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How Explanatory Analytics Enables Marketing Leadership BY J O SH R E YNO L DS HE AD O F MAR K E TING & CL IE NT CO NSULTING Q UANTIF IND 1 ©2016 Quantifind, All rights reserved. How Explanatory Analytics Enables Marketing Leadership The role of the Chief Marketing Officer has always been somewhat nebulous. CMOs wear multiple hats. No matter the role, they are held accountable to drive increasingly hard-to-control outcomes – and the recent proliferation of new technologies, new consumer behaviors, and unpredictable business pressures have only further complicated things. What’s more, this identity crisis is exacerbated by the fact that CMOs don’t feel in control of driving bottom-line results, and are clamoring for meaningful digital insights to help them. As a result, marketing leaders are asking themselves: • Which CMO hat do I need to wear today? • Which roles feel most natural to me? • How can I enhance my skill sets across all the various roles I need to play? The answer lies in recent advances in marketing analytics. Until now, most analytics approaches haven’t helped CMOs set their own agenda, primarily because none of them correlated to growth. Social media listening measures buzz and sentiment, but that doesn’t map to revenue. Predictive analytics may tell you what’s likely to happen if you don’t change course, but that doesn’t tell you who’s driving revenue, why, or how to change the outcome. And traditional brand health metrics, while sometimes helpful, are often time-intensive, backwardslooking, and rarely correlated to sales. Perhaps that’s why all these approaches are facing increasing scrutiny and skepticism in the boardroom. Think about it: In order to be a strategic asset to the company, a CMO needs to explain why something is happening and how to change it. And that’s where explanatory analytics comes in. Explanatory analytics looks not only at which results marketing is driving, but also why, with whom, and how to change them. That knowledge allows marketers to shape their own roles, reinvigorate their relationships with their CEOs, and bridge internal silos across departments. What’s more, explanatory analytics helps to unite the quantitative tribes of research, data, and analysis with the creative tribes of brand, campaigns, and messaging. Perhaps most importantly, this new approach keeps humans in the loop as big data computing continues to proliferate throughout the marketing discipline. It helps marketers use numbers to shape narrative. And it allows marketing executives to explore, understand, and change their impacts on revenue by intuitively searching through data visualizations correlated to KPIs CEOs already care about. In other words, explanatory analytics shifts marketers from playing defense to offense. It changes the question from “So what?” to “Now what?” And that’s a game-changer. 2 ©2016 Quantifind, All rights reserved. Introduction And now, for the first time, CMOs can use data to shape their own objectives, drive growth with surgical precision, and determine what kind of marketing leader they want to be. Let’s begin with a look at the science behind explanatory analytics, and then we’ll examine how it applies to five CMO roles as identified and categorized by Deloitte.1 Explanatory Analytics At-a-Glance The trouble with most forms of marketing data is that so much of it is noise and static. On average, only about 20% of social data has any explanatory value when it comes to revenue. But once you find that 20%, all kinds of new growth-driving possibilities emerge. The graph below represents real data from an extremely popular fast-food restaurant illustrating the difference between noise and signal. The shaded gray area represents “buzz;” the green line represents sales, and the blue line represents social data tracking to sales. Within that blue line are all kinds of additional data points — verbatims, affinity groups, demographics, regions, etc. — that explain why revenue is moving the way it is, and how to change it. 83 160 78 140 73 120 68 100 63 80 58 60 53 40 48 20 43 Brand Mentions (thousands) Sales ($ millions) Explanatory analytics systematically filters out everything that … doesn’t correlate to sales — and measures how strongly the remainder does correlates to sales. 0 Jan 20, 2014 ● Mar 20 Brand Buzz ● May 20 Sales ● Jul 20 Sep 20 Nov 20 Jan 20, 2015 Mar 20 May 20 Jul 20 Buyers 1 Diana O’Brien, The multiple roles of the 21st century CMO, February 1, 2016, Deloitte, http://cmo.deloitte.com/the- multiple-roles-of-the-21st-century-cmo/, accessed May 5, 2016. Used with permission. 