“A lot of times, people don’t know what they want until you show it to them.” Steve Jobs.
Deciding on the best development path for a new product or service can be challenging. Conjoint analysis helps you to choose the best feature and price trade-offs to maximise consumer appeal within the given constraints.
How does conjoint analysis work?
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 (alternative measures can be used if so desired). The mathematical model can then translate purchase likelihood into a preference share. Preference shares can be simulated for any particular brand, feature and price combination in order to test different scenarios.
Who is conjoint analysis for?
Conjoint analysis is relevant to a wide range of organizations; and is useful in evaluating both products and services. Roles may include - but are not limited to - engineers, R&D / NPD specialists, developers, marketers, entrepreneurs and executives.
- Estimate potential share of volume (preference share) for your product and competitor products (if you have no competitors, other options are available). Preference share is a function of brand equity, product features and pricing and is isolated from external factors (such as awareness and distribution levels) that may vary across competitors and time.
- Estimate preference 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 of preference, without needing to run in-market experiments.
- Understand approximately what price change may compensate for feature loss, or what increases may be possible with feature additions.
- Obtain an indication of your brand's equity, in terms of its ability to gain preference share when holding all features equal across all brands.
Why Acentric's CVA conjoint?
- Unlike traditional CVA conjoint that may use fixed decision rules (such as BTL) to translate rating scales into shares; a linking function is used to emulate CBC conjoint choice probabilities, and to adapt to the sensitivity of choice probabilities to ratings.
- CVA has the advantage of working well with small screens (e.g. smartphones) which CBC (discrete choice models) do not, as they stack multiple profiles on one screen in each choice set.
- Simulators include demographic weights for representation.
- Simulators include individual purchase volume weights (necessary in situations where repeats of the same behaviour will occur in the time period of interest).
- Limited to 6 attributes, and 24 levels in total (i.e. summed across all attributes). If you have more attributes, you can either choose the 6 most important or bundle. If you have any questions about this, please don't hesitate to ask. An example/template is provided during the price and launch process.
- Limited space for additional questions (see price and launch tool for options).
- Minimum incidence currently allowed = 50%.
Approximately 20 to 30 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.