1. Line up all your results. (conversions, webvisits, sales, sign-ups…). This is the Y’s
2. Line up all your possible influencers (TV Trp, banner exposures, impressions, Outdoor, print adds, sun-hours, whether it is a week-day or not, share of voice, DM’s …..) This is the X’s
3. Do a correlation test between all your X’s. If the correlation between two X’s are 1 (100%), one X describes the other perfectly. If there is a correlation of 50% or higher between two X’s, one of the should be removed. Which one, you ask?. Good question. I look at each of the X’s correlation with other X’s, and remove the one who has the highest correlation with others. When no X correlates more than 50% with any other X, you are cleared for the next level.
4.Do a regression modelling with your X’s against the Y you are trying to simulate.
5. Check the X’s for any P-value above 0,05. If there are more than one X with such a value, remove the X with the highest P-value.
6. Go to step 4 and redo the regression modelling. Continue removing the largest P above 0,05, one P at the time.
7. When all P’s are belov 0,05 you are done.
8. Using the coefficients you can create the formula describing your model.
E.g. Web visits = 631 + (0,02*TV TRP’s) + (10*Impressions) + (15,2*taboid print adds)
Why haven’t anyone made it so simple and easy before? Because all of this fits on one page, and one page makes a pretty sad book. Damn, there goes my carreer in publishing