3 ©2016 Quantifind, All rights reserved. HOW EX PLANATORY ANALYTICS ENABLES MAR KETING LEAD ER S H IP How do you find that magical blue line? Explanatory analytics systematically filters out everything that isn’t about the brand, everything that isn’t from a real human, and everything that doesn’t correlate to sales — and measures how strongly the remainder does correlates to sales. Then the data is categorized into searchable dimensions that let marketers explore the who, what, where, when and why behind their revenue. raw data brand buzz: statement about the brand, including marketing messages and Tweets. brand buzz organic data: statements by people offering their unaided views. organic buyer data: statements made by people whose views correlate with KPI movement. buyer topics: themes of conversation about the brand, including marketing messages and Tweets. topics drivers: brand conversations that most align with buyer decision-making. Now, let’s take a look at five CMO roles, and examine how explanatory analytics unlocks the full potential of marketing leaders. The Deloitte Model At its Next Generation CMO Academy in 2016, Deloitte delineated its five roles Chief Marketing Officers play. It's a great model for understanding the modes with which you need to engage your colleagues, the different skill sets and perspectives you need to tap into, and for identifying where a fresh approach to data and insights can help boost your effectiveness as a marketing leader. Almost every senior marketer plays each one of these roles at one time or another, and marketers must be able to move from role to role at the appropriate time in order to lead effectively: • Customer Champion • Capability Builder • Innovation Catalyst • Growth Driver • Chief Storyteller Here’s how explanatory analytics empowers each one… 4 ©2016 Quantifind, All rights reserved. DELO I TTE D EFINITION Customer Champion /'kəs-tə-mər 'cham-pē-ən/ Deloitte asserts that CMOs acting as Customer Champions align the organization around customer centricity. They utilize data insights and analytics to deliver superior, personalized customer experiences and measurable business results. DEFAULT DATA SOLU T ION S Consumer insights, focus groups, advisory councils, customer journey analysis Customer Champions have leaned heavily on focus groups and other forms of customer intelligence to help them win cases. But those approaches are too easy for skeptical executives to question. When wearing this hat, a CMO champions the preferences and priorities of the customer across the C-Suite. The success metrics most commonly associated with a Customer Champion are customer satisfaction scores, Net Promoter Scores, customer loyalty/customer churn, share of wallet, and brand engagement. Their job is to know how close the company is to its customers, and figure out how to get closer. The challenge is that all these metrics, while helpful in many regards, are one or more steps removed from the most important metric C-suite peers are tracking — revenue. That’s why Customer Champions have typically faced an uphill battle when it comes to internal credibility. With seemingly “soft” metrics, it’s not particularly easy to go ask a COO to adjust operations, or a CFO to change pricing models, or a head of product strategy to change the UX. Those kinds of strategic bets are only made after serious due diligence. And yet Customer Champions must act as trusted advisors to other senior executives if they ever hope to be more than a passive conveyer of customer sentiment. If they truly want to become change agents and successfully advocate for a better relationship with customers, they need to drive new outcomes, and that requires quantitative evidence. Traditionally, Customer Champions have leaned heavily on focus groups and other forms of customer intelligence to help them win cases. But those approaches are too easy for skeptical executives to question. The sample sizes are too small. The responses aren’t unaided or organic. We haven’t accounted for various statistical biases. We’re just hearing what we want to hear. This is where explanatory analytics can shift the burden of proof for Customer Champions. By filtering out all the noise and static, Customer Champions are empowered with a world of organic and unaided customer comments quantitatively connected with revenue. In an explanatory analytics model, the Customer Champion can not only predetermine which topics, demographics and issues to explore, they can also let data whisper to them. Organically-emerging topics and data clusters may suggest new demographics to consider, unexpected affinities to leverage, and hidden opportunities to explore. For example, a fast-food restaurant wanted to increase breakfast sales. They had detailed day-part sales data that told them what they were selling, where, and when. And they had a good running hypothesis that the best way to grow breakfast sales was by selling more breakfast to teens. The food options and the creative tested well with teens, yet sales didn't grow. But by taking an explanatory analytics approach, and exploring the data that mattered, the 5 ©2016 Quantifind, All rights reserved. HOW EX