For any existing product, consumers already know the current prices. So, one should test prices that can be realistically changed and not a wide range of prices. As it can generate either random response or respondents heavily focus on price which leads to incorrect results
2. Testing too many price levels
Sometimes, in order to get greater accuracy, large number of levels are created for price, which increases the importance of price because of the “level effect”. The number of levels in price should be kept similar to other features being tested in the conjoint study
3. Not using information outside conjoint
Often, there is external data available that can supplement the findings of research, and one should
liberally use this external authentic source of data to enhance results of conjoint. For example, elasticity of existing price points from POS data can be used to calibrate the price elasticity for price points not tested in the market
4. Focusing only on pricing and not entire value chain
Often, price is not the only variable to play with. One can increase price by bundling in another feature(s) , or increase the price/Kg by reducing the quantity (often followed in CPG) at the same price. These options should be explored before taking the pricing decision.
5. Focusing only on current pricing model
Nowadays, onetime payment is not the only option of purchasing the product/service. Monthly or yearly payments is becoming common and if applicable, should be included as a part of the research.
6. Assuming Price as linear variable
There are threshold points / inflection points that are present and a linear variable will miss that.
7. Not including competition in the study
Brand plays a big part in any purchase decision and when doing a pricing research, own brand and competition brands should be included as a part of study to make choices more realistic for consumers. Also, price elasticity may be very different for one brand to the other and hence brand and price interactions should be included.
8. Assuming same price elasticity for every feature
In today’s mass customization age, not every feature will have same price preference and elasticity. So, using a more sophisticated technique that can give price elasticity for every feature can impact pricing decisions and revenues immensely
9. Testing similar ranges for all products
No two brands are same, no two SKUs are same either. Thus, one should not limit oneself to testing same price range / number of levels for every product. Same range / number of levels is neither a requirement nor a limitation of conjoint analysis and there are ways to test these differences effectively.
10. Targeting the incorrect / non representative group for research
Respondents provide the most valuable element of any research “data”. Having the right sample representing the population is a must and should be carefully executed to get robust results from the analysis
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