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  • >> Predictive analytics has an incredible amount of potential

    >> 已擁有驚人的預測分析論 可能的數量

  • power and could lead to fantastic success, or your competitors

    電源,且可能會造成超棒的 成功或您的競爭者

  • could leverage the principles to gain an advantage.

    可以利用原則 以取得優勢。

  • Learn the secret to managing your data to gain the competitive edge

    了解的密碼,來管理您 若要取得競爭優勢的資料

  • next on "Modern Workplace."

    下一步"現代職場"。

  • [Music]

    [音樂]

  • >> Prediction is the way of the future. Today we have access

    >> 預測是的方法 未來。今天我們就可以存取

  • to the roughly 5 exabytes of data created globally every single day.

    若要建立資料的大約 5 exabyte 全域每一天。

  • That's the same as 1 billion gigabytes every day. But how can

    這是 1 億 gb 相同 每一天。但要如何

  • this lead to success? According to Gartner, by 2016, 70 percent

    如此成功?依據 若要 Gartner,由 2016,70%

  • of the most profitable companies will be using this data and

    獲利最豐的公司 將會使用這項資料和

  • realtime predictive analytics to rapidly grow their businesses.

    到的即時預測分析論 快速地生長其業務。

  • Those who don't start today risk becoming obsolete. Today's topic

    今日的風險就不要啟動的任何人 成為過時。今天的主題

  • is actionable insights, how to turn data into success. We are

    方式是可行的洞察力到 您可以將 [資料轉換成功。我們

  • delighted to welcome the best selling author of "Predictive Analytics,"

    歡迎最佳的銷售大師當然非常樂意 "預測分析論,"的作者

  • Eric Siegel. He says that perfect prediction is not possible,

    Eric Siegel。他說該完美 預測不可行,

  • but it's important to make your bets on predictive analytics

    但請務必讓您 在預測分析論首選

  • now in order to get value from them tomorrow.

    ﹛ a 以取得值 從這些明天。

  • Also with us today, we have the CEO of Aryng and author of "Behind

    與我們今天,我們還有 CEO Aryng 以及 「 延後的作者

  • Every Good Decision," Piyanka Jain. She says that deciphering

    每個良好決策,"Piyanka Jain。 她說: 該解密

  • data is not rocket science. Thank you both for being here.

    資料不難懂。感謝 你們雙方為這裡。

  • First question, Eric, I'm interested, is BI, big data, that's

    第一個問題,Eric,我想, 是商業情報,大的資料的

  • been a trend that most people are well aware of. Is predictive

    已是大多數人的趨勢 了解。是預測

  • analytics kind of the same thing as that, or is it different?

    分析一種相同的動作 項目,或為不同嗎?

  • >> When people say big data, I'd say more than half the time they're

    >> 當人說大的資料時,我應該說: 它們是超過一半的時間

  • referring to a specific use of predictive analytics, but big

    指的自己的專屬的用法 但大的預測分析論

  • data is a much broader area. Predictive analytics is a subset.

    資料是一個多更廣泛的區域。預測 分析是子集。

  • Big data doesn't refer to any one specific method or technology.

    大的資料不指向任何一個 特定的方法或技術。

  • It just means there's a lot of excitement because there's a lot

    它只是表示有很多的樂趣 因為一大堆

  • of data, there's a lot of things to do with it, a lot of value

    資料,有很多的項目 如何處理它,許多價值

  • to get.

    若要取得。

  • >> Big data is just a pile of data whereas predictive analytics

    >> 大的資料是一堆資料 而預測分析論

  • is what you can do with it?

    是您能用它做什麼?

  • >> Exactly, right. So the data is what's so exciting about data,

    >> 完全吧。因此資料是 什麼是相關資料,如此令人振奮

  • the most valuable thing about it is that it's predictive.

    最有價值的好處 就是預測。

  • It is a recording of things that have happened. It's a bunch

    它是事情的錄製, 所有發生。它是一堆

  • of experience sort of the collective experience of an organization,

    經驗排序的集合 組織中的經驗

  • from which it's possible to apply predictive analytics to learn

    從中也可套用 若要了解的預測分析論

  • from it how to make predictions per individual. So in most cases,

    從它如何讓每個預測 個別的。因此在大多數情況下,

  • predictive analytics is per individual, like per individual customer

    預測分析論是每個人, 每位個別客戶喜歡

  • or applicant for credit, this kind of thing.

