Using marketing analytics to drive superior growth
善用營銷分析學(xué)促進銷售更快增長
Companies have so many analytical options at their disposal that they often become paralyzed, defaulting to just one approach.公司可用的分析工具太多, 這令他們常常變得猶豫不決, 最后默認使用某一種分析方法。
June 2014 | by Rishi Bhandari, Marc Singer, and Hiek van der Scheer
2014年6月號，Rishi Bhandari, Marc Singer, and Hiek van der Scheer合寫。
 
There’s no question that the development of better analytical tools and approaches in recent years has given business leaders significant new decision-making firepower. Yet while advanced analytics provide the ability to increase growth and marketing return on investment (MROI), organizations seem almost paralyzed by the choices on offer. As a result, business leaders tend to rely on just one planning and performance-management approach. They quickly find that even the most advanced single methodology has limits. 毫無疑問,近年來開發(fā)出來的性能更加卓越的分析工具和方法,為企業(yè)領(lǐng)導(dǎo)人進行決策提供了全新的動力。然而盡管高級分析學(xué)可以為促進銷售增長和提高營銷投資回報率提供幫助，有許多企業(yè)因為選擇過多，幾乎有點無所適從。結(jié)果，企業(yè)領(lǐng)導(dǎo)人傾向于只使用一套計劃和經(jīng)營管理方法。他們很快意識到，單一方法就算是再先進，也有其自身的不足。
 
The diverse activities and audiences that marketing dollars typically support and the variety of investment time horizons call for a more sophisticated approach. In our experience, the best way for business leaders to improve marketing effectiveness is to integrate MROI options in a way that takes advantage of the best assets of each. The benefits can be enormous: our review of more than 400 diverse client engagements from the past eight years, across industries and regions, found that an integrated analytics approach can free up some 15 to 20 percent of marketing spending. Worldwide, that equates to as much as $200 billion that can be reinvested by companies or drop straight to the bottom line. 營銷活動的多樣化、營銷費用通常支持的受眾以及投資時間界限的多樣化要求使用更加精密的方法。根據(jù)我們的經(jīng)驗，企業(yè)領(lǐng)導(dǎo)人提升營銷效益的最佳途徑是將營銷投資回報率各種方案以一種兼顧各種資產(chǎn)最大優(yōu)點的方式進行整合。這么做效果卓著：我們通過對過去8年來遍布各地、橫跨各行業(yè)的400多位各種各樣的客戶案例總結(jié)分析，發(fā)現(xiàn)整合分析方法可以節(jié)省15%到20%的營銷費用。從全世界范圍來看，這相當于2000億美元，公司可以用來進行重新投資或者直接將營銷費用壓到最低。
Here’s one example. A property-and-casualty insurance company in the United States increased marketing productivity by more than 15 percent each year from 2009 to 2012. The company was able to keep marketing spending flat over this period, even as related spending across the industry grew by 62 percent. As the chief marketing officer put it, “Marketing analytics have allowed us to make every decision we made before, better.”
我們來看一個例子。美國的一家財產(chǎn)和意外保險公司，從2009年到2012年，營銷效率以每年超過15%的速度增長。這種情況下，該公司還能夠在這段時間內(nèi)將營銷費用持平，而行業(yè)內(nèi)相關(guān)營銷費用增長了62%。該公司首席營銷經(jīng)理說：“營銷分析法使我們做的每個決定都比以前更好。”
Anchoring analytics to strategy
將分析學(xué)融入戰(zhàn)略
A company’s overarching strategy should ground its choice of analytical options. Without a strategy anchor, we find companies often allocate marketing dollars based largely on the previous year's budget or on what business line or product fared well in recent quarters. Those approaches can devolve into “beauty contests” that reward the coolest proposal or the department that shouts the loudest rather than the area that most needs to grow or defend its current position. 一個公司的整體戰(zhàn)略應(yīng)該建立在對各種分析方案的選擇上。我們發(fā)現(xiàn)，如果沒有一個戰(zhàn)略定位，許多公司經(jīng)常在很大程度上會依據(jù)上一年度的財政預(yù)算或最近幾個季度哪些業(yè)務(wù)線或產(chǎn)品表現(xiàn)不俗來分配營銷費用。這些方法可能會退化成“選美賽”—獎勵最酷的方案或者嚷嚷得最厲害的部門，而不是分配給那些最需要成長或者保持當前位置的部門。
A more useful approach measures proposals based on their strategic return, economic value, and payback window. Evaluating options using such scores provides a consistent lens for comparison, and these measurements can be combined with preconditions such as baseline spending, thresholds for certain media, and prior commitments.
一個更為有用的方法基于戰(zhàn)略回報、經(jīng)濟價值和回報期限等方面對方案進行評估。評估方案采用這種分數(shù)提供了一個持續(xù)的圖景用以作比較，而且這些評估可以和一些前提條件如基本費用，特定媒體的門檻，優(yōu)先承諾等聯(lián)合使用。
The other prerequisite in shaping an effective MROI portfolio is understanding your target consumers’ buying behavior. That behavior has changed so radically in the past five years that old ways of thinking about the consumer—such as the marketing “funnel”—generally don’t apply. 要形成一個有效的營銷投資回報率組合，另一項前提是了解目標消費者的購買行為。在過去的5年里消費者購買行為發(fā)生了巨大的變化，那些考慮消費者的如營銷“漏斗”的老路子，已經(jīng)基本上行不通了。Where the funnel approach prioritized generating as much brand awareness as possible, the consumer decision journey recognizes that the buying process is more dynamic and that consumer behavior is subject to many different moments of influence.¹ 1.See David Court et al., “The consumer decision journey,” McKinsey Quarterly, June 2009. For insight into the impact of digitization, see David Edelman, Kelly Ungerman, and Edwin van Bommel, “Digitizing the consumer decision journey,” June 2014.   (A sidebar, “Five questions for maximizing MROI,” highlights additional considerations.) 營銷“漏斗”法重點放在樹立品牌意識，而消費者決策過程分析法則承認消費者購買過程更加靈活多變，消費者行為受到許多不同環(huán)節(jié)的影響。（1參見David Court 《消費者決策過程》，麥肯錫期刊，2009年6月。要了解更多數(shù)字化的影響，參見David Edelman, Kelly Ungerman, and Edwin van Bommel，《消費者決策過程數(shù)字化》，2014年6月）（補充：《營銷投資回報率最大化的五個問題》，強調(diào)其他一些要考慮的方面。）
One home-appliance company, for example, typically spent a large portion of its marketing budget on print, television, and display advertising to get into the consideration set of its target consumers. Yet analysis of the consumer decision journey showed that most people looking for home appliances browsed retailers’ websites—and fewer than 9 percent visited the manufacturer’s own site. When the company shifted spending away from general advertising to distributor website content, it gained 21 percent in e-commerce sales. 例如：一家家用電器公司，通常會將其營銷預(yù)算的很大一部分花在印刷、電視和廣告展示以引起特定目標消費者的關(guān)注。而對消費者決策過程的分析表明，大多數(shù)消費者通過瀏覽零售商的網(wǎng)站尋找家用電器，只有不到9%的人瀏覽生產(chǎn)商自身的網(wǎng)站。當公司將營銷費用從一般廣告轉(zhuǎn)移到分銷商內(nèi)容網(wǎng)站上，公司電子商務(wù)銷售額增加了21%。
Making better decisions 更好地決策
While new sources of data have improved the science of marketing analytics, “art” retains an important role; business judgment is needed to challenge or validate approaches, but creativity is necessary to develop new ways of using data or to identify new opportunities for unlocking data. These “soft” skills are particularly useful because data availability and quality can run the gamut. For instance, while online data allow “audience reached” to be measured in great detail, other consumer data are often highly aggregated and difficult to access. But such challenges shouldn’t impede the use of data for better decision making, provided teams follow three simple steps. 新的數(shù)據(jù)來源改善了營銷分析學(xué)的科學(xué)性，但分析學(xué)的“藝術(shù)性”仍起著重要作用；商業(yè)判斷需要勇于挑戰(zhàn)或者驗證各種方法, 但創(chuàng)新能力對開發(fā)使用數(shù)據(jù)新方法或使用解鎖數(shù)據(jù)確定新的機會非常重要。這些“軟”技能特別有用，因為數(shù)據(jù)可用性和質(zhì)量可以覆蓋全過程。比如，線上數(shù)據(jù)允許對 “到達受眾”進行詳細評估，別的消費者數(shù)據(jù)經(jīng)常是高度集成并難以進入。但假如團隊遵循下面三個簡單步驟，這些挑戰(zhàn)就不會影響數(shù)據(jù)的使用，從而做出更好的決策。
1. Identify the best analytical approaches　確定最佳的分析方法
To establish the right marketing mix, organizations need to evaluate the pros and cons of each of the many available tools and methods to determine which best support their strategy. When it comes to nondirect marketing, the prevailing choices include the following:
