A few days ago I was at the checkout in a grocery store, when the cashier asked me: “Can I scan your App?” then I got back home, opened the App Store and the itch started!

几天前，我在一家杂货店的结帐处，出纳员问我：“我可以扫描您的应用程序吗？” 然后我回到家，打开了App Store，开始了！

In this article, I would like to explain why retailers are interested in promoting their mobile app to their customer base, what advantages it brings for them, what kind of data they collect, and how it can be used under a data science point of view.

在本文中，我想解释一下为什么零售商有兴趣向其客户群推广其移动应用程序，它为他们带来什么好处，他们收集了什么样的数据以及如何在数据科学的角度下使用它。 。

介绍 (Introduction)

Besides my little adventure at my local grocery store, as a Data Scientist, I couldn’t stop myself from noticing that in recent times more and more retailers of every kind (even ice cream retailers) are inviting their customers to download their apps and scan some kind of barcode at the moment of the transaction.

除了我在本地杂货店的小冒险活动外，作为一名数据科学家，我也无法阻止自己注意到最近越来越多的各种零售商(甚至是冰淇淋零售商)正在邀请其客户下载他们的应用程序并进行扫描交易时使用某种条形码。

Sometimes they promise discounts, sometimes they deliver products to your door, sometimes you can use it as a note-taking app to prepare your shopping list and sometimes a mix of all.

有时他们承诺打折，有时将产品交付到您家，有时您可以将其用作记笔记应用程序来准备购物清单，有时还可以混合使用所有商品。

应用程序： (The Apps:)

Those apps are available and free to downloads from different app stores depending on the platform, however, they are structured more or less all in the same way:

根据平台的不同，这些应用程序可用并且可以免费从不同的应用程序商店下载，但是，它们的结构或多或少都采用相同的方式：

The app’s home screen has a recap of how many points I collected, a carousel with different call to action, and an image suggesting the main way to use this app: scan your barcode at the checkout. Scrolling a bit more to the bottom I noticed an interesting section with some offers that are available for a limited time.

该应用程序的主屏幕概述了我收集的点数，带有不同号召性用语的轮播以及一张图像，该图像提示了使用该应用程序的主要方式：在结帐时扫描条形码。 滚动到底部更多一点，我注意到了一个有趣的部分，其中提供了在有限的时间内提供的一些优惠。

According to an interesting report by Nielsen, the main reasons retailers are interested in an app for their customers are:

根据尼尔森(Nielsen )的一份有趣的报告，零售商对其客户感兴趣的主要原因是：

• Everything is social: many apps push social media integration since it helps to collect different kinds of data. Also, social integration makes it easier for the user to login (think about the “Login with …” button).

  一切都是社交的：许多应用推动社交媒体集成，因为它有助于收集各种数据。 另外，社交集成使用户更容易登录(请考虑“使用…登录”按钮)。

• It helps decision making: since it is very easier (once you have a user base) to get user location and taste, grocery companies can have help in deciding where to set up future brick and mortar stores.

  它有助于决策：由于很容易(一旦拥有用户群)就可以确定用户的位置和品味，因此杂货公司可以在确定将来在哪里建立实体店方面提供帮助。

• Customer care improves: based on the information your customers shared with you, along with purchases, transactions, and demographics relations with customers can be improved.

  改善客户服务：基于与您共享的客户信息，可以改善与客户的购买，交易和人口统计关系。

• It creates a new field where suppliers can compete: having a section like “recommended products” might give retailers the chance to put out bids for a spot there, this opens the doors to competition among suppliers and/or increase in bargaining power.

  它创造了供应商可以竞争的新领域：拥有“推荐产品”之类的区域可能会给零售商提供机会在那里竞标，这为供应商之间的竞争和/或议价能力的提高打开了大门。

• The magic of data: collecting user transactions, contents of their baskets and preferences open the doors for retailers to the world of predictive analysis.

  数据的魔力：收集用户交易，他们的购物篮内容和偏好，为零售商打开了进行预测分析之门。

• Retaining existing customers leads to higher profits: it is known that customer acquisition costs are generally higher than retention costs, moreover, reports suggest that existing customers, compared to new customers, are 50% more likely to try new products and spend up to 31% more.

  留住现有客户可带来更高的利润：众所周知，客户获取成本通常高于保留成本，而且报告显示，与新客户相比，现有客户尝试新产品并花费高达31％的可能性要高50 ％更多。

他们收集什么样的数据？ (What kind of data do they collect?)

To answer this question I would first focus on which data we input in those apps, from the moment we download the app the first time, to the moment when we scan our barcode at the checkout.

为了回答这个问题，我将首先关注从第一次下载应用程序到在结帐时扫描条形码的那一刻，我们在这些应用程序中输入的数据。

First, when we register, we are usually prompted for our email address, date of birth (because of regulations there might be some age barriers to register), name, surname. If we login with an external provider (think of “Login With Facebook”) we delegate the external provider to give that information on our behalf.

