I'm trying to measure the impact of a promotional activity and want to see whether it is contributing to business performance.
Let's say there are 100 campaigns. For some campaigns, there is no promotional activity at all. If a campaign has promotional activity, the % of items included in the campaign might differ.
I used '% of items included in the promotional activity' as my independent variable and revenue as my dependent variable. The results show that promotional activity is insignificant with a p value of 0.7 and the coefficient is negative. Also tried binary categorization (promotion vs no promotion) and the results are similar. My R squared values are close to zero.
I'm aware that this is a very limited analysis so far. But I'm quite new to this and any comments would be highly appreciated. To understand the impact of an activity, do I need to build a whole model that explains the variance correctly?
- What else should I do to improve it?
- Should I add more variables, such as number of # of visitors, into regression and why?
- Would running the same regression on subsets, like only campaigns in category x, make sense to see if promotion had an impact on that specific campaign? Would I gain anything by disaggregating data on the subsets above?