# Your personas probably suck. Here’s how you can build them better.

https://medium.com/uxr-content/your-personas-probably-suck-heres-how-you-can-build-them-better-b2b32a45c93b

Personas. Maybe you love them. Maybe you hate them. Either way, you’ve probably concluded at some point in your career that your current personas are failing you.

人物角色。也許你愛他們。也許你討厭他們。無論哪種方式，您可能已經在職業生涯的某個時刻得出結論，您當前的角色讓您失望。

It’s pretty much accepted that the concept of personas is a key tool in a user researcher’s professional tool kit. UX researchers have done such a good job of embedding the idea that empathy-building is critical to developing a successful product, that stakeholders cry out for another persona-driven deliverable.

角色的概念是用戶研究人員的專業工具包中的一個關鍵工具，這一點已被廣泛接受。用戶體驗研究人員在嵌入這樣一個想法方面做得非常好，即建立同理心對於開發成功的產品至關重要，利益相關者迫切需要另一個角色驅動的可交付成果。

So why do you feel like the skill of creating valuable personas; personas that actually resonate with the users we know, love, and advocate for, is a dark art that you can’t conquer. Why has the tool of personas transitioned from being the go-to output to a concept that ruffles the feathers of most UX Researchers?

那麼為什麼你覺得創造有價值的人物角色的技能呢？真正與我們認識、喜愛和擁護的用戶產生共鳴的角色是一種你無法征服的黑暗藝術。為什麼人物角色工具從首選輸出轉變為讓大多數用戶體驗研究人員感到惱火的概念？

What’s killed personas?

什麼是被殺死的人物？

Personas have become plug and play templates

角色已成為即插即用模板

As a profession, we have slept walked into the habit of creating personas that are templates. They all look and feel the same. Let us be clear, there is nothing wrong with a template. The problem comes when people use it as the endpoint instead of the starting point.

作為一種職業，我們已經養成了創建模板角色的習慣。他們看起來和感覺都一樣。讓我們明確一點，模板沒有任何問題。當人們將其用作終點而不是起點時，問題就來了。

Personas are too specific but not useful

角色過於具體但沒有用

Creating content that is ultra-specific to populate your personas cards, feels useful, it may even feel like the thing you must do. It's not. Placing, ultra-specific content like salaries or photographs next to important content like attitudes and approaches is damaging to the goal you want to achieve. It tells the reader, this person can only share the attitudes and interests only exists if the user looks exactly like this photograph or earns this amount. That is simply not true.

創建非常具體的內容來填充您的角色卡片，感覺很有用，甚至可能感覺像是您必須做的事情。它不是。在態度和方法等重要內容旁邊放置諸如薪水或照片之類的超具體內容會損害您想要實現的目標。它告訴讀者，這個人只能分享態度和興趣，只有當用戶看起來和這張照片一模一樣或賺到這個數額時才存在。那明顯是錯的。

Personas are built from pre-existing research

人物角色是根據已有的研究建立的

Researchers are rarely given the time and space to build personas what they set out to do; build a holistic understanding of users in your product space. Instead, we try and squeeze personas out of existing research we already have done, or worse still build them off ideas we have about who our users are. This won’t work. You can’t build personas from pre-existing research that was not conducted for the purpose of building researchers. So don’t do it.

研究人員很少有時間和空間來建立他們打算做的人物角色。全面了解您的產品空間中的用戶。相反，我們嘗試從我們已經完成的現有研究中擠出角色，或者更糟糕的是，仍然根據我們對用戶是誰的想法來構建它們。這行不通。你不能從不是為了培養研究人員而進行的預先存在的研究中建立角色。所以不要這樣做。

Personas are creating stereotypes, not archetypes

角色正在創造刻板印象，而不是原型

Deep down, all UX researchers know these three things but it can be hard to shake a bad habit and that's how we end up with personas like these.

在內心深處，所有 UX 研究人員都知道這三件事，但很難改掉壞習慣，這就是我們最終得到這樣的人物角色的原因。

Examples of personas that do not represent a user group

不代表用戶組的角色示例

Legalease Leonard, Rachel, Pete, Brad, and Jennifer are not accurate pictures of our user groups. They are not accurate pictures of anyone’s user groups! They are shortcuts that put our users in boxes, they oversimplify the complexity of individuals and they are not useful.