    申請人的信用貸款,或 這種事所處的狀況。

  • >> The same sort of stuff as like the nudge theory, where you

    >> 相同,像是排序的項目 微調理論上,其中您

  • can sort of predict what people are going to do based on previous actions?

    有點可以預測人什麼 如何根據先前的動作?

  • >> Yeah, exactly. And the kind of prediction, as you said in the

    >> 是,完全相同。和種類的 預測,當您在說:

  • opening here, is about putting odds on their behavior, whether

    開啟 [在這裡,是介紹加勝算不大 在它們的行為是否

  • they're going to click, buy, lie, or die, or do something you

    他們要按一下、 購買、 位於此項目, 死去,或做某件事您

  • don't want them to do, commit an act of fraud or a crime.

    不希望他們執行這項,認可 詐騙或一種犯罪行為的動作。

  • >> One of the things we also said in the open, there was the Gartner

    >> 的事情之一也是我們說過中 開啟時,發生 Gartner

  • statistic of a lot of companies who are already using predictive

    統計資料的許多公司人員 已經使用預測

  • analytics today. Where are some people getting value out of this,

    今天分析。有些人的位置 取得值,超出

  • like right now?

    現在要?

  • >> Well, most large organizations are using it in multiple ways.

    >> 好吧,大多數的大型組織 正在使用多種方式。

  • Marketing is a really hot area, which is just in terms of targeting

    行銷就是真正作用的區域,其中 是只是指目標

  • your direct marketing to customers more likely to respond or

    您給客戶的直接行銷 比較容易回應或

  • targeting your retention offers to customers that are more likely

    針對您保留建議 比較可能的客戶

  • to leave.

    若要離開。

  • The ultimate sales force and the most visible example of marketing

    最終的銷售人員和最 行銷的可見範例

  • campaigns in this country are the U.S. presidential campaigns

    是此國家或地區中的行銷活動 美國總統戰役

  • for becoming the most powerful person in the world. And the

    成為最強大的 全世界的人。和

  • sales force are the armies of volunteers who go and knock on

    銷售人員是自願的軍隊 誰去破壞

  • the doors. Those are the sales calls. It's a marketing campaign.

    門。這些是銷售電話。 它是行銷活動。

  • They're selling a presidential candidate. That's the product

    他們正在兜售蓋塔總統 候選。這是產品

  • being sold. So very much the same type of marketing analytics,

    在賣。非常相同 行銷分析,型別

  • predictive analytics, specifically, is being used by multiple candidates.

    具體來說,是預測分析論, 正在使用多個候選項目。

  • It was really a game changer in 2012 where the Obama campaign

    它是不是真的在遊戲交換器 2012 where Obama 戰役

  • used predictive analytics for the first time.

    使用預測分析論 第一次。

  • >> Now, that strikes me as interesting because we're sitting here

    >> 可達到現在,我為有趣 因為我們坐在這裡

  • in 2015, almost 2016. We're well in the midst of another presidential

    在 2015 中,幾乎是 2016年。我們在正常 大型另總統

  • cycle, yet we're still highlighting something a few years ago.

    循環,但我們仍醒目提示 有幾年前。

  • Is it still the case that that remains the poster child? Or have

    它仍然是大小寫,維持 海報子系嗎?或有

  • others really superseded what we saw there in that 2012 race?

    其他人真的取代我們 2012 比賽時,看到那里?

  • >> Did you see yesterday's ad sorry to jump in.

    >> 您看到嗎昨天的 ad 中跳抱歉。

  • Hillary's ads? Hillary Clinton has changed her entire advertising

    Hillary 的廣告?有 Hillary Clinton 變更她的整個廣告

  • game, and I think it's coming from the same analytics kind of perspective.

    遊戲時,我和我認為它來自 分析同樣的觀點來看。

  • She now has four ads, which came out yesterday, and it's highlighting

    她現在有四個的廣告、 附出 昨天,而且它反白顯示

  • four economic issues for women of certain age.

    四個的經濟的問題 women 的某年紀的群組。

  • And the polls which have followed since then have already showed

    和有跟投票 已經有顯示此後

  • a difference in her so I think it's powerful. That story will

    因此我認為她差異 它是功能強大。故事就是

  • read in 2016 for selection.

