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Made this for our Decision Support Systems class, we took real California homeowner data to target customers that resembled our customer market base using KNN and clustering algorithms.
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How to choose
canvassing turfsknocking area
efficient
Complete SolarSolution
Installs solar systems on residential homes
Market base: Bay Area, Chino Hills & San Diego CA
Prominent canvassing team of 40 canvassers
no presence of pool
presence of pool
Turf 1 VS. Turf 2
Turfs currently chosen based
on:
intuition&
pool presence density
~17% 80%
Canvassing areas are chosen poorly,
not based on demographic data or analytics
Data has found that our
consists mostly of homeowners that:
client market base
Are Married
[existing customers]
Middle-aged Lived in their home for over 5 years
, , ,
Ages46-65
Credit score of over 650
Presence of pool
,
Decision Support System
Allow managers to choosemost efficient turfs
Based on demographical data & pool density
optimizes canvasser’s time & leads
Decision Support System
consists of two parts:
1K means clustering algorithm
2K-nearest neighbours (KNN) algorithm
&
Constraints Age ≥ 40 years oldMarital Status
Married
Income $175,000+ per yearHome Value $350,000+Credit Rating A-D (A being the best and
H being the worst)
1K means clustering algorithm
to divide potential customers into groups according to characteristics
33.885 33.89 33.895 33.9 33.905 33.91 33.915 33.92 33.925 33.93117.95
117.96
117.97
117.98
117.99
118
118.01
118.02
118.03
118.04
Distribution of Potential Clients
Location
Latitude
Long
itude
1K means clustering algorithm
2K-nearest neighbours (KNN) algorithmto determine which cluster is the closest and likelihood of purchase
Probability of purchase
LATITUDE &
LONGITUDE
INPUT OUTPUT
The End. thank you for your time
Questions?
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