科學(xué)價(jià)值 社交關(guān)系 大數(shù)據(jù)

A crucial part of building a product is understanding exactly how it provides your customers with value. Understanding this is understanding how you fit into the lives of your customers, and should be central to how you build on what already exists. It is a way of ensuring that every decision taken will be positive and ultimately improve the value you deliver.

構(gòu)建產(chǎn)品的關(guān)鍵部分是準(zhǔn)確了解產(chǎn)品如何為客戶提供價(jià)值。 了解這一點(diǎn)就是了解您如何適應(yīng)客戶的生活，并且應(yīng)該成為您如何基于已有資源構(gòu)建的核心。 這是確保所做出的每個(gè)決定都是積極的并最終提高您提供的價(jià)值的一種方法。

In the last couple of months the value question has been hot on the lips of some of us at Jeff. Considering the current global situation and the resulting difficulty of continuing to expand a rapidly growing platform, it was a good moment to take a step back and really think about how our customers were taking advantage of “the good, good life”.

在過去的幾個(gè)月中，價(jià)值問題一直困擾著我們杰夫(Jeff)的某些人。 考慮到當(dāng)前的全球形勢以及持續(xù)擴(kuò)展快速增長的平臺(tái)所帶來的困難，現(xiàn)在是退后一步，真正考慮我們的客戶如何利用“美好，美好生活”的好時(shí)機(jī)。

我們?nèi)绾蔚竭_(dá)這里 (How we got here)

The company wide project started with the new user experience team, who got data science involved after some early conversations. Understanding our service’s value was one of their first initiatives, and they saw that a necessary part of this was drawing the typical “customer journey”. These are the typical life cycles that users have on the platform and they help us clearly pinpoint the different moments where customers are delivered value. To draw this they needed a general overview of our different users’ behaviours.

公司范圍內(nèi)的項(xiàng)目始于新的用戶體驗(yàn)團(tuán)隊(duì)，該團(tuán)隊(duì)在進(jìn)行了一些初期交談之后就參與了數(shù)據(jù)科學(xué)的研究。 了解我們服務(wù)的價(jià)值是他們的首批舉措之一，他們認(rèn)為其中的必要部分正在吸引典型的“客戶旅程”。 這些是用戶在平臺(tái)上擁有的典型生命周期，它們可以幫助我們明確指出為客戶交付價(jià)值的不同時(shí)刻。 為此，他們需要對(duì)我們不同用戶的行為進(jìn)行總體概述。

Getting in touch with the data science team to see if we could demystify some aspects, everyone quickly realised that one of the biggest challenges facing us was that most of the understanding of our company was dispersed, based on intuition, and not easily accessible. Hypotheses were unconfirmed, and complex topics were relatively unexplored. This is pretty typical for many organisations, especially when they are still young, rapidly changing, or don’t have a strong research culture.

與數(shù)據(jù)科學(xué)團(tuán)隊(duì)聯(lián)系，看看我們是否可以揭開某些神秘面紗，每個(gè)人都Swift意識(shí)到，我們面臨的最大挑戰(zhàn)之一是，基于直覺，對(duì)我們公司的大多數(shù)理解是分散的，并且不易獲得。 假設(shè)尚未得到證實(shí)，而相對(duì)復(fù)雜的話題尚未得到探討。 這對(duì)于許多組織來說是非常典型的，尤其是當(dāng)它們還很年輕，變化Swift或者沒有強(qiáng)大的研究文化時(shí)。

While this made the task at hand more difficult, it also has several repercussions for decision making. The first main issue is that teams are mostly blind to research not done by themselves, and are doomed to either waste time on ad hoc investigations, miss out on what other teams already know, or make decisions for the wrong reasons. The other is a struggle to gauge the impact of any changes to the platform. Should we encourage more users to subscribe? Is that more important than improving onboarding of new users? This is hard to understand on the fly and makes prioritising a vague process.

