Attribution Modeling
When evaluating the effectiveness of your channels, use attribution models that reflect your advertising goals and business models. Regardless of the model(s) you use, test your assumptions by experimenting. Increase or decrease investment in a channel as guided by the model output, then observe your results in the data.
（译文：评估渠道的有效性时，请使用反映您的广告目标和商业模式的归因模型。 不管你使用的模型如何，通过试验来测试你的假设。 在模型输出的指导下增加或减少渠道投资，然后观察数据中的结果。）
About the Models
- The Last Interaction model attributes 100% of the conversion value to the last channel with which the customer interacted before buying or converting. Google Analytics uses this model by default when attributing conversion value in non-Multi-Channel Funnel reports. When it's useful: Because the Last Interaction model is the default model used for non-Multi-Channel Funnel reports, it provides a useful benchmark to compare with results from other models. In addition, if your ads and campaigns are designed to attract people at the moment of purchase, or your business is primarily transactional with a sales cycle that does not involve a consideration phase, the Last Interaction model may be appropriate. （译文：最后一个交互模型将100％的转化价值归因于客户在购买或转换之前与之交互的最后一个渠道。 在非多渠道路径报告中归因转化价值时，Google Analytics默认使用此模型。 何时有用：由于“最终交互”模型是用于非多渠道路径报告的默认模型，因此它提供了一个有用的基准，可与其他模型的结果进行比较。 此外，如果您的广告和广告系列旨在吸引购买时的人员，或者您的业务主要是交易性的，而且销售周期不涉及考虑阶段，则最终交互模式可能是适当的。）
- The First Interaction model attributes 100% of the conversion value to the first channel with which the customer interacted. When it's useful: This model is appropriate if you run ads or campaigns to create initial awareness. For example, if your brand is not well known, you may place a premium on the keywords or channels that first exposed customers to the brand. （译文：First Interaction模型将转化价值的100％归功于与客户互动的第一个渠道。 何时有用：如果您运行广告或广告系列来创建初始感知，则此模型是适当的。 例如，如果您的品牌不是众所周知的，那么您可能会重视首先将客户置于品牌上的关键字或渠道。）
- The Linear model gives equal credit to each channel interaction on the way to conversion. When it's useful: This model is useful if your campaigns are designed to maintain contact and awareness with the customer throughout the entire sales cycle. In this case, each touch point is equally important during the consideration process. （译文：线性模型给予转换途中每个渠道互动的平等分数。 何时有用：如果您的广告系列旨在与整个销售周期中的客户保持联系和了解，则此模式非常有用。 在这种情况下，每个接触点在审议过程中同样重要。）
- Time Decaymodel may be appropriate. This model most heavily credits the touch points that occurred nearest to the time of conversion. When it's useful: If you run one-day or two-day promotion campaigns, you may wish to give more credit to interactions during the days of the promotion. In this case, interactions that occurred one week before have only a small value as compared to touch points near the conversion. The Time Decay model allows you to appropriately credit touch points during the day or two leading up to conversion. （译文：时间Decaymodel可能是适当的。 这个模型对最接近转换时间的触点进行评分最高。 何时有用：如果您进行为期一天或两天的促销活动，您可能希望在促销期间给予互动更多的荣誉。 在这种情况下，与转换附近的触点相比，一周前发生的交互只有很小的值。 时间衰减模型允许您在一两天内将触点适当归功于转换。）
- The Position Based model allows you to create a hybrid of the Last Interaction and First Interaction models. Instead of giving all the credit to either the first or last interaction, you can split the credit between them. One common scenario is to assign 40% credit each to the first interaction and last interaction, and assign 20% credit to the interactions in the middle. When it's useful: If you most value touchpoints that introduced customers to your brand and final touchpoints that resulted in sales, use the Position Based model. （译文：基于职位的模型允许您创建最后的互动和第一互动模型的混合。 不要把所有的功劳归于第一次或最后一次的互动，你可以分开他们之间的信用。 一种常见的情况是将第一次互动和最后一次互动分配给40％的学分，并将20％的学分分配给中间的互动。 有用时：如果您最看重将客户引入品牌的接触点和导致销售的最终接触点，请使用基于职位的模型。）
- causal attribution modeling. Attribution measurement is really a study of cause and effect http://m6d.com/2012/05/03/causal-attribution-a-pipe-dream-or-ad-techs-dance-with-relativity-theory/（译文：因果归因建模。 归因测量实际上是对因果关系的研究）
- survival models for marketing attribution. PhD thesis by John Chandler-Pepelnjak, published in 2010. His thesis investigated Modelling conversions in online advertising.（译文：营销归因的生存模式。 John Chandler-Pepelnjak于2010年发表论文，研究了在线广告中的建模转换。）
