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扩展到具有覆盖面和频次的模型
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前面部分中所述的定义可以扩展,以用于具有覆盖面和频次数据的渠道。更笼统地说,潜在结果可以写成 \( \overset \sim Y_{g,t}^{ \left( \left\{ x_{g,t,i}^{(\ast)}
\right\}, \left\{ r_{g,t,i}^{(\ast)} \right\}, \left\{ f_{g,t,i}^{(\ast)}
\right\} \right) } \)
具有覆盖面和频次数据的 \(q^{th}\) 渠道的增量效果定义如下:
$$
\text{IncrementalOutcome}^{[RF]}_q = E \Biggl(\sum\limits_{g,t} \biggl(
\overset \sim Y_{g,t}^{ \left(
\left\{ x_{g,t,i} \right\},
\left\{ r_{g,t,i} \right\},
\left\{ f_{g,t,i} \right\} \right) } -
\overset \sim Y_{g,t}^{ \left(
\left\{ x_{g,t,i} \right\},
\left\{ r_{g,t,i}^{(0,q)} \right\},
\left\{ f_{g,t,i} \right\} \right) }
\biggr) \bigg| \left\{ z_{g,t,i} \right\} \Biggr)
$$
其中, \(r^{(0)}_{g,t,i}\) 表示所有渠道的观测覆盖面值,但渠道 \(q\)除外(该渠道在所有位置的观测覆盖面值均设置为零)。更具体地说:
- \(r^{(0,q)}_{g,t,q} = 0\ \forall\ g,t\)
- \(r^{(0,q)}_{g,t,i} = r_{g,t,i}\ \forall\ g,t,i \neq q\)
请注意,覆盖面值为零时,频次反事实值并不重要;无论后者为何,增量效果都应为零。在本定义中,将这些值任意设置为历史值。
具有覆盖面和频次数据的 \(q^{th}\) 渠道的投资回报率定义如下:
\[\text{ROI}^{[RF]}_q = \dfrac{\text{IncrementalOutcome}^{[RF]}_q}{\text{Cost}^
{[RF]}_q}\]
其中 \(\text{Cost}^{[RF]}_q=\sum\limits_{g,t} \overset \sim r_{g,t,q}\)。
请注意,在定义响应曲线时,可以通过多种方式对具有覆盖面和频次数据的渠道缩放支出。对于任何给定的支出水平,都可以通过任意数量的覆盖面和频次组合来达到该支出水平。Meridian 主要关注两类响应曲线:
覆盖面响应曲线定义为以下函数:
$$
\text{IncrementalOutcome}_q^{[reach]} \left( \omega \cdot \text{Cost}_q^{[RF]} \right) =
E \Biggl(
\sum\limits_{g,t} \biggl(
\overset \sim Y_{g,t}^{
(\{x_{g,t,i}\},\{r_{g,t,i}^{\omega,q}\},\{f_{g,t,i}\})
} -
\overset \sim Y_{g,t}^{
(\{x_{g,t,i}\},\{r_{g,t,i}^{(0,q)}\},\{f_{g,t,i}\})
}
\biggr) \bigg| \{z_{g,t,i}\}
\Biggr)
$$
其中, \(r_{g,t,i}^{(\omega,q)}\) 表示所有渠道的观测覆盖面值,但渠道 \(q\)除外(该渠道在所有位置的观测覆盖面值均需乘以系数 \(\omega\) )。更具体地说:
- \(r_{g,t,q}^{(\omega,q)}=\omega \cdot r_{g,t,q}\ \forall\ g,t\)
- \(r_{g,t,i}^{(\omega,q)}=r_{g,t,i}\ \forall\ g,t,i \neq q\)
频次响应曲线定义为以下函数:
$$
\text{IncrementalOutcome}_q^{[freq]} \left( \omega \cdot \text{Cost}_q^{[RF]} \right) =
E \Biggl(
\sum\limits_{g,t} \biggl(
\overset \sim Y_{g,t}^{
(\{x_{g,t,i}\},\{r_{g,t,i}\},\{f_{g,t,i}^{(\omega,q)}\})
} -
\overset \sim Y_{g,t}^{
(\{x_{g,t,i}\},\{r_{g,t,i}^{(0,q)}\},\{f_{g,t,i}\})
}
\biggr) \bigg| \{z_{g,t,i}\}
\Biggr)
$$
其中, \(f_{g,t,i}^{(\omega,q)}\) 表示所有渠道的观测频次值,但渠道 \(q\)除外(该渠道在所有位置的观测频次值均需乘以系数 \(\omega\) )。更具体地说:
- \(f_{g,t,q}^{(\omega,q)}=\omega \cdot f_{g,t,q}\ \forall\ g,t\)
- \(f_{g,t,i}^{(\omega,q)}=f_{g,t,i}\ \forall\ g,t,i \neq q\)
请注意,如果 \(\omega < \dfrac{1}{min_{g,t} f_{g,t}}\),对于某些 \(g,t\)组合,反事实频次 \(\omega \cdot f_{g,t,q}\) 将小于 1。虽然平均频次值不可能小于 1,但 Meridian 的模型规范允许针对此类不太可能出现的值估计增量效果。在解读 \(\omega\)值如此小的响应曲线时务必要小心谨慎。
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最后更新时间 (UTC):2025-08-04。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-08-04。"],[[["\u003cp\u003eThis document extends marketing outcome definitions to channels with reach and frequency data, calculating incremental outcomes by comparing scenarios with and without a specific channel's reach.\u003c/p\u003e\n"],["\u003cp\u003eIt defines ROI for these channels as the ratio of incremental outcome to cost, with cost determined by the total reach of the channel.