    讀取 2016年中的選取範圍。

  • >> It sounds like it's already being employed. Eric, do you think

    >> 看起來已經在進行 採用。你以為 Eric

  • it's still are they still leading the game?

    它的仍仍然是它們 前置遊戲?

  • >> The thing is we haven't had another chance since 2012 because

    >> 的事情是我們已經有另一個 因為,因為 2012年機率

  • we haven't had another presidential like there have been other

    我們已經有另一個總統 像過去曾經發生過其他

  • election campaigns done on the smaller scale doing this, but

    完成的選舉戰役 較小擴充這麼做,但

  • the presidential elections where they really have the budget

    總統選舉位置 他們真的有適當的預算

  • and the oomph to do it. Hillary for America has actually advertised,

    與這麼 oomph。為 Hillary 美國有實際通告,

  • not just for predictive analytics staff, but more specifically

    不只是針對預測分析論 工作人員,但多特別

  • for what's called persuasion modeling, which is a specific advanced

    目的為何,然後按一下 [建立模型的眼光 這是進階的特定

  • form of predictive analytics >> You

    表單的預測分析論 >> 您

  • mean micro targeting.

    表示微目標。

  • >> that the Obama campaign did. Now, all predictive analytics

    >>,Obama 的行銷活動。 現在,所有的預測分析論

  • you could consider micro targeting, but in this case, it's the

    您可以考慮微目標, 但在此情況下,會有

  • definition of what's being targeted, which isn't just who's going

    什麼作為目標的定義 這不是只誰去

  • to vote for us or who's not going to vote for us, it's who can

    若要為我們的一票或誰不敢 若要進行投票的我們,最好是誰可以

  • be persuaded. They call it persuasion modeling, also known as

    引誘。它們會呼叫它眼光 模型化,也就

  • uplift modeling.

    uplift 模型。

  • >> So that's happening in the presidential campaign, and that's

    >> 這樣的狀況中總統 行銷活動,以及的

  • all well and good, but how would you relate that to someone who's

    所有好,很好,但要怎麼會 給其他人與相關之人員的

  • sitting at their desk in a commercial organization today?

    坐在辦公桌中廣告 組織若是?

  • What are the things that they can learn and apply from that example?

    它們可以了解的事項是什麼 並套用該範例?

  • >> Well, we see lots of case studies from large corporations using

    >> 好吧,我們看到很多案例研究 從使用的大型公司

  • this same type of technology. So predictive analytics, normally,

    相同類型的技術。因此 預測分析論、 一般情況下,

  • you're predicting who's going to respond in order to target an

    您要預測誰敢 若要為目標的回應

  • offer or direct marketing, direct mail, what have you, and that's

    提供或直銷,直接 郵件,有什麼關係呢,和的

  • the traditional use. That's the most common way to use it.

    傳統的使用中。的的 最常見的方式使用它。

  • It's very valuable. You see time after time again, PREMIER Bankcard,

    它是非常有用。您會看到的時間 同樣地,總理 Bankcard 時間

  • for example, reporting a savings of $12 million in their marketing

    例如,報告可節省 $12 萬其行銷部門

  • costs because they're defining that audience and they reach their

    因為它們正在定義的成本 對象,而且達到其

  • targeting that much more effectively. But then the next step

    鎖定的目標更有效率。 但再下一個步驟

  • we're seeing in a smaller handful, but even more successful,

    我們看見在較小的少數, 但更成功,

  • is where you don't just predict who's going to respond if I contact,

    是,您不只是預測誰的 回應 [如果我連絡,

  • you predict, who am I going to influence to respond because of contact?

    預測時,誰我會不會影響 若要回應由於連絡人?

  • That is, they wouldn't buy otherwise. So you're literally predicting

    也就是說,他們不會購買否則。 所以您實際上預測

  • influence or persuasion in order to optimize for persuasion.

    影響或順序的眼光 若要最佳化的眼光。

  • >> Much more nuanced?

    >> 許多更 nuanced?

  • >> Yes.

