Predictive Analytics for Franchise Expansion Helping franchises identify ideal neighbourhoods for expansion through data mining techniques
CASE STUDY - Williams Fresh Cafe
We begin by obtaining an existing set of loca-
tions of Williams Fresh Cafe, from which we will
build our model on. The figure to our left shows
the current locations where Williams currently has
their storefronts. A quick glance tells us that these
locations are predominately based around
South-Western Ontario.
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We then obtain a set of new locations after
running our algorithm. Shown above, we see an
overlap between the new locations and the ex-
isting franchise locations. However, the set of
predictions also includes locations within Ottawa
and Kingston, and it is exactly these locations we
are most interested in because they represent
uncaptured market-share for Williams.
Finally, we examine the new locations outside of the overlapping set for further
analysis. In the city of Ottawa, see that a handful of the new recommended locations are
close to post-secondary educational establishments as well as central business districts. In
Kingston, we can see that the new recommended locations are also located close to post-
secondary educational establishments. In general, the predicted set can be interpreted as
a short list of possible locations for Williams to expand into, thus greatly reducing the amount
of time required to make franchise expansion decisions, as well as helping management
make more informed decisions through data-driven analysis.
1. BACKGROUND Current Site Selection Procedure:
i. Slow (takes weeks)
ii. Labour intensive
iii. Inconsistent and unscientific
2. OBJECTIVE Given a set of existing locations for a QSR (Quick
Service Restaurant) franchise, identify a set of
favourable neighbourhoods for further expansion within
Ontario.
3. APPROACH Utilize Ontario demographic data, existing storefront
location data, and locations of post-secondary
institutions for the purpose of performing data mining
algorithms.
4. MODEL i. Consolidate, cleanse, and standardize the data sets
ii. Identify key demographic attributes that improve
model performance (including but not limited to):
Population
Age
Gender
Minority Status
Occupation
Distance to Educational
Establishments
Mode of Transportation
Income
Marital Status
Number of Children
Education Attainment
Type of Household
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iv. A/B Testing of various attributes for the purpose of
improving model accuracy
v. For each existing franchise store location, calculate
Euclidean distance between itself and predicted
neighbourhoods in Ontario (with p(success) ≥ 95%) to
determine similar neighbourhoods in terms of
demographic features
vi. Find the predicted neighbourhoods in Ontario that
appear most often in the Euclidean distance calculations
across all of a franchise’s store locations filtered by lowest
Euclidean distance value
5. RESULTS Using 10-Fold Cross Validation, our Logistic Regression
model has a classification accuracy of 76% for Williams,
64% for Second Cup and 51% for Tim Horton’s.
6. IMPACT
7. CONCLUSION Data mining can be an effective tool to help franchises
identify new neighbourhoods to expand into while
expecting a similar set of demographics to their existing
locations. However, demographic information is not the
only determinant for predicting locations. Other important
factors to look at are the number of competitors, real-
estate availability, and intangible factors such as
management quality. There are still challenges with a
data-driven approach as it relates to site expansion, as
quality of data plays a pivotal role.
DATA SOURCE
Core Demographic Data -
Ontario
2013 Projected Data & RSI
Canada
Existing Storefront
Locations
Williams Website
Second Cup Website
Tim Horton’s Website
Post Secondary
Institutions
Ministry of Education
Team 11: Chad Xu, Jason Wang, Jonathan Ong, Lamin Ceesay
Faculty Advisors: Dr. Mark D. Smucker, Dr. Lukasz Golab
Sponsor: Piinpoint (Jim Robeson)
iii. Utilize logistic regression to obtain a set of
probabilistic likelihood of “success” for all Ontario
neighbourhoods
AREA IMPACT
Ethical Increased chance of capitalistic abuse of ethnic
and gender information
Health Higher exposure to fast foods
Safety Minimize financial risks for franchises looking to
expand operations
Society Identify markets for new service
