Multi-touch attribution (MTA) tools attempt to report past events through a bottom-up framework often consisting of pixel views, tracked link clicks, and attributed conversions.
MTA tools can measure attributed conversions by tracking a customer via a cookie or using a UTM tracking parameter on a link click. However, they are not effective for measuring the value of offline channels, and they do not provide value for measuring activities of opted-out or protected users (e.g., due to iOS14+, AppTrackingTransparency, ad blockers, GDPR)
On the other hand, media mix modeling (MMM) uses advanced statistical techniques to quantify the value of revenue or new customer acquisition that is likely to have occurred due to the marketing investments. MMM provides a consistent modeling methodology across all channels (even offline).
For example, there's no way to 'click' on an offline ad like a billboard – so how do you measure performance? Media Mix Modeling techniques have been used to measure the return of offline campaigns for many years by larger brands and can even account for a lift on other metrics. This technique also works for other non-clickable channels, like TV, print, radio, and modern media like podcasts and influencers.
As MTA becomes less effective due to privacy regulations, MMM is well-positioned to emerge as the leading methodology for measurement and planning. MMM uses predictive techniques and relies on first-party, future-proof datasets such as your company's revenue and media cost datasets.
OutPoint’s machine learning-enabled media models improve efficiency and effectiveness by helping brands discover optimal spend levels and re-allocating growth dollars to higher returning areas.