Legalease Leonard、Rachel、Pete、Brad 和 Jennifer 不是我們用戶組的準確圖片。它們不是任何人的用戶組的準確圖片！它們是將我們的用戶放在盒子裡的捷徑，它們過度簡化了個人的複雜性，而且它們沒有用。

As UX researchers, we a letting a critical tool in our toolkit be used badly. That leads to poor design decisions and that leaves our colleagues with a poor reflection of our offering. That’s a big problem for us as a UX community but it was becoming a damaging problem for us because our users are already one of the most stereotyped groups on the planet.

作為 UX 研究人員，我們允許我們工具包中的一個關鍵工具被濫用。這會導致糟糕的設計決策，並使我們的同事對我們的產品產生不良反應。作為一個 UX 社區，這對我們來說是一個大問題，但對我們來說卻是一個破壞性的問題，因為我們的用戶已經是這個星球上最刻板印象的群體之一。

Why did we take on the task of resuscitating personas?

我們為什麼要承擔復活角色的任務？

Our users are prisoners. I work as a senior user researcher at the Ministry of Justice, in the United Kingdom. Whenever a member of the public interacts with the services or policies of the Ministry of Justice they are probably going through some of the worst moments of their lives: imprisonment, divorce, power of attorney, or a court case.

我們的用戶是囚犯。我在英國司法部擔任高級用戶研究員。每當公眾成員與司法部的服務或政策互動時，他們可能正在經歷一生中最糟糕的時刻：監禁、離婚、委託書或法庭案件。

But in particular, the work I have been doing focuses solely on prisoners. The staff that they work with to build products and services for this user group, cannot meet their users. They are physically locked behind bars, out of sight and mind. This means they often build products and services for people they will never meet but that could impact the course of a prisoner's life forever. That’s a lot of responsibility.

但特別是，我一直在做的工作只關注囚犯。與他們一起為該用戶組構建產品和服務的員工無法滿足他們的用戶。他們被身體鎖在監獄裡，看不見也看不見。這意味著他們經常為他們永遠不會見面的人提供產品和服務，但這可能會永遠影響囚犯的生活進程。這是很大的責任。

If that wasn’t enough, stereotypes about prisoners are reinforced daily by the media, in the tabloids, and in shows like Prison Break and Orange Is the New Black.

如果這還不夠，媒體、小報以及《越獄》和《橙色是新黑人》等節目每天都在強化對囚犯的刻板印象。

Images of a Daily Mail cover, characters from Orange Is The New Black and Michael Scofield from Prison Break

《每日郵報》封面圖片、Orange Is The New Black 中的人物和越獄中的 Michael Scofield

This left myself and my peers Carol Pizatto, Faye Mitchell and Lindsey Martin— who are also part of the UX Research and Design community in the Ministry of Justice — in a pretty dire position.

這讓我和我的同齡人 Carol Pizatto、Faye Mitchell 和 Lindsey Martin——他們也是司法部用戶體驗研究和設計社區的一員——處於非常可怕的境地。

We had to help our colleagues build services with empathy and understanding, for a user group they will never meet, whilst challenging long embedded media and societal stereotypes, all with a tool that sucks.

我們必須幫助我們的同事以同理心和理解力為他們永遠不會遇到的用戶組構建服務，同時挑戰長期嵌入的媒體和社會刻板印象，所有這些都使用了一個糟糕的工具。

We weren’t left with much of a choice. We needed personas to work, so we started from scratch to address the skeleton in the cupboard that is personas. We did the hard work, so you don’t have to!

我們沒有太多選擇。我們需要角色來工作，所以我們從頭開始解決櫥櫃中角色的骨架。我們做了艱苦的工作，所以你不必！

How to revive the tool of personas

如何重振人物角色工具

We asked ourselves what would good personas actually look like? And what a question that turned out to be.

我們問自己，好的角色實際上應該是什麼樣的？結果是一個多麼好的問題。

First, we turned to academia. They have been doing personas right for decades, they even have a who field dedicated to it, it’s called qualitative data analysis and there is a lot of good practice we can borrow and build on. I went to the University of Oxford, so we went back and got in touch with some professors. They reminded us to be systematic about collecting our data and to apply rigorous consistent principles when analysing it.

首先，我們轉向學術界。他們幾十年來一直在做正確的人物角色，他們甚至有一個專門的領域，它被稱為定性數據分析，有很多我們可以藉鑑和建立的良好實踐。我去了牛津大學，所以我們回去和一些教授取得了聯繫。他們提醒我們要係統地收集數據，並在分析數據時應用嚴格一致的原則。

Next, we explored anatomy, folklore, and mythology and checked how archetypes were built in those worlds. This deeply inspired the design phase as you will see later.

