An edited version of this article was first published on ClickZ: Marketer’s guide to data-driven marketing attribution.

本文的编辑版本首次发布在ClickZ：基于市场营销人员的数据驱动营销归因指南中 。

Marketing attribution is a way of measuring the value of the campaigns and channels that are reaching your potential customers. The point in time when a potential customer interacts with a campaign is called a touchpoint, and a collection of touchpoints forms a buyer journey. Marketers use the results of an attribution model to understand what touchpoints have the most influence on successful buyer journeys, so that they can make more informed decisions on how to optimise investment in future marketing resources.

营销归因是一种衡量吸引潜在客户的广告系列和渠道的价值的方法。 潜在客户与广告系列互动的时间点称为接触点，接触点的集合构成了购买者的旅程。 营销人员使用归因模型的结果来了解哪些接触点对成功的购买者旅程具有最大的影响，以便他们可以就如何优化对未来营销资源的投资做出更明智的决策。

Buyer journeys are rarely straightforward and the paths to success can be long and winding. With so many touchpoints to consider it is difficult to distinguish between the true high and low impact interactions, which can result in an inaccurate division of credit and a false representation of marketing performance. This is why choosing the best attribution model for your business is so important.

买家的旅程很少是直截了当的，成功的道路可能漫长而曲折。 考虑到这么多的接触点，很难区分真正的高影响力互动和低影响力互动，这可能导致信贷分配不准确和营销绩效的错误表述。 这就是为什么为您的业务选择最佳归因模型如此重要的原因。

In this post, I provide some insight into how Cloudera has used Cloudera products to build a custom, data-driven attribution model to measure the performance of our global campaigns.

在本文中，我将提供一些有关Cloudera如何使用Cloudera产品来构建自定义，数据驱动的归因模型以衡量我们的全球活动绩效的见解。

传统模式的局限性 (Limitations of traditional models)

All attribution models have their pros and cons, but one drawback the traditional models have in common is that they are rules based. The user has to decide up front how they want the credit for sales events to be divided between the touchpoints. Traditional models include:

所有归因模型都有其优缺点，但是传统模型的一个缺点是它们都是基于规则的。 用户必须预先决定他们如何希望在接触点之间分配销售活动的功劳。 传统模型包括：

Luckily there are more sophisticated data-driven approaches that are able to capture the intricacies of buyer journeys by modelling how touchpoints actually interact with buyers, and each other, to influence a desired sales outcome. A data-driven model provides marketers with deeper insight into the importance of campaigns and channels, driving better marketing accountability and efficiency.

幸运的是，存在更复杂的数据驱动方法，这些方法可以通过对接触点实际上如何与买方交互以及如何与买方交互以影响期望的销售结果进行建模来捕获买方旅程的复杂性。 数据驱动的模型为营销人员提供了对广告系列和渠道重要性的更深入的了解，从而提高了营销责任和效率。

Cloudera的数据驱动方法 (Cloudera’s data-driven approach)

The first attribution model we evaluated was based on the Shapley value from cooperative game theory. I covered the details of this model in a previous post. This popular (Nobel prize winning) model provided much more insight into channel performance than the traditional approaches, but in its most fundamental implementation it didn’t scale to handle the number of touchpoints we wanted to include. The Shapley model performed well on a relatively small number of channels, but our requirement was to perform attribution for all campaigns, which can equate to hundreds of touchpoints along a buyer’s journey.

我们评估的第一个归因模型是基于合作博弈理论的Shapley值。 我在上一篇文章中介绍了该模型的详细信息。 与传统方法相比，这种流行的(获得诺贝尔奖的)模型提供了对渠道性能的更多了解，但是在其最基本的实施中，它无法扩展以处理我们想要包含的接触点数量。 Shapley模型在相对较少的渠道上表现良好，但我们的要求是对所有广告系列进行归因，这可以等同于买方整个旅程中的数百个接触点。

Before investing time into scaling out the Shapley algorithm, we researched alternate methods and decided to evaluate the use of Markov models to solve the attribution problem. We used the ChannelAttribution R package for the implementation and found that it produced similar results to the Shapley model, it could scale to a large number of touchpoints, and was easy to set up and use in Cloudera Data Science Workbench (CDSW).