要建立正確的營銷組合，企業(yè)需要評估許多可用工具和方法的利弊以確定哪些才能最有力地支持他們的戰(zhàn)略。當涉及到非直接營銷，主流的選擇包含以下方面：

• Advanced analytics approaches such as marketing-mix modeling (MMM).MMM uses big data to determine the effectiveness of spending by channel. This approach statistically links marketing investments to other drivers of sales and often includes external variables such as seasonality and competitor and promotional activities to uncover both longitudinal effects (changes in individuals and segments over time) and interaction effects (differences among offline, online, and—in the most advanced models—social-media activities). MMM can be used for both long-range strategic purposes and near-term tactical planning, but it does have limitations: it requires high-quality data on sales and marketing spending going back over a period of years; it cannot measure activities that change little over time (for example, out-of-house or outdoor media); and it cannot measure the long-term effects of investing in any one touchpoint, such as a new mobile app or social-media feed. MMM also requires users with sufficiently deep econometric knowledge to understand the models and a scenario-planning tool to model budget implications of spending decisions.　 高級分析方法，比如營銷組合模式。營銷組合模式使用大數(shù)據(jù)以確定渠道費用的效果。這個方法通過數(shù)據(jù)將營銷投資與其它銷售動力聯(lián)系起來，且常常包括外部變量如季節(jié)性、競爭者和促銷活動以揭示縱向效應(yīng)（一段時間內(nèi)個人和某些部分的變化）及互動效應(yīng)（離線、在線以及大多數(shù)高級模型里都有社會媒體活動之間的區(qū)別）營銷組合模型既可以用以長期戰(zhàn)略目標也可以用來作為短期戰(zhàn)術(shù)計劃，但該模型也有不足：它需要高質(zhì)量的銷售數(shù)據(jù)和以及過去幾年內(nèi)的營銷費用數(shù)據(jù)；它不能評估一段時期內(nèi)變化很小的營銷活動（如戶外媒體）；另外，它也不能評估任何一個觸點里的長期投資效應(yīng)，比如一個新的手機應(yīng)用軟件或社交媒體反饋。營銷組合模型也為用戶提供相當深度的計量經(jīng)濟學(xué)知識以理解模型和情景計劃工具以形成支出決策預(yù)算模型，揭示各種隱含關(guān)聯(lián)。
• Heuristics such as reach, cost, quality (RCQ).RCQ disaggregates each touchpoint into its component parts—the number of target consumers reached, cost per unique touch, the quality of the engagement—using both data and structured judgment. It is often used when MMM is not feasible, such as when there is limited data; when the rate of spending is relatively constant throughout the year, as is the case with sponsorships; and with persistent, always-on media where the marginal investment effects are harder to isolate. RCQ brings all touchpoints back to the same unit of measurement so they can be more easily compared. It is relatively straightforward to execute, often with little more than an Excel model. In practice, though, calibrating the value of each touchpoint can be challenging given the differences among channels. RCQ also lacks the ability to account for network or interaction effects and is heavily dependent on the assumptions that feed it. 探試學(xué)，比如RCQ法，即范圍、成本和質(zhì)量。這個方法將每個觸發(fā)點分解成各個成分—目標消費者的范圍數(shù)量、每個觸發(fā)點的平均成本、活動的質(zhì)量—使用數(shù)據(jù)和結(jié)構(gòu)判斷做分析。當營銷組合模式方法不可行，比如數(shù)據(jù)有限時常常使用探試法；當活動包含贊助使整年的費率相對穩(wěn)定時；以及在持續(xù)不斷投放媒體廣告邊際效益變得更難分離時。RCQ法將所有觸發(fā)點帶回到同一個評估單元，這么一來可以很容易對觸發(fā)點做比較。該方法相對直接易于執(zhí)行，往往和一個EXCEL模型差不多。盡管在實際運用中，考慮到各種渠道之間的差異，要確定每個觸發(fā)點的價值很有挑戰(zhàn)性。RCQ方法也不能解釋網(wǎng)絡(luò)或互動效應(yīng)，并且十分依賴用于該方法的各種假設(shè)。
