Pricing products and services – Part 1
One of the most critical challenges for any company is its understanding of customer preferences in order to choose the right price for its goods or services. However, deciding the ‘Willingness To Pay’ (WTP) of its customer can be quite a challenge and several market research techniques have been developed to help in this process.
Traditionally, the monadic price experiment has been the popular choice. In this method several possible pricing options are grouped in sets and different groups of sample customers are asked how likely they were to buy at that price. Though easy to use, this method is costly and cumbersome and will need to be carried out for a long period to be useful.
Generally, customers make purchasing decisions based on a combination of several factors including price. A process called the ‘Choice Based Conjoint’ was developed to leverage this important fact. Here, customers are presented with a scenario comprising several attributes including price which are then carefully varied thereby uncovering how important these factors were for the customer. This approach helps create a reasonably accurate model of how the customer weighs his choices and the company can explore the customer’s preferences further to tune its product offering as well as price. However, this method is costly and requires good technical capability as well as specialised software tools.
Other methods use simple ‘quick and dirty’ approaches. The Gabor-Granger Method presents a proposition at a specified price and asked if the price is acceptable. If the answer is affirmative, a higher price is then presented and so on. If the answer is negative to the initial query, the price is systematically lowered. The Price Sensitivity Meter (PSM) is a variation of this method in which the survey subject is shown an option and asked what price would be too expensive to buy, what price would be viewed as too costly but still purchasable, what would be a good value buy and what would be viewed as cheap and hence of suspect quality.
All the above methods presume that a customer behaves logically all the time. However, recent research has observed that the customer’s judgement is affected by other factors. One effect is called “Attraction Effect” because people find it easier to compare similar choices than dissimilar ones. Even a totally unrelated fact can significantly sway a person’s judgement and this is called “Arbitrary Coherence”. This is where big data concept would help. Glaringly unrelated variables play a role in purchase until you measure the co-relationships. Dan Ariely’s “Predictably Irrational” is a fantastic book that elaborates on this concept.
To assess the various methods, two studies were carried out on pricing for Adult Day Care (ADC) services. The first study used the PSM approach and the results were discouraging as they predicted an optimum price of $25 per hour which happened to be far lower than the national average. The second approach used the CBC approach with 10 levels of pricing (6 related to ADC and 2 each to assisted living and home health aide), client-staff ratio for ADC, maximum ADC clients and transportation availability (included, extra cost or not available). The results were dramatic and explained customer preferences somewhat cogently.
In conclusion, the CBC approach is very attractive and has a wealth of research studies to ensure continuous improvement. Potential bias in results can be carefully filtered out by fine tuning the model specifications and using the right statistical tools. Now think about social pressures that make you buy things! Is there a real need to buy a smart phone ?