Every marketer wants to accurately measure the impact of their advertising spend. And “digital” was supposed to make that really easy (especially for digital advertising). But, that promise has rarely been realized—it’s becoming increasingly difficult to track users across touchpoints, thanks to privacy regulations and browser updates that block or aggressively expire cookies, and many marketers grapple with an uneasy feeling that the attribution reported by their agencies and from their digital analytics platforms do not feel "right." In this session, we will review four different approaches to marketing attribution: heuristic modeling (first touch, last touch, linear, time decay, etc.), algorithmic ("data-driven") modeling, media mix modeling (MMM), and randomized controlled trials (RCTs, matched market testing). These are distinct approaches, and the tradeoffs between them tend to be either unspoken or misunderstood, which this session will rectify.
Takeaways for attendees of this session will include:
Learning Objectives:
A clear understanding of incrementality, its importance, and which attribution approaches do (and do not) account for it
The benefits of incorporating techniques that do not rely on user-level tracking while still measuring the impact of digital investments on offline results
Why it is important to push media partners to go "beyond conversion pixels" when measuring results