接下來，我們探索了解剖學、民間傳說和神話，並檢查了原型是如何在這些世界中構建的。正如您稍後將看到的，這深深地激發了設計階段的靈感。

Finally, we looked at plays, museums and books to better understand storytelling and learn how to curate the complexity and depth of people’s lives.

最後，我們查看了戲劇、博物館和書籍，以更好地理解講故事並學習如何策劃人們生活的複雜性和深度。

Using all of this research, we took a long hard look at that template and we came up with a seismic shift.

使用所有這些研究，我們仔細研究了該模板，並提出了一個地震式轉變。

Good personas don’t have heuristic shortcuts that drive biases. The time has come for personas with:

好的人物角色沒有驅動偏見的啟發式捷徑。角色的時代已經到來：

No ages

沒有年齡

No photographs

沒有照片

No salaries

沒有工資

No names

沒有名字

No genders

無性別

This may be quite shocking to a lot of people. It’s rare to see personas like this, and you might be thinking if I don’t have any of that, what am I going to put on my personas!?

這對很多人來說可能是相當震驚的。很少看到這樣的角色，你可能會想如果我沒有這些角色，我要在我的角色上放什麼！？

We want you to:

我們希望您：

Focus on your users as people, not what defines them in a census.

將您的用戶作為人關注，而不是在人口普查中定義他們的內容。

Allow the depth of data and richness of insights to flourish. Humans are complex.

讓數據的深度和洞察的豐富性蓬勃發展。人是複雜的。

Stop letting people judge your personas by their cover, strip out looks and personification.

不要讓人們通過他們的封面來判斷你的角色，去掉外表和擬人化。

Image of a slide that compares the current approach to a meaningful alternative to designing personas

將當前方法與設計角色的有意義的替代方法進行比較的幻燈片圖像

Don’t panic, we’ve got a five-step framework to help you figure out how to do just this.

不要驚慌，我們有一個五步框架來幫助您弄清楚如何做到這一點。

A five-step framework

五步框架

In summary, the five steps that we will walk you through are:

總之，我們將引導您完成的五個步驟是：

Ask rich questions, not dumb questions

提出豐富的問題，而不是愚蠢的問題

Write a codebook

寫一個密碼本

Code your data

編碼您的數據

Map your data

映射您的數據

Form your personas

形成你的角色

Before you get started

在開始之前

Before you start on the road to good personas, disregard any existing data you already have. Don’t be tempted to borrow from it, we have no doubt it was great research that might well feel appropriate but it was collected for other purposes. Start from fresh! Your personas will emerge from the data you collect and you can’t do that recycling existing evidence.

在開始通往良好角色的道路之前，請忽略您已經擁有的任何現有數據。不要試圖借用它，我們毫不懷疑這是一項偉大的研究，可能感覺很合適，但它是為其他目的而收集的。從新鮮開始！您的角色將從您收集的數據中浮現，而您無法回收現有證據。

What we are proposing might feel overwhelming but you can do it with very little time and a small team. We did it one day a week over 10 months alongside our other project work, using tools you probably already know!

我們提出的建議可能會讓人感到不知所措，但您可以用很少的時間和一個小團隊來完成。我們在 10 個月的時間裡每週一天與我們的其他項目工作一起使用您可能已經知道的工具！

The tools you will need

您需要的工具

We used these tools, but you can use whatever you have access to that has similar functionality:

我們使用了這些工具，但您可以使用任何您有權訪問的具有類似功能的工具：

Notion: A wiki tool for documenting and recording the project

概念：用於記錄和記錄項目的 wiki 工具

Dovetail: A synthesis tool for analyzing, tagging and storing our data

Dovetail：用於分析、標記和存儲數據的綜合工具

Miro: A collaborative whiteboard for analysis and affinity sorting

Miro：用於分析和親和排序的協作白板

Google Sheets: A data processor to find patterns in our data.

Google Sheets：一個數據處理器，用於在我們的數據中查找模式。

Step 1: Ask rich questions, not dumb questions

第 1 步：提出豐富的問題，而不是愚蠢的問題

Your users don’t exist in a vacuum of your product and only your product. Their approach to your product is driven by events in their wider lives but also bigger, more holistic ideas like wants, wishes, stresses, and attitudes.

您的用戶不存在於您的產品的真空中，而只存在於您的產品中。他們對你的產品的態度是由他們更廣泛的生活中的事件驅動的，但也有更大、更全面的想法，比如需求、願望、壓力和態度。

Stop asking people just about your product, start asking people about themselves.