首先，当我们注册时，通常会提示我们输入电子邮件地址，出生日期(由于法规的缘故，年龄可能会有所限制)，姓名，姓氏。 如果我们使用外部提供商登录(例如“使用Facebook登录”)，我们将委托外部提供商代表我们提供该信息。

Next, when we do our regular shopping, we are prompted by the cashier to scan our barcode. This barcode contains an ID associated with our profile, which is used to create an association between the transaction and us.

接下来，当我们进行常规购物时，收银员会提示我们扫描条形码。 该条形码包含与我们的个人资料关联的ID，该ID用于在交易与我们之间建立关联。

When doing a transaction the following data (at least) are recorded:

进行交易时，至少会记录以下数据：

• The transaction timestamp.交易时间戳。
• The store where the transaction happened.发生交易的商店。
• The content of our basket (it might be a list of product IDs).购物篮中的内容(可能是产品ID的列表)。
• The transaction total.交易总额。
• If we used any coupon/discount and which.如果我们使用了任何优惠券/折扣，哪个。
• If we used any promo code.如果我们使用任何促销代码。

It is also possible, depending on the way the app was designed, to acquire a much broader set of data, containing information such as if the basket content was previously prepared inside the app, what technology was used for the payment(Google pay, cash, credit cards…), which mobile OS was used and so on.

根据应用程序的设计方式，还可能会获取更广泛的数据集，其中包含诸如是否在应用程序内部预先准备购物篮内容，用于付款的技术(Google付款，现金)等信息。 ，信用卡...)，使用了哪个移动操作系统等等。

哪些零售商(可以)处理这些数据？ (What retailers (can) do with those data?)

We can see that even without an App some of those data listed in the previous section can still be recorded, devices such as the POS located in-store already records transaction data. However, having an association between a transaction and a user opens different possibilities for retailers, such as tailored marketing, user clustering, personal recommendations, and offers.

我们可以看到，即使没有App，仍可以记录上一节中列出的某些数据，而位于商店中的POS等设备已经记录了交易数据。 但是，在交易和用户之间建立关联会为零售商带来不同的可能性，例如量身定制的营销，用户集群，个人推荐和要约。

A similar way to create a relation between a transaction and a user is the loyalty cards from a convenient store, they are around for as long as I remember. However, this strategy is still considered an analogic way of keep tracking track of the customer base, since it does not take into account, and it is not able to records events such as the preparation of a shopping list.

在交易和用户之间建立关系的一种类似方式是来自便利店的会员卡，只要我记得，它们就一直存在。 但是，此策略仍被认为是跟踪客户基础的一种类似方法，因为它没有考虑到这一点，并且它无法记录诸如准备购物清单之类的事件。

Since there are many ways, retailers can exploit such a “live” data collection of transactions and users I would like to group them into two groups:

由于有很多方法，零售商可以利用交易和用户的“实时”数据收集，我想将它们分为两类：

• Descriptive analysis

  描述性分析

• Predictive Analysis

  预测分析

The descriptive analysis is used to get acquainted with our data, to generate a set of business reports, to cluster our customers base on different categories, and to find/prepare correlations and aggregations for further modeling the data, with, as a final goal the predictive analysis.

描述性分析用于了解我们的数据，生成一组业务报告，根据不同类别对客户进行聚类，并查找/准备相关性和汇总以进一步对数据建模，最终目的是预测分析。

The predictive analysis has a goal to deliver predictions about the future using data from the past. It can be possible, for example, to forecast future sales, for different categories of products, in this case, the data must be processed correctly (we might want to aggregate and sum all the sales)

预测分析的目标是使用过去的数据对未来进行预测。 例如，可以预测不同类别产品的未来销售，在这种情况下，必须正确处理数据(我们可能希望汇总和汇总所有销售)

Another common application of transactional data is price optimization: prices of articles are tweaked by calculating the price elasticity of demand.

交易数据的另一个常见应用是价格优化：通过计算需求的价格弹性来调整商品价格。

Have you ever saw those e-ink price tags in a store? The information displayed on those price tags is controlled by a remote system, that can be updated anytime.

您是否在商店中看到过这些电子墨水价格标签？ 这些价格标签上显示的信息由远程系统控制，可以随时更新。

It seems that we’ve already seen something very similar somewhere else…

看来我们已经在其他地方看到了非常相似的东西……

Another (more related to retail) example of the techniques described until now is the Alibaba Hema store. Hema is a Chinese grocery store which basically is the definition of the future or grocery shops: customers use the Hema’s app to scan and pay for groceries, the app shows additional information for every product, such as recipes (think about having n items in your basket and the app tell you which recipes you can cook and what is missing).

到目前为止，所描述的技术的另一个示例(与零售更相关)是阿里巴巴Hema商店。 Hema是一家中国杂货店，基本上是未来或杂货店的定义：客户使用Hema的应用程序扫描杂货并付款，该应用程序会显示每种产品的其他信息，例如食谱(例如，您的商品中有n商品篮和应用程序会告诉您可以烹饪哪些食谱以及缺少什么)。

The stores also serve as a distribution center, where employees gather online orders and through a system of conveyors belt hanging from the ceiling order bags are moved throughout the store. Normally a customer within the range of 3KM can get its groceries in less than 30 minutes.

