predictive customer analytics: what your customers really want and what it’s worth to them

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Current market research methods, and in particular surveys, don’t provide reliable insights on how your customers will really behave - nor can they put a precise monetary value on their choices. Failed product launches, sub-optimal product designs & ineffective marketing campaigns are often the result. Using intelligent software simulations, Managility Predictive Customer Analytics help you to precisely predict outcomes and cost effectively test scenarios. Managility Predictive Customer Analytics combine the most comprehensive, patented choice modelling technology framework available on the market with in-depth subject matter and industry expertise. Choice modelling, a concept developed by Nobel Prize winner Daniel McFadden, provides a scientific framework to uncover how humans make decisions when faced with alternatives.

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Page 1: Predictive Customer Analytics: What your customers really want and what it’s worth to them
Page 2: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Predictive Customer Analytics (PCA)

Beyond the Surface

What is it about?

Page 3: Predictive Customer Analytics: What your customers really want and what it’s worth to them

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

Page 4: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Can we really

predict

customer

behaviour?

Page 5: Predictive Customer Analytics: What your customers really want and what it’s worth to them

What are those choices worth?

Page 6: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Predictive Customer Analytics (PCA)

Demos

Winter Festival - Video

http://vimeo.com/96051392

Page 7: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Predictive Customer Analytics (PCA)

Demos

Winter Festival - Model

http://managility.com.au/PCA/event/index.html

Page 8: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Predictive Customer Analytics (PCA)

Demos

Insurance - Model

http://managility.com.au/PCA/insurance/index.html

Page 9: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Predictive Customer Analytics (PCA)

Demos

Magazine Cover - Model

http://managility.com.au/PCA/mag/index.html

Page 10: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Benefits & Power of PCASimulation can test the demand for a number of attributes, such as:

product features, pricing and packaging

Page 11: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Benefits & Power of PCAUnderstand the value attributed by key customer

segments

Page 12: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Benefits & Power of PCAUnderstand the price elasticity and

demand curve for your product or

services

Page 13: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Benefits & Power of PCAAssess the customer’s ‘Willingness-to-Pay’

for product features

Page 14: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Benefits & Power of PCAPredict the shifts in market shares

Page 15: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Benefits & Power of PCAUnderstand the true value of your

brand

Page 16: Predictive Customer Analytics: What your customers really want and what it’s worth to them

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

Page 17: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Contact Us

1300 00 PALO (+61 1300 00 7256)

[email protected]

www.managility.com.au

facebook.com/managility

Page 18: Predictive Customer Analytics: What your customers really want and what it’s worth to them

Q + A