    >> [是]。

  • >> Awesome. We're going to have to take a break now, but it's

    >> 極了。我們要有 若要分現在,但它的

  • a great time to let us know what you think. You can tweet me

    讓我們知道什麼的好時機 您認為。您可以 tweet 我

  • directly @amcbg, using #modernworkplace. Next we're going to

    直接 @amcbg,使用 #modernworkplace。 接下來我們要

  • take a deep dive into how you can start using predictive analytics

    採取深度的剖析成您要如何 開始使用預測分析論

  • in your organization today.

    在今天組織。

  • >> Every day I'm flooded with new information e mail, calendar

    >> 我以新淹沒每一天 資訊的電子郵件、 行事曆

  • events, social feeds. Keeping my head above water long enough

    事件,共享的摘要。保留我 水長到足以上方的標頭

  • to find what I need used to be nearly impossible.

    若要尋找我所需要使用 為幾乎不可能。

  • Until now. As I work, Office Graph used machine learning to map

    直到現在。當我工作時,「 Office 圖表 使用學習,以對應的電腦

  • the relationships between the content and people I interact with

    內容之間的關聯性 和我互動的人員

  • all across Office 365, then uses what it learns to make my job

    所有跨 Office 365,然後使用 它讓我的工作可學習

  • easier in all kinds of new ways. For instance, Outlook relies

    更容易在所有類型的新方法。 舉個例說,Outlook 必須藉助

  • on that intelligence to remove the clutter from my inbox so I

    若要移除該智慧上 從我的收件匣的雜亂度讓我

  • can focus on important e mails first. And whenever I need to

    可以專注於重要的電子郵件 第一個。而且每當我需要

  • get ready for a meeting, Office Graph checks my calendar to learn

    準備進行會議時,Office 圖表 檢查我的行事曆,了解

  • the topic, then pulls the latest content together onto prep cards

    主題,然後提取最新版的 內容到準備卡在一起

  • that I can review on the way.

    我可以檢閱的方式。

  • And if I need a file from OneDrive while I'm there, I just type

    如果我需要從 OneDrive 的檔案 雖然我,我只需要輸入

  • a question to find it. No more fumbling around in folders.

    若要尋找它的問題。不必再苦苦 fumbling 資料夾中。

  • Even the groups I work with benefit from Office Graph. It not

    即使我所合作利益的群組 從 [Office 圖表。它不

  • only tells us what's trending within the group, it also pulls

    只告訴我們什麼趨勢內 群組中,它也會提取

  • in relevant content and resources from outside the group so we

    在相關的內容與資源 從群組之外,我們

  • can be even more productive.

    可以甚至更具生產力。

  • Office Graph intelligence also powers Delve, a brand new app

    Office 也圖形智慧 力量 Delve,全新的應用程式

  • that presents and collects content from anywhere in Office videos,

    所提供,並收集內容 從任何地方在 Office 視訊

  • links, documents, the works. It almost feels like the right information

    連結、 文件、 運作方式。它幾乎 感覺像正確的資訊

  • finds me. So even though the daily flood isn't likely to slow

    找出我的資訊。因此即使每日 大量封包不可能會變慢

  • down any time soon, I'm not worried. Office Graph's got my back,

    停機很快就任何時間,我不擔心。 Office 圖表重回戰場了我上一步],

  • making sure I'm more proactive and better connected with my network

    確認我更主動和 更四通八達跟我的網路

  • than ever.

    比以往。

  • >> In a moment, our experts will share some tips on how you can

    >> 在等一下,我們的專家將會 共用一些秘訣,如何

  • start utilizing your insights. But first, if you're not already

    開始使用您的見解。但 首先,如果您還沒有

  • registered, please check out modernworkplace.com. You can register

    登錄,請簽出 modernworkplace.com。 您可以註冊

  • to receive reminders about future shows and get access to exclusive

    若要收到關於未來的提醒 顯示,以及到獨占的 get 存取

  • white papers and other key information to help your organization thrive.

    白皮書的內容及其他重要的資訊 若要協助組織興盛的方法。

  • Please register at modernworkplace.com. Today we're focused

    請註冊在 modernworkplace.com。 今天我們的重心是

  • on how you can stay ahead of the competition using predictive analytics.

    在您如何保持競爭優勢 使用預測分析論。

  • Piyanka, it's now time to get down to brass tacks. In your consulting

    Piyanka,它現在是時候了向下 若要黃銅 tacks。在您的顧問

  • business, how do you advise customers to get started in this field?