PLANATORY ANALYTICS ENABLES MAR KETING LEAD ER S H IP company discovered that moms didn’t like the restaurant’s coffee all that much, and the teens were relying on a ride from mom. So the answer to the question “How do we sell more breakfast to teens?” was “Give moms better coffee.” That kind of insight gives a Customer Champion persuading power. That kind of insight helps the Customer Champion speak boldly on behalf of the people, with quantifiable evidence to back up assertions. That kind of insight is what gives a Customer Champion the ability to go to their CEO with a decisive agenda for driving revenue. DELO I TTE D EFINITION Capability Builder /̩kā-pə-'bi-lə-tē 'bil-dər/ Deloitte describes CMOs as Capability Builders when they develop robust marketing capabilities (e.g., technology fluency, digital expertise, customer data and insights, data analytics) to help the organization compete in the future. DEFAULT DATA SOLU T ION S Predictive analytics, ROI calculations, after-the-fact KPI tracking When a CMO is acting in Capability Builder mode, they’re focused on unlocking the value of existing investments. Capability Builders focus on what functions, skill sets, and expertise the company needs to succeed. They’re typically operational marketing executives, focused on infrastructure and process. They’re constantly tracking things like KPIs, workflow efficiencies, and reporting structures. They’re constantly on the lookout for signs of trouble, proof of success, and insights that help their organization compete now and in the future. So the Capability Builder understands the power of math. They want to quantify the effectiveness of individuals and teams. They want to know the CPM and CPL of every ad buy. They’re constantly monitoring the lifetime value of a customer. They want to know how they stack up against the competition, quantitatively, along multiple vectors. And they want to be able to predict success and make the rights bets. In other words, the Capability Builder needs to know exactly how much revenue a dollar of marketing spend can bring back to the bottom line. As a result, marketers operating as Capability Builders often find it easier to earn the respect and credibility of their C-Suite peers, particularly the CFO and COO. In a few cases, the CMO may actually be a former CFO. But what they don’t always have is the trust of the CEO or the head of sales who doubts they have the insights needed to do more than just track outcomes. That’s because most metrics Capability Builders use tell them what’s already happened, not what to do next. ROI is inherently backwards-looking. Nobody wants to be relegated to marketing in the rear-view mirror. In recent times, some Capability Builders have turned to predictive analytics, hoping to get a sense of ROI before the investment is made. But even predictive models fall short of the mark, as they only predict the likely outcome if you don’t make any changes. They don’t tell you which changes to make or why. And yet the Capability Builder knows, intuitively, the enormous value of the data sets at their disposal. The question is how to tap into the math that matters. 6 ©2016 Quantifind, All rights reserved. Explanatory analytics lets the Capability Builder feel assured they’re advocating for the right capabilities for the right reasons. And by taking an investigative approach that uses explanatory analytics, the Capability Builder can become the future-proofer of growth for the organization. By tapping into the 20% of the external data sets that actually matters, the Capability Builder can discover the correlations to their internal performance data and explore the why behind the what. Let’s say a car company is looking for a way to sell more cars, and they’ve already identified millennials as the target market offering the most potential for growth. On the one hand, it takes time to retool an assembly line and materially change what goes into the drive train, engine or chassis, so it’s a real challenge to use customer data to change the capabilities of the organization. But with explanatory analytics, that car company can discover what really drives sales among millennials. And as it turns out, what’s under the hood doesn’t matter as much as what’s in the interior. That car company can pinpoint which aspects of the interior most impact sales and strategically decide how to prioritize the drivers’ experience. Seat heaters, displays, leather seats, sound systems, Bluetooth capabilities — all these have the theoretical potential to lift sales with millennials. But why guess? Explanatory analytics lets the Capability Builder feel assured they’re advocating for the right capabilities for the right reasons. Most importantly, explanatory analytics gives marketers wearing the Capability Builder hat a metric that matters — one that explains not only where their brand is winning, but also why and how to accelerate