在花时间扩展Shapley算法之前，我们研究了替代方法，并决定评估使用Markov模型解决归因问题。 我们使用ChannelAttribution R包进行实施，发现它产生了与Shapley模型相似的结果，可以扩展到大量接触点，并且易于在Cloudera Data Science Workbench(CDSW)中设置和使用。

马尔可夫归因模型 (Markov attribution models)

Markov is a probabilistic model that represents buyer journeys as a graph, with the graph’s nodes being the touchpoints or “states”, and the graph’s connecting edges being the observed transitions between those states. For example, a buyer watches a product Webinar (first state) then browses to LinkedIn (transition) where they click on an Ad impression for the same product (second state).

马尔可夫是一个概率模型，它以图表的形式表示买方的旅程，图表的节点是接触点或“状态”，图表的连接边是在这些状态之间观察到的过渡。 例如，买主观看产品网络研讨会 (第一状态)，然后浏览到LinkedIn(过渡)，在该处他们单击同一产品的广告展示(第二状态)。

The key ingredient to the model is the transition probabilities (the likelihood of moving between states). The number of times buyers have transitioned between two states is converted into a probability, and the complete graph can be used to measure the importance of each state and the most likely paths to success.

该模型的关键要素是转移概率(状态之间移动的可能性)。 买家在两个州之间转换的次数转换为概率，并且完整的图表可用于衡量每个州的重要性以及最可能的成功之路。

For example, in a sample of buyer journey data we observe that the Webinar touchpoint occurs 8 times, and buyers watched the webinar followed by clicking on the LinkedIn Ad only 3 times, so the transition probability between the two states is 3 / 8 = 0.375 (37.5%). A probability is calculated for every transition to complete the graph.

例如，在购买者旅程数据的样本中，我们观察到网络研讨会接触点发生了8次，并且购买者观看了网络研讨会，随后仅点击了LinkedIn 广告 3次，因此两种状态之间的转换概率为3/8 = 0.375 (37.5％)。 计算每个过渡完成图的概率。

Before we get to calculating campaign attribution, the Markov graph can tell us a couple of useful nuggets of information about our buyer journeys. From the example above you can see that the path with the highest probability of success is “Start > Webinar > Campaign Z > Success” with a total probability of 42.5% (1.0 * 0.425 * 1.0).

在计算广告系列归因之前，马尔可夫图可以告诉我们一些有用的关于购买者旅程的信息。 从上面的示例中，您可以看到成功概率最高的路径是“ 开始>网络研讨会>广告系列Z>成功 ”，总概率为42.5％(1.0 * 0.425 * 1.0)。

The Markov graph can also tell us the overall success rate; that is, the likelihood of a successful buyer journey given the history of all buyer journeys. The success rate is a baseline for overall marketing performance and the needle for measuring the effectiveness of any changes. The example Markov graph above has a success rate of 67.5%:

马尔可夫图还可以告诉我们总体成功率； 也就是说，根据所有买家旅程的历史记录，成功的买家旅程的可能性。 成功率是整体营销绩效的基准，是衡量任何变化的有效性的关键。 上面的示例马尔可夫图的成功率为67.5％：

广告活动归属 (Campaign attribution)

A Markov graph can be used to measure the importance of each campaign by calculating what is known as the Removal Effect. A campaign’s effectiveness is determined by removing it from the graph and simulating buyer journeys to measure the change in success rate without it in place. Removal Effect is a proxy for weight, and it’s calculated for each campaign in the Markov graph.

