Better decisions, faster for non-profits

Jay Fuller

NPOGROW

+

What they do:1. NPO consulting

2. Sell their analytics platform

What they need:A revenue prediction

model

?

?

?

?

?

Where can a NPO expand and hit their revenue target?

64%

35%

52%

43%

48%

Where can a NPO expand and hit their revenue target?

the data: revenue for 874 NPOs

$2.1M

$1.7M

$4.9M

$1.2M

$0.4M

the data: revenue for 874 NPOs

approach: estimate probability distribution for each market

$2.1M

$1.7M

$4.9M

$1.2M

$0.4M

PSF

PSEA

Revenue ($M)

# of

NPO

s

Lognormaldistribution

Like

lihoo

d of

m

issi

ng ta

rget Bootstrapped

95% confidence

intervals

Revenue ($M)

Like

lihoo

d of

hi

tting

targ

et

New York Distribution(PNY)

Cumulative distribution function

Survival function

C

D

A

B

30% of NPOs in NYC raise

>$5M

New York

Input: $5M revenue target

Risk estimate using a survival function

Revenue ($M)

Likelihood of hitting target

Plug in any revenue target to get the probability of hitting it$ Millions

Cities where you are most likely to your revenue target

Demo

about me: PhD, neuroscienceHow does the brain control movement?

thank you!npogrow.org

Philanthropic market predicts philanthropic revenue.

Philanthropic market ($M)Estimated philanthropic market size ($millions)

Phila

nthr

opic

reve

nue

($m

illio

ns)

Philanthropic market predicts philanthropic revenue.

Philanthropic market ($M)Philanthropic market ($M)

Phila

nthr

opic

reve

nue

($M

)

?

My objective: Predict the likelihood of reaching a revenue

target in each market

Desired solution properties:1. Interpretable

2. Stand alone prototype3. Get better with the data being collected

Project take-aways:1. Probabilistic method of

estimating revenue2. Gets better with the data Room40

is collecting3. Prototype is driving sales of

business analytics platform

Plug in any revenue target to get the probability of hitting it

OptimizationProblem: