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We’re moving on to ice cream. Specifically, we’re a group of DSs working at an ice cream company. A few months ago, the sales and marketing teams approached us, asking for a model that will help identify when to send coupons to customers to increase the chance of them seeing those coupons in their inboxes. The marketing group’s standard behavior is to send out a bulk mailing every Monday morning at $8 \mathrm{a} . \mathrm{m}$. Our project aims to generate a day-and-hour combination to send the emails out on an individualized (personalized) basis.

The top of figure 11.2 shows the components and examples of our prior state. The bottom of the figure shows what the model output fashions as part of an image component generator, personalized to each of our members.

We’ve built this MVP and have shown some promising results based on our shadow runs. Through tracking our pixel data (a $1 \times 1$ pixel embedded in our emailed coupon codes that show the open and click rates for our marketing ads), we’re finding shockingly accurate results from our model based on our monitoring of actual open and usage rates of our coupons.

While this news is exciting, the business isn’t convinced by our delta error in minutes from prediction to actual opening time of the emails. What they really want to know is this: “Does this increase sales?” To begin to answer that question, we should analyze that metric, shown in figure 11.3.

How can we determine whether a causal relationship exists between sending targeted coupons to customers at times that they are most likely to see the coupons, and the customers’ use of those coupons? It all begins with determining what to measure, who to measure it on, and what tools to utilize to determine if the model has a causal influence.

## 计算机代写|机器学习代写Machine Learning代考|Measuring prediction performance

The first step that we need to think about in measuring our model’s performance is the same as we would engage in for any design of experiments (DOE) exercise. We start by talking to the experts who engage in the email marketing campaigns (our internal customers for this project) well before the production release date of our solution. This team, after all, has a fundamentally deep understanding of both our customers and their interactions with our product line.

During these discussions, we’ll want to focus on the marketing team’s knowledge of our customer. That deep understanding of the customer base will aid us in determining which data that we collect about them can be used to limit the latent effects in order to minimize variance in our results. Table 11.1 shows the conjectures that the SME groups and the DS team have, along with the results of the analysis.

We know that we need to minimize the latent variable effects that are causing behavior imbalance. We can’t get the data that conclusively identifies the behaviors that we’re seeing (multimodality), but we certainly can improve our attribution if we control for it. But how can we do that? How do we group our users most effectively?

Based on our discussions with the SME group, we set about analyzing approaches that can reduce the inherent variability within our population. By listening to the marketing team, we find that its tried-and-true methodology for evaluating customer cohorts is the most optimal solution. By combining the recency of purchases, the number of historical purchases, and the total amount of spending sent our way by customers, we can define a standard metric to classify our cohorts (see the following sidebar regarding RFM for the power of this segmentation technique).

RFM: A great way to group humans if you’re selling things to them RFM, an acronym for recency, frequency, and monetary value, is a direct marketing term coined by Jan Roelf Bult and Tom Wansbeek. In their article “Optimal Selection for Direct Mail,” they postulated RFM as a significantly powerful means of assigning value to customers. The pair estimated that $80 \%$ of a company’s revenue actually comes from $20 \%$ of its customers.

While prescient in the extreme, the success of this methodology has been proven time and again in many industries (not relegated to only business-to-consumer companies, either). The principal concept is to define five quantile-based buckets of customers on each of these three observational variables. Customers with a high value in monetary value, for instance, would be the top $20 \%$ of spenders, receiving a value of 5 for M. Customers with a low value in frequency (the number of total purchases over the lifetime of the account), typically consisting of one-time purchasers, would have an $\mathrm{F}$ value of 1.

When combined, RFM values create a matrix of 125 elements ranging from the lowestvalue customer (111) to the highest-value (555) customer. Applying business-specific and industry-specific meta-groupings atop these raw 125 matrix entry values allows for a company (and a DS team) to have points of latent-variable-lessening stratification points for the purposes of hypothesis testing.

I once was a bit incredulous at this technique of grouping human behavior in such a simplistic way-until I analyzed it for a third time at a third company. I’m now a pretty firm believer in this seemingly simplistic but wondrously powerful technique.

## 计算机代写|机器学习代写Machine Learning代考|Measuring prediction performance

RFM:如果你向他们销售产品，RFM是将人们分组的好方法。RFM是近时性、频率和货币价值的首字母缩略词，是由Jan Roelf Bult和Tom Wansbeek创造的一个直接营销术语。在他们的文章“直销邮件的最佳选择”中，他们假设RFM是为客户分配价值的一种非常强大的手段。两人估计，一家公司80%的收入实际上来自20%的客户。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。