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ROI, mROI e curvas de resposta
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Resultado incremental
Para um determinado canal de mídia \(q\), o resultado incremental é definido como:
\[\text{IncrementalOutcome}_q = \text{IncrementalOutcome} \left(\Bigl\{
x_{g,t,i}^{[M]} \Bigr\}, \Bigl\{ x_{g,t,i}^{[M](0,q)} \Bigr\} \right)\]
Em que:
- \(\left\{ x_{g,t,i}^{[M]} \right\}\) são os valores de mídia observados.
- \(\left\{ x_{g,t,i}^{[M] (0,q)} \right\}\) indica os valores de mídia observados para todos os canais, exceto \(q\), que é sempre definido como zero. Mais especificamente:
- \(x_{g,t,q}^{[M] (0,q)}=0\ \forall\ g,t\)
- \(x_{g,t,i}^{[M](0,q)}=x_{g,t,i}^{[M]}\ \forall\ g,t,i \neq q\)
ROI
O ROI do canal \(q\) é definido como:
\[\text{ROI}_q = \dfrac{\text{IncrementalOutcome}_q}{\text{Cost}_q}\]
Em que \(\text{Cost}_q= \sum\limits _{g,t} \overset \sim x^{[M]}_{g,t,q}\)
O denominador do ROI representa o custo de mídia em um período especificado que está alinhado com o período em que o resultado incremental é definido.
Assim, o resultado incremental no numerador inclui o efeito defasado da mídia executada antes dessa janela temporal e exclui o efeito futuro da mídia executada durante a janela. Portanto, o resultado incremental no numerador não está totalmente alinhado com o custo no denominador.
No entanto, esse desalinhamento será menos significativo em um período razoavelmente longo.
O cenário de mídia contrafactual (\(\left\{ x_{g,t,i}^{[M](0,q)}
\right\}\)) talvez não seja representado nos dados. Quando isso acontece, é necessário extrapolar os pressupostos do modelo para inferir o contrafactual.
Curvas de resposta
Generalizando a definição de resultado incremental, a curva de resposta do canal \(q\) é uma função que retorna o resultado incremental como uma função do gasto no canal \(q\):
\[\text{IncrementalOutcome}_q (\omega \cdot \text{Cost}_q) =
\text{IncrementalOutcome} \left(\left\{ x^{[M](\omega,q)}_{g,t,i} \right\},
\left\{ x^{[M](0,q)}_{g,t,i} \right\}\right)\]
Em que \(\left\{ x^{[M](\omega,q)}_{g,t,i} \right\}\) indica os valores de mídia observados para todos os canais, exceto \(q\), que é sempre multiplicado por um fator de \(\omega\) . Mais especificamente:
- \(x^{[M](\omega,q)}_{g,t,q}=\omega \cdot x^{[M]}_{g,t,q}\ \forall\ g,t\)
- \(x^{[M](\omega,q)}_{g,t,i}=x^{[M]}_{g,t,i} \forall\ g,t,i \neq q\)
ROI marginal (mROI)
O ROI marginal (mROI) do canal \(q\) é definido como:
$$
\text{mROI}_q = \left(\dfrac{1}{\delta \cdot \text{Cost}_q} \right) \text{IncrementalOutcome} \left( \left\{ x^{[M](1+\delta,q)}_{g,t,i} \right\},
\left\{x^{[M](1,q)}_{g,t,i}\right\} \right)
$$
Em que \(\delta\) é uma quantidade pequena, como \(0.01\).
As definições de curva de resposta e ROI marginal pressupõem implicitamente um custo constante por unidade de mídia que é igual à média histórica do custo por unidade de mídia.
Exceto em caso de indicação contrária, o conteúdo desta página é licenciado de acordo com a Licença de atribuição 4.0 do Creative Commons, e as amostras de código são licenciadas de acordo com a Licença Apache 2.0. Para mais detalhes, consulte as políticas do site do Google Developers. Java é uma marca registrada da Oracle e/ou afiliadas.