停止詢問人們關於你的產品，開始詢問人們關於他們自己的事情。

Cropped image of the topic guide used for interviews

用於採訪的主題指南的裁剪圖像

Take these questions up here. When we set out to interview prisoners we could have asked the obvious questions like:

把這些問題帶到這裡。當我們開始採訪囚犯時，我們可以問一些顯而易見的問題，例如：

What’s is it like to be locked up? What do you miss?

被關起來是什麼感覺？你錯過了什麼？

We might have got some good data but its more likely we would have got circumstantial, specific evidence, which we know isn’t useful. In fact, most of the time, we could have learned from simply observing the environment they occupy. The point of asking rich questions is to unearth the data that is not so obvious or observable but is fundamentally shaped by the environments people occupy. So, instead, we asked them questions like this,

我們可能有一些很好的數據，但更有可能我們會得到間接的、具體的證據，我們知道這些證據沒有用。事實上，大多數時候，我們可以通過簡單地觀察它們所處的環境來學習。提出豐富問題的目的是挖掘那些不那麼明顯或可觀察但從根本上受人們所處環境影響的數據。所以，相反，我們問了他們這樣的問題，

What are you most proud of? What does a good day here look like?

你最自豪的是什麼？這裡的美好一天是什麼樣的？

These questions are not looking to understand the surface level. These questions tackled family, staying healthy, setting goals, equality, and a fair justice system. Ideas and topics that matter to all of us, but which are experienced very differently in a prison environment. For our users, in particular, this approach was surprising and refreshing, prisoners rarely get the chance to talk about themselves just as ‘themselves’

這些問題並不是想了解表面層次。這些問題涉及家庭、保持健康、設定目標、平等和公平的司法系統。對我們所有人都很重要的想法和話題，但在監獄環境中的體驗卻大不相同。特別是對於我們的用戶來說，這種方法令人驚訝和耳目一新，囚犯很少有機會像「他們自己」一樣談論自己

Questions like this are called rich questions. In order to gather the evidence we needed to design a set of personas, we temporarily took off our product research hats and had a conversation. Sure, that conversation was guided around topics we as a business are interested in but it was not a set of rigid questions like a questionnaire.

像這樣的問題被稱為豐富的問題。為了收集我們設計一組角色所需的證據，我們暫時脫下了產品研究的帽子並進行了交談。當然，該對話是圍繞我們作為企業感興趣的主題進行的，但這不是一組像問卷調查那樣的嚴格問題。

This approach is so important because rather than imposing what you want to talk about, it lets users showcase what they think is important to them as individuals. And if you know what’s important to individuals, you can design services and products that meet speak to those users directly.

這種方法非常重要，因為它不是強加你想談論的內容，而是讓用戶展示他們認為對他們個人來說很重要的東西。如果您知道什麼對個人最重要，您就可以設計與這些用戶直接對話的服務和產品。

It can take a while to craft these questions. It can feel hard and challenging, you might even need to test drive some of your questions. You will know when you have got there when it feels less like an interview and more like a really insightful and rich conversation.

制定這些問題可能需要一段時間。這可能會讓人感到困難和具有挑戰性，您甚至可能需要試駕一些問題。你會知道當你到達那裡時，感覺不像是一次採訪，而更像是一次真正有見地和豐富的談話。

The key aim here is to get to the heart of what matters to your users, not your product. If you understand your users, you can drive the development of a successful product.

這裡的主要目標是深入了解對您的用戶而言重要的事情，而不是您的產品。如果您了解您的用戶，您就可以推動成功產品的開發。

So there you have it. Step one: identify your users, collect your data but make sure you ask rich questions not dumb questions.

所以你有它。第一步：識別你的用戶，收集你的數據，但要確保你問的是豐富的問題而不是愚蠢的問題。

Step 2: Write a codebook

第 2 步：編寫代碼本

First things first, do some housekeeping. Get your data in order. Get it into a position to be analysed. If you were able to record the interviews, you should transcribe them verbatim. Put somewhere you can easily access. We used Dovetail for this.

首先，做一些家務。讓您的數據井然有序。把它放在一個可以分析的位置。如果您能夠記錄採訪，您應該逐字轉錄。放在您可以輕鬆訪問的地方。為此，我們使用了 Dovetail。

Only then, can you sit down and write a codebook.

只有這樣，你才能坐下來寫一本密碼本。

What is a codebook, I hear you cry. A codebook is something that academics in this field use all the time. Put simply, a codebook is a map to help you analyse your data.

什麼是密碼本，我聽到你哭了。碼本是該領域的學者一直使用的東西。簡而言之，碼本是幫助您分析數據的地圖。

Screenshot of part of our codebook

我們部分代碼本的屏幕截圖

The codebook, on the whole, tends to write itself but this is how you go about making one. It's a simple sequence.

