Advertising finds itself in a tenacious spot these days serving two masters: creativity and data.

如今，廣告業處于一個頑強的位置，服務于兩個大師：創造力和數據。

On the one hand, it values creativity; and it’s not hard to understand why. Creativity helps make brands distinct from each other and meaningful to consumers, it wins awards and improves recognition of the brands, agencies and people involved. And according to Hurman (2016) — who analysed three decades of data — more creative advertising is more effective advertising, driving sales and revenue.

一方面，它重視創造力。 而且不難理解為什么。 創意有助于使品牌彼此不同并對消費者有意義，它可以贏得獎項并提高對品牌，代理商和相關人員的認可。 根據赫爾曼(Hurman，2016)的研究，他分析了三十年的數據，創意廣告越有效，廣告的銷售量和收入就越高。

On the other hand, we are seeing the rise and rise of data. From its beginnings in advertising as post-campaign performance measurement to prove the effectiveness of advertisements, and as an input to strategy development; data has morphed into a powerful force that can influence every aspect of advertising that cannot be ignored. We now have programmatic advertising, personalisation at every point of contact, content-matching algorithms and interactive OOH.

另一方面，我們正在看到數據的興起。 從廣告開始就作為廣告活動后的績效衡量指標，以證明廣告的有效性，并作為戰略制定的依據； 數據已演變成強大的力量，可以影響廣告不可忽視的各個方面。 現在，我們有了程序化廣告，每個聯系點的個性化，內容匹配算法和交互式OOH。

But the two are not easy bedfellows, as Tim Nudd of Adweek says: “data has always been a bit terrifying to creative people. It’s often seen — sometimes fairly, sometimes not — as a replacement for intuition rather than a way to supplement it”.

但正如Adweek的蒂姆·納德 ( Tim Nudd )所說，這兩者并不容易。 人們經常(有時是公平，有時不是)將其視為直覺的替代，而不是補充直覺的方式。”

Yet data science — as a discipline — is not just about ‘data’. It includes research, analysis and ultimately method and reasoning. These other aspects of data science could be easily applied to creative decision making without taking away from intuition, expression and creative experience. As Payam Cherchian of AKQA says: “more and more clients are coming to the table expecting agencies to have data/ research on every slide to back up their creative ideas, assumptions and strategies”.

然而，作為一門學科的數據科學不僅與“數據”有關。 它包括研究，分析以及最終的方法和推理。 數據科學的這些其他方面可以很容易地應用于創意決策，而無需脫離直覺，表達和創意經驗。 正如AKQA的Payam Cherchian所說：“越來越多的客戶來到餐桌上，期望代理商在每張幻燈片上都進行數據/研究，以支持他們的創意，假設和策略。”

創意者的問題 (The Creative’s Problem)

The time between receiving a proposal request and submitting it is short, and creatives need to perform a number of mental tasks in that timeframe. They need to understand the brief, ideate, decide on an idea (or two), develop the idea, detail how to make it happen, and budget the idea.

從收到提案請求到提交提案請求的時間很短，并且廣告素材需要在該時間范圍內執行許多心理任務。 他們需要了解摘要，構思，決定一個(或兩個)構思，發展該構思，詳細說明如何實現該構思并為該構思進行預算。

Because time is limited the idea(s) presented in the proposal to the client are not always fully fleshed out. The general idea is there, but either the intricate details are missing or assumptions are made; and once the client has signed off on the idea, it needs to move into production. This is where conjoint analysis could be employed to give some data to the creatives decisions.

由于時間有限，建議書中呈現給客戶的想法并不總是完全充實。 總體思路就在那里，但是要么錯綜復雜的細節遺漏，要么做出假設。 一旦客戶批準了該想法，就需要投入生產。 在這里可以使用聯合分析為創意決策提供一些數據。

聯合分析簡介 (Intro to Conjoint Analysis)

Conjoint analysis is a choice modelling technique used in market research primarily to find the optimal combination of attributes for a new product. The methodology helps uncover the preferences individuals have by designing different versions of the same product (i.e. 3 ice cream flavours and choice of cone = 9 combinations) and asking potential consumers to rank the different versions of a product from best to worst.

聯合分析是一種用于市場研究的選擇建模技術，主要用于為新產品找到最佳屬性組合。 該方法通過設計同一產品的不同版本(即3種冰淇淋口味和9種組合選擇)，并要求潛在消費者對產品的不同版本(從最佳到最差)進行排名，從而幫助發現個人的喜好。

Choices can be influenced by many psychological, situational and social factors such as habit, inertia, experience, advertising, peer pressure, opinions, etc… but underneath all that it is assumed there is this thing called utility (or value) in the consumers mind that represents how important/ preferred/ desirable each option is (Louviere et al, 2000).

