And I give you a 100% assurance that this would be better than the last. Hope so. Fingers Crossed.
Class just got over and I glanced to look at all my peers. BA got over. Some gave a sigh of relief as they got done with another “subject with numbers” and some wished the subject had more lectures. There was an intense discussion as to what all one learnt over the last week.
Well, on my part, I learnt a lot. Reflecting back to my SI, I just wished I had known SPSS before the internship. It would have probably helped me a lot and I would have produced a better quality work.
So today I will elaborate on Conjoint Analysis.
Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.
Area of Application:
Conjoint analysis is appropriate when a researcher wants to measure preference for a product or service, the source of that preference, or the impact on preference caused by product design changes.
While there are a wide number of uses for conjoint analysis, four of the most common will be discussed below.
• Product Design Research
• Market Segmentation Research
• Brand Equity Research
• Price Sensitivity Research
Conjoint analysis takes these attribute and level descriptions of product/services and uses them in interviews by asking people to make a number of choices between different products.
Let’s demonstrate the use of conjoint analysis in a quirky example:
For instance who would Mr.X choose Angelina or Megan?
Age 36 25
Height 5’8’’ 5’5’’
Intelligence 7/10 2/10
(Hypothetical assumptions; sorry if someone’s sentiments got hurt .... NOT)
In practice you can see how difficult some of the choices can be... :p.
Mr.X’s thought process might be:
“Angelina is older, but has the height and intelligence, but Megan Fox is younger and dumber. But for me Intelligence is more important so I’d rather focus on getting Angelina.”
By asking for enough choices one can work out how valuable each of the factors is relative to the others around it – this value is known as the ‘utility’ of the level.
In this instance we can see that for X, the optimum age is 24. 18 is too young and above 24 is too old. We can see that there is no benefit in spending exorbitant amount of money on make-up and amazing clothes, to attract Mr.X if the age is above 24years.
However, we can also compare across attributes to see which attributes may have the greatest impact in his decision. We can therefore say which attribute is most important and measure importance by taking the relative impact of one attribute compared to the others. For example:
In this dummy example, being intelligent is more than twice as important as the height of the person and is the most important variable. So that would be a more important criteria.
So, we come to the end.
Till we meet again.....
Marketing Group 5