In R I have
day promotion profit new_users 1 105 45662 33 2 12 40662 13 3 44 46800 20 4 203 54102 46
day is simply the day (and is in order).
promotion is simply the promotion-value for the day, the
profit is the profit that day and
new_users is the number of new users that day.
I want investigate the relationships between
new_users. We see a clear positive correlation between
profit, and there is also a positive correlation between
In R I simply test correlation
cor.test(data$promotion, data$profit, method="kendall", alternative="greater" ) cor.test(data$promotion, data$new_users, method="kendall", alternative="greater")
which both gives a low p-value, ie we have a positive correlation.
I want to find a point where where the increase of
promotion don't increase
new_users that must, ie a sweet spot.
Here is 2 plots and the R code for these
plot(data$promotion, data$profit, col="brown") plot(data$promotion, data$new_users)
How should this be done?
My thoughts where to make a regression model. For the first one "promotion vs. new_users" one could use a poisons model because it's a count-process, so a model like this would be a good chose?
glm(formula= data$new_users ~ data$promotion, family="poisson", data=data)
Next what regression model should one chose for the next one. Is it fair to say that this regression model is a good chose ? (I use sqrt command)
glm(formula=data$profit ~ sqrt(data$promotion) , data=data)
Or maybe it's not even necessary to use a regression model at all to find a sweet spot?
I have now looked at 'good' new users. For each
day we have a
promotion value and we have a
count value which is the number of new good users. This plot shows us the number of good new users we get for a promotion for each day. For example for promotion value 90 we have a day where we got 8 new good users and a day where we got 14 new good users.
What would be the right approach to find a sweet spot for the use of promotion ?