盡管這使手頭的任務(wù)變得更加困難，但對(duì)決策也有一些影響。 第一個(gè)主要問題是，團(tuán)隊(duì)大多對(duì)自己無法完成的研究視而不見，并且注定要浪費(fèi)時(shí)間進(jìn)行臨時(shí)調(diào)查，錯(cuò)過其他團(tuán)隊(duì)已經(jīng)知道的知識(shí)或出于錯(cuò)誤的原因做出決定。 另一個(gè)是努力評(píng)估平臺(tái)任何更改的影響。 我們應(yīng)該鼓勵(lì)更多的用戶訂閱嗎？ 這比改善新用戶的加入更為重要嗎？ 這是很難即時(shí)理解的，并且會(huì)使模糊的過程成為優(yōu)先事項(xiàng)。

With both of our teams being relatively young, we saw this as a good opportunity to not only analyse our value proposition, but also to deliver a broad, unified understanding that could be used by anyone in the company when it came to decision making.

由于我們的兩個(gè)團(tuán)隊(duì)都相對(duì)年輕，我們認(rèn)為這是一個(gè)很好的機(jī)會(huì)，不僅可以分析我們的價(jià)值主張，還可以提供廣泛，統(tǒng)一的理解，供公司中的任何人在決策時(shí)使用。

細(xì)分客戶 (Segmenting our customers)

One of the beautiful things about the data — user experience partnership is that both sides can readily contribute to a common goal in ways that the other cannot.

關(guān)于數(shù)據(jù)的美麗之處之一-用戶體驗(yàn)合作關(guān)系是，雙方可以輕易以雙方無法做到的方式為共同的目標(biāo)做出貢獻(xiàn)。

Part of the initial problem was understanding exactly what the status quo was — understanding what users come to us for. This is a daunting task, considering the thousands of different users with all of their peculiarities. As a data scientist however, detecting and quantifying diverse behaviours should be your bread and butter.

最初問題的一部分是確切地了解現(xiàn)狀-了解用戶向我們尋求什么。 考慮到成千上萬的不同用戶的特殊性，這是一項(xiàng)艱巨的任務(wù)。 但是，作為數(shù)據(jù)科學(xué)家，檢測和量化各種行為應(yīng)該是您的頭等大事。

This seemed like a typical case that calls for a user segmentation, which is basically the division of users into different common behaviours. This classic concept is quite simple, but in practice, a good segmentation is nuanced and defined by a central trade off. For it to be useful, we need to design segments that contain specific, uniform behaviours, that all carry some business meaning. The trade off is that many tiny groups, created using all of the variables available to you, are all specific and uniform — but a few big groups designed using only a few company level KPIs are far, far easier to understand and use practically. The bonus difficulty is that there isn’t a single metric that will evaluate the quality of your segments.

這似乎是一個(gè)典型的案例，需要進(jìn)行用戶細(xì)分 ，這基本上是將用戶劃分為不同的常見行為。 這個(gè)經(jīng)典概念非常簡單，但是在實(shí)踐中，通過權(quán)衡取舍可以很好地細(xì)分和定義良好的細(xì)分。 為了使其有用，我們需要設(shè)計(jì)包含特定且統(tǒng)一的行為的細(xì)分市場，這些行為均具有一定的業(yè)務(wù)意義。 需要權(quán)衡的是，使用您可用的所有變量創(chuàng)建的許多小型小組都是特定且統(tǒng)一的，但是僅使用幾個(gè)公司級(jí)KPI設(shè)計(jì)的幾個(gè)大型小組實(shí)際上就容易得多，而且更容易理解和使用。 額外的困難在于，沒有一個(gè)可以評(píng)估細(xì)分受眾群質(zhì)量的指標(biāo)。

This problem typically arises when data scientists are too quick to shove a whole database into their favourite algorithm. In our particular case, the platform combines online, offline, subscribers, and occasional users — without even mentioning our other customers, the franchise owning partners — a lot of combinations and the need to create a more or less unified framework. Considering that to start with we were interested in a general overview of behaviour, we alleviated the dilemma by focusing on variables that reflect the core of the business, and by creating the segments focusing on interpretable “cut off” points — the limits we used to define different behaviour groups. All of this while taking into account the entire lifecycle of our users on the platform.