马尔可夫图可通过计算所谓的“ 去除效果”来衡量每个活动的重要性。 广告活动的效果是通过将其从图表中删除并模拟买家的旅程来衡量成功率变化(而不进行设置)来确定的。 去除效果是权重的代表，它是针对马尔可夫图中的每个广告系列计算得出的。

Using Removal Effect for marketing attribution is the final piece of the puzzle. To calculate each campaign’s attribution value we can use the following formula: A = V * (Rt / Rv)

使用“去除效果”进行市场营销归因是最后一个难题。 要计算每个广告系列的归因值，我们可以使用以下公式： A = V *(Rt / Rv)

• A = Campaign’s attribution value

  A =广告系列的归因值

• V = Total value to divide. For example, the total USD value of all successful buyer journeys used as input to the Markov model

  V =要除的总值。 例如，所有成功买家旅程的总美元价值用作马尔可夫模型的输入

• Rt = Campaign’s Removal Effect

  Rt =广告系列的移除效果

• Rv = Sum of all Removal Effect values

  Rv =所有去除效果值的总和

Let’s walk through an example. Say that during the first quarter of the fiscal year the total USD value of all successful buyer journeys is $1M. The same buyer journeys are used to build a Markov model and it calculated the Removal Effect for our Ad campaign to be 0.7 (i.e. The buyer journey success rate dropped by 70% when the Ad campaign was removed from the Markov graph). We know the Removal Effect values for every campaign observed in the input data, and for this example let’s say they sum to 2.8. By plugging the numbers into the formula we calculate the attribution value for our Ad campaign to be $250k:

让我们来看一个例子。 假设在会计年度的第一季度，所有成功的买家旅程的总美元价值为100万美元 。 使用相同的买家旅程来构建马尔可夫模型，并计算出我们的广告系列的去除效果为0.7 (即，当从Markov图中删除广告系列时，买家旅程成功率下降了70％)。 我们知道在输入数据中观察到的每个活动的“去除效果”值，对于这个示例，假设它们的总和为2.8 。 通过将数字插入公式，我们得出广告系列的归因价值为25万美元 ：

$250,000 = $1,000,000 * (0.7 / 2.8)

$ 250,000 = $ 1,000,000 *(0.7 / 2.8)

In addition to this, we calculate campaign ROI by subtracting the cost of running a campaign over the same period of time from its attribution value.

除此之外，我们通过从广告活动的归因值中减去在相同时间段内运行广告活动的成本来计算广告活动的投资回报率。

What’s nice about the ChannelAttribution R package is it does all of this for you and even includes implementations for three of the traditional rules-based algorithms for comparison (first-touch, last-touch, and linear-touch). Theres a new Python implementation too.

ChannelAttribution R软件包的好处是它可以为您完成所有这些工作，甚至包括三种传统的基于规则的比较算法(初次触摸，最后一次触摸和线性触摸)的实现。 也有一个新的Python实现。

Cloudera上的Cloudera (Cloudera on Cloudera)

We’re proud of our data practice at Cloudera. The marketing attribution application was developed by Cloudera’s Marketing and Data Centre of Excellence lines of business. It’s built on our internal Enterprise Data Hub and the Markov models run in Cloudera Data Science Workbench (CDSW).

我们为Cloudera的数据实践感到自豪。 营销归因应用程序是由Cloudera的营销和卓越数据中心业务部门开发的。 它基于我们内部的企业数据中心构建，并且Markov模型在Cloudera Data Science Workbench(CDSW)中运行 。

By leveraging a data-driven attribution model we have eliminated the biases associated with traditional attribution mechanisms. We have been able to understand how various messages influence our potential customers and the variances by geography and revenue type. Now that we have solid and trusted data behind attribution, we’re confident in using the results to inform and drive our marketing mix strategy and investment decisions. And we can rely on the numbers when we partner with sales teams to drive our marketing strategies going forward.

通过利用数据驱动的归因模型，我们消除了与传统归因机制相关的偏见。 我们已经能够了解各种消息如何影响我们的潜在客户以及按地理位置和收入类型划分的差异。 既然归因于背后的是可靠且可靠的数据，我们有信心使用结果来指导和推动我们的营销组合策略和投资决策。 与销售团队合作时，我们可以依靠数字来推动我们的营销策略。