I'm asking for your help in a real world price test evaluation. I've had two years of stats and econometrics back at uni a couple of years ago and am trying to scramble all my knowledge together - would be awesome if you could help me a bit .

[Here a bit of background info for anybody interested]

I am working for a startup in the Netherlands and we just ran a price test on an online webstore for the last couple of months.

The setup is as follows:

People visiting the online store are randomly put into either of 5 price groups

A - cheapest price offered by any of our competitors for any given product

B - + 7,5%

C - + 7,5%

D - + 7,5%

E - + 7,5%

The data currently consists of around 2000 unique user ID's and looks somehow like this:

User ID ; Price group(A/B/C/D/E); Conversion (0/1)

I'd now like to run a logit regression on the data so that I can say: A consumer paying 7,5% more than the cheapest price on the market (group B) is X% less likely to buy than a consumer seeing the lowest price (A).

From what I remember I would let the regression be:

ln(conversion) = ß0 + ß1(B) + ß2(C) + ß3(D) + ß4(E) + e

where the the independent variables are dummies and taking the lowest group A as the base group.

My questions are:

- does that make sense from a practical perspective?

- anything else I have to look out for/ test? (autocorrelation, homoscedasticity etc)

- anything else you would do with this set of data that would be interesting from a business perspective?

Really grateful for any help!

Andre