AB Testing

One of the most common tasks in marketing analytics (after post-campaign analytics) is determining which variation of a campaign was most effective.  Small tweaks to your subject line will increase your open rate, and small tweaks to your content will increase your CTR and eventual conversion rate.

This analysis works.  Some email deployment tools even have it built in, so that you can set up a few variations, deploy it slowly and based on the relative open rates from the first recipients / test subjects, the tool will automatically discard unsuccessful variations so the rest of the deployment get the best one.

My problem with the whole approach is that it treats your audience as homogeneous.  What if one version resonates best with women and the other with men?  To me the problem is not about simple maximisation, it is about selecting something relevant.

Another problem with the technique is that the goal should not be maximising your open rate.  It should be maximising your relevance.  If a recipient is not going to complete the purchase then getting them to open or click through is wasting that person's time and destroying your reputation with them.  

I'd like to propose an alternative approach.  Instead let's score each message based on its affinity to the person - i.e. a winner-takes-all problem rather than a global maximisation problem.  Reframed this way we get a very different solution, we need to represent both every message and every person in space, and the task is to find the closest message for each person.  Naturally we start off not knowing where each person (or message) is, so initial feedback is needed

The practical implementation of this are simple.  Where we can build an understanding about which products you will like, we can make far better recommendations.  That is a well studied problem with plenty of good solutions.  For example we can use purchase history to predict your next purchase and then present it in your next email.  We can use your website behaviour to infer the probability of you clicking on two different messages in order to select the right news for you.  We can even manually place new products in space by looking at products they are similar to, in order to send relevant messages without purchase or clickthrough history.

So why would you do this?  Because if done well you can create an expectation that your emails are worth reading even when nobody else's are.  This isn't easy... you will need to be significantly more relevant than people expect for approximately ten emails in a row before the pattern is noticed.  A single slip-up will consign your brand to the same position as your competition. However the benefits are huge.  Customers will automatically open and carefully read your emails without you having to shout to gain their attention.  

Anyway, please comment or otherwise get in contact with me if you find this interesting.  I'd like to make 2017 the year where broadcast advertising starts to die.