How to estimate cut off percentiles to classify cost per metric?

Actual costs by ad type. Here are cost per outcome scores (in dollars, pesos, bitcoin, or whatever) for three hypothetical types of ads. I will explore them to see whether various kinds of possible summaries may be useful.

set.seed(2020)
a = round(rnorm(500, 20, 3), 2)
 summary(a)
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    10.66   17.86   19.79   19.84   21.89   29.60 
b = round(rnorm(100, 10, 2), 2)
 summary(b)
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    4.680   8.777   9.890   9.973  11.165  14.900 
c = round(rnorm( 70, 30, 4), 2)
 summary(c)
     Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    20.94   27.10   30.19   29.89   31.95   39.99 

For Type a ads, Medium cost is between 17.86 and 21.89, Low below 17.86 and High above 31.95; for Type b ads Medium is between 8.78 and 11.17; and for Type c ads Medium is between 27.10 and 31.95.

In the boxplots below (of varying widths as a reminder that sample sizes differ), the values inside the boxes correspond to Medium-cost ads.

boxplot(a,b,c, names=c("a","b","c"), varwidth=T,
        col="skyblue2", pch=20, main="Cost per Outcome")

Now, how would you make comparisons between Types? "Medium-cost" adds have very different cost ranges, depending on ad Type.

"Standard" costs. You might try standardizing the scores for each type separately. To do that, for each cost in a you would subtract the average cost for a, then divide by the standard deviation of costs for a. Similarly for b and c. You could call the standard costs $A, B$ and $C.$ They not longer represent actual costs, but relative costs within their ad Type. That might help make comparisons of ads among various Types, but if you start to talke about budgets, you'll have to go back to the actual costs $a, b,$ and $c.$

A = (a-mean(a))/sd(a)
 summary(A)
      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  -2.87313 -0.61880 -0.01607  0.00000  0.64222  3.05702 
B = (b-mean(b))/sd(b)
 summary(B)
      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  -2.62840 -0.59362 -0.04117  0.00000  0.59198  2.44674 
C = (c-mean(c))/sd(c)
  summary(C)
      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  -2.18049 -0.67830  0.07431  0.00000  0.50211  2.46318 

boxplot(A,B,C, names=c("A","B","C"), varwidth=T,
        col="skyblue2", pch=20, main="Standard Cost per Outcome")

Now the lowest relative costs for Medium ads would be $-0.62, -0.59,$ and $=0.67$ for Types A, B, and C, respectively. They are nearly the same on the relative scale, but of very different actual costs. Again with relative costs within Types, the medium cost ads lie within the boxes of the respective boxplots.

Because I don't fully understand the differences among ad Types, the goal of your analysis of costs, and so on, I am not necessarily recommending you use any of the summaries or graphics above. But I'm hoping that thinking about these methods may help you clarify what you should do.