Choice Modelling is a scientific methodology used by academics, economists and policy-makers to measure consumer preferences. It is regarded as the most scientifically robust method to investigate and understand how choices are made.
As well as academics, a constant challenge facing companies is to understand the level and nature of the demand for new and existing products or services. Traditional surveys and market research methods capture a rough picture of consumer intentions that are often not supported by science using weak methods such as ratings or reliance on respondents’ own explanations of their intentions.
Choice Modelling is different.
Using a controlled experiment, influences leading to a decision, even those outside the conscious awareness of the respondent, can be identified and measured.
Choice Modelling allows organisations to accurately estimate demand and know exactly why customers are making decisions.
The applications of Choice Modelling are numerous. It has been used to estimate the demand for existing products and to forecast demand for new ones. It has been used to observe the effects of subtle layout and wording changes to marketing materials and even to measure the direct dollar valuation of brands.
Choice modelling allows the exploration of vast numbers of possible configurations, of the order of many many trillions, something unfeasible with traditional research methods.
Choice Modeling can answer questions such as:
- What aspects of a product do customers value when choosing?
- How can I make my company’s offerings more attractive?
- Is there a real demand for my new service?
- How can I make our marketing messages more effective?
This is an invaluable gift to marketers and business strategists for planning purposes and is particularly powerful when linked to a company’s business planning or budgeting process.
This allows the evaluation of countless combinations of offer design alternatives and potential competitive responses in real-time. By running a series of “what if” scenarios on the models, managers can assess not only the demand and profit potential of strategic moves but also the risk exposure if competitors react defensively.
Choice Modelling is used by:
- Financial institutions such as CBA and Wespac to model customer valuation of price variables such as interest rates, fees and rewards.
- Agencies such as Saatchi and DDB for concept testing, new product development and brand equity tracking.
- Healthcare including BUPA and RTA to understand how people value aspects of personal health and healthcare options for insurance packages and policy decisions.
- Construction for CSR and Hebel to develop pricing strategies for commodity building products for CSR and Hebel. Experiments covered pricing, product benefits, after-sales service and warranties allowing the user to price products competitively against alternatives.
- Fast food companies including McDonald’s, Subway and CocaCola to explore the myriad combinations of fast food ingredients, drinks, special offers, price, promotional messages and delivery to develop optimal bundled orders.
- Fast Moving Consumer Goods like Burgen to explore the billions of possible pricing and packaging configurations of new FMCG product variants.
Is Choice Modelling a new science?
The science of Choice Modelling has been around for more than 30 years and in development for at least 20 years before that. Fortune 500 companies are using it and have so for many years.
It is only now with the ease of customer contact and powerful computing and custom software that it is becoming part of everyday use. Choice Modelling is currently transforming the larger world of marketing.
The field of Choice Modelling is one of the most vibrant in the social sciences, with two recent Nobel Prizes for economics awarded to contributors: Daniel McFadden (2000) and Daniel Kahneman (2002).
While the academic origins are complex, the first important steps in the field of Choice Modelling are attributed to R. Duncan Luce and his psychological theories of choice and utility – the fulfilment that people derive from behaviours such as consuming a good or service. Along with other mathematicians and psychologists in the late 1950s, Luce pioneered a study to understand how people make decisions when faced with alternatives. Daniel McFadden and other economists in the 1960s saw a direct application of this psychological theory to economics since customer choice behaviour is fundamental to economic demand.
McFadden’s early and best-known application of discrete choice analysis was in his work with the California Bay Area Rapid Transit Authority (BART) to understand the potential demand for a mass-transit system – “If we build it, will they come?” McFadden helped BART analyse the way people make transportation decisions when faced with various alternatives such as automobiles, buses and trains all with different costs, convenience levels and trip times. McFadden’s model was highly accurate, predicting a 6.4% share of commuter travel for BART, being very close to the actual 6.2% share achieved by the system.
What started in psychology and transportation economics has evolved into perhaps the most important tool that marketers have. The world’s top marketers understand the significance of Choice Modelling. Professor Jordan Louviere published an article in 1983 alerting the business world to the practical applications of Choice Modelling. Oliver Wyman was one of the first consulting firms to apply the technique in telecommunications, financial services, and many other industries. In 1998 Ben Kennedy-White of SurveyEngine, while working with leaders in the field including Louviere and Ben-Akiva, developed a standardised system for practical commercial use of Choice Modelling. In 2014 SurveyEngine had patented this system as general-purpose software allowing rapid and inexpensive modelling of human decision making.
The initial costs borne by frontier clients was enormous, requiring expert Mathematicians and Economists and often taking over a year to execute. The cost of individual Choice Modelling projects is now of the order of 100’s rather than the 10’s of 1000’s of dollars. For example, in 2002 the Australian Defence Forces, using the Survey Engine system, paid around $500,000 to model retention of personnel, whereas today, those same services would be $10,000. The Independent Energy Regulator in SA and NSW engaged SurveyEngine, Professor Louviere and KPMG to model the effects of the electricity market post-deregulation at a cost of circa $2,000,000. Today for the same outputs the cost would also be around $10,000.
As marketing noise and advertising clutter continue to increase, scientific experimentation Choice Modeling allows marketers to better communicate with their customers and match product and service features to customers willingness-to-pay. substantially raising the odds that their marketing efforts will pay off.