Maximize the value of your media mix

Media Mix Modeling (MMM), also known as Marketing Mix Modeling, is a statistical process that enables teams to quantify the relationship of changes in various marketing inputs (in this case, channel spends) on the desired output (in this case, revenue, or customer acquisition targets).

The strength of the MMM approach over click attribution and other methodologies is that, as a top-down model, the primary inputs are changes in spend (independent variables) and changes in total return (dependent variable) over time; no clicks or user tracking required. As a result, all channels, offline and online, can be modeled consistently. Further, unlike other reporting tools, MMM's are prescriptive in recommendations, allowing mixes of spending to optimize business outcomes.

Advertisers use media mix models to measure the effectiveness of their advertising and provide predictive insight for future budget allocation decisions. Marketing investments typically display incremental lift (i.e., incrementality) and marginal returns, which OutPoint can accurately model using automated data pipelines, machine learning, and causal inference.

Traditionally an expensive, manual process, MMM is now no longer confined to F500 brands with massive budgets and large data teams. With OutPoint, you can access advanced MMM and data science tooling in a lower-cost, intuitive, and automated software-as-a-service (SaaS) platform.

At a high-level, correlations between ad spends and revenue lifts inform our return models.

First, we train an ML model to compute a return value for 'Direct Return', allocating credit for 'direct response' multi-touch UTM sessions associated with the channel spend. Next, we train the ML model to predict 'Brand Lift' and 'Email Lift' aggregated return and allocate partial credit between channels using causal inference techniques. These terms are combined to compute return as a function of spends across channels:

Estimated Return = Direct Return + Brand Lift + Email Lift

At a high level, OutPoint's media mix modeling software can help you scale revenue growth, understand marginal costs and incrementality, and optimize your spending plans to maximize business results. 

Here’s how OutPoint can drive further success for your business:

Are you spending effectively?

• Understand where diminishing returns may be occurring on current channels
• Measure offline channel performance (in addition to online) using a consistent modelling framework

Where should we spend our next dollars?

• Justify ad spend and budget decisions with predicted return lift 
• Identify high return channels that can help you hit revenue targets
• Unlock more revenue with optimal spend recommendations

OutPoint's machine learning models optimize your budget for return lift and marginal performance, allowing you to scale faster and improve efficiency.

OutPoint's predicted return models allow you to see how much additional budget you should invest into a channel (or how much you should dial back!) based on a prediction of marginal returns and incremental lift by spend level.

By identifying points of diminishing returns, you can optimize your media mix for maximum efficiency.

OutPoint's channel insights tool lets you dive into various predictive scenarios for every modelled channel, taking into account marginal performance as spend changes.

For example, you may observe that your customer acquisition cost rises by a disproportionate amount when you increase ad spend. Rising acquisition costs are related to the concepts of Diminishing Returns and Marginal CAC.

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.

We refresh your Media Mix Models every month. The utility and clear use case for MMM is in high-impact marketing capital allocation decisions. We still encourage you to continue to use your day-to-day tool for micro-optimizations and reporting.