that growth. DELO I TTE D EFINITION Innovation Catalyst /̩i-nə-'vā-shən 'ka-tə-ləst/ Deloitte asserts CMOs in the Innovation Catalyst role create breakthrough customer offerings and advance marketing by experimenting with new technology platforms, alternative media, new tools and techniques to transform the customer’s experience while simultaneously improving internal processes. DEFAULT DATA SOLU T ION S Social Listening platforms, web/ mobile/application analytics, customer engagement metrics We all have friends more technologically advanced than the rest of us. There’s always that early adopter at the cocktail party sporting a pair of augmented reality glasses. During the holidays they give friends gifts made with their 3D printers. They’re already over SnapChat because it’s “so 2015.” They’re technological trendsetters who like discovering something first. There are CMOs with similar technological proclivities who enjoy disrupting their categories. Deloitte calls these people in this role Innovation Catalysts. This leader wearing this hat is always looking for new platforms and possibilities. Marketing automation, new advertising technologies, customer experience advances, emotional and gesture recognition, advances in programmatic approaches — Innovation Catalysts view all these technologies through the lens of the entrepreneur looking for a game-changing advantage. Their default mode is experimentation. The marketing leader performing this role is like an athlete always on the lookout for the latest breakthroughs in sports science. They use performance-enhancing data to monitor progress and outpace the competition. That’s why Innovation 7 ©2016 Quantifind, All rights reserved. HOW EX PLANATORY ANALYTICS ENABLES MAR KETING LEAD ER S H IP Without a strong understanding of what is and isn’t driving revenue before the experimentation begins, the Innovation Catalyst could get labeled a cost center. Catalysts are attracted to the latest advances in social media listening and mobile, web and application analytics. They’re probably the first in their vertical to experiment with emotion-recognition technologies and eye tracking. They’re looking for clues into how their latest innovations are resonating with customers. The challenge here, however, is that the reporting on the effectiveness of these new platforms always lags behind results. Experimentation, while bold and innovative, is often like shooting in the dark. And while there may indeed be a first-mover advantage, that advantage is quickly lost unless the movement is in the right direction. In other words, Innovation Catalysts are often their own worst enemy. Particularly in uncertain economic times, the appetite of the CEO or CFO for “experimentation” is limited, at best. Without a strong understanding of what is and isn’t driving revenue before the experimentation begins, the Innovation Catalyst could get labeled a cost center. Or worse yet, when they do discover the winning innovation formula, without hard metrics to show it was an intentional success, the CMO may not get a fair share of the credit. With explanatory analytics, the Innovation Catalyst knows what problem to solve for. They know whom they need to reach, and how. Most importantly, they have a real-time view into what’s working, what’s not, and why. For example, a financial institution recently wanted to expand its reach into existing customers. They started with the reasonable hypothesis that targeting younger consumers with mobile banking solutions would be a smart bet. But when they engaged with explanatory analytics, they soon discovered that in fact older customers were more drawn to the mobile banking experience, while younger customers cared more about in-branch experiences. This kind of insight allows an Innovation Catalyst to focus on the right innovations that matter most to the right target audiences. What’s more it gives them the ability to take a smarter business case to internal stakeholders by showing insights correlated to real KPIs. And as entrepreneurs who value “failing fast” and accelerated learning curves, Innovation Catalysts can lean on explanatory analytics to show them quickly which bets are paying off, which ones aren’t, and how to adjust course. What’s more, they can bring their CEO and CFO a business case in need of an innovative solution — and not the other way around. 