總的來說，密碼本傾向於自己編寫，但這就是您製作密碼本的方式。這是一個簡單的序列。

Read through your data carefully. As you read through your data, you will start to see themes emerging.

仔細閱讀您的數據。當您通讀數據時，您將開始看到出現的主題。

Once these themes emerge a handful of times you label it. This theme then becomes what we call a code.

一旦這些主題出現了幾次，你就給它貼上標籤。這個主題就變成了我們所說的代碼。

As you write the name of the code down, in your codebook you should give a description of what that code is about. You should also give an example from your data. Then finally you should give an example of close but not quite. This will stop your coders from misinterpreting important nuances.

當您寫下代碼的名稱時，您應該在代碼簿中描述該代碼的內容。您還應該從您的數據中給出一個示例。那麼最後你應該舉一個close但不完全的例子。這將阻止您的編碼人員誤解重要的細微差別。

Repeat this process as you read through your data a couple of times. You might only have a handful of initial codes, perhaps five or maybe ten. That’s okay because these codes will change and evolve as you analyse the data more critically. In our case, these definitions were continuously challenged by each other and iterated as we became more familiar with the data.

在您通讀數據幾次時重複此過程。您可能只有少數初始代碼，可能是五個或十個。沒關係，因為這些代碼會隨著您更嚴格地分析數據而改變和發展。在我們的例子中，隨著我們對數據越來越熟悉，這些定義不斷地相互挑戰和迭代。

You can learn more about the process of coding and how to write a codebook by reading this book Qualitative Data Analysis by Saldana.

您可以通過閱讀 Saldana 的《定性數據分析》一書，了解有關編碼過程以及如何編寫代碼本的更多信息。

Step 3: Code your Data

第 3 步：編碼您的數據

Print that codebook. Have it easily to hand. We kept ours in Notion and projected it onto a big screen and gave people print outs.

打印那個密碼本。輕鬆掌握。我們將其保存在 Notion 中，並將其投影到大屏幕上，並讓人們打印出來。

For this step, you will need to put your transcripts into a tool like Dovetail that lets you attribute tags to the data.

對於這一步，您需要將您的成績單放入 Dovetail 等工具中，該工具可讓您將標籤歸因於數據。

Screenshot of the coding process on Dovetail

Dovetail 上編碼過程的屏幕截圖

You should work in pairs to analyse the data, this improves what we call inter-coder reliability and makes sure your inherent bias is not creeping in. It sounds complicated but all it means is that you sit together and work through the transcript line by line and attribute codes from your codebook. If there isn’t a code in your codebook and you think the data is important enough you should make one. These codes might change over time as you go through the process.

你應該結對工作來分析數據，這提高了我們所說的編碼器間的可靠性，並確保你固有的偏見不會蔓延。這聽起來很複雜，但這意味著你們坐在一起，逐行完成抄本和來自您的碼本的屬性代碼。如果您的密碼本中沒有代碼並且您認為數據足夠重要，您應該製作一個。這些代碼可能會隨著您的流程而改變。

That’s okay, let the data tell you what is going on. Don’t tell the data what’s going on.

沒關係，讓數據告訴你發生了什麼。不要告訴數據發生了什麼。

What is important here is to take your time. This is the longest part of the process. You will need to read each line carefully. No line should go untagged! Even questions and chit-chat.

這裡重要的是要花時間。這是整個過程中最長的部分。您需要仔細閱讀每一行。任何行都不應未標記！甚至問題和閒聊。

After you have coded all of your data it might be possible to start grouping your code into tiers. For example, we have the thematic code of ‘relationships before prison’ was the sub-code of ‘positive relationships’.

在您對所有數據進行編碼後，可能可以開始將您的代碼分組到層中。例如，我們有「監獄前的關係」的主題代碼是「積極關係」的子代碼。

When you have finished you should have fully coded data and a completely documented codebook. It’s important to keep this codebook and the transcripts safe because the aim is that if someone was to repeat this exercise they would find similar outcomes to you.

完成後，您應該擁有完整編碼的數據和完整記錄的密碼本。保持這個密碼本和抄本的安全很重要，因為目的是如果有人要重複這個練習，他們會發現與你相似的結果。

We invited a small group of designers, product managers, business analysts and user researchers to help us with this. We had to devote a whole week to this process as we had a lot of transcripts to get through.

我們邀請了一小群設計師、產品經理、業務分析師和用戶研究人員來幫助我們解決這個問題。我們不得不花費整整一周的時間來完成這個過程，因為我們有很多成績單要完成。

By following steps one, two, and three you have taken a robust and replicable approach to ensure that your data has integrity.

