Are Personalized Ads a Waste of Money?個(gè)性化廣告是浪費(fèi)錢嗎？

巴爾特·德朗熱 斯特凡諾·蓬托尼　謝紅/譯

Several major tech companies have recently built platforms that claim to educate companies about how best to market themselves and their products online. Examples include Meta for Business （formerly Facebook for Business; “Get step-by-step guidance， industry insights and tools to track your progress， all in one place”）， Think with Google （“Take your marketing further with Google”）， and Twitter for Business （“Grow your business with Twitter ads”）.

幾家大型科技公司最近都創(chuàng)建了自己的平臺(tái)，聲稱將教企業(yè)如何最有效地在線推廣自己和自己的產(chǎn)品，比如Meta for Business（前身是Facebook for Business；“提供一站式服務(wù)，讓您獲得循序漸進(jìn)的指引、行業(yè)視野和跟蹤進(jìn)展的工具”），Think with Google（“讓谷歌助您進(jìn)一步開拓市場”），以及Twitter for Business（“推特廣告助您業(yè)務(wù)攀升”）。

These sites are very appealing. They offer a variety of advertising tools and services designed to help those companies boost their performance.

這些網(wǎng)站極具吸引力，它們提供各種廣告工具和服務(wù)以幫助企業(yè)迅速提升業(yè)績。

All of these sites have the same basic goal. They want you to invest your marketing dollars in them.

所有這些網(wǎng)站都有共同的基本目標(biāo)：賺取你的市場推廣費(fèi)。

Not as simple as it looks

并非看起來那么簡單

In recent weeks， Facebook has been broadcasting ads that tell all sorts of inspiring stories about the small businesses that it has helped with its new services. My Jolie Candle， a French candlemaker， “find[s] up to 80% of their European customers through Facebook platforms.” Chicatella， a Slovenian cosmetics company， “attributes up to 80% of their sales to Facebooks apps and services.” Mami Poppins， a German baby-gear supplier， “uses Facebook ads to drive up to half of their revenue.”

最近幾周，臉書公司一直在播放廣告，講述各種勵(lì)志故事，宣傳它的新服務(wù)如何幫助小企業(yè)提升了業(yè)績。廣告中稱，法國蠟燭生產(chǎn)商My Jolie Candle有“高達(dá)80%的歐洲客戶來源于臉書公司的平臺(tái)”；斯洛文尼亞化妝品公司Chicatella將“高達(dá)80%的銷售額歸功于臉書的應(yīng)用程序及服務(wù)”；德國嬰幼兒用品供應(yīng)商Mami Poppins “一半的收入得益于臉書公司的廣告”。

That sounds impressive， but should businesses really expect such large effects from advertising？ The fact is， when Big Tech companies “educate” small businesses about their services， they often are actually encouraging incorrect conclusions about the causal effects of advertising.

聽起來振奮人心，但企業(yè)真能指望廣告帶來如此巨大的效益嗎？事實(shí)上，當(dāng)大型科技公司向小企業(yè)“宣傳”他們的服務(wù)時(shí)，他們通常是在鼓勵(lì)人們得出廣告因果效應(yīng)的錯(cuò)誤結(jié)論。

Consider the case of a consulting client of ours， a European consumer goods company that for many years has positioned its brand around sustainability. The company wanted to explore if an online ad that makes a claim about convenience might actually be more effective than one that makes a claim about sustainability. With the help of Facebook for Business， it ran an A/B test1 of the two ads and then compared the return on advertising spend between the two conditions. The return， the test found， was much higher for the sustainability ad. Which means thats what the company should invest in， right？

以我們的一個(gè)咨詢客戶為例，這是一家歐洲消費(fèi)品公司，多年來其品牌定位在可持續(xù)性方面。該公司想知道一條在線廣告如果主打產(chǎn)品的便利性是否比主打可持續(xù)性更有效。在 Facebook for Business的幫助下，該公司對(duì)兩種廣告做了“A/B測試”，然后比較兩種情況下的廣告投入所獲收益。測試發(fā)現(xiàn)，主打可持續(xù)性廣告的收益要高得多。這意味著公司應(yīng)該在這方面投資，對(duì)吧？

Actually， we dont know.

實(shí)際上，我們不知道。

Theres a fundamental problem with what Facebook is doing here： The tests it is offering under the title “A/B” tests are actually not A/B tests at all. This is poorly understood， even by experienced digital marketers.

這里臉書的操作存在一個(gè)根本問題：它提供的所謂“A/B測試”根本不是A/B測試。這不太好理解，哪怕對(duì)于經(jīng)驗(yàn)豐富的數(shù)字營銷師來說也是如此。

So whats really going on in these tests？ Heres one example：

這些測試到底是怎么回事？下面來看一個(gè)例子：

1） Facebook splits a large audience into two groups—but not everybody in the groups will receive a treatment. That is， many people actually wont ever see an ad.

1）臉書將大量用戶分成兩組——但不是組中的每個(gè)人都能收到測試項(xiàng)目，就是說，很多人實(shí)際上根本看不到廣告。

2） Facebook starts selecting people from each group， and it provides a different treatment depending on the group a person was sampled from. For example， a person selected from Group 1 will receive a blue ad， and a person selected from Group 2 will receive a red ad.

2）臉書開始在每組中選人，并根據(jù)組別給選中的人發(fā)送不同的測試項(xiàng)目。比如，來自1組的人將收到藍(lán)色廣告，而來自2組的人將收到紅色廣告。

3） Facebook then uses machine-learning algorithms to refine its selection strategy. The algorithm might learn， say， that younger people are more likely to click on the red ad， so it will then start serving that ad more to young people.