選擇可能會受到許多心理，情境和社會因素的影響，例如習慣，慣性，經驗，廣告，同伴壓力，意見等……但是，在所有假定的前提下，消費者的腦海中都存在一種稱為效用 (或價值)的東西。代表每種選擇的重要性/偏好/理想性(Louviere等，2000)。

While it would be nice to assume we could just analyse the rankings of product versions with each specific attribute (i.e. how high is chocolate ice cream ranked?), conjoint analysis calculates utilities (called part-worths) for each attribute based on the rankings of consumers (H?rdle and Simar, 2012: 413) that are used to:

最好假設我們可以分析具有每個特定屬性的產品版本的排名(例如，巧克力冰淇淋的排名是多少？)，聯合分析基于以下屬性的排名來計算每個屬性的效用 (稱為部分價值)：習慣于以下方面的消費者(H?rdle和Simar，2012：413)

1. determine the optimal combination of attributes to maximise utility
   確定屬性的最佳組合以最大化效用
2. determine the importance of each attribute according to consumers
   根據消費者確定每個屬性的重要性
3. design alternative combinations and analyse their utility
   設計替代組合并分析其效用

This is how market research is able to determine consumers are interested in a zero-interest, red credit card with a loyalty program that gives you 300 bonus points upon sign-up vs a 5% interest, blue card without a loyalty program.

這就是市場研究如何確定消費者對具有忠誠度計劃的零利率紅色信用卡感興趣的原因，該計劃在注冊后為您提供300的獎勵積分，而對于沒有忠誠度計劃的5％利息的藍色信用卡。

What if we made one… small… change…

如果我們做了一個……很小的……改變……

Instead of comparing product attributes we were comparing the components of a creative idea that we are thinking of pitching to a client? This would tell us which combination of attributes for our creative idea have the most appeal to the target audience. It would also tell us which attributes are most and least important in their choice (which we should emphasise).

我們沒有比較產品屬性，而是比較了我們考慮向客戶推銷的創意的組成部分？ 這將告訴我們，我們創意的哪些屬性組合對目標受眾最有吸引力。 它還會告訴我們哪些屬性在選擇中最重要和最不重要(我們應該強調)。

一個例子 (An Example)

Let’s assume we’ve received a RFP and the creative team have struck upon an idea to run a secret concert that people have to locate. As the turnaround time for the proposal is incredibly short, that’s about as far the creative team have come with developing the idea. Many questions remain:

假設我們已經收到了RFP，而創意團隊想出了一個舉辦人們必須尋找的秘密音樂會的想法。 由于該提案的周轉時間非常短，因此創意團隊已經提出了這個想法。 仍然有許多問題：

• how many bands are playing at this concert?
  這個音樂會上有多少支樂隊在演奏？
• is there just one concert or several, in different cities?
  在不同的城市里只有一場音樂會還是幾場？
• do we provide clues or should people follow a signal?
  我們提供線索還是人們應該遵循信號？

The team could make decisions based on their opinions or budget, or we can use data to see what consumers think using traditional conjoint*.

團隊可以根據他們的意見或預算做出決定，或者我們可以使用數據來查看消費者使用傳統的聯合體*的想法。

The first thing we need to do is design the choice model. In this example we have three attributes each with two levels (options):

我們需要做的第一件事是設計選擇模型。 在此示例中，我們具有三個帶有兩個級別(選項)的屬性：

Because the number of attributes and levels is small, the total number of combinations is small, 8 in fact, leading to a factorial design (which represents every possible combination):

因為屬性和級別的數量很少，所以組合的總數很小，實際上是8，這導致了階乘設計 (代表每種可能的組合)：

• one band, in Melbourne, send out clues
  一支樂隊在墨爾本發出線索
• one band, in Melbourne, follow the signal
  墨爾本的一個樂隊，跟隨信號
• one band, in 5 capital cities, send out clues
  5個省會城市的一支樂隊發出線索
• one band, in 5 capital cities, follow the signal
  5個省會城市中的一個樂隊，跟隨信號
• several bands, in Melbourne, send out clues
  墨爾本的幾支樂隊發出線索
• several bands, in Melbourne, follow the signal
  墨爾本有幾個樂隊跟隨信號
• several bands, in 5 capital cities, send out clues
  5個省會城市的幾個樂隊發出線索
• several bands, in 5 capital cities, follow the signal
  5個省會城市的幾個樂隊跟隨信號

Side note: when the number of attributes and levels start to increase, the number of possible combinations gets very high. Too high for anyone to rank the combinations in a sensible order. In these cases we take only a subset of combinations — such that will still give us enough information to run the analysis on all the attributes. This is called a fractional factorial design. As a rule of thumb, when there are more than 10 combinations, you should use a fractional factorial design.