    商務,您要如何通知客戶 若要開始在此欄位中?

  • >> There are two aspects to it. One is at the leadership level,

    >> 有兩個方面。其中一個 在領導層級,

  • how do we start competing in analytics? How do we start leveraging

    我們如何啟動競爭中分析? 我們如何啟動運用

  • the troll of data that we are been collecting? Maybe we started

    資料,我們已經食人妖 收集?也許我們開始

  • collecting day one. How do we start leveraging that to start

    收集第一天。我們該如何啟動 運用,即可開始

  • making decisions? That's >> How

    決策?>> 如何

  • do we do it?

    我們是否能?

  • >> How do we do it? So we start, we do that by what we call three

    >> 我們該如何進行?讓我們一開始,我們 否則請讓我們對三個呼叫

  • key questions to ask your data. We do that by not looking at

    詢問您的資料的主要問題。 這是藉由不查看

  • data and expecting the answer to pop up.

    資料與預期 快顯的答案。

  • >> Not like The Matrix?

    >> 不是像矩陣?

  • >> No, the data doesn't speak. It only responds. It responds

    >> 否,不會讀出資料。 它只會回應。它會回應

  • to intelligent questions.

    智慧型的問題。

  • >> That's an interesting way of putting it. It doesn't speak,

    >> 的一種有趣的方式 將它置於。它不會說,

  • it only responds, but you have to know the questions to ask it.

    它只會回應,但是您必須 知道請它的問題。

  • >> Exactly. And there are many, many questions you can ask the

    >> 完全相同。而且有許多, 您可以要求的許多問題

  • data, but where should you begin? Where you begin is by asking

    資料,但是,您應該開始嗎? 詢問您的開始位置是

  • the three key questions. The first one is how am I doing?

    三個主要的問題。第一個 其中一個是如何我執行?

  • Which is another way of saying how do you measure your own success?

    這是說的另一種方式 您評量自己成功?

  • And for different organizations, it will be different. For some

    並於不同的組織, 它會不同。某些

  • companies it might be profit. Sometimes it might be shareholder value.

    公司可能蠻利潤。有時候 它可能股東價值。

  • Sometimes it might be >> So

    有時候可能蠻 >> 因此

  • this is more about knowing your business and knowing what's important.

    這是更詳細了解您的公司 然後,了解什麼重要的。

  • >> What's important for you. So that's the first part, which is

    >> 什麼是很重要。這樣的 第一部分中,也就是

  • how am I doing? That's the first question. Second is what drives

    我做的如何?這是第一個 問題。第二個是什麼磁碟機

  • how am I doing? What drives the success metrics? What drives

    我做的如何?哪些磁碟機 成功的標準嗎?目標磁碟機

  • those metrics you define to be your KPI?

    您定義這些度量資訊 為您的 KPI?

  • >> This is where you need to think about what are leading indicators

    >> 這是您需要考慮的位置 什麼是一種前置字元的指標

  • and lagging indicators.

    然後,lagging 指標。

  • >> All those, right, exactly. Leading and lagging are basically drivers.

    >> 所有這些,以滑鼠右鍵,完全相同。前置 而遲延基本上是驅動程式。

  • Think of what do I really if I really want to have better margins

    想到什麼我真的如果我真的 希望自己能夠更好的邊界

  • or better growth, what levers can I pull within my organization

    或以上成長,桿的可以 我在我的組織內提取

  • to move that, to drive growth forward? So that's the second part.

    若要移動,向前磁碟機成長? 因此,這會是第二個部分。

  • What drives the success?

    目標磁碟機是否成功?

  • >> What do I actually care about, and what influences that?

    >> 該怎麼做實際上還有 而且,什麼會影響的?

  • >> Exactly. And the third one is customers. Who are my customers?

    >> 完全相同。而第三個是客戶。 我的客戶是誰?

  • And how do I engage with them? So we all have hundreds and millions,

    和我交戰與其?所以我們 所有有上百和百萬,

  • sometimes tens of thousands, different customer sets. How do

    有時候數萬個,不同 客戶的設定。怎麼做

  • you do you understand your customers? What are their needs?

    只要您瞭解您的客戶? 他們的需求為何?

