Business Intelligence Best Practice Summit: Introduction to Choice Modelling
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Predictive customer analytics uses techniques developed by Nobel Prize-winning researchers to understand customer behavior and predict how customers will respond to different product features, pricing, and marketing strategies. It allows companies to simulate demand for various attributes, understand what different customer segments value, analyze price elasticity, assess what customers are willing to pay for features, and predict how market shares may shift. The goal is to provide deep insights into customer preferences and uncover the true value of a company's brand.
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• The Art of Positioning is Marketing
• Positioning the game of Mind and Heart
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- 1. PREDICTIVE CUSTOMER ANALYTICS Mike McGee | General Manager | Managility
- 2. 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
- 3. Can we really predict customer behaviour?
- 4. What are those choices worth?
- 5. Predictive Customer Analytics (PCA) Beneath the Surface
- 6. Benefits & Power of PCA Simulation can test the demand for a number of attributes, such as: product features, pricing and packaging
- 7. Benefits & Power of PCA Understand the value attributed by key customer segments
- 8. Benefits & Power of PCA Understand the price elasticity and demand curve for your product or services
- 9. Benefits & Power of PCA Assess the customer’s ‘Willingness-to-Pay’ for product features
- 10. Benefits & Power of PCA Predict the shifts in market shares
- 11. Benefits & Power of PCA Understand the true value of your brand