通過執行第一步、第二步和第三步，您已經採取了一種穩健且可複制的方法來確保您的數據具有完整性。

Step 4: Map your data

第 4 步：映射您的數據

So you’ve got your data. You have coded it. It’s time to find the patterns and begin uncovering your personas. This is the time to bring stakeholders along, start building trust in your process. Invest their time and then they will be more likely to buy into the personas you produce.

所以你有你的數據。你已經編碼了。是時候找到模式並開始揭示您的角色了。現在是時候讓利益相關者一起來，開始在您的流程中建立信任。投資他們的時間，然後他們將更有可能購買您製作的角色。

This is when you are trying to find out if any of your participants share similar opinions and ideas in one theme?

這是當您試圖找出是否有任何參與者在一個主題上有相似的意見和想法時？

This is when the traditional technique of affinity sorting comes to hand. If you don’t know what affinity sorting is you can read about it here.

這是傳統的親和排序技術派上用場的時候。如果你不知道什麼是親和排序，你可以在這裡閱讀。

We had 2700 minutes of interviews so we had a real need for keeping things organised. We extracted the data from Dovetail and moved it to Miro. Each piece of coded data became a sticky note. Basically, we transformed 45 coded scripts into a thousand sticky notes. Each sticky note contained a coded piece of information i.e a quote and the relevant code attributed to that quote. It also contained the participant number. We used colours here to identify different participants. So all sticky notes, containing all coded quotes belonging to participant 36 were coloured purple for example.

我們有 2700 分鐘的採訪，所以我們真的需要讓事情井井有條。我們從 Dovetail 中提取數據並將其移至 Miro。每一段編碼數據都變成了便利貼。基本上，我們將 45 個編碼腳本轉換為一千個便簽。每個便簽都包含一段編碼信息，即引用和歸因於該引用的相關代碼。它還包含參與者編號。我們在這裡使用顏色來識別不同的參與者。因此，例如，包含屬於參與者 36 的所有編碼引用的所有便簽都被塗成紫色。

We then created a three-part scale template that was replicated for each theme. The sub-theme denoted the scale points on that theme. So picking up on our theme from earlier Relationships before prison, our three-point scale would go positive, mixed, and negative.

然後我們創建了一個由三部分組成的比例模板，為每個主題複製。子主題表示該主題的刻度點。因此，從入獄前的早期關係中汲取我們的主題，我們的三分制將分為正面、混合和負面。

The template we used for mapping data points on Miro

我們用於在 Miro 上映射數據點的模板

Then it was about matching! Did the participant have data on that sub-code? If so, we would move their sticky note with the relevant quote to the template.

然後是關於匹配！參與者是否有關於該子代碼的數據？如果是這樣，我們會將帶有相關引用的便簽移動到模板中。

By doing this we were able to visualize which participants had similar experiences, beliefs, and attitudes within themes and across themes. For example, green and yellow always go together or red doesn’t have any views on a particular theme, just like purple.

通過這樣做，我們能夠形象化哪些參與者在主題內和跨主題具有相似的經歷、信念和態度。例如，綠色和黃色總是搭配在一起，或者紅色對特定主題沒有任何看法，就像紫色一樣。

This will organically move you onto the next (and last!) step.

這將使您有機地進入下一個（也是最後一個！）步驟。

Step 5: Form your personas

第 5 步：形成你的角色

Colouring your participants allows you to track which clusters of participants share the same response across themes but if you have got a lot of data like this, it might be hard to do this with the naked eye. This is when you can make use of computing power!

為您的參與者著色可以讓您跟踪哪些參與者集群在不同主題之間共享相同的響應，但如果您有大量這樣的數據，用肉眼可能很難做到這一點。這是您可以利用計算能力的時候！

We decided to build a really simple algorithm on Google Sheets to do this for us but you don’t have to. Our algorithm looked for where participants shared three or more responses on our data points across all the scales we mapped. For example, participants who have the same reasoning for why they did the crime, maybe it was about money, they then also had quite negative prior relationships outside prison before they came in and they then also may have trouble maintaining relationships while in prisons, etc across all themes. The algorithm would tell us that seven people, for example, share these responses. It in essences tells us the persona group size and what the common data points are.