\u003c/p\u003e\n"],["\u003cp\u003eTwo types of response curves are introduced: reach response curves (scaling reach while keeping frequency constant) and frequency response curves (scaling frequency while keeping reach constant), allowing for analysis of marketing outcomes based on different spend scaling strategies.\u003c/p\u003e\n"],["\u003cp\u003eThe response curves are functions that estimate the incremental outcome at different spend levels, achieved by scaling either reach or frequency while keeping the other constant at historical values.\u003c/p\u003e\n"],["\u003cp\u003eWhile the model allows for calculating incremental outcomes even with implausible frequency values (less than one), caution is advised when interpreting results in such cases.\u003c/p\u003e\n"]]],["The core content details extending definitions for channels with reach and frequency data. It defines the incremental outcome, which quantifies the impact of a specific channel, and ROI, which is the ratio of incremental outcome to cost. Two types of response curves are introduced: reach and frequency. The reach curve scales the reach of a channel while holding frequency constant; conversely, the frequency curve scales frequency while holding reach constant. Each response curve measures the incremental outcome based on these adjustments.\n"],null,["# Extension to models with reach and frequency\n\nThe definitions described in the previous sections can be extended for channels\nwith reach and frequency data. The potential outcomes can be written more\ngenerally as \\\\( \\\\overset \\\\sim Y_{g,t}\\^{ \\\\left( \\\\left\\\\{ x_{g,t,i}\\^{(\\\\ast)}\n\\\\right\\\\}, \\\\left\\\\{ r_{g,t,i}\\^{(\\\\ast)} \\\\right\\\\}, \\\\left\\\\{ f_{g,t,i}\\^{(\\\\ast)}\n\\\\right\\\\} \\\\right) } \\\\)\n\nThe incremental outcome of the \\\\(q\\^{th}\\\\) channel with reach and frequency data\nis defined as: \n$$ \\\\text{IncrementalOutcome}\\^{\\[RF\\]}_q = E \\\\Biggl(\\\\sum\\\\limits_{g,t} \\\\biggl( \\\\overset \\\\sim Y_{g,t}\\^{ \\\\left( \\\\left\\\\{ x_{g,t,i} \\\\right\\\\}, \\\\left\\\\{ r_{g,t,i} \\\\right\\\\}, \\\\left\\\\{ f_{g,t,i} \\\\right\\\\} \\\\right) } - \\\\overset \\\\sim Y_{g,t}\\^{ \\\\left( \\\\left\\\\{ x_{g,t,i} \\\\right\\\\}, \\\\left\\\\{ r_{g,t,i}\\^{(0,q)} \\\\right\\\\}, \\\\left\\\\{ f_{g,t,i} \\\\right\\\\} \\\\right) } \\\\biggr) \\\\bigg\\| \\\\left\\\\{ z_{g,t,i} \\\\right\\\\} \\\\Biggr) $$\n\nWhere \\\\(r\\^{(0)}_{g,t,i}\\\\) denotes the observed reach values for all channels\nexcept channel \\\\(q\\\\), which is set to zero everywhere. More specifically:\n\n- \\\\(r\\^{(0,q)}_{g,t,q} = 0\\\\ \\\\forall\\\\ g,t\\\\)\n- \\\\(r\\^{(0,q)}_{g,t,i} = r_{g,t,i}\\\\ \\\\forall\\\\ g,t,i \\\\neq q\\\\)\n\nNote that the frequency counterfactual values don't matter when the reach is\nzero; the incremental outcome should be zero regardless. These are arbitrarily\nset to historical values in this definition.