10-19-2012 02:10 PM
Suppose a company has retail stores in two regions, A & B and ran a marketing campaign in region B for 3 months.
The average monthly store sales of last three months BEFORE the campaign of A is 5% higher than those of B.
The average monthly store sales of last three months AFTER the campaign of A is 8% higher than those of B.
I would like to know how to test 8% is significantly higher than 5%.
10-19-2012 02:41 PM
suppose the data look like below and month 1, 2,3 are before the marketing campaign and month 4, 5, 6 doing marketing campaign.
region, store#, month, sales
A, 001, 1, 100
A, 002, 1, 102
B, 101, 1, 67
B, 102, 1, 105
A, 001, 4, 99
A, 002, 4,110
B, 101, 4, 105
B, 102, 4, 121
10-19-2012 03:53 PM
My first question would be: did the marketing campaign affect the sales in B? If so by how much?
And instead of worrying about the 5% and 8% difference I would look first if the mean sales in A changed significantly regarless of what happened in B.
There is a time factor here as well. By any chance was there a similar change in the ratio of sales between A and B the same time last year?
10-22-2012 07:45 AM
I hate it when this happens. Region and marketing campaign are completely confounded in this design. I do not see how you can attribute any differences found to the effect of the marketing campaign when there are certain to be regional differences. You may be saved by the time factor, if the measures of sales in each region are pre and post campaign values. A repeated measures model that looks at the interaction between time and campaign, with region as a random effect might be able to salvage your data.