商店还充当配送中心，员工在此收集在线订单，并通过悬挂在天花板上的传送带系统将订单袋移动到整个商店。 通常，在3KM范围内的客户可以在不到30分钟的时间内获得其食品杂货。

Customers use Hema’s mobile app, using it to scan barcodes throughout the store to find out things such as product information and recipe ideas. Alibaba knows everything a customer has purchased, so it offers users the option in the future to quickly order the same goods to be delivered to their home.

客户使用Hema的移动应用程序，通过它扫描整个商店中的条形码，以查找诸如产品信息和食谱创意之类的内容。 阿里巴巴了解客户购买的所有物品，因此将来可以为用户提供选择，以快速订购要运送到家中的相同商品。

一个小例子： (A small example:)

An interesting presentation never misses the live demo, this is not a presentation, but anyway I wanted to give some tastes of how it looks like a very simple basket analysis model, based on a very common and widely used algorithm: apriori.

有趣的演示永远不会错过现场演示，这不是演示，但是无论如何，我想尝试一下它的外观，它基于非常普遍且广泛使用的算法apriori，它是一个非常简单的篮子分析模型。

For this example, I will use a very cool and open-source data mining software called Orange, it provides a series of tools for data mining, exploration, and even some machine learning! After you downloaded it make sure to install the Associate add-on (head over Options, then Add-ons). Next, you can get the dataset for this demo here.

在此示例中，我将使用一个非常出色的开源数据挖掘软件Orange ，它提供了一系列用于数据挖掘，探索甚至是机器学习的工具！ 下载后，请确保安装“关联”加载项(“选项”前是“附加”)。 接下来，您可以在此处获取此演示的数据集。

Once fitted the Frequent Itemset algorithm it is possible to have a look at the output tables:

一旦安装了Frequent Itemset算法，就可以查看输出表：

This table shows a summary for each item (with nested itemsets), for example mineral waterappears in 23.83% of the transactions; the itemset {mineral water, spaghetti}in 5.97%.

该表显示了每个项目(带有嵌套项目集)的摘要，例如， mineral water占交易总量的23.83％； 项目集{mineral water, spaghetti}的含量为5.97％。

The second (definitely more interesting) table is the one generated by the Association Rules algorithm, it introduces two very important concepts: support and confidence.

第二个表(肯定更有趣)是由关联规则算法生成的表，它引入了两个非常重要的概念：支持和置信度。

Support measures how frequent an itemset appears in the transactions: from our data, we can see how the rule milk -> chocolatehas more than twice the support of the rule milk -> soup .

支持度量项目集在交易中出现的频率：从我们的数据中，我们可以看到规则milk -> chocolate的支持率是规则milk -> soup两倍以上。

While with confidence we can identify how likely the consequent item is purchased when the antecedent is in the basket.

我们可以放心地确定当前件放在购物篮中时，购买后续产品的可能性。

Using a combination of the two metrics can help retailers understand where to place, what to promote, and how to promote different items (or itemsets!).

结合使用这两个指标可以帮助零售商了解在哪里放置，要促销什么以及如何促销不同的项目(或项目集！)。

结论： (Conclusions:)

While doing research for writing this article I came across another very interesting trend: shop-agnostic grocery apps.

在进行撰写本文的研究时，我遇到了另一个非常有趣的趋势：与商店无关的杂货应用程序。

The way those apps work is slightly different than apps coming from a single retailer: some have a built-in recommendation system to help you with meal planning and grocery lists, then they offer partnerships with different retailers. Jaw, a French startup that recently got 7M$ in funding offers partnerships with different supermarkets, among them giants such as Carrefour, Auchan, and Leclerc.

这些应用程序的工作方式与来自单个零售商的应用程序略有不同：一些应用程序具有内置的推荐系统，可帮助您计划饮食和购物清单，然后与其他零售商提供合作伙伴关系。 Jaw是一家法国创业公司，最近获得了700万美元的资金，与多家超市建立了合作伙伴关系，其中包括家乐福，欧尚和Leclerc等巨头。

Another interesting move is the acquisition of szopi by supermcato24 which besides the copy-paste of their Italian website to the Polish one (suggesting the willingness for a rebranding of szopi) shows that this trend is growing.

另一个有趣的举动是supermcato24对szopi的收购，除了将其意大利网站复制粘贴到波兰语网站(暗示愿意对szopi进行品牌重塑)，这种趋势还在增长。

A question spikes me: at this moment, it seems to me that this market is pretty segmented (especially in Europe), what will happen in the next months?

一个问题激起了我的印象：此刻，在我看来，这个市场已经细分了(尤其是在欧洲)，接下来的几个月会发生什么？

Will we install on our smartphones tons of different apps (think of UberEats, Glovo, Deliveroo) meaning different players fighting for their market share or the Zip’s law will also come into play here?

我们是否会在智能手机上安装大量不同的应用程序(例如UberEats，Glovo，Deliveroo)，这意味着为争夺市场份额而战的不同玩家或Zip定律也将在这里发挥作用？

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