11–20 of 22 posts
Fun reading for a university class, but the real world doesn’t work this way. Optimizing for a target cpa is never a possibility because at an agency or in house marketing team your given x budget and expected to spend it. I’ve never seen someone underspend a budget and be thanked for it.
Really, you're going to use a logarithm because it fits one data point better? Besides you might as well just use all your datapoints directly. There's no real need to interpolate between them (and if you really want to optimize your ad costs that finely, don't use a function that predicts -infinty gross profit if you don't use ads).
> The graph above features a linear line of best fit. Clearly we can see above that the data isn't linear, and a linear line of best fit doesn't make sense
I think the reason for not using a linear fit was that it "doesn't make sense", but the reasoning is not given. It would be interesting if the article explicitly said why it doesn't make sense, and why the somewhat arbitrary choice of the log function does. As you point out, gross profit will not be massively negative when there is 0 ad spend.
By my eye, the gross profit as a function of ad spend looks approximately linear in the region where data was sampled: say gross profit = 400 + 0.5*ad spend . If you plug that in & then optimise for total profit the most profitable non negative choice of ad spend is of course zero!
So in this case a structural modelling assumption that isn't well explained or justified (log vs linear vs any other function) has a very large impact on the answer. It seems like we're trying to maximise a function in a region where we don't have observed data.
Since the problem as given is data poor and modelled in a way that is trivial to compute, perhaps it is a reasonable candidate for: running some more experiments to get more data points ; or using a statistical method that can express the uncertainty of our structural modelling decisions & parameter estimation (e.g. a Bayesian analysis starting with a prior distribution of possible fits over a richer class of functions with plausible behaviour near zero) since we don't have a physical theory that justifies a particular functional form of the assumed shape of the relationship.
Fun reading for a university class, but the real world doesn’t work this way. Optimizing for a target cpa is never a possibility because at an agency or in house marketing team your given x budget and expected to spend it. I’ve never seen someone underspend a budget and be thanked for it.
That's far too strong a statement. Plenty of small businesses could be more flexible than this if it made commercial sense.
I’ve never seen someone underspend a budget and be thanked for it.
That's a problem with the businesses you've seen, not the principle of not spending money unnecessarily.
Hi folks, I'm the author of the post — I wrote it because I think a lot of brands tend to pluck numbers out of thin air when they're talking about marketing targets, without knowing how they're affecting their bottom line. This might seem like econ 101 as zwaps mentioned, but there are a lot of brands who don't take anything like this approach still. Keen to hear people's thoughts :)
Hi folks, I'm the author of the post — I wrote it because I think a lot of brands tend to pluck numbers out of thin air when they're talking about marketing targets, without knowing how they're affecting their bottom line. This might seem like econ 101 as zwaps mentioned, but there are a lot of brands who don't take anything like this approach still. Keen to hear people's thoughts :)
Is it common for the cost per conversion curve to be a parabola?
If so, great question. I haven't looked into this much but wouldn't expect it to be parabolic - that was just an approximation to illustrate the points raised.
Hi folks, I'm the author of the post — I wrote it because I think a lot of brands tend to pluck numbers out of thin air when they're talking about marketing targets, without knowing how they're affecting their bottom line. This might seem like econ 101 as zwaps mentioned, but there are a lot of brands who don't take anything like this approach still. Keen to hear people's thoughts :)
It started me thinking about the distribution of customers who convert, viewed as a function of ad spend. I am not sure there is any reason this distribution need have a particularly simple shape, or even be continuous. E.g. maybe some customers can be addressed and convert at a modest ad spend, while if you double the ad spend you don't get any more conversions, then if you double ad spend again maybe suddenly you're out bidding a competitor and the number of conversions shoots up, perhaps giving you a better overall net profit than if you stopped earlier with a modest budget.
This might mean that the curve we're trying to maximise (net profit) has more than one local maxima, or might not even be continuous.
There's probably also an explore/exploit tradeoff here as well: how much of the total budget should you spend sampling to try out different ad spends across the whole range of plausible values (from 0 up to the long term value of a conversion, I guess) to get enough data to start optimising.
Hi folks, I'm the author of the post — I wrote it because I think a lot of brands tend to pluck numbers out of thin air when they're talking about marketing targets, without knowing how they're affecting their bottom line. This might seem like econ 101 as zwaps mentioned, but there are a lot of brands who don't take anything like this approach still. Keen to hear people's thoughts :)
Interesting post. "All models are wrong, some models are useful" - Box. This one seems useful and is simple to explain. It started me thinking about the distribution of customers who convert, viewed as a function of ad spend. I am not sure there is any reason this distribution need have a particularly simple shape, or even be continuous. E.g. maybe some customers can be addressed and convert at a modest ad spend, whi…
I original thought this was a nice "set it and forget it" marketing scheme, but your comment makes me think otherwise.
Really, you're going to use a logarithm because it fits one data point better? Besides you might as well just use all your datapoints directly. There's no real need to interpolate between them (and if you really want to optimize your ad costs that finely, don't use a function that predicts -infinty gross profit if you don't use ads).
If we're considering the most effective ad spend, then there's no reason that the model has to make sense for 0 ad spend.