. Failing to
understand and address these dynamics can lead to poor business performance. Since variation in
customers’ preferences over time is an inherent condition facing all marketers, an effective
marketing strategy must manage these dynamics (MP#2).
Ai
nb’s problem would appear to fit the second fundamental marketing problem all firms face
while formulating marketing strategy, i.e. multiple factors were working together in multifaceted
ways to make all customer change in the market. So Ai
nb must constantly analyze which needs,
desires, and preferences across their buyers are most important to attracting (onboarding)
customers and retaining hosts. Thus, Ai
nb needs answers to the following questions:
Which product attributes are most desired by customers during acquisition and retention?
How can the market be effectively segmented to decide:
o Which customers to acquire?
o Which customers to expand?
How should Ai
nb work its targeting and positioning strategy to deal with custome
dynamics?
To help inform the decision, suppose Ai
nb embarked on an analytics exercise in Eugene, OR, to
power a choice-model-based analysis.
Data
Traveler Data
As a local Ai
nb manager, you partner with Alaska Airlines to promote your firm. As a pilot test,
Ai
nb will pay Alaska Airlines $9,000 for the ability to email a promotion to the next 3,000
passengers who book a flight from Seattle to Eugene.
You design three emails: 1) promotion of $25 off an Ai
nb booking, 2) promotion of free
transportation from the airport to the Eugene destination booked on Ai
nb (taxi cost is $25, pre-
a
anged through Oregon Taxi), and 3) a simple welcome email from Ai
nb with a 30 second
video that shows some of Eugene’s highlights (no additional cost). The email promotions are
andomly distributed to the first 3,000 passengers who booked a flight to Eugene on Alaska
Airlines and did not already have an Ai
nb account based on an email-matching algorithm. Each
promotion was mailed to 1,000 passengers.
After the email campaign, Alaska Airlines shares the following information about the collected
data:
Email promotion type: 1=$25 off, 2=free taxi, 3=welcome. These are converted into
“dummy variables” of 1/0 for $25 off and 1/0 for taxi, and the welcome email is used as
the default reference category.
Marketing Strategy: Based on First Principles and Data Analytics
4
Customer email: The variable is used to do the following:
o Determine if the recipient uses any of the following email accounts:
@gmail.com;
@yahoo.com;
.edu;
or other. (These are also converted to dummy variables, with “other” as the
default reference category.)
Alaska account status: 1=not an Alaska frequent flier member, 2=frequent flier member,
3=MVP frequent flier member.
Address: 1=out of state, 2=Oregon but not Eugene or Springfield, 3=Eugene or Springfield
(converted to two dummy variables with “out of state” as the default reference category).
Age.
Outcome: Indicates whether the person responded to the email by booking a stay on Ai
n
in Eugene: 1=yes, 0=no.
Number of tickets booked on the itinerary.
Type of ticket: 1=round trip departing Eugene in less than 14 days, 0=one way to Eugene.
Host Data
Separately, Ai
nb has data on hosts—those who list their property on the Ai
nb platform for
travelers to rent. It is useful to know what predicts a host’s likelihood to remain on the platform
over time. With a host’s retention rate and information on how much revenue they earn, you could
prioritize which host to target based on their “lifetime value” (calculation can be estimated based
on a customer lifetime value formula).
You have the following data about 400 hosts:
Whether the accommodation is still listed on Ai
nb: 1=yes, 0=no.
How long they were on Ai
nb in days.
Listing type: 0=shared room or private room, 1=entire home.
Number of guests the listing accommodates.
Zillow estimate of property value in thousands of U.S. dollars.
Average listing price on Ai
nb per night.
Average rating by Ai
nb guests.
Revenue generated in one year for Ai
nb via its property fee.
Location: 1=campus to downtown to Whitaker, 2=Fairmont to Amazon to South Eugene
Hills, 3=Friendly to West Eugene, 4=North Eugene, 5=Springfield, 6=other outskirt
locations. These are converted to dummy variables with “other outskirt locations” used as
the default reference category.
Untitled Spreadsheet
Frontpage
Enginius
Predictive Modeling
Steve Gallegos
Copyright (c) 2022, DecisionPro Inc.
Data description
Cali
ation data
Cali
ation data contains 3000 rows and 12 columns. It contains 11 predictors, to which we added an intercept.
Summary of predictors
These data have been used for model cali
ation.
Email_25 Email_Taxi Gmail yahoo Edu AlaskaFF Add_Ore Add_Eug Age Tickets RoundTrip
Average 0.3330 0.3333 0.3093 0.2237 0.1053 1.7413 0.2510 0.3797 38.04 1.990 0.8697
Standard deviation 0.4714 0.4715 0.4623 0.4168 0.3070 0.5188 0.4337 0.4854 10.34 1.176 0.3367
Median 0.0000 0.0000 0.0000 0.0000 0.0000 2.0000 0.0000 0.0000 38.00 1.000 1.0000
Minimum 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 18.00 1.000 0.0000
Maximum 1.0000 1.0000 1.0000 1.0000 1.0000 3.0000 1.0000 1.0000 71.00 5.000 1.0000
Frequency table for observed choices
Alternative 0 Alternative 1
Count 2556 444
Frequency 85.2% 14.8%
Cali
ation data
choice Email_25 Email_Taxi Gmail yahoo Edu AlaskaFF Add_Ore Add_Eug Age Tickets RoundTrip
cu_id_1 1 1 0 1 0 0 1 0 0 46 1 1
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cu_id_248 0 1 0 1 0 0 1 0 0 47 1 1
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cu_id_250 1 1 0 1 0 0 2 0 0 20 1 1
cu_id_251 0 1 0 1 0 0 2 1 0 42 1 1
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cu_id_265 0 1 0 1 0 0 2 0 0 36 3 1
cu_id_266 1 1 0 1 0 0 3 1 0 33 2 1
cu_id_267 0 1 0 1 0 0 2 0 0 41 1 1
cu_id_268 1 1 0 1 0 0 2 0 1 41 1 1
cu_id_269 0 1 0 1 0 0 2 0 0 33 1 1
cu_id_270 0 1 0 1 0 0 2 0 1 49 3 1
cu_id_271 0 1 0 1 0 0 2 1 0 31 1 1
cu_id_272 0 1 0 1 0 0 1 0 0 34 1 1
cu_id_273 1 1 0 1 0 0 2 1 0 23 1 1
cu_id_274 0 1 0 1 0 0 2 1 0 40 3 1
cu_id_275 0 1 0 1 0 0 2 0 1 32 1 1
cu_id_276 0 1 0 1 0 0 2 0 1 28 3 1
cu_id_277 0 1 0 1 0 0 2 0 0 37 4 1
cu_id_278 0 1 0 1 0 0 2 0 1 39 2 0
cu_id_279 1 1 0 1 0 0 1 0 0 42 1 1
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