Maximize the value of your media mix

Unlock actionable insights to scale paid media profitably across channels

Trusted by high-growth B2C brands around the 🌎

Unlock incremental growth with data

đź‘‹ Welcome to OutPoint, a data science platform built to enable profitable allocation of paid media budgets.

OutPoint's machine learning-enabled media mix models help you to optimize a cross-channel budget for incremental value and marginal performance, so you can improve growth efficiency & scale faster.

Improve performance of every dollar, across any channel (even offline!)

Diversification

Diversify your growth channels and determine the optimal media mix for maximum efficiency

Incremental Lift

Smart cross-channel ROAS, CAC, and revenue predictions with insight into incrementality and brand lift

Marginal Returns

Identify and monitor diminishing returns to adjust spend on current channels and mitigate the risk of pesky CAC increases

channel insights

Transform your approach to budget allocation

OutPoint generates models tailored to your business

  • Return, ROAS, and CAC curve modelling
  • Brand lift + offline channel measurement
  • Media Mix Modelling and allocation recommendations
  • Channel-level predicted outcomes by spend amounts

Use OutPoint's recommendations to achieve the efficient frontier for your budget allocation

Super helpful analysis... if OutPoint is right, it'll grow the valuation of our business by 10-20% this year

Co-founder and COO

B2C Healthcare Brand

We discovered where we were hitting diminishing returns on Facebook and successfully diversified to TikTok

Head of Growth

B2C Fintech Brand

Frequently Asked Questions

  • What is Media Mix Modelling? How can it help?

    Media Mix Modelling (MMM), also known as Marketing Mix Modelling, is a statistical technique that enables teams to quantify the impact of various marketing inputs on Revenue or Return on Ad Spend (ROAS).

    The goal of MMM is to explain how much each marketing channel contributes to revenue and how much to spend on each marketing channel. It is the foundation for effectively optimizing a paid media budget across different channels.

    OutPoint's models apply MMM principles and rely on machine learning and causal inference techniques. Our models are optimized to simulate new and current channels to empower your team to make more reliable spend decisions.

    Read more on our approach here.

  • Is OutPoint an attribution or marketing analytics tool?

    No. OutPoint is a data science suite focused on predictive modelling of marketing investments. Analytics tools are focused on reporting past events, often using limited click or pixel-based reporting technologies.

    Unlike existing Marketing Attribution tools focused on historical analysis and reporting (i.e., "how did we do?"), OutPoint's utility is in future decision-making and paid channel investments (i.e., "where should we invest next?").

    We believe there is more value in predicting future outcomes than reporting on the past. With OutPoint, you can simulate optimal paid media mix across current and new paid growth channels to achieve higher revenues, lower costs, or both.

  • What is the difference between attribution & incrementality?

    Attribution ≠ Incrementality. Attribution attempts to understand the past, through a bottoms-up linkage of views, clicks and conversions (often based on limited pixel or click tracking). On the other hand, incrementality modelling uses advanced statistical techniques to quantify the value of revenue or new customer acquisition that likely will not occur without various marketing tactics.

    OutPoint’s machine learning models improve efficiency and effectiveness by helping brands discover optimal spend levels and re-allocating growth dollars to higher returning areas.

  • How do we decide how much to spend on a given channel?

    OutPoint's marginal return curve analysis allows you to see how much additional budget you should invest into a channel (or how much you should dial back!)

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

  • How should we analyze and measure offline channels?

    OutPoint's advanced growth modelling imputes value to each of your online and offline channels using econometrics, machine learning, and causal inference.

    Other methods we can help your team incorporate and model include Brand Lift/Incrementality tests and "How Did You Hear About Us" surveys.

  • What will happen if we double spend next month?

    OutPoint lets you understand 'what-if' scenarios, taking into account diminishing marginal returns. 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.

With these data-driven insights, companies can maintain their existing budgets yet achieve improvements of 10% to 30% (sometimes more)

hbr_logo

Harvard Business Review

Wes Nichols

Media mix modeling allows advertisers to map spend to revenue on the basis of incremental value & diminishing returns 

Mobile Dev Memo, 

Eric Benjamin Seufert 

Partner with an experienced team

Smart mobile app

Sean Billings, CTO

Senior technologist with production machine learning experience at Amazon and early-stage companies

rob2

Rob Palumbo, CEO

Growth marketing expert who scaled 3x B2C brands from Seed to Series B+ using data-driven growth strategies

erika_fabian

Erika Fabian, Head of Sales

Sales leader with 10+ years of media industry experience in content development and advertising

Backed by world-class investors

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