3）隨后臉書用機(jī)器學(xué)習(xí)算法完善其選擇策略。算法可能會(huì)發(fā)現(xiàn)，比如說，年輕人更喜歡點(diǎn)擊紅色廣告，于是它開始將紅色廣告更多地推送給年輕人。

Do you see whats happening here？ The machine-learning algorithm that Facebook uses to optimize ad delivery actually invalidates the design of the A/B test.

所以你發(fā)現(xiàn)了什么？臉書用來優(yōu)化廣告投放的機(jī)器學(xué)習(xí)算法實(shí)際上讓A/B測試的設(shè)計(jì)失效了。

Heres what we mean. A/B tests are built on the idea of random assignment. But are the assignments made in Step 3 above random？ No. And that has important implications. If you compare the treated people from Group 1 with the treated people from Group 2， youll no longer be able to draw conclusions about the causal effect of the treatment， because the treated people from Group 1 now differ from the treated people from Group 2 on more dimensions than just the treatment. The treated people from Group 2 who were served the red ad， for example， would end up being younger than the treated people from Group 1 who were served the blue ad. Whatever this test is， its not an A/B test.

這就是我們想表達(dá)的。A/B測試的設(shè)計(jì)基于隨機(jī)分配理念。但上述第三步的分配是隨機(jī)的嗎？不是。而且它產(chǎn)生了重大影響。此時(shí)比較1組和2組中收到測試項(xiàng)目的人，你無法再得出關(guān)于測試項(xiàng)目因果效應(yīng)的結(jié)論，因?yàn)?組的人和2 組的人現(xiàn)在不僅僅是在收到的測試項(xiàng)目上有所不同。比如，最終2組中收到紅色廣告的人要比1組中收到藍(lán)色廣告的人年紀(jì)小。不管這是什么測試，反正不是A/B測試。

Twitter for Business works with a data broker to get access to cookies， emails， and other identifying information from a brands customers. And then Twitter adds information about how these customers relate to the brand on Twitter—whether they click on the brands promoted tweets， for example. This supposedly allows marketing analysts to compare the average revenue from customers who engaged with the brand to the average revenue from customers who did not. If the difference is large enough， the theory goes， then it justifies the advertising expenditure.

Twitter for Business利用數(shù)據(jù)代理獲得某品牌客戶的網(wǎng)頁瀏覽信息、郵件和其他身份識(shí)別信息，然后添加這些客戶在推特上與該品牌互動(dòng)的相關(guān)信息——比如他們是否會(huì)點(diǎn)擊該品牌的推廣推文。據(jù)稱，這讓營銷分析師可以做如下比較：與品牌互動(dòng)的客戶和不與品牌互動(dòng)的客戶分別能帶來多少平均收入。按照這種理論，如果差異足夠大 ，則證明廣告支出是有效的。

This analysis is comparative， but only in the sense of comparing apples and oranges. People who regularly buy cosmetics dont buy them because they see promoted tweets. They see promoted tweets for cosmetics because they regularly buy cosmetics. Customers who see promoted tweets from a brand， in other words， are very different people from those who dont.

這個(gè)分析是在比較，但比較的是兩個(gè)沒有可比性的東西。經(jīng)常購買化妝品的人不是因?yàn)榭吹搅舜黉N推文才買。她們看到化妝品促銷推文是因?yàn)樗齻兘?jīng)常購買化妝品。換句話說，看到某品牌推廣推文的和看不到推文的是截然不同的兩類人。

Causal confusion

因果混淆

Companies can answer two types of questions using data： They can answer prediction questions （as in， “Will this customer buy？”） and causal-inference questions （as in， “Will this ad make this customer buy？”）. These questions are different but easily conflated2. Answering causal inference questions requires making counterfactual3 comparisons （as in， “Would this customer have bought without this ad？”）. The smart algorithms and digital tools created by Big Tech companies often present apples-to-oranges comparisons to support causal inferences.

利用數(shù)據(jù)公司可以回答兩種類型的問題：預(yù)測類問題（比如“這位顧客會(huì)購買嗎？”）和因果推斷類問題（比如“該廣告會(huì)促使這位顧客購買嗎？”）。這兩類問題不同但很容易混淆?；卮鹨蚬茢囝悊栴}需要進(jìn)行反事實(shí)比較（比如“沒有這條廣告這位顧客還會(huì)購買嗎？”）。大型科技公司開發(fā)的智能算法和數(shù)字工具經(jīng)常比較無可比性的事物來支持因果推斷。

Big Tech should be well aware of the distinction between prediction and causal inference. Targeting likely buyers with ads is a pure prediction problem. It does not require causal inference， and its easy to do with todays data and algorithms. Persuading people to buy is much harder.

大型科技公司應(yīng)該清楚地意識(shí)到預(yù)測與因果推斷之間的區(qū)別。針對(duì)潛在顧客投放廣告是純粹的預(yù)測問題，不需要因果推斷，且在當(dāng)今的數(shù)據(jù)和算法下很容易實(shí)現(xiàn)。而說服人購買卻要困難得多。

Small and medium-sized businesses should be aware that advertising platforms are pursuing their own interests when they offer training and information， and that these interests may or may not be aligned with those of small businesses.

中小企業(yè)應(yīng)該意識(shí)到，廣告平臺(tái)提供培訓(xùn)和信息是為了追求自身利益，這些利益可能與小企業(yè)的利益一致，也可能不一致。

（譯者為“《英語世界》杯”翻譯大賽獲獎(jiǎng)?wù)撸?/p>