更新模型
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
如何更新模型以及多久更新一次,取决于数据频率(如每天或每周)以及营销团队制定决策的时间范围。如果每季度制定一次决策,我们建议每季度运行一次模型。每次都可以扩大数据窗口,这样较早的数据仍然会对最新的估计产生影响。
请注意以下几点:
在模型中,Meridian 并未将媒体效果设为随时间而变化。因此,决定是丢弃旧数据还是附加新数据需要您在方差与偏差之间进行权衡。附加新数据会减少方差,因为您有了更多的数据;如果媒体效果和策略随着时间的推移发生了巨大变化,这可能会增加偏差。
附加少量数据会对结果产生很大影响,因为一般来说,MMM 估计值的方差很大。
在附加新数据时,可以设置先验,使其与附加数据前的结果后验相匹配。这一操作会促使旧结果与新结果相匹配,而且这样做也有合理的业务理由。我们建议您根据先验知识和直觉来设置先验,而这种直觉完全可以参考以往的 MMM 结果。至于您希望以往的 MMM 结果在多大程度上影响您的先验知识和直觉,则由您自己决定。不过,要考虑到,设置与以往的 MMM 结果相匹配的先验,实际上是对以往的数据进行了两次计算。
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最后更新时间 (UTC):2024-12-23。
[[["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):2024-12-23。"],[[["Model refresh frequency should align with data frequency and marketing team's decision-making timeframe (e.g., quarterly)."],["Expanding the data window with each refresh allows older data to influence newer estimates while balancing bias and variance."],["Even small data additions can significantly impact results due to the inherent high-variance nature of MMM estimates."],["Prior settings can be adjusted to balance new and old data influences, informed by past results and business intuition."]]],["Model refreshing frequency should align with data frequency and marketing decision timelines, such as quarterly updates for quarterly decisions. Appending new data reduces variance but may introduce bias if media strategies change. Appending small amounts of data can significantly impact estimates due to their high variance. When appending, setting priors to match previous results can align old and new data, although this risks double-counting data. Prior knowledge should influence prior selection, and prior MMM results can inform this.\n"]]