At the moment I’m studying the “Customer Analytics in Python” course.
I was about to do the Brand Choice Models homework, the final lecture of the Modeling Brand Choice section and I suddenly got confused about something: I think there might be a mistake in the example.
To calculate the Own Price Brand Choice Elasticity and the Cross Price Brand Choice Elasticity for different segments, I think we have to take different coefficients, regarding what we have obtained for that segment. Following the course example:
For the average customers, we obtain the next coefficients Data Frame:
Since in the example we are interested in Brand 5, we select the appropriate coefficient and we name it beta5 (-1.09).
And we use it to calculate the Price Brand Choice Elasticity for average customers.
When in the example are obtained the coefficients for the Segment ‘Well-Off’, the Data Frame displays the following values:
and we obtain the value of -0.44, but we don’t use it and I think that it is the value we must use.
Instead, we still use the old value beta5 = -1.09 as we can see in the figure below, extracted directly from the code in the lecture:
Am I wrong in my reasoning? Is there something I am not understanding? Is a mistake in the lecture?
Thank you in advance for your answer, and I wish you a Very Merry Christmas and a Happy New Year.
thanks for reaching out and for your question! The idea here is that though we’re examining cross brand 4, our own brand remains brand 5, this is why we still use the same coefficient. When examining the price elasticity of a brand (or indeed with most of marketing analysis) we’re interested in our own brand and that’s the perspective we take a look at.
In our case the beta coefficient is the coefficient for the Brand variable, and not for the segment variable. Thus, it does not change across segments, it would change for a different brand.
Hope this helps!
The 365 Team