Proving the monetary value of A/B testing


Proving the monetary value of A/B testing

Written by X Jansen,
July 2017
Written by a human, not by AI

How to go from “4% conversion uplift” to “€636K — €854K extra profit this year”. Example calculation sheet included!

Also see my previous article: The CRO Process.

I’ve created a method to calculate the impact of A/B testing in terms
of yearly profit. And not unimportant: this has been accepted by the
whole company. This method isn’t perfect, but it gives you as a CRO
manager quite some power to prove the effect of your team.

The prerequisites to do this:

  • showing what you normally would expect to get in terms of traffic,
    number of orders and average revenue (or even average profit) per order
    for the next 12 months. If you don’t have this, you could take data of
    the past 12 months as an alternative baseline.
  • You ran some successful A/B test :).

* I take 12 months so I don’t have to worry about any seasonal effects.

BI/Finance team can already provide me with the baseline prediction
(based on current numbers and including predictions on competitors,
market changes, investments etc. etc.) so I don’t have to worry about
that. Of course we also have some successful A/B tests at our disposal.

Now I can perform these steps:

  1. Calculate the extra profit by just running this single A/B test.
  2. Prediction/extrapolation: A) Increase the baseline 12 month prediction with the changes in conversion rate and/or average order value proven by the test.B) Make some corrections on A) to make sure you’re not overselling yourself.

1) Calculate the extra profit by just running this single A/B test

is just a simple calculation only based on your A/B test data. You
calculate the revenue (or better: profit) from your original variant and
all other variants. Then you calculate the difference if you would have
had the original running 100% versus running the test with all your

works for all tests (successful or not) and tells you if you made any
loss/gain during the test itself. Of course you only implement the
winner, but if you test multiple variants it could be that the net
result during the testing period is negative. This helps you to get a
grip on the monetary cost/risk of performing A/B test.

2A) Increase the baseline 12 month prediction

one is actually quite simple: you take your prediction (we have it on a
day-by-day basis) and apply the changes in conversion rate and average
order value that we saw in the test.

2B) Make some corrections

this is where it gets tricky. 2A) will get you a very precise (and
usually quite high) number which I feel will need some corrections
(downwards) to make it realistic. I also prefer to put it in a range
instead of just one single number.

Some of the assumptions/corrections I do:

  • I
    assume a diminishing effect per month. I’ve set this to 2% which means
    that after a full year, you will only have 78% of the original effect
  • When
    I do a test in a single or just a couple of countries, I always assume
    the effect will be less when applied in other countries. In the same
    region I’ve set this to 25%, for countries outside the tested region
    this is set to 50% (this is not included in the example sheet below).
  • A
    correction for the significance level (we test at 95%). This correction
    makes no sense when applied to one test, but when you do this for all
    experiments that you implement, you can assume a certain part (around

all corrections I turn the single number that comes out of this
equation into the upper limit of a range and subtract a percentage to
get to a lower limit. For a country this is 20%, when applied to the
same region it’s 40% and for the rest of the group this is 50%. All
arbitrary numbers.

And the result of all this? Instead of saying “we have improved conversion by 4%” I can now claim “after implementation this test gives us €636K — €854K extra profit this year”.

Try it yourself!

obvious reasons I can’t share our own internal sheet (containing our
complete budget), but I made a simplified version in Google Sheets that
should make the basic calculations clear:

Example of the calculation sheet

Let me know if you think some things are off or if you have any improvements.

Write down your assumptions

highly recommend writing down all your assumptions in your calculation
sheet. You will need to make quite some of them to make this work and
writing it down makes it tangible and easier to adjust afterwards if you
have more data to update your assumptions and the calculations based on

Some more written down assumptions I have in my sheet:

  • We
    work across 12 countries with different currencies while the budget
    predictions are all int he same currency. Changes in exchange rates will
    not be taken into account.
  • During
    A/B tests we optimize for CR and AOV but the testing system cannot
    optimize for profit (technical limitation). Profit is a percentage of
    AOV but this is different for different countries and different
    products. This calculation assumes a stable relation between AOV and
    profit which is the best we can do right now.

This also helps when sharing this sheet internally because: buy-in!

Don’t do this on your own, get buy-in

I didn’t build this sheet on my own: I also got our business analyst
and our financial controller involved to check the numbers, the
calculations and all assumptions. They helped improving the sheet to
match how finance calculate numbers and to get an official stamp of

Some final remarks

course this is just a prediction and even in hindsight after 12 months
you will not be able to calculate if the changes in your numbers were a
result of your A/B test implementation.

fact means it still feels a bit weird to me to do such a prediction.
But because we made this together with BI/Finance this is currently the
best way we have to quantify the effect of our experiments. It also
provide us with a way to compare our experiments.

the best reason to do this is that it works really well. Putting a
dollar sign in your experiment reports makes a lot more people
interested in the results and helps you to increase your budget.

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