8 ©2016 Quantifind, All rights reserved. Growth Driver /'grōth 'drī-vər/ Deloitte describes CMOs in the Growth Driver role as leaders who create and manage a plan to achieve sustainable, profitable growth. DEFAULT DATA SOLU T ION S Revenue models, sales analyses and predictive analytics When CMOs are primarily focused on revenue they are Growth Drivers. They have explicitly embraced the mandate to drive growth. Ultimately, all CMOs are held to this goal, but the Growth Driver leans into it. They speak the language of sales. They look for ways to sell more to existing customers and more ways to sell to new customers. They’re constantly tracking results and are most likely to report how they’re doing to the CEO and CFO before the CEO and CFO have to ask. Brand health metrics, buzz, sentiment and vanity metrics mean nothing to the Growth Driver; they’ve already discovered the profound disconnect between brand studies and the bottom line. In other words, this CMO role looks the most like a Chief Revenue Officer, and is the least likely to be displaced or disrupted by one. They live and die by the revenue data. The challenge for Growth Drivers is that although they may know what the revenue is doing, they may not know why. Think about it. If a CMO goes into a board meeting with great revenue results, but can’t explain why they achieved them or how to improve upon them, how big of a win is that? But if a CMO walks in with mixed results and can explain why, and can show exactly how to improve results next quarter, that’s a great meeting. That’s a CMO who can drive growth. And that’s what explanatory analytics does for the Growth Driver — it solves revenue mysteries. Nobody likes a KPI line he or she can’t explain, even if it’s a positive one. For CFOs and shareholders, predictability is as important as profitability. And the successful Growth Driver is the one who can zoom in on the “why” axis of any revenue chart and explain it. Growth Drivers are the ones who benefit most directly from using explanatory analytics to identify new opportunities and competitive strategies. And by filtering out all the external data that doesn’t correlate to revenue, explanatory analytics opens up a whole new world of insights that actually matter. Of all the marketing leader roles, Growth Drivers are the ones who benefit most directly from using explanatory analytics to identify new opportunities and competitive strategies. And by filtering out all the external data that doesn’t correlate to revenue, explanatory analytics opens up a whole new world of insights that actually matter. Now they can use explanatory analytics platforms to understand, explore, and change their impact on revenue. Let’s say a beer company needs to find a way to grow sales. Let’s say they know the demographics they appeal to most, and they have a strong sense of brand identity. But what if they didn’t know how to steal market share from aggressive competitors in a declining category? With an explanatory analytics approach, a Growth Driver is able to benchmark the company’s performance against the overall category, and specific competitors. It lets them systematically target specific demographics, topics, and issues that 9 ©2016 Quantifind, All rights reserved. HOW EX PLANATORY ANALYTICS ENABLES MAR KETING LEAD ER S H IP give them the best chance to beat the competition. It gives them the ability to surgically identify distribution strategies, creative campaigns, sponsorships and promotions that boost sales. And it helps them define the terms of the engagement against competitors and pick and choose the battles they engage in — the ones most likely to lead to victory. DELO I TTE D EFINITION Chief Storyteller /'chēf 'stȯr-ē-̩te-lər/ Deloitte asserts that CMOs, as Chief Storytellers, act as both architect and steward of the brand by creating and telling brand stories and inviting consumers to participate in the narrative. Develop, preserve, and promote the brand’s relevance and consistency while evolving its position, meaning, and messaging. DEFAULT DATA SOLU T ION S Focus groups, creative testing, brand metrics, consumer intelligence This marketing leader in this role tests and tweaks brand narratives. Chief Storytellers dig deep to make sure the brand is not just top of mind in the marketplace, but also pragmatically positioned to drive sales. They know that a product is more than a sum of its constituent parts. They know that myth and story around a product creates value, opens the door to price premiums, and drives customer loyalty. But of all the CMO roles, the Chief Storyteller is arguably the farthest removed from hard ROI and has the toughest case to make when describing contributions to revenue. After all, how do you measure the ROI of a storyline? At best, marketers can demonstrate a coincidental relationship between brilliant creative strategy and movements in revenue. And that’s the challenge. Chief Storytellers understand what drives revenue on an intuitive level. And unfortunately for them, the business world has yet to recognize intuition for the brilliant data algorithm it really is. It doesn’t help that “storytelling” has become one of the most over-used, and therefore least meaningful, terms in all of marketing. So we impose all kinds of other mathematical equations on top of human intuition and creativity in a retroactive measurement of its effectiveness. But despite all the hype around storytelling, there is in fact a