旁注：當屬性和級別的數量開始增加時，可能的組合數量會非常多。 太高了，無法以合理的順序對組合進行排名。 在這些情況下，我們僅采用組合的子集-這樣仍將為我們提供足夠的信息以對所有屬性進行分析。 這稱為分數階乘設計 。 根據經驗，當組合超過10個時，應使用分數階乘設計。

Now we can design a survey and administer it to a group of consumers in the target audience. According to Peduzzi et al. (1996), the minimum sample size for a choice experiment can be worked out with the following formula:

現在，我們可以設計調查并將其管理給目標受眾中的一組消費者。 根據Peduzzi等。 (1996)，選擇實驗的最小樣本量可以用以下公式計算：

where q is the number of questions asked in the survey, a is the number of combinations per question, and c is the maximum number of levels of any attribute in the choice model. Given we have 1 question, asking respondents to rank 8 combinations and the most levels of any attribute is 2, this reveals we need a sample of at least 250.

其中q是調查中提出的問題數量， a是每個問題的組合數量， c是選擇模型中任何屬性的最大級別數。 假設我們有1個問題，則要求受訪者對8個組合進行排名，并且任何屬性的最高水平為2，這表明我們需要至少250個樣本。

We can ask each respondent to rank these combinations from best (1) to worst (8), plug the raw data into a spreadsheet or R to run the analysis. I’ve setup a Google Sheet to run this type of conjoint analysis that can handle up to 10 combinations of 6 attributes and a sample size of 500, but you could do more in R or specialist software like Sawtooth and Q.

我們可以要求每個受訪者對這些組合從最佳(1)到最差(8)進行排名，將原始數據插入電子表格或R中以運行分析。 我已經設置了一個Google表格來運行這種類型的聯合分析，最多可以處理6個屬性的10個組合，樣本大小為500，但是您可以在R或Sawtooth和Q之類的專業軟件中進行更多操作。

Assume we get the following results:

假設我們得到以下結果：

The importance metric tells us how ‘important’ or ‘preferred’ each attribute was in the consumers ranking of the options. It tells us that the number of bands was the most important factor in their decisions.

重要程度指標告訴我們，每個屬性在選項的消費者排名中是“重要”還是“優先”。 它告訴我們，樂隊的數量是他們決定的最重要因素。

The part-worths breakdown the attribute preference further to show which levels ‘increase’ perceived utility (consumer preference) and which detract. Here, the results suggest the best combination is:

部分價值進一步分解屬性偏好，以顯示哪些水平“增加”了感知效用(消費者偏好)，哪些降低了。 在這里，結果表明最佳組合是：

• one band, in Melbourne, follow the signal
  墨爾本的一個樂隊，跟隨信號

This now tells the creative team not to worry about sourcing several bands for the concert nor worry about finding locations in other cities; giving them more time and energy focus on developing a better experience that people actually want.

現在，這告訴創意團隊不必擔心會為音樂會籌集多個樂隊，也不必擔心會在其他城市找到地點。 給他們更多的時間和精力專注于開發人們真正想要的更好的體驗。

Conjoint analysis doesn’t replace the role of the creative in ideation, nor does it leave data as an end product to evaluate creative performance. It helps support creative decision making with rigour and enables data to play a larger role than just an API connection to a database for personalising advertisements.

聯合分析不會取代創意在創意中的作用，也不會將數據作為評估創意效果的最終產品。 它不僅可以幫助支持嚴謹的創意決策，還可以使數據發揮更大的作用，而不僅僅是與數據庫的API連接以個性化廣告。

實現它 (Making it happen)

Implementing conjoint analysis to creative decision making does not require much change to current processes. In fact it requires a bit more communication between creatives and analysts and some patience:

在創意決策中實施聯合分析不需要對當前流程進行太多更改。 實際上，這需要廣告素材和分析師之間進行更多的交流，并且要有一定的耐心：

第1步：創建廣告素材感興趣的屬性和級別的列表 (Step 1: create a list of attributes and levels the creatives are interested in)