我們決定在 Google 表格上構建一個非常簡單的算法來為我們執行此操作，但您不必這樣做。我們的算法尋找參與者在我們映射的所有尺度上對我們的數據點共享三個或更多響應的位置。例如，對於他們犯罪的原因有相同推理的參與者，也許是為了錢，然後他們在進入監獄之前也有非常消極的監獄外關係，然後他們在監獄期間也可能難以維持關係等跨越所有主題。例如，該算法會告訴我們有 7 個人分享這些回答。從本質上講，它告訴我們角色組的大小以及常見的數據點是什麼。

Screenshot of the algorithm we created using Google Sheets

我們使用 Google Sheets 創建的算法的屏幕截圖

A computer is really good at doing that, much better than a human is.

計算機真的很擅長這樣做，比人類要好得多。

Your personas sizes may differ in the number of participants they have, this okay, it’s just something to be aware off but you should set a minimum threshold for what a persona size is based on the overall number of participants. The important thing is not to set out expecting to find five personas, the data will naturally cluster into the appropriate number of personas if you set an appropriate threshold.

您的角色大小可能因參與者的數量而異，這沒關係，這只是需要注意的事情，但您應該根據參與者的總數設置角色大小的最低閾值。重要的是不要期望找到五個角色，如果設置適當的閾值，數據自然會聚集到適當數量的角色中。

What makes personas work?

是什麼讓角色發揮作用？

So you’ve followed the steps for producing the insight that goes into your personas.

因此，您已經遵循了生成進入您的角色的洞察力的步驟。

So what’s next? How can you ensure your personas will work and won’t end up in a drawer?

下一個是什麼？您如何確保您的角色能夠正常工作並且不會最終出現在抽屜中？

When it comes to designing artefacts, here is what matters. Take a look at this complete example of one of our personas.

在設計人工製品時，這才是重要的。看看我們的一個角色的完整示例。

Mockup of what one of our personas looks like

我們的角色之一的樣機

Our aim was that no matter where you delve into these personas, you should be able to take away a consistent idea of what that persona is. This way every type of learner should feel that these personas are accessible and not be alienated by the type of template they may have seen before.

我們的目標是，無論您在哪裡深入研究這些角色，您都應該能夠對該角色是什麼有一個一致的想法。通過這種方式，每種類型的學習者都應該覺得這些角色是可訪問的，並且不會被他們以前可能見過的模板類型所疏遠。

These are the five personas we ended up with:

這些是我們最終得到的五個角色：

Front cover of our five personas

我們五個角色的封面

We associated colour to the persona card which reflected elements of the personality that we wanted to communicate. This colour tone was then used consistently throughout. For example, shades of blue. We also designed an abstract graphic, to illustrate a dominant feature of the characteristic. Here our persona Zeus is in charge of the wing.

我們將顏色與反映我們想要傳達的個性元素的角色卡片相關聯。然後一直使用這種色調。例如，深淺不一的藍色。我們還設計了一個抽像圖形，以說明特徵的主要特徵。在這裡，我們的角色 Zeus 負責機翼。

Memorable symbols for visual learners

視覺學習者的難忘符號

Our tag lines and names were carefully selected. The use of a greek god was to create a powerful and memorable anchor. Nobody remembers Dave the Marketer but you can be sure that something like a greek god sticks in people’s minds. We chose greek gods because we could align their fables with the own narratives of our personas. It’s also a provocation to associate prisoners with gods, forcing people to abandon their preconceptions of prisoners.

我們的標語和名稱都是經過精心挑選的。希臘神的使用是為了創造一個強大而令人難忘的錨。沒有人記得 Dave the Marketer，但你可以肯定，像希臘神一樣的東西會留在人們的腦海中。我們選擇希臘諸神是因為我們可以將他們的寓言與我們自己的角色敘述結合起來。將犯人與神聯繫起來也是一種挑釁，迫使人們放棄對犯人的先入之見。

You could use any kind of metaphor elements for example or seasons. We would recommend staying away from purely human characteristics. It should be unique and easily distinguishable so that people can quickly refer to it.

例如，您可以使用任何類型的隱喻元素或季節。我們建議遠離純粹的人類特徵。它應該是獨一無二的並且易於區分，以便人們可以快速參考它。

We also put a snappy one-liner that acts as another summary point for this type of learner. This is something we borrowed from mythology.

我們還放置了一個活潑的單線，作為此類學習者的另一個總結點。這是我們從神話中藉來的東西。

Narratives for those who learn through storytelling

為那些通過講故事學習的人提供的敘述

For more qualitative learners we wrote a longer narrative to tell the story of the persona. Data can often lose that rich storytelling perspective that brings the depth and complexity of an individual to light. This is something we borrowed from soliloquies in plays.

