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.

1. How to choose
canvassing turfs
knocking area
efficient

2. Complete Solar
Solution
Installs solar systems
on residential homes
Market base: Bay Area, Chino
Hills & San Diego CA
Prominent canvassing team of 40 canvassers

3. no presence of pool
presence of pool
Turf 1 VS. Turf 2
Turfs currently chosen based on:
intuition
&
pool presence density
~17% 80%

4. Canvassing areas are chosen poorly,
not based on demographic data or analytics

5. 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
, , ,
Ages
46-65

6. Credit score of
over 650
Presence of
pool
,

7. Decision Support System
Allow managers to choose
most efficient turfs
Based on demographical data & pool density
optimizes canvasser’s time & leads

8. Decision Support System
consists of two parts:
K means clustering algorithm
K-nearest neighbours (KNN) algorithm
&

9. Constraints
Age ≥ 40 years old
Marital Status Married
Income $175,000+ per year
Home Value $350,000+
Credit Rating A-D (A being the best and H
being the worst)
K means clustering algorithm
to divide potential customers into
groups according to characteristics

10. 117.98
117.99
118
118.01
118.02
118.03
118.04
33.885 33.89 33.895 33.9 33.905 33.91 33.915 33.92 33.925 33.93
Longitude
Latitude
Distribution of Potential Clients
Location
K means clustering algorithm

11. K-nearest neighbours (KNN) algorithm
to determine which cluster is the
closest and likelihood of purchase

12. Probability of
purchase
LATITUDE
&
LONGITUDE
INPUT OUTPUT

13. The End.
thank you for your time 
Questions?