We live in the dictatorship of the narrative. There is a whole philosophical debate about psychological Narrative, ethical Narrative and the Self (see Strawson). From the oral story tellers, to the novelists, to the TV dramas, to the social (Snapchat) and traditional media stories – story is everywhere. A mediated world creates more stories.

But it’s too much, story means simplification, means extraction of what could appeal to others – what they might understand. It’s a judgment on the listener or reader. Stories are abundant in our attention seeking world. But they are hardly new, too many templates. Stories get recycled and retold.

Every book cover comes with a story about the author (difficult childhood etc). Even in the business world, employees in large corporations tell stories so they stand out – emulating conviction and passion. Business consulting also adepts stories, how to capture the mind of the CEO so he will go away and remember. Politics has long used the story to persuade the electorate. The truth doesn’t matter, but the timely appeal of the political narrative does.

Resist the urge to tell stories, just describe the chaotic nature of the ever changing facts. Don’t let the story you have told others and yourself, hold you back.

Marketing Mix Modelling has been around for a long time, digital attribution (non last click) has been around for maybe the last 5 years. The challenge is how to come up with a holistic evaluation approach that gives all media the correct credit.

This is tricky because digital data is at user/cookie level whereas traditional media (TM) data is aggregated. However I think they can be combined. The trick is to merge the data at a particular level. For instance if the TM is at region and date/hour, then you can insert a hypothetical, weighted  TM event into the digital data. For example if the GRP for region A at hour 10 is 120 then insert such an event for all users that had a digital event a few hours after 10 in region A. The assumption here is that usually TM precedes a digital event (eg search). Here you can use Adstock or other sequential functions to create a lasting effect. Once you have created such artificial events you can run your model of choice to predict conversion and hence assign credit to channels. Note that the TM->digital assumption introduces some bias in your model.

You could also give a higher weight to Outdoor media for (digital) mobile users as they usually are on the go. If you have demographics in your TM and digital data you can match even better.