Some very common questions for marketers:

• How is this campaign/tactic/creative/content/CRO experiment performing?
• Are we investing the right amount in media?
  • What is our baseline of sales?
  • How do we get an accurate read on MROI?
  • What’s the incremental impact of our marketing campaigns?
  • How much less can we spend for the same outcomes?
  • How much more we can spend before ROI drops?
• If we have an extra $1 million to spend, how should we spend it and what will the results be?
  • if we have to cut the budget, how do we minimise the impact (or demonstrate to the business the bottom-line impact of cutting marketing spend)
• How do we balance the need for both top-of-funnel and bottom-of-funnel results?
  • How will our various marketing KPIs be impacted by adjusting the mix of brand and tactical campaigns?

Underpinning these questions is a need to be able to attribute value to marketing activities. 

Some businesses use last-touch attribution where all the value is given to the last marketing activity that can be attributed to the conversion. This typically sees paid search and paid social pick up the conversion, but even these channels are being disrupted by signal loss and cookie issues, which means the click/cost is counted, the conversion happens, but the ad platform doesn’t see the conversion, resulting in a higher (reported) CPA, lower conversion rate and ROI etc. Last-touch is also commonly used for assessing digital creative (ideally you want to evaluate performance based on conversion rather than CTR) as well as how well landing pages or A/B content tests are performing. 

Multi-touch attribution uses path-to-conversion data to stitch together all the different ad exposures that a user experienced in the run-up to the conversion. This is trying to attribute value to the various media activities that may finally lead someone to click on the paid search ad (or organic result) before converting, but is limited to what’s seen in the digital realm. This data is also massively impacted by signal loss, ad blockers and cookie-lifespan issues (ITP/ETP).

These digital attribution methods will be boosted significantly with RescueMetrics, which is an almost-unbelievably easy piece of tech to deal with signal loss issues without the need of a server-side implementation (and will even make existing server-side implementations work better). There is nothing else that will boost your campaigns with as little effort. 

Whilst great for in-the-moment analysis of campaigns (last touch) or retrospective attribution (MTA), these approaches only tell part of the story – missing offline media, brand strength, non-media and external factors – and have little predictive capability. 

Marketing Mix Modelling, and its closely-related analytical sibling, Media Mix Modelling (both with the same acronym of  MMM) have been around for decades and provide the holistic understanding of media channels required to optimise media allocation. Built at a time when there were far fewer channels and publishers, traditional MMM has its share of limitations, namely speed and granularity. To address that,  a number of modern SaaS MMM platforms that use cloud computing, machine learning and AI have come to market, promising holistic, unified marketing measurement. Some of the limitations remain, and there is now a new generation of tech, underpinned by synthetic data and generative AI, that offers all the benefits of MMM, but faster and using a person-level approach bringing new levels of actionability to media insights and optimisation. We call this people-based marketing attribution, and it’s powered by Marketing Evolution’s MEVO.