• Keep to 2–6 attributes and 2–4 levels per attribute
  保留2–6個屬性，每個屬性2-4個級別
• Avoid using attributes that are hard to specify or quantify — like high/ low quality
  避免使用難以指定或量化的屬性，例如高質量/低質量
• More attributes provide a more accurate picture, but becomes too much information for a respondent to think about
  更多的屬性可以提供更準確的描述，但對于受訪者而言，它成為太多信息而無法考慮
• Check how much did your mental image of the idea change with the inclusion of each component, if not much, then it might not be relevant
  檢查您對創意的心理印象在包含每個成分的情況下發生了多少變化，如果變化不大，那么可能不相關

步驟2：設計人們會排名的替代組合 (Step 2: design alternative combinations that people will rank)

• With a small number of attributes and levels, you could have people rank every possible combination
  使用少量的屬性和級別，您可以使人們對每種可能的組合進行排名
• In cases that the number of combinations is large, you will need to select a smaller number of combinations using a special methodology (your analyst will need to do this)
  如果組合的數量很大，則需要使用特殊的方法來選擇較小數量的組合(您的分析師將需要執行此操作)
• Avoid prohibited pairs (i.e. speed and fuel economy) because that is not realistic
  避免使用禁止對(即速度和燃油經濟性)，因為這是不現實的

• You can work out the minimum number of combinations needed (Cordella et al, 2013) where t is the total number of levels across all attributes and a is the number of attributes

  您可以計算出所需的最小組合數(Cordella等，2013)，其中t是所有屬性的級別總數， a是屬性的數量

• Because choice experiments are dependent on the design of the study, you cannot simply remove faulty combinations from the survey. You cannot add or remove attributes or levels either
  由于選擇實驗取決于研究的設計，因此您不能簡單地從調查中刪除錯誤的組合。 您不能添加或刪除屬性或級別

步驟3：確定樣本量并發送調查 (Step 3: determine the sample size and send out survey)

• Who are you surveying? They might be existing customers from the clients CRM, or leads from a web form, or fans from the clients Facebook page. You might be fortunate enough to run it via Google Surveys for a representative sample of the general population.
  您正在調查誰？ 他們可能是客戶CRM的現有客戶，也可能是Web表單的潛在客戶，也可能是客戶Facebook頁面的粉絲。 您可能很幸運，可以通過Google調查來針對一般人群進行代表性采樣。
• The data collection should be quick given that the survey is rather short (1 question). I’d expect a turnaround time of 1–2 weeks and close it after that point.
  鑒于調查時間較短(1個問題)，因此數據收集應Swift。 我希望周轉時間為1-2周，然后將其關閉。

步驟4：進行分析并將結果與??創意團隊的初步想法進行比較 (Step 4: run the analysis and compare results to creative team’s initial thoughts)

• The two key metrics are importance and part-worths. Importance tells you to what extent each attribute influenced people’s preferences and the part-worths break that down into the individual levels of each attribute.

  兩個關鍵指標是重要性和部分價值 。 重要性告訴您每個屬性在多大程度上影響了人們的偏好，而部分價值則將其分解為每個屬性的各個層次。

• The analysis should also produce a little simulator that allows you to calculate the utility value of any combination of attributes and levels. This is useful when creative ideas are constrained by budget or client (i.e. can no longer use the follow the signal hint, so what’s our next best option?)

  分析還應產生一個小的模擬器，使您可以計算屬性和級別的任何組合的效用值。 當創意受到預算或客戶的限制時，此功能很有用(即無法再使用遵循信號提示，那么我們的下一個最佳選擇是什么？)

最后的想法 (Final Thoughts)

I hope that I’ve given you a new opportunity to introduce data and analytical thinking to your creative work, and shown you that the two can be harmoniously integrated in a much better way than they currently are. If you enjoyed this post, please share it with your friends.

希望我給您了一個新的機會，可以將數據和分析思維引入您的創意工作，并向您展示可以以比目前更好的方式將兩者融為一體。 如果您喜歡這篇文章，請與您的朋友分享。

*Of the three conjoint methods: traditional (TC), menu-based (MBC), and choice-based (CBC) I think traditional is probably easiest to apply to creative problem solving because (1) it can be setup in a spreadsheet (MBC and CBC require specialist software), and (2) the number of attributes they would be interested in would be low (3–5)

*在三種聯合方法中：傳統(TC)，基于菜單(MBC)和基于選擇(CBC)，我認為傳統可能最容易應用于創意問題解決，因為(1)可以在電子表格中進行設置( MBC和CBC需要專業軟件)，并且(2)他們感興趣的屬性數量很少(3-5)