• Emerging approaches such as attribution modeling.As advertising dollars move online, attribution becomes increasingly important for online media buying and marketing execution. Attribution modeling refers to the set of rules or algorithms that govern how credit for converting traffic to sales is assigned to online touchpoints, such as an e-mail campaign, online ad, social-networking feed, or website.。Those credits help marketers evaluate the relative success of different online investment activities in driving sales. The most widely used scoring methods take a basic rules-based approach, such as “last touch/click,” which assigns 100 percent of the credit to the last touchpoint before conversion. But newer methods that use statistical modeling, regression techniques, and sophisticated algorithms that tie into real-time bidding systems are gaining traction for their analytical rigor. While these approaches are a step up from methods tied to rules, they still typically depend on cookie data as an input, which limits the richness of the data set and consequently makes it difficult to accurately attribute the importance of each of the online touchpoints. 歸因建模等新興方法的出現(xiàn)。由于廣告費用流向在線網(wǎng)絡(luò)，歸因?qū)τ诖_定在線媒體購買和營銷執(zhí)行方面變得日益重要。歸因建模是指一套規(guī)則或算法，用以管理流量轉(zhuǎn)化為銷售的分值如何分配給各個在線觸發(fā)點，比如EMAIL活動、網(wǎng)絡(luò)廣告、社交網(wǎng)絡(luò)反饋或者網(wǎng)絡(luò)。這些分值幫助營銷人員評估推動銷售增長的不同在線投資活動，以確定有哪些相對成功之處最廣泛使用的計分方法采用基本的基于規(guī)則的方法，比如“最后觸發(fā)/點擊”，把全部的分數(shù)分配給轉(zhuǎn)換前的最后觸發(fā)點。 但新出現(xiàn)的方法使用統(tǒng)計模型，回歸技術(shù)和更為精密的算法與實時的競價系統(tǒng)相連，這些方法為他們進行嚴密分析提供了助力。盡管這些方法比那些依賴規(guī)則的方法更進了一步，它們通常仍然依賴作為輸入數(shù)據(jù)的cookie（文本信息）數(shù)據(jù)，這限制了數(shù)據(jù)集的豐富性，最終導(dǎo)致對每個在線觸發(fā)點的重要性準確歸因變得很困難。

2. Integrate capabilities to generate insights 
整合各種能力以獲得深刻見解
Although some companies rely on just one analytical technique, the greatest returns come when MROI tools are used in concert. An integrated approach, which includes pulling in direct-response data and insights, reduces the biases inherent in any one MROI method and provides business leaders with the flexibility to shift the budget toward activities that produce the most bang for their buck. 雖然有一些公司只依賴于某一種分析方法，但是最好的回報還是來自對營銷投資回報率方法的整合使用。作為一種綜合的分析方法，包括了：可以拉動直接反應(yīng)的數(shù)據(jù)和見解、可以減小任何一種營銷投資回報率方法偏見性、能令企業(yè)領(lǐng)導(dǎo)人靈活決策將預(yù)算投向可產(chǎn)生最大效益的營銷活動。
 
So how do these techniques work together? A company may find, for instance, that TV, digital, print, and radio make up about 80 percent of its marketing spending. Since those activities generate audience-measurement data that can be tracked longitudinally, it makes sense to use MMM. But digital spending can be refined further using attribution modeling to pinpoint the activities within broad categories—such as search or display—that are likely to generate the most conversion. The company could then use heuristics analysis such as RCQ to monitor the remaining 20 percent of its spending, which may go toward sponsorships and out-of-home advertising to capture the company’s non-TV-watching target audience. 怎樣才能使這些方法協(xié)同工作呢？或許公司會覺得，例如電視廣告、數(shù)字化廣告、印刷廣告以及廣播廣告加起來差不多占了整個營銷費用的80%。既然這些活動生成的受眾評估數(shù)據(jù)可以縱向跟蹤，使用營銷組合模型方法就變得可行。但是通過使用歸因模型在大范圍類別里精確定位---比如搜索或展示等最有可能產(chǎn)生銷售轉(zhuǎn)化的活動，數(shù)字營銷費用可以得到進一步精確化。然后公司可以使用探索式分析法，如RCQ（范圍、成本、質(zhì)量）法來監(jiān)控剩余20 ％的支出，這些支出可能會轉(zhuǎn)向贊助活動及戶外廣告，以便為公司獲取不看電視的目標受眾。
Developing common response curves across analytical techniques helps marketers put the values of different approaches on common footing. The organization can then use a decision-support tool to integrate the results, allowing business leaders to track and share marketing performance on a near-real-time basis and course correct as needed.