當(dāng)數(shù)據(jù)科學(xué)家太快而無法將整個(gè)數(shù)據(jù)庫推入他們喜歡的算法時(shí)，通常會(huì)出現(xiàn)此問題。 在我們的特殊情況下，該平臺(tái)將在線，離線，訂戶和偶爾的用戶結(jié)合在一起-甚至沒有提及我們的其他客戶，特許經(jīng)營擁有者-很多組合，而且需要?jiǎng)?chuàng)建或多或少統(tǒng)一的框架。 考慮到我們首先對(duì)行為的總體概況感興趣，因此我們通過關(guān)注反映業(yè)務(wù)核心的變量并創(chuàng)建關(guān)注可解釋的“截止”點(diǎn)的細(xì)分市場(我們過去的限制)來緩解困境。定義不同的行為組。 所有這些都考慮了平臺(tái)上用戶的整個(gè)生命周期。

Two good examples are the frequency and number of orders for a user. The frequency is easily divisible into interpretable “categories”, like users who order once a week or once a month — especially since this lines up with how our subscriptions work. Looking at how many users followed different behaviours, we can easily make more or fewer frequency segments like this. The number of orders was a bit more complicated. We saw that the more orders a user had, the more likely they were to be retained long term, but only marginally. Comparing users with more than 5 and 20 orders, for example, we saw that while users with more than 5 were slightly less likely to churn, there were way more of them than those with over 20. We accepted this trade off to define a “retained users” segment that had plenty of customers, only marginally losing out on uniformity of behaviour.

兩個(gè)好的例子是用戶的訂單頻率和數(shù)量。 頻率很容易劃分為可解釋的“類別”，例如每周或每月訂購一次的用戶-尤其是因?yàn)檫@與我們的訂閱工作方式保持一致。 觀察有多少用戶遵循不同的行為，我們可以輕松地像這樣創(chuàng)建更多或更少的頻率段。 訂單數(shù)量稍微復(fù)雜一些。 我們看到用戶擁有的訂單越多，則越有可能長期保留訂單，但僅保留一部分訂單。 例如，比較訂單數(shù)量超過5和20的用戶，雖然用戶數(shù)量超過5的用戶流失的可能性略小，但與用戶數(shù)量超過20的用戶相比，用戶流失的可能性更大。我們接受了這種權(quán)衡來定義“擁有大量客戶的“保留用戶”細(xì)分受眾群，但在行為統(tǒng)一性方面僅微不足道。

This approach meant different behaviour groups are easy to understand and immediately relevant to current strategy, while being defined in a purposeful and meaningful way thanks to our interpretable “cut offs”. For example, it becomes very clear how much we stand to gain from converting new users to “retained” users, the potential target audience for subscription up-selling (frequently ordering users without a subscription), as well as how users’ behaviour evolves (how they move from one segment to another over time).

這種方法意味著不同的行為群體易于理解，并與當(dāng)前策略直接相關(guān)，同時(shí)由于我們可解釋的“臨界值”，以有目的和有意義的方式對(duì)其進(jìn)行了定義。 例如，非常清楚的是，從將新用戶轉(zhuǎn)換為“保留”用戶，潛在地進(jìn)行訂閱向上銷售(經(jīng)常訂購沒有訂閱的用戶)的目標(biāo)受眾以及用戶行為的演變，我們將獲得多少收益(隨著時(shí)間的推移，它們?nèi)绾螐囊粋€(gè)細(xì)分轉(zhuǎn)移到另一個(gè)細(xì)分)。

We drew a Sankey diagram to track transitions between segments, a 100% data based customer journey我們繪制了一個(gè)Sankey圖來跟蹤細(xì)分之間的過渡，這是100％基于數(shù)據(jù)的客戶旅程

Throughout the process of building our segments, we also adopted a methodology of continuously delivering concrete insights about what we were finding, ranging from how long users take to make their first order, to how much subscribers contribute to the financial health of a hub. These were all key pieces of knowledge that we found to be missing when we started the project. The continuous delivery of these conclusions gave our young team plenty of low hanging fruit to make some quick impact.

