Predictive Customer Analytics (PCA)
Beyond the Surface
What is it about?
FOUNDATIONS
Professor
Daniel McFadden
2000 Nobel Prize Winner
Berkeley, MIT, University
of Southern California
Focus Area: Discrete
Choice Theory
Professor
Daniel Kahneman
2002 Nobel Prize Winner
Princeton, Berkeley
Focus Areas: Behavioural
Economics, Hedonic
Philosophy
Can we really
predict
customer
behaviour?
What are those choices worth?
Predictive Customer Analytics (PCA)
Demos
Winter Festival - Video
http://vimeo.com/96051392
Predictive Customer Analytics (PCA)
Demos
Winter Festival - Model
http://managility.com.au/PCA/event/index.html
Predictive Customer Analytics (PCA)
Demos
Insurance - Model
http://managility.com.au/PCA/insurance/index.html
Predictive Customer Analytics (PCA)
Demos
Magazine Cover - Model
http://managility.com.au/PCA/mag/index.html
Benefits & Power of PCASimulation can test the demand for a number of attributes, such as:
product features, pricing and packaging
Benefits & Power of PCAUnderstand the value attributed by key customer
segments
Benefits & Power of PCAUnderstand the price elasticity and
demand curve for your product or
services
Benefits & Power of PCAAssess the customer’s ‘Willingness-to-Pay’
for product features
Benefits & Power of PCAPredict the shifts in market shares
Benefits & Power of PCAUnderstand the true value of your
brand
The PCA ProcessAssess strategic objectives: e.g., find
optimal price, product features, identify real
motives of customers
Configure PCA Model according to objectives, &
model the choices in realistic environment: e.g.,
different product/price choices in fridge
Define and supply relevant sample: i.e.,
target group that will go through model
Run actual process:
sample group cycles through
choice models
Formulate final outcomes &
recommendations report
Contact Us
1300 00 PALO (+61 1300 00 7256)
www.managility.com.au
facebook.com/managility
Q + A
Recommended