跨分析技術(shù)開發(fā)普通反應(yīng)曲線幫助營銷人員把不同方法的價值建立在普通立腳點上。企業(yè)可以用一個決策支持工具整合結(jié)果，讓企業(yè)領(lǐng)導(dǎo)人可以跟蹤并分享基于接近實時銷售的營銷業(yè)績并可根據(jù)需要改變進程。
An international power company, for example, used RCQ analysis to adjust its out-of-home and sponsorship mix, efforts that increased reach within its target audience and raised the efficiency of marketing communications by 10 to 15 percent. 又例如，一個國際電力公司，采用RCQ分析法調(diào)整了公司的戶外廣告和贊助組合投資方案，這一努力使公司擴大了目標受眾的范圍并將營銷傳播的效率從10%提高到15%。The company then turned to MMM to get a more granular MROI assessment of its spending on digital versus traditional media. It found that while each €1 million invested online generated 1,300 new consumers, the same investment in TV, print, and radio helped the company retain 4,300 consumers (40 percent of whom were likely to stay loyal to the brand over the long term). Those insights helped the company understand where to best focus its spending and messaging for both attracting new customers and keeping existing ones. 然后公司轉(zhuǎn)向營銷組合模型方法以得到對數(shù)字媒體與傳統(tǒng)媒體對照的支出更細化的營銷投資回報率評估。公司發(fā)現(xiàn)每投入在線廣告100萬產(chǎn)生1300個新客戶，而100萬投入到電視，印刷和廣播則幫助公司留住4300個消費者（有40%左右的人在很長時間里會忠于公司品牌）。這些見解讓公司知道應(yīng)該將費用和信息聚焦何處，以吸引新客戶并留住老客戶。In fine-tuning the mix, it can be tempting to allocate money to short-term initiatives that generate high ROI. That bias is fed by the fact that so much data comes from consumers engaging in short-term behavior, such as signing up for brand-related news and promotions on a smartphone or buying a product on sale. That short-term effect typically comprises 10 to 20 percent of total sales, while the brand, a longer-term asset, accounts for the rest. Businesses need to ensure their mix models are capable of examining marketing effectiveness over both time horizons.在細調(diào)組合方案時，公司很有可能經(jīng)不住高回報的誘惑而把錢投入到短期活動中去。這種傾向是有事實依據(jù)的，有許多來自消費者的短期行為的數(shù)據(jù)可以證明，例如為品牌相關(guān)消息或智能手機促銷注冊登記或購買打折產(chǎn)品。短期效應(yīng)通常占到總銷售額的10-20%，而品牌作為長期資產(chǎn)，則占到總銷售額的80-90%。企業(yè)需要確定組合模型能夠基于時間和范圍兩個緯度對營銷效果進行檢測。
One consumer food brand almost fell into this short-term trap. It launched a campaign using Facebook advertising, contests, photo-sharing incentives, and shared-shopping-list apps. At a fraction of the cost, the approach delivered sales results similar to those generated by more traditional marketing, which included heavy TV and significant print advertising. Not surprisingly, the brand considered shifting spending from TV and print advertising to social-media channels. Yet when long-term effects were included in its calculations, the impact of its digital efforts was cut by half. If the company had proceeded with significantly cutting its TV spending, as traditional MMM suggested, it would have reduced the net present value of the brand’s profit.