在構(gòu)建細(xì)分市場的整個(gè)過程中，我們還采用了一種方法，不斷對(duì)發(fā)現(xiàn)的結(jié)果提供具體的見解，范圍從用戶下達(dá)第一筆訂單所需的時(shí)間到訂戶對(duì)樞紐財(cái)務(wù)狀況的貢獻(xiàn)有多大。 這些都是我們?cè)陂_始項(xiàng)目時(shí)發(fā)現(xiàn)缺少的關(guān)鍵知識(shí)。 這些結(jié)論的不斷傳遞使我們的年輕團(tuán)隊(duì)垂涎三尺，可以Swift產(chǎn)生影響。

The next step specific to our segments is to understand how they can best be used directly to improve the development of new features and strategy. We are also in the process of understanding how they might be useful for our franchise partners, so that they can improve how they run their businesses.

我們細(xì)分市場的下一步是了解如何最好地將其直接用于改進(jìn)新功能和策略的開發(fā)。 我們也在了解它們對(duì)我們的特許合作伙伴可能有用的方式，以便他們可以改善其業(yè)務(wù)運(yùn)作方式。

用戶體驗(yàn)和我們的價(jià)值主張 (User experience and our value proposition)

With these findings, we were able to get rid of the vague, general context that we started with, and replace it with a detailed, specific overview of the different customer behaviours. This allowed the user experience team to start to really flesh out their understanding of the user journey, adding new paths, expanding on existing ones, and supplementing the design with concrete statistics on behaviour. We could now understand where we are fulfilling our promised value, where we are not, and where users are finding value in unexpected ways.

有了這些發(fā)現(xiàn)，我們就擺脫了最初的含糊，籠統(tǒng)的背景，而用不同的客戶行為的詳細(xì)，詳細(xì)的概述代替了它。 這樣，用戶體驗(yàn)團(tuán)隊(duì)就可以開始真正充實(shí)他們對(duì)用戶旅程的理解，添加新路徑，擴(kuò)展現(xiàn)有路徑，并通過行為的具體統(tǒng)計(jì)數(shù)據(jù)來補(bǔ)充設(shè)計(jì)。 現(xiàn)在，我們可以了解我們?cè)谀睦飳?shí)現(xiàn)了承諾的價(jià)值，在哪里沒有實(shí)現(xiàn)，以及用戶在哪里以意想不到的方式找到價(jià)值。

Having reached this point, the user experience team is also in a much better position to plan future research activity. They can interview our customers and ask exactly why they do certain things, and deliver the company insights that data science alone could not. It allows us to understand the motivations behind user behaviour, including external factors that affect this.

到此為止，用戶體驗(yàn)團(tuán)隊(duì)也可以更好地計(jì)劃未來的研究活動(dòng)。 他們可以采訪我們的客戶，并確切詢問他們?yōu)槭裁匆瞿承┦虑?#xff0c;并提供僅憑數(shù)據(jù)科學(xué)無法做到的公司見解。 它使我們能夠了解用戶行為的動(dòng)機(jī)，包括影響用戶行為的外部因素。

We are confident that this will be the first major step on our way to fully understand how we improve the lives of our customers. By taking advantage of data science’s ability to summarise how users behave, user experience is able to both fully understand the customer journey with the service, as well as round off our overall understanding with their own differential research. This puts us well on the path to understanding what role Jeff plays in the lives of its customers, and will hopefully drive real, research backed improvements to the service in the future.

我們相信，這將是我們?nèi)媪私馊绾胃纳瓶蛻羯畹牡谝徊健?通過利用數(shù)據(jù)科學(xué)總結(jié)用戶行為的能力，用戶體驗(yàn)既可以全面了解客戶使用該服務(wù)的旅程，又可以通過他們自己的差異研究來完善我們的整體理解。 這使我們能夠更好地理解Jeff在客戶生活中所扮演的角色，并有望在未來推動(dòng)對(duì)服務(wù)進(jìn)行真正的，研究支持的改進(jìn)。

We will be sure to post an update on how things are going in the near future, but in the meantime don’t hesitate to get in touch if you have any questions, comments, or feedback!

我們一定會(huì)發(fā)布一個(gè)更新的事情是如何在不久的將來打算，但在此期間不要猶豫 取得聯(lián)系 ，如果您有任何疑問，意見或反饋！

翻譯自: https://medium.com/jeff-tech/the-value-of-a-service-data-science-and-user-experience-investigate-the-good-good-life-cdc7044e06a7

科學(xué)價(jià)值 社交關(guān)系 大數(shù)據(jù)

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