\n\nThe ROI of the \\\\(q\\^{th}\\\\) channel with reach and frequency data is defined as:\n\n\\\\\\[\\\\text{ROI}\\^{\\[RF\\]}_q = \\\\dfrac{\\\\text{IncrementalOutcome}\\^{\\[RF\\]}_q}{\\\\text{Cost}\\^\n{\\[RF\\]}_q}\\\\\\]\n\nWhere \\\\(\\\\text{Cost}\\^{\\[RF\\]}_q=\\\\sum\\\\limits_{g,t} \\\\overset \\\\sim r_{g,t,q}\\\\).\n\nTo define response curves, note that there are many ways to scale spend for\nchannels with reach and frequency data. For any given spend level, there are any\nnumber of reach and frequency combinations that can result in that spend level.\nMeridian focuses primarily on two types of response curve:\n\n- A *reach* response curve where reach is scaled, holding frequency constant\n at the historical values for each geo and time period.\n\n- A *frequency* response curve where frequency is scaled, holding reach\n constant at the historical values for each geo and time period.\n\nThe reach response curve is defined as the following function: \n$$ \\\\text{IncrementalOutcome}_q\\^{\\[reach\\]} \\\\left( \\\\omega \\\\cdot \\\\text{Cost}_q\\^{\\[RF\\]} \\\\right) = E \\\\Biggl( \\\\sum\\\\limits_{g,t} \\\\biggl( \\\\overset \\\\sim Y_{g,t}\\^{ (\\\\{x_{g,t,i}\\\\},\\\\{r_{g,t,i}\\^{\\\\omega,q}\\\\},\\\\{f_{g,t,i}\\\\}) } - \\\\overset \\\\sim Y_{g,t}\\^{ (\\\\{x_{g,t,i}\\\\},\\\\{r_{g,t,i}\\^{(0,q)}\\\\},\\\\{f_{g,t,i}\\\\}) } \\\\biggr) \\\\bigg\\| \\\\{z_{g,t,i}\\\\} \\\\Biggr) $$\n\nWhere \\\\(r_{g,t,i}\\^{(\\\\omega,q)}\\\\) denotes the observed reach values for all\nchannels except channel \\\\(q\\\\), which is scaled by \\\\(\\\\omega\\\\) everywhere. More\nspecifically:\n\n- \\\\(r_{g,t,q}\\^{(\\\\omega,q)}=\\\\omega \\\\cdot r_{g,t,q}\\\\ \\\\forall\\\\ g,t\\\\)\n- \\\\(r_{g,t,i}\\^{(\\\\omega,q)}=r_{g,t,i}\\\\ \\\\forall\\\\ g,t,i \\\\neq q\\\\)\n\nThe frequency response curve is defined as the following function: \n$$ \\\\text{IncrementalOutcome}_q\\^{\\[freq\\]} \\\\left( \\\\omega \\\\cdot \\\\text{Cost}_q\\^{\\[RF\\]} \\\\right) = E \\\\Biggl( \\\\sum\\\\limits_{g,t} \\\\biggl( \\\\overset \\\\sim Y_{g,t}\\^{ (\\\\{x_{g,t,i}\\\\},\\\\{r_{g,t,i}\\\\},\\\\{f_{g,t,i}\\^{(\\\\omega,q)}\\\\}) } - \\\\overset \\\\sim Y_{g,t}\\^{ (\\\\{x_{g,t,i}\\\\},\\\\{r_{g,t,i}\\^{(0,q)}\\\\},\\\\{f_{g,t,i}\\\\}) } \\\\biggr) \\\\bigg\\| \\\\{z_{g,t,i}\\\\} \\\\Biggr) $$\n\nWhere \\\\(f_{g,t,i}\\^{(\\\\omega,q)}\\\\) denotes the observed frequency values for all\nchannels except channel \\\\(q\\\\), which is scaled by \\\\(\\\\omega\\\\) everywhere. More\nspecifically:\n\n- \\\\(f_{g,t,q}\\^{(\\\\omega,q)}=\\\\omega \\\\cdot f_{g,t,q}\\\\ \\\\forall\\\\ g,t\\\\)\n- \\\\(f_{g,t,i}\\^{(\\\\omega,q)}=f_{g,t,i}\\\\ \\\\forall\\\\ g,t,i \\\\neq q\\\\)\n\nNote that for \\\\(\\\\omega \\\u003c \\\\dfrac{1}{min_{g,t} f_{g,t}}\\\\), the counterfactual\nfrequency \\\\(\\\\omega \\\\cdot f_{g,t,q}\\\\) will be less than one for some combinations\nof \\\\(g,t\\\\). Although it is not possible to have average frequency values below\none, Meridian's model specification allows incremental outcome to be\nestimated for such implausible values. Be careful when interpreting response\ncurves for such small values of \\\\(\\\\omega\\\\)."]]