Cossatot Community Restaurant Grades & Regression Statistics Case Analysis Hi Again I have another case to analyze ,all the needed docs are attached. Pleas

Cossatot Community Restaurant Grades & Regression Statistics Case Analysis Hi Again I have another case to analyze ,all the needed docs are attached. Please follow the instructions. 9-916-038
REV: MARCH 5, 2020
Advertising Experiments at RestaurantGrades
Shirley A. Critic, founder of RestaurantGrades (RG), a restaurant review platform (similar to Yelp
or TripAdvisor) with an impressive stock of online reviews written by ordinary restaurant-goers, was
happy with RG’s growth and position in the industry, and had compelling evidence that RG reviews
had an important influence on the restaurant choices people made. However, doubts had surfaced
about the efficacy of the company’s main source of revenue—selling advertisements (ads) to
restaurants. To better understand this issue, Critic decided to run a large-scale randomized controlled
trial with a control group and two treatment groups: one to test the impact of RG’s current ads on
restaurant sales, and the other to test the impact of an alternative ad design to which she was
considering switching. Her team ran the experiment and found the results in the spreadsheet
attached in the data supplement. Being a data nerd (in the best possible sense) and knowing that the
devil was often in the details, Critic wanted to look at the results herself. She wanted to understand
whether her company’s ads really worked, and whether they should stick with the current design or
switch to the alternative design.
Background on RG Advertisements
On RG, each restaurant had a profile page with operating information including its hours, phone
number, and location, where RG users who visited the restaurant could leave reviews for other users.
Users could also discover and search for restaurants on the platform using filters, and could make
reservations and order food through a restaurant’s profile page.
The majority of RG’s revenues stemmed from selling ads through its sales team, which cold-called
restaurants to try to convince them to advertise on the platform. Advertisements, labeled as
sponsored search results, were placed in a separate section above the organic results for searches
conducted by users. Packages of ads were purchased for about $300 per month, and advertisers were
required to sign up for a one-year contract. While RG used a search algorithm much like Google’s
that determined when and which ads were shown, depending on a user’s search for restaurants on
the platform, restaurants had little say in what search terms would trigger their ads. However, RG
guaranteed that their ads would be shown a minimum of 1,000 times per month to users.
RG’s current search algorithm showed ads for restaurants triggered by type of cuisine within a 0.5-mile
radius of a user’s search. For example, if a user searched for Italian restaurants in Harvard Square, the
algorithm chose two Italian restaurants within Harvard Square to advertise. The engineering team had run
a variety of tests looking at how users responded to different types of ads in different searches,
916-038
Advertising Experiments at RestaurantGrades
and had designed an alternative search algorithm. Rather than choosing two restaurants by cuisine,
the alternative algorithm showed ads when a user searched for a specific restaurant and chose to
display two restaurants with similar ratings and hours. While Critic’s team was reasonably satisfied
with how the current algorithm showed ads, they were open to the possibility that the alternative
design might be significantly better (or significantly worse) in providing benefit for their advertisers.
Experiment
For the experiment, Critic’s team randomly selected 30,000 restaurants that were active on its
platform but were not currently advertising, yielding a sample that was representative of its
population of restaurants in the U.S.
For the one- month duration of the experiment, 10,000 of these restaurants were randomly chosen
to receive free ads using the current advertising approach, and another 10,000 restaurants were
randomly selected to receive the alternatively designed ads. The main difference between these two
treatment groups was that the alternative design used a very different algorithm to decide when to
deliver ads and which ads to pair with each search, as described above. The remaining 10,000
restaurants received no advertisements. None of these restaurants were informed about the
experiment or the advertisements.
Spreadsheet
The spreadsheet supplement for this exercise (HBS No. 916-702) contains the outcomes that
Critic’s team observed for the 30,000 restaurants during the one-month experiment. The unit of
observation is a restaurant- month, so the data for each restaurant are in a single row. For example, in
a row, “pageviews” refers to the number of unique visits to a restaurant’s RG page during that
month. The outcome variables included are as follows:
Variables
Variable Name
Definition
business_id
The restaurant’s unique identifier
treatment
= 0: in the control group
= 1: in the first treatment group (ads using current algorithm)
= 2: in the second treatment group (ads using alternative algorithm)
restaurant type
= chain: chain restaurants
= independent: independent restaurants
2
pageviews
# of visits to the restaurant’s RG page per month
calls
# of phone calls made from the restaurant’s mobile RG page per month
reservations
# of reservations made from the restaurant’s RG page per month
Advertising Experiments at RestaurantGrades
Harvard Business School Case # 916-038
Courseware # 916-702
This courseware was prepared solely as the basis for class discussion. Copyright © 2016 President and
Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-5457685, write Harvard Business School Publishing, Boston, MA 02163, or go to
www.hbsp.harvard.edu/educators. This publication may not be digitized, photocopied, or otherwise
reproduced, posted, or transmitted, without the permission of Harvard Business School.
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