What is OutPoint?

OutPoint is a media mix modeling tool that enables the profitable allocation of media budgets across paid channels. OutPoint’s predictive media mix models (MMM) optimize ad budgets for incremental lift and marginal performance to generate clear next dollar spend recommendations.

OutPoint uses advances in machine learning and media mix modeling to help consumer growth teams map ad spend to revenue based on incremental lift and predicted performance. We help answer the two hardest questions marketers face: are you spending effectively, and where should you allocate your dollars to get more revenue?

High-growth brands use OutPoint's automated MMM tooling to measure the effectiveness of their advertising and provide insight in making future budget allocation decisions. OutPoint build custom casual inference and regression-based models that map the relationship between marketing spending and business outcomes such as revenue or new customers acquired.

How does it work?

Causal Inference is a process where causes are inferred from data. Many kinds of data can be used for causal inference, as long as have enough signal (even observational data). The OutPoint predictive model is the engine that powers the product, using regression-based analysis to determine the efficiency and scalability of each advertising platform.

Causal inference refers to an intellectual discipline that considers the assumptions, study designs, and estimation strategies that allow researchers to draw causal conclusions based on data. As detailed below, the term ‘causal conclusion’ used here refers to a conclusion regarding the effect of a causal variable (often referred to as the ‘treatment’ under a broad conception of the word) on some outcome(s) of interest.

For OutPoint's application, causal inference is the process of determining the independent, estimated lift from spending on a particular channel that is a component of a larger cross-channel portfolio. We specifically use causal inference to derive incremental lift signals from both variations in spending and variations in revenue across channels over time.

Learn more about causal inference here.

Why use MMM?

The benefit of MMM over click or pixel-level attribution is that, as a top-down predictive model, the primary inputs required are changes in spend and changes in the overall outcome, typically revenue. OutPoint ingests a historical set of signals to make the models actionable, and once we have enough of these signals, we can infer the estimated efficiency of each channel, allowing us to construct an estimated return curve for each outlet. Since our models rely on inferred "cause and effect" vs click-based attribution alone, OutPoint media mix modelling (MMM) can capture the value of economic lift from less-attributable media such as Influencers, TV, and Radio, which do not always provide impression or click data at a per user level.

While MMM as a general methodology does not require granular click data, OutPoint's models will in some cases incorporate first & last click data as an input improving modelled inference around cause and effect, specifically for direct response-oriented channels like Paid Search.

How does OutPoint predict return?

Independent channel-level correlations between ad spending and revenue lifts inform our return models. First, we train an ML model to compute a return value for 'Direct Return,' allocating and predicting 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

Direct models are simpler and represent the relationship between a single channel’s spend and that channel’s direct revenue (tagged to that channel using multi-touch attribution). Lift models are more complex, they represent the relationships between all advertising channels and a specific type of lift revenue, such as organic revenue or email revenue.

Why Is This Important?

It is OutPoint’s goal to apply MMM to determine how much “incremental lift” each marketing channel is contributing to revenue, and thus how much to spend on each channel.✔️

Diminishing Returns

OutPoint's product can predict the various spend levels where spending will increase marginal customer acquisition cost (CAC). Being aware of the rate at which marketers see diminishing returns helps determine optimal spend levels for profitability or faster, more predictable growth.

External Factor Impact:

External factors such as sales, variations in consumer sentiment, purchasing power, etc. may affect revenue and have nothing to do with advertising spend. Thus, it is important to be able to model the effects of these factors and use them to remove non-advertising driven revenue or explain inflection points that may occur. OutPoint's MMMs are designed to account for external factors such as seasonality and pricing (e.g. Black Friday, Cyber Monday), showing a clearer picture of what is happing beyond average metrics.

The OutPoint Process

The process we follow aims to help answer the question, "How do I allocate my ad spend efficiently across channels?" The final result creates a future roadmap on how you can improve performance on every dollar spent and achieve faster growth.

The "Open Secret"

Scaled B2Cs like Wayfair, Netflix, and Amazon have already been investing heavily in the above areas and the concepts behind MMM. OutPoint’s thesis is to make these types of approaches accessible to all to help level the playing field and give growth stage companies the tools they need to enter their competitive landscapes.

Google and Facebook have also published open-source code and research validating the methodologies of incrementality and MMM, making similar models available to help researchers build their own tools.

Limitations of MMM

MMM is not a silver bullet and should be used in addition to experimentation and more tactical daily analytics tools. For example, there is a constraint around granular user-level attribution. A brand can use MMM to infer that a particular level in television spend likely drove a specific dollar amount in incremental revenue, but not the specific incremental user. Some business environments also deal with an extended purchase journey or a lot of noise in the buying process. In these situations, arguments can be made about distinguishing between causation and correlation.

More Information On Incrementality + MMM

OutPoint is a suite of data science tools used by high-growth B2C marketers to allocate paid media budgets optimized for incremental lift and improved marginal returns.

Contact us to schedule a demo or meet with the OutPoint team to learn how you can use MMM to lift your business.

🚀Sign up, or email erika.fabian@outpoint.app if you are interested in learning more.