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Conjoint Analysis has evolved to be one of the most powerful analytic tools in research, with far reaching tactical and strategic uses.

Predictive analysis, discrete choice conjoint, price elasticity - these are the many aliases for what is commonly known as trade-off analysis.  Before the invention of microprocessors for desktop computers, choice conjoints would demand floor-to-ceiling of computing hardware, months of data crunching, and large teams of data scientists.  Furthermore, technology allows us to simulate real life shopping trips on web-based surveys using virtual reality.  In the conjoint design, respondents are asked to make a purchase selection from a series of competing mock-ups with varying attributes e.g. brand, price, speed, material, etc.  Econometric modelling allows for the estimation of utility functions, which ultimately gives insight into how sensitive customers are to slight changes in product features.  The strategic implications can be profound, guiding pricing scenarios, advertising expenditures and market share.  Collaborative workshops with strategists and cross-functional teams are key to leveraging insights.

Price elasticity studies can be profoundly complex and expensive, especially when your offering has a wide variety of features and competitors.  There are some "pre-packaged" conjoint solutions e.g. Qualtrics, but these assume your model design is quite simplistic and restricted in product "features" (e.g. brand, price, etc.) and "levels" (e.g. choices within brand, ranges within price, etc.).

A Choice Based Conjoint (CBC) study maintains its reliability and predictability the closer we capture real-life decision-making.  For traditional, Non-adaptive CBC, it's best utilized when we have 8 or less “Features” and 10 or less “Levels”:
 

Feature Examples = 1) Brand 2) Price 3) # Seats 4) # Work Graph Objects 5) Boards 6) Dashboards…. 8)
Levels for Brand Example:  1) Asana 2) Jira 3) monday.com 4) Microsoft Project …. 10)

 

Below is an example of a simple, Non-Adaptive Choice Based Conjoint (CBC) where participants are shown 3 Smartphones with 6 features. In this choice exercise, participants are asked to choose from one of the four options.  This is repeated 12-18 times (depending on the complexity of the study), where differing smartphones and their features are rotated in.  Results from a CBC would allow us to...

 

+ understand the relative importance of one feature over the other e.g. camera features are more important than operating systems

+ predict the hypothetical market share that would result if Amazon were to lower its fire phone price or introduce a 16 Megapixel camera

+ understand the price elasticity for the brand and its associated product features

One important thing to keep in mind is that marketing campaigns, advertising, how competitors react to price reductions are dynamic, so though CBC can give you a hypothetical prediction of market share, it is a snapshot in time that assumes all else are equal in the marketplace.

Features below can also vary to include shipping costs and retailer (eg Amazon.com vs Apple.com vs Walmart.com).  In the design, it's crucial to carefully consider the attributes that go into the collective decision-making of the consumer.      

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For more complex services or product offerings, with >25 features/attributes, Adaptive Choice Based Conjoint (ACBC) is preferred.   

ACBC has three main survey design elements:

  • BUILD-YOUR-OWN ideal package:  The Build-Your element gives respondents the ability to choose products and services across package types AND across brands.  This further allows us to present conjoint exercises that are more refined and nuanced around the BYO package.  

  • ELIMINATING IRRELEVANT features:  ACBC gives the respondent a series of scenarios to determine which ones are irrelevant and can be eliminated from the final choice exercises.  This is a very important aspect to ACBC that allows us to have many product and service features, while respondent refines them throughout the exercise.

  • ADAPTIVE CHOICE EXERCISES:  ACBC has been shown to be more appealing and relevant to respondents, thus giving them the higher level of attention that is needed to perform the preference exercises.  

If you'd like to discuss how ACBC could assist your brand, reach out to us at collaborate@incitefusion.com