Conjoint Analysis Module
Conjoint Analysis is a well-established method for simulating consumer response to new products and changes to existing products. Conjoint analysis helps you to choose the best feature and price trade-offs to gain market share against competitors.
Survey participants are shown alternative profiles (generated by computer) of a product, with different features, prices and competing brands (if applicable). Survey participants are asked to evaluate each profile. Enough alternatives are shown to estimate the impact of different features and pricing on preference.
These estimates of impact take the form of a statistical model, with each attribute level (feature), price and brand having a parameter (also called a part worth) assigned. The parameters indicate the impact of alternative attribute levels, pricing and brand and are useful for understanding what is important. It can also be used to segment the market by need. This can be taken a step further by creating a market simulator, allowing different scenarios to be tested.
Example - UHT Milk Conjoint Analysis
How does conjoint analysis work?
Conjoint analysis is based on the brilliant insight that competing products and services in a market can be reduced to component parts called attributes. This means that once the attributes are identified and mapped, a computer can create new synthetic products from these attributes called profiles.
Survey participants in the relevant market are asked to evaluate alternative product or service profiles. The alternatives are created based on a computer generated experimental design. The survey doesn’t need to test every possible alternative, since the goal is parameter estimation, rather than direct evaluation of the profiles shown.
Once the survey is completed, the model parameters can be estimated. These parameters indicate the impact of brand, features and pricing on claimed purchase likelihood or appeal. The mathematical model can then translate purchase likelihood into a predicted choice or choice probability using mathematical functions called decision rules. Consumer reactions can be simulated for any particular brand, feature and price combination - at the individual level - in order to test different scenarios.
Who is conjoint analysis for?
Conjoint analysis is extremely flexible and has found use in a broad range of applications and fields such as: new product concept development, product reformulation, pricing strategy, brand equity measurement and even HR and medical fields.
- Estimate preference share or ballpark market share for your product and competitor products (if you have no competitors, such as is the case with radical new inventions, conjoint can also be used but in a slightly different way). Note: Preference share is a function of brand equity, product features and pricing. It is useful to product developers and pricing strategists as it levels the playing field between competitors, isolating the effects of differential promotion and accessibility (distribution).
- Estimate share for products that don’t exist yet, or to improve existing products.
- Identify the best combinations of features, or compromises that improve results at acceptable cost.
- Determine the relative importance of different product attributes without asking consumers directly.
- Run scenarios in a simulator to see if your changes would have a positive or negative impact.
- Run 'what if' scenarios regarding competitor responses.
- Obtain a rough idea of the impact of price changes on share, without needing to run in-market experiments that may harm your brand.
- Understand approximately what price change may compensate for feature loss, or what increases may be possible with feature additions to remain competitive.
- Obtain an indication of your brand's equity.
Why Acentric's Conjoint Analysis Approach?
- Acentric uses a more advanced form of metric conjoint analysis which has numerous advantages over both traditional metric and choice-based conjoint (discrete choice models). These include:
- Acentric's metric conjoint has the unique ability to include a 'none' or 'no buy' option which is not possible with traditional metric conjoint.
- It works better with the small screens that most survey respondents use nowadays (e.g. smartphones) than choice-based conjoint, because it only requires one profile to be shown at a time.
- More cost effective, as sample sizes do not need to be as large.
- More attributes and levels can be included due to Lower levels of fatigue because survey respondents don't need to evaluate as many profiles when using metric conjoint.
- The model is estimated at the individual level, meaning part-worths can be used to segment using cluster-analysis.
- Unlike traditional metric conjoint analysis (e.g. IBM SPSS) that uses fixed decision rules (such as BTL or Max Util) to translate rating or ranking scales into shares; Acentric's approach adapts the sensitivity of choice probabilities to ratings by using a unique form of randomized first choice, allowing for greater accuracy in situations where Max Util shares are too extreme and where BTL's IIA assumption is not appropriate.
- Acentric's RFC implementation eliminates the IIA assumption and if needed allows the emulation of both Max Util and RFC decision rules, or anything in-between. Unlike the older RFC implementations, Acentric RFC does not rely on the Gumble distribution which has been criticized by academics.
- Test the impact of external effects. These marketing mix elements are often ignored in conjoint analysis projects. This helps to bring predictions closer to volume market share before resorting to forced calibrations, which means that the calibrations are milder.
For a case study see "Modelling consumer response to cow-free milk" and for more insight on why to use metric conjoint see "Seven reasons to choose CVA over CBC conjoint analysis" for some related insights and
What's included in the price?
The price is all inclusive. The following is included:
- Mapping the relevant attributes and levels that define the competing products in the market.
- Questionnaire setup.
- 300 completed interviews in the target consumer group (recuited from an online panel that pays incentives to enhance response rates). Minimum incidence 50%.
- Data preparation.
- Conjoint analysis model fitting.
- Simulator build in excel. This is the final deliverable which is emailed to you.
Approximately 15 to 20 working days depending on the target group, questionnaire length and analysis complexity. Note: timelines are provided as a guideline only. Timelines are calculated from the date you approve the final questionnaire for launch.