Lunch & Learn Presentation - Complete Solar Solution
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This document contains the resume of Merisa Lee, which details her educational and professional background working on optimization engineering and strategic marketing projects for solar energy companies, with a focus on analyzing customer data to improve canvassing routes and identify the key factors that predict potential solar customers. It also outlines her internship experiences analyzing data to suggest improvements to applications used for solar canvassing and discussions of roadblocks and next steps in continuous development of their customer targeting strategies.
Lunch & Learn Presentation - Complete Solar Solution
- 1. MERISA LEE
- 2. MERISA LEE From Leamington, ON Leamington!
- 3. MERISA LEE tomato capital of Canada
- 5. MERISA LEE Interested in renewable energy & the automotive industry
- 8. 2011 2016
- 9. LEAD OPTIMIZATION ENGINEERING INTERN Analyze data coming from app Make suggestions on how to improve routes Build algorithms to determine best homes to visit
- 10. CHINO HILLS CANVASSING Observe how canvassing is done, tablet based app in action, make hypotheses to improve data going in to application
- 11. CHINO HILLS CANVASSING Aggressive Annoying Rude Unpassionate Easy Genuine Smart Kind Passionate Hard working
- 12. What factors predict a potential solar customer?
- 13. Credit score High usage Human Dynamics Home-owner Presentation Pool/non pool Marital status Time of day Square footage Length of Residence Signs of life Climate Age # of previous attempts Children Home value Presentation Internet leads Income Canvasser tone Electric/hybrid car Education Neighbors with solar
- 14. STRATEGIC MARKETING What is strategic marketing? Target consumers that will actually be interested Why? Optimize our resources (canvasser time) to increase appointments and sales How? Find consumers similar to our clients customer base
- 16. SCORE
- 17. Why use the tablet? No need for data entry Able to record homes already knocked Integrate efficient data in app Tons of promise in analyzing data
- 19. Status Count Percentage Come Back 3 2% Inaccessible 4 2% Not Home 120 60% Refused 69 35% Successful 2 1% Vacant 2 1% TOTAL 200 Statuses 1% 1% 2% 2% 35% 60% Come Back Inaccessible Not Home Refused Successful Vacant
- 20. Knocks VS Time 250 200 150 100 50 0 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30
- 21. ROADBLOCKS Battery life inefficient GPS runs in background 2-4 hours
- 22. ROADBLOCKS Hard to test with very few Bay Area canvassers
- 23. ROADBLOCKS Having a 3rd party maintain our application
- 24. ROADBLOCKS Adjusting scoring method, thinking of different methods
- 25. NEXT STEPS NEW INTERN Patrick Roncal 3B Math
- 26. NEXT STEPS NEW CANVASSERS 20 in Dublin 12 in Chino Hills