I work as an agency analyst in the media and advertising sector, and by far the most commonly requested work is attribution analysis.
For the non-layman, this is the process of modelling which adverts have helped generate incremental business revenue, be that in the form of new customer interest as a precursor to sale or the sale itself, and then in some manner awarding nominal credit to that advert. The ratio between cost and credit is used to understand which strategies are generating the best return on investment for the next planning cycle.
An example then: let us imagine a customer who responded to seeing our TV advert by entering our brand name in to an internet search engine. Clicking on a resulting paid-for link they landed on our web site where they completed an order. Our customer also happened to have read a magazine in which one of our adverts had been placed, but it failed to resonate with them and they skipped over it.
Before bandying numbers and models around, it’s worth just remembering that the true purpose of attribution lies in answering these questions:
- How do we know the customer saw the TV advert and responded to it?
- How do we know the magazine advert failed to resonate?
- What role did the search click play?
- What was the relative importance of each advert to the sale, and ultimately, was it worth me spending the money on it?
I feel these basic concepts are often not given due appreciation in the rush to produce “a model” – merely having the latter seemingly more important than the question it is intended to answer.
Simply exposing a potential customer to an advert, for example, doesn’t mean it was effective. Likewise, how a customer navigates their web browser to your site might just be a route of convenience rather than via an opinion influencing step. And lastly; at what point does the customer journey cease to be influenced by advertising, and instead move over to being driven by the customer-service experience?
For an individual customer, these questions are admittedly impossible to answer. But across a larger sample size we can use data to identify repeat patterns between advertising and customer response. We can identify the channels that typically favour successful sales over missed ones. Crucially though, in the process of quantifying this influence we have to apply some assumptions about how we believe our customers respond. This is where the art mixes with the science: having strong insight in to your customer journey can really shape the solution for the better.