一個食物消費品牌幾乎掉進了這種短期陷阱。該品牌通過臉譜網(wǎng)的廣告、競賽、相片分享激勵機制和共享購物清單應(yīng)用軟件發(fā)起了一場營銷活動。在成本這個環(huán)節(jié)，這個方法得到與較為傳統(tǒng)的營銷方法產(chǎn)生的基本一致的銷售結(jié)果，傳統(tǒng)的營銷方法包括密集的電視廣告、大量的印刷廣告。該品牌考慮將營銷費用從電視和印刷廣告轉(zhuǎn)移到社交媒體渠道上來，這毫不奇怪。然而將長期效應(yīng)一起考量時，數(shù)字化努力的效果就會減半。正如傳統(tǒng)的營銷組合模型方式所顯示，如果公司大量地縮減電視廣告費用，該公司的凈收益價值就會降低。
3. Put the analytical approach at the heart of the organization
把分析方法置于企業(yè)的核心地位
It’s not uncommon for teams to outsource analysis or throw it over the wall to an internal analytics group. When the findings come back, however, those same teams may be reluctant to implement them because they don’t fully understand or trust the numbers. 對許多團隊來說，進行數(shù)據(jù)分析外包或者將這一工作扔給內(nèi)部的分析團隊，是很平常的事情。然而，當分析結(jié)果返回公司后，這些營銷團隊可能不愿意執(zhí)行，因為他們不能夠充分理解或信任這些數(shù)據(jù)。
To solve that problem, marketers must work closely with data scientists, marketing researchers, and digital analysts to question assumptions, formulate hypotheses, and fine-tune the math. Companies also need to cultivate “translators,” individuals who both understand the analytics and speak the language of business. One financial-services company, for instance, set up councils within its marketing function to bring the creative and analytical halves of the department together. The councils helped analysts understand the business goals and helped creatives understand how analysis could inform marketing programs. We’ve seen such collaboration cut the duration of MROI efforts in half. 要解決這個問題，營銷人員必須和數(shù)據(jù)專家緊密工作，數(shù)據(jù)分析師要質(zhì)疑舊假設(shè)，提出新假設(shè)，并對數(shù)學(xué)模型進行微調(diào)。公司需要培養(yǎng)既懂得分析學(xué)又懂得商務(wù)的“翻譯人員”。例如，一個金融服務(wù)公司，在營銷功能框架下設(shè)立了委員會以便將創(chuàng)意部門和分析部門融合到一起。委員會幫助分析師理解商業(yè)目標，又幫助創(chuàng)意人員理解分析方法怎么激活營銷項目。我們已見過這樣的組合，將營銷投資回報率分析過程縮短了一半時間。
Speed and agility are also important. Insights from the consumer decision journey and the marketing-mix allocation should inform the tactical media mix. Actual results should be compared with target figures as they come in, with the mix and budget adjusted accordingly. Attribution modeling can be especially helpful with in-process campaign changes, since digital spending can be modified on very short notice. Our research shows that the best-performing organizations can reallocate as much as 80 percent of their digital-marketing budget during a campaign. 速度與靈活性也非常重要。來自消費者決策過程和混合營銷分配的深刻見解可以激活戰(zhàn)術(shù)媒體組合。隨著實際結(jié)果數(shù)據(jù)的進入，應(yīng)當將實際結(jié)果數(shù)據(jù)和目標數(shù)字相比較，并對組合制和預(yù)算進行相應(yīng)調(diào)整。由于數(shù)字化營銷費用能夠在得到短期通知后進行調(diào)整，歸因建模對處理進程內(nèi)的活動變化特別有用。我們的研究表明，最佳經(jīng)營公司能夠?qū)�營銷活動中80%的數(shù)字營銷預(yù)算進行重新分配。
The pressure on business leaders to demonstrate return on investment from a diverse portfolio of marketing programs is only increasing. The data to make smarter decisions are available, as are the analytical tools. We believe that taking an integrated analytics approach is the key to uncovering meaningful insights and driving above-market growth for brands.
企業(yè)領(lǐng)導(dǎo)者通過多種組合的營銷計劃來證明投資回報率的壓力有增無減。要用數(shù)據(jù)做出更明智的決策，就應(yīng)當使用分析工具。我們相信采用整合分析方法發(fā)掘非凡見解和推動品牌超越營銷增長的關(guān)鍵所在。
For more from McKinsey on the topic of marketing and sales, visit the McKinsey on Marketing & Sales website. 要從麥肯錫了解更多營銷和銷售主題，請訪問麥肯錫營銷和銷售網(wǎng)站。
About the authors 作者介紹
Rishi Bhandari is a senior expert in McKinsey’s Chicago office, Marc Singer is a director in the San Francisco office, and Hiek van der Scheer is a consultant in the Amsterdam office.
Rishi Bhandari 是麥肯錫芝加哥辦公室的高級專家，Marc Singer是舊金山辦公室的一位董事，Hiek van der Scheer是阿姆斯特丹辦公室的一位咨詢顧問。

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