genuine and welldocumented methodology behind persuasive storytelling. Most of it is based on the work of Joseph Campbell and his Hero’s Journey model. And thanks to explanatory analytics, the science of corporate storytelling has evolved beyond merely generating buzz and sentiment; it can now be used to motivate and measure behavioral change. Explanatory analytics opens up a new world of creative possibilities for storytellers. Social media data, voice-of-customer (VOC) data, spending patterns. and all kinds of external data points can be filtered and correlated to revenue. What results is a treasure trove of topics, themes and associations that provide before-the-fact inspiration for storytellers, not after-the-fact judgments. What’s more, these findings can be used across a variety of storytelling methodologies, all of which share a few common characteristics. 10 ©2016 Quantifind, All rights reserved. All too often, Storytellers try to concoct some surprise element of their own. But sometimes — not all the time, but sometimes — organic patterns bubble up in the data to suggest new and unanticipated creative strategies to pursue. The second job of the storyteller is to connect on an emotional level. Again, the trap here is to pay too much attention to sentiment analysis and be steered in the wrong direction by the wrong audience. By correlating social data to revenue and other KPIs, explanatory analytics gives you insights into which voices to listen to, and which emotional hooks to leverage in your narrative. Another critical component to any good story is a plot twist or an unexpected discovery. This is perhaps where explanatory analytics provides the most distinct value. All too often, Storytellers try to concoct some surprise element of their own. But sometimes — not all the time, but sometimes — organic patterns bubble up in the data to suggest new and unanticipated creative strategies to pursue. The data itself can often provide the element of surprise many storytellers are looking for. In other words, numbers can be used to shape narrative. All kinds of data flows back and forth across the cerebral cortex when people hear a story — it’s just being processed in a black-box organic operating system that doesn’t translate to a CMO dashboard as neatly as Google Analytics. Here’s an example. A movie studio was looking to promote a buddy cop movie featuring a well-known comic actor and a lesser-known hunk. Naturally, the ads focused on promoting the comedian. And naturally, they targeted guys. And as a result, they focused on drug humor and the kind of lowbrow comedy that appeals broadly to guys. But the movie was projected to open at levels far below what the studio was hoping for. Explanatory analytics revealed that the biggest opportunity to drive more people into the theater wasn’t with guys, it was with females. And they were more interested in the hunk than the funny man. What’s more, they were turned off by the drug humor and instead were hoping to see another hunk make a muchanticipated cameo in the movie. So the studio changed the creative storytelling in their movie trailers, shifted their focus to the female audience – and boom the opening box office numbers jumped significantly. That’s the power of explanatory analytics. It helps Chief Storytellers tell the stories that change behavior. After all, by their very nature, Chief Storytellers are full of stories. With explanatory analytics, their creative genius and informed intuition can be targeted intelligently at the right audiences, incorporate the right story elements, and inspire the right behavioral changes to lead to revenue growth. 11 ©2016 Quantifind, All rights reserved. HOW EX PLANATORY ANALYTICS ENABLES MAR KETING LEAD ER S H IP Three Takeaways: 1 2 3 Understanding your strengths as a marketing executive can help bring clarity to your day-to-day activities. The Deloitte Model is a great place to begin the personal inventory. Explanatory analytics is a great tool for unlocking the full potential behind the kind of CMO role that helps you excel. What’s more, learning to use explanatory analytics is a great way for CMOs to set the strategic agenda with their CEO and become the kind of CMO they most want to be — and the one that’s most valuable to their organization. Quantifind Quantifind's mission is to empower people to make better decisions that combine human intuition with the voice of intelligent data. Our on-demand insights platform, Signum, uses explanatory analytics to help marketers explore, understand, and change their impact on revenue. Quantifind’s clients are some of the world’s most recognized brands, spanning industries such as automotive, consumer packaged goods, entertainment, financial services, pharmaceutical, retail, and telecom. Quantifind is proud to be backed by investors such as AME Cloud Ventures, Andreessen Horowitz, Cathay Innovation, Comcast Ventures, Iris