對於更多的定性學習者，我們寫了更長的敘述來講述角色的故事。數據通常會失去豐富的講故事視角，而這種視角可以揭示個人的深度和復雜性。這是我們從戲劇中的獨白中藉來的東西。

Quantitative data for those who learn through numbers

通過數字學習的人的定量數據

Lastly, we created these quick data points. For the more quantitative data consumers out there, these are quick reference points to summarise that personas key characteristics. This is something that we borrowed from anatomy. All of these data points are also covered in the narrative just in a different manner.

最後，我們創建了這些快速數據點。對於更多定量數據的消費者，這些是總結角色關鍵特徵的快速參考點。這是我們從解剖學中藉來的東西。所有這些數據點也以不同的方式包含在敘述中。

Don’t forget to Socialise those personas

不要忘記社交這些角色

We firmly believe that a person should not be strictly bound to the card format alone. There is no rule book that says personas have to be cards and only cards. We had so much data that didn’t make it into our personas that we decided to make lots of other assets.

我們堅信，一個人不應該只受制於卡片格式。沒有規則書說角色必須是卡片，而且只有卡片。我們有太多的數據沒有進入我們的角色，所以我們決定製作很多其他資產。

Try and find new ways to communicate and share the knowledge you have spent time collecting. You have probably invested a fair bit of time in this so make the work you have done work for you.

嘗試尋找新的方式來交流和分享您花時間收集的知識。您可能在這方面投入了相當多的時間，因此讓您所做的工作為您服務。

Quote cards

報價卡

We took all of the great rich quotes that we had and made them into 31 quote cards, one for each day of the month. In our workplace, we read one of these quotes out at stand up every day in front of the team to remind the team of our users and what they think. You could even give this to senior stakeholders to sit on their desk or set up a slack bot.

我們把我們擁有的所有偉大的豐富報價製作成 31 張報價卡，每個月的每一天一張。在我們的工作場所，我們每天在團隊面前站起來朗讀這些引語之一，以提醒團隊我們的用戶和他們的想法。您甚至可以將其提供給高級利益相關者，讓他們坐在他們的辦公桌上或設置一個 Slack 機器人。

Mockup of the deck of quote cards we designed

我們設計的一副報價卡的模型

Website

網站

Finally like all teams in COVID19, we pivoted to a digital world. We designed a website to ensure everyone in the organisation can easily access the insights we gathered and use them. The website allows users to interact with stories, quotes, images and audios clips that can help them empathise with the looks, sounds and people in prison.

最後，像 COVID19 中的所有團隊一樣，我們轉向了數字世界。我們設計了一個網站，以確保組織中的每個人都可以輕鬆訪問我們收集的見解並使用它們。該網站允許用戶與故事、引述、圖像和音頻片段進行互動，這可以幫助他們對監獄中的外表、聲音和人產生共鳴。

A sneak peek of the website we designed to socialise our work

偷看我們設計的用於社交工作的網站

Principles for good persona artefacts

良好人物模型的原則

So here are our principles. You don’t have to follow them to the letter but you should test what you produce against them.

所以這是我們的原則。您不必完全遵循它們，但您應該針對它們測試您生產的產品。

Make them inclusive

讓他們具有包容性

Make them playful

讓他們好玩

Make them consistent

使它們一致

Make them accessible

使它們易於訪問

It’s up to you now

現在由你決定

So there we have it. We have talked you through it.

因此，我們有它。我們已經和你討論過了。

Your personas suck but you still build them the same way. If you weren’t convinced about that before, we hope you are by now.

你的角色很糟糕，但你仍然以同樣的方式構建它們。如果您以前不相信這一點，我們希望您現在已經相信了。

Don’t keep pretending they are working, when you know deep down they are not! Getting things right for your users is what inspires you to go to work every day. You are their advocate and you shouldn’t be happy with the current way of doing personas.

不要一直假裝他們在工作，當你內心深處知道他們沒有工作時！為您的用戶做正確的事情是激勵您每天上班的動力。您是他們的擁護者，您不應該對當前的角色扮演方式感到滿意。

The good news is they don’t need to suck. Now you know how to, build them properly. Do the thing, but do it better.

好消息是他們不需要吮吸。現在您知道如何正確構建它們了。做這件事，但做得更好。

Get our resources and templates

獲取我們的資源和模板

We are still figuring out the best way to share resources and support you on the road to building personas.

我們仍在尋找共享資源的最佳方式，並在構建角色的道路上為您提供支持。

If you are interested in staying up to date, register your interest so we can send you stuff straight to your inbox.

如果您有興趣了解最新信息，請註冊您的興趣，以便我們可以將內容直接發送到您的收件箱。