How can I enhance my linear regression model?

[Layo]

Hello, I am looking for suggestions to better predict the number of loyalty cards sold.

A company I work at has a 'pay for' loyalty program. We track how many loyalty cards we sell daily. I was able to build a linear regression model based on NumberOfCardsSold, TrafficInStore, GrossRevenue for a specific day. R-square is 97.5%. All p-values are less than 0.05.

In order to drive loyalty card sales from time to time, we execute different card-related promotions and sometimes these promotions may last for several days. At first, I was thinking to add dummy variables for different promotion types but then realized that it doesn't take into account the number of days we run these promotions for.

Essentially, I'm trying to estimate the boost that a particular promotional card-related event would have on a number of loyalty cards we'd sell that day. So if we're missing a monthly plan, we could launch certain promotions to get us back on track.

I'm not strong in various statistical methods, just basics like moving avg or simple linear regression. What approach would you recommend in my case?

Thank you!

 

[hlsmith]

Is your data inputted as the obs being day's values. So one row will be all of the variables you listed. If so, a dummy variable for that day could work. I will note that if you are inputting your data as days, this may be better modeled via time series. Issues being the days sells are not independent, there could be seasonality, trends and autocorrelation.

Lastly, an r-sq of 97.5% is VERY high.

 

[schan]

Hello, I am looking for suggestions to better predict the number of loyalty cards sold.

A company I work at has a 'pay for' loyalty program. We track how many loyalty cards we sell daily. I was able to build a linear regression model based on NumberOfCardsSold, TrafficInStore, GrossRevenue for a specific day. R-square is 97.5%. All p-values are less than 0.05.

In order to drive loyalty card sales from time to time, we execute different card-related promotions and sometimes these promotions may last for several days. At first, I was thinking to add dummy variables for different promotion types but then realized that it doesn't take into account the number of days we run these promotions for.

Essentially, I'm trying to estimate the boost that a particular promotional card-related event would have on a number of loyalty cards we'd sell that day. So if we're missing a monthly plan, we could launch certain promotions to get us back on track.

I'm not strong in various statistical methods, just basics like moving avg or simple linear regression. What approach would you recommend in my case?

Thank you!

If you are trying to measure the boost a particular promotional event would have on sales, then you need to add a dummy variable for each event, exclude one from the model to avoid collinearity and run the regression to see the effect. If you take daily sales data as the Outcome Variable, then the number of days you run the promotion for will not matter because you have observations for each day.
Hope this clarifies.