Conjoint Analysis — what makes customers buy?
All customers are looking for the buyer’s trifecta: quality + speed + cheap. Customers older than age 5, however, know that is not how it works.
Conjoint analysis, developed in the ’60’s and 70’s and led by Wharton School of Business professor Paul Green, is a statistical technique that compares how respondents value the different attributes (features, function, benefits) that make up a product or service.
For you, it’s a way to prioritize and price your investments and offerings.
Does this rate $$ or $$$$?
Backed by years of research and using industry-leading analytical software, iSight studies discover what customers value most, and what they do not. Data can be spliced and diced and used to determine market segments.
iSight studies can help you determine the best possible combination of attributes in your new offering and the customers that find it the most compelling.
How it works
The best possible way for you to see how iSight studies work is by example. So here’s a little demo that will shed some light on how customers (in this demo, that’s you) make purchasing decisions –and illustrate exactly how conjoint analysis works.
What matters to you in a hotel?
Imagine you and a special companion are off to a getaway weekend at an oceanside hotel. Consider all the possible features: view, brand, beds, price, fitness center, restaurants, transport and more. What would be important to you, as a target customer?Contact us for a test drive
Craft the study
Your customers are your best source of market information. Their needs and wants are what should guide you. Not opinions. Not competitor pricing. Not guesswork.
iSight studies start with surveys that quickly get to the meat of which features your customers see as benefits and measures them on a relative scale.
It starts like this: in a workshop that includes your sales and technical people, we define and prioritize the key attributes (features) of a proposed technology development or available offering.
The differing values or features of these attributes are then grouped in various combinations, which will be tested by your team before being ranked by customers.
The analysis of prioritized feature combinations can then be used to determine pricing, market segments, and targets for go to market strategies.