Without knowing more about your objective, it is difficult to suggest a suitable criterion that could be used for all accounts. But here are some ideas. [Computations in R.]

In your example, 'impressions' are

x = c(1563, 400, 100, 1400, 1, 10)

Sorted data are

sort(x)
[1]    1   10  100  400 1400 1563

So you might throw out a row in your table if: (a) the impression count is to low to matter, perhaps below 20; (b) the impression count is among a client's lowest 25% of impressions, which is below about 30 in your example; (c) if the number of impressions is a low outlier, which in your example, disregards nothing. (I'm using the "boxplot 1.5 IQR rule" for outliers.) This would work better with more data, say at least a dozen rows.)

quantile(x, .25)
 25% 
32.5

boxplot(x, horizontal=TRUE)

Then, having disregarded rows with too few impressions, you might use another criterion to pick "relatively good" conversion rates for each client.

In your example, suppose now that rows F, G, and H are gone on account of low impression counts.

That might be anything: (a) at or above the median (1.3); (b) at or above the 75th quantile (10); (c) more than one SD above the mean (18). [Even if you had enough data to show outliers, I think requiring a high outlier in order to be called "reasonably good" is too harsh a test.]

y = c(1.3, 10, 24, .4, .5)
median(y);  quantile(y, .75)
[1] 1.3
75% 
 10 
mean(y) + sd(y)
[1] 17.43819
mean(y)
[1] 7.24

[Notice that with rows F, G, and H gone, the average conversion rate is now down to 7.24.]

Even if you don't like my specific suggestions, based on quick orientation and only one dataset, maybe you will find something useful in the general approach.