Última atualização 2025-07-30 UTC.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Não contém as informações de que eu preciso","missingTheInformationINeed","thumb-down"],["Muito complicado / etapas demais","tooComplicatedTooManySteps","thumb-down"],["Desatualizado","outOfDate","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Problema com as amostras / o código","samplesCodeIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-07-30 UTC."],[[["\u003cp\u003eIncremental outcome measures the change in outcome attributed to a specific media channel by comparing observed media values to a scenario where that channel's values are zero.\u003c/p\u003e\n"],["\u003cp\u003eROI is calculated by dividing the incremental outcome of a media channel by its cost, reflecting the return on investment for that channel.\u003c/p\u003e\n"],["\u003cp\u003eResponse curves illustrate the relationship between media spend on a specific channel and the resulting incremental outcome, providing insights into channel effectiveness at different investment levels.\u003c/p\u003e\n"],["\u003cp\u003eMarginal ROI measures the incremental outcome gained by increasing spend on a specific channel by a small percentage, indicating the return on additional investment in that channel.\u003c/p\u003e\n"],["\u003cp\u003eThese metrics rely on counterfactual scenarios, sometimes requiring model-based extrapolation when observed data doesn't fully represent those scenarios.\u003c/p\u003e\n"]]],["Incremental outcome for a media channel is calculated by comparing observed media values to a scenario where that channel's values are zeroed out. ROI is the incremental outcome divided by the channel's cost. Response curves show how incremental outcome changes with varying spend on a channel. Marginal ROI (mROI) measures the change in incremental outcome from a small increase in channel spend, assuming a constant cost per media unit. Counterfactual scenarios where channels are zeroed out might need to be inferred by the models.\n"],null,["# ROI, mROI, and response curves\n\nIncremental outcome\n-------------------\n\nFor a given media channel \\\\(q\\\\), the incremental outcome is defined as:\n\n\\\\\\[\\\\text{IncrementalOutcome}_q = \\\\text{IncrementalOutcome} \\\\left(\\\\Bigl\\\\{\nx_{g,t,i}\\^{\\[M\\]} \\\\Bigr\\\\}, \\\\Bigl\\\\{ x_{g,t,i}\\^{\\[M\\](0,q)} \\\\Bigr\\\\} \\\\right)\\\\\\]\n\nWhere:\n\n- \\\\(\\\\left\\\\{ x_{g,t,i}\\^{\\[M\\]} \\\\right\\\\}\\\\) are the observed media values\n- \\\\(\\\\left\\\\{ x_{g,t,i}\\^{\\[M\\] (0,q)} \\\\right\\\\}\\\\) denotes the observed media values for all channels except channel \\\\(q\\\\), which is set to zero everywhere. More specifically:\n - \\\\(x_{g,t,q}\\^{\\[M\\] (0,q)}=0\\\\ \\\\forall\\\\ g,t\\\\)\n - \\\\(x_{g,t,i}\\^{\\[M\\](0,q)}=x_{g,t,i}\\^{\\[M\\]}\\\\ \\\\forall\\\\ g,t,i \\\\neq q\\\\)\n\nROI\n---\n\nThe ROI of channel \\\\(q\\\\) is defined as:\n\n\\\\\\[\\\\text{ROI}_q = \\\\dfrac{\\\\text{IncrementalOutcome}_q}{\\\\text{Cost}_q}\\\\\\]\n\nWhere \\\\(\\\\text{Cost}_q= \\\\sum\\\\limits _{g,t} \\\\overset \\\\sim x\\^{\\[M\\]}_{g,t,q}\\\\)\n\nNote that the ROI denominator represents media cost over a specified time period\nthat aligns with the time period over which the incremental outcome is defined.\nAs a result, the incremental outcome in the numerator includes the lagged effect\nof media executed prior to this time window, and similarly excludes the future\neffect of media executed during this time window. So, the incremental outcome in\nthe numerator does not perfectly align with the cost in the denominator.\nHowever, this misalignment will be less material over a reasonably long time\nwindow.\n\nNote that the counterfactual media scenario (\\\\(\\\\left\\\\{ x_{g,t,i}\\^{\\[M\\](0,q)}\n\\\\right\\\\}\\\\)) may not actually be represented in the data. When this happens,\nextrapolation based on model assumptions is necessary to infer the\ncounterfactual.\n\nResponse curves\n---------------\n\nGeneralizing the incremental outcome definition, the response curve is defined\nfor channel \\\\(q\\\\) as a function which returns the incremental outcome as a\nfunction of the spend on channel \\\\(q\\\\):\n\n\\\\\\[\\\\text{IncrementalOutcome}_q (\\\\omega \\\\cdot \\\\text{Cost}_q) =\n\\\\text{IncrementalOutcome} \\\\left(\\\\left\\\\{ x\\^{\\[M\\](\\\\omega,q)}_{g,t,i} \\\\right\\\\},\n\\\\left\\\\{ x\\^{\\[M\\](0,q)}_{g,t,i} \\\\right\\\\}\\\\right)\\\\\\]\n\nWhere \\\\(\\\\left\\\\{ x\\^{\\[M\\](\\\\omega,q)}_{g,t,i} \\\\right\\\\}\\\\) denotes the observed\nmedia values for all channels except channel \\\\(q\\\\), which is multiplied by a\nfactor of \\\\(\\\\omega\\\\) everywhere. More specifically:\n\n- \\\\(x\\^{\\[M\\](\\\\omega,q)}_{g,t,q}=\\\\omega \\\\cdot x\\^{\\[M\\]}_{g,t,q}\\\\ \\\\forall\\\\ g,t\\\\)\n- \\\\(x\\^{\\[M\\](\\\\omega,q)}_{g,t,i}=x\\^{\\[M\\]}_{g,t,i} \\\\forall\\\\ g,t,i \\\\neq q\\\\)\n\nMarginal ROI (mROI)\n-------------------\n\nThe marginal ROI (mROI) of channel \\\\(q\\\\) is defined as: \n$$ \\\\text{mROI}_q = \\\\left(\\\\dfrac{1}{\\\\delta \\\\cdot \\\\text{Cost}_q} \\\\right) \\\\text{IncrementalOutcome} \\\\left( \\\\left\\\\{ x\\^{\\[M\\](1+\\\\delta,q)}_{g,t,i} \\\\right\\\\}, \\\\left\\\\{x\\^{\\[M\\](1,q)}_{g,t,i}\\\\right\\\\} \\\\right) $$\n\nWhere \\\\(\\\\delta\\\\) is a small quantity, such as \\\\(0.01\\\\).\n\nNote that the response curve and marginal ROI definitions implicitly assumes a\nconstant cost per media unit that equals the historical average cost per media\nunit."]]