  • What do they want from you? How do you engage with them? So these

    他們想要從您什麼?怎麼做 您正在從事與其?因此這些

  • are the three key questions that you start with at your highest

    有三個索引鍵質疑, 您在您的最高開頭

  • level of the organization and start peeling the onion of your

    層級的組織和開始 剝除的洋蔥您

  • it looks like three questions, but as soon as you have answered

    它看起來像是三個問題,但 一旦您已回答

  • them, there will be more questions coming up. Now, once you've

    它們會有更多的問題 接下來。現在,一旦您

  • identified like, let's say you're going for profits or margin,

    識別此選項,例如,假設您是 利潤或邊界,將

  • and you realize really it's the churn, the customer churn that

    和您瞭解您真的 客戶變換的變換,

  • is killing us. Then the question is how do you reduce that churn?

    ,殺光我們。然後問題 是您該如何減少該變換?

  • And the question you may start with is our customer churn is

    然後您可以開始的問題 就是我們的客戶變換

  • 39 percent. How do we get it down to 35 percent? Once you have

    39 百分比。我們該如何它向下 為 35%?一旦開啟了

  • a question defined at that level, you use a framework we call

    在該層級上定義的問題 您使用的架構,我們呼叫

  • BADIR, which is a five step method of really starting from defined

    BADIR,也就是五個步驟方法 其實從啟動的定義

  • business question, then laying out an analysis plan, which is

    商務問題,再以配置圖形 分析計劃,也就是

  • the second step of B A in BADIR, collecting only relevant data

    B A BADIR,在第二個步驟 收集相關的資料

  • or pulling only relevant data, deriving insights, and making recommendations.

    或正在提取只的相關資料,衍生 洞察力及建議事項。

  • >> So just recap that, and then I'd love Eric's thoughts on how

    >> 只要複習一下,然後我會 愛 Eric 的想法,如何

  • to get started.

    若要開始使用。

  • >> BADIR. Business question, what's the real business question?

    >> BADIR。商務問題為何的 真實的商業問題?

  • Lay out an analysis plan, which has all the hypotheses and so on.

    配置分析計劃,其中有 所有的假設和等等。

  • >> Business question, analysis.

    >> 分析商務問題。

  • >> Data collection. And then do your insights.

    >> 資料集合。和 然後執行您的見解。

  • >> That's actually getting it, getting the data.

    >> 的實際取得 它,取得的資料。

  • >> And then making the recommendations. So BADIR is the short term.

    >>,然後建議。 BADIR 是短期。

  • >> Eric, what are your thoughts?

    >> Eric,您的想法為何?

  • >> So to build on what Piyanka just said, everything she just

    >> 因此,若要在何種 Piyanka 只建置 換句話說,她只的所有項目

  • said has to do with figuring out exactly how to position and

    說不想找出 如何確切位置,

  • make the best use of predictive analytics, which ultimately means

    善用預測分析論, 這最後表示

  • what operational actions, decisions, treatments of customers,

    何種操作的動作,決策, treatments 的客戶,

  • whether it's regards to marketing or sales calls or customer

    它是否與行銷 銷售電話或客戶

  • service, what operation is going to be improved with predictions?

    服務,即將哪項作業 若要加以提昇預測?

  • So if it turns out, for example, you want to try to retain customers,

    因此,如果結果顯示,例如,您 要保留的客戶,

  • you're trying to figure out which ones are at risk of leaving

    您正嘗試哪一個 如果出版物有風險的離開

  • in order to target those retention offers. Which customer's

    若要為目標的保留 提供此項目。哪位客戶的

  • at risk of committing fraud, in terms of triaging the use of

    在認可詐騙的風險 分級的使用中的條款

  • auditors' time. So one way or another, it's improving mass scale operations.

    稽核員的時間。所以一或多種方式, 它會增進全面性的作業。

  • This is where the rubber meets the road or what all organizations

    這是橡膠符合的位置 道路或什麼的所有組織

  • are doing to treat and serve us. It's all the mass scale operations

    會進行處理,並提供我們。它有 全面性的所有作業

  • in terms of deciding upon credit applications for a credit card,

    以決定信用 與信用卡的應用程式

  • marketing, fraud detection all these things can stand to be improved

    行銷、 詐騙偵測所有這些 要改善可以獨立的項目