Back in 2008 I thought fractional attribution was a complete solution but after the last four years working with brands, my opinion has changed.

I have discovered that while this method is not entirely without merit, it is disappointingly limited and doesn’t do much to help a marketer re-allocate spend meaningfully.

In 2008 Microsoft said ‘the company can now provide a scientifically based standard showing how well different media affect an eventual conversion’, this was engagement mapping, a classic example of fractional attribution and what have we learnt from it? 

My take on it is that user interface sliders and shiny bubble graphs are sexy but technology has to do more than look good, it must synthesise, it can’t just echo back what you tell it.

It’s important to try and learn about the successes and failures of your marketing spend, this is how we build successful programmes. Attribution analysis should enable you to examine the empirical value of your media and reallocate your spend accordingly.

Here we are, four years on from engagement mapping and most technology suppliers have still not developed any meaningful alternative to attributing returns to media investment on a last-in basis. 

In my opinion, at the centre of this problem we are plagued by two facts:

Correlation is not the same as causation

A can be correlated to B but that doesn’t necessarily mean A caused B. 

Take for example the tight relationship between shark attacks and ice-cream sales, however we all realise that we can’t save lives by banning ice-cream sales because the one is not causing the other even though the two appear to be co-related.

We all want to know which elements of our media investments are causing sales so we can re-allocate spend into those elements and cause more sales. 

Simples! Except it’s not that easy!

Return On Advertising Spend (ROAS) can only really be considered at the level of the entire budget. 

The moment we consider a subset of the total media spend such as investments in digital sub-channels like search, social and display, we need to address the attribution of sales revenue correlated to these sub-channel media investments. 

Suddenly we are now talking about degrees of assumed causality, how much of your reattributed spend are you going to allocate to each sub-channel? 

This is where our industry has willingly ignored the true challenge/opportunity of attribution. What has happened is that to simplify matters, the industry has favoured a dumbed-down approach in which the degree of assumed causality is fractioned out according to the marketer’s arbitrary reckoning.

Typically we see equal share across these digital sub-channels, a model sometimes referred to as “flat attribution”. What’s even worse is that some agencies/vendors would have the capacity to up-weight elements of the media plan that make them more margin.

So what is the true opportunity here? We are talking ourselves out of the last click standard, so what could replace it?

We may be comfortable with the idea that a physical action, like a click, is a more qualified engagement compared to a display impression but strictly speaking we can no more claim causation from a correlated click as we can from a correlated impression.

The good news is that identifying media effectiveness is not like proving innocence or guilt in a court of law. 

It’s OK to let some clicks go that were actually responsible for sales and lock up some clicks in media plans that were caught up in circumstantial evidence and which had nothing to do with developing purchase intent – nothing unjust is going to happen, we’re looking for marginal improvements in performance not proof cases.

What I recommend is working with proven statistical models. The statistical approach identifies highly correlated media events, not just all correlated events and most importantly it synthesises a degree of correlation.

By isolating single variables such as creative, placement or retargeting context while controlling all other variables (in so far as possible) it can yield statistically significant indicators of incremental media value. 

This provides the confidence in the assumption that there was causation effect (remember, you can’t get away from the fact that this will always be an assumption). 

Here are three key takeaways for considering an approach to your attribution strategy:

  • Fractional re-attribution can provide insights but don’t be afraid to question a vendor that offers this as their only solution. How much use is a report that echoes back what you told it?
     
  • Statistical modelling takes into account media that didn’t correlate to sales meaning you also take into account media investments that didn’t result in sales.  Best not to ignore the media that didn’t convert as it’s likely the bulk of your media spend.
     
  • Statistical modelling can provide an actual number that quantifies the contributing effect of elements in your media plan so you can confidently reallocate spend. Proper statistical analysis ensures these actual numbers are only drawn from statistically significant sample sizes.

This is mathematical marketing: let’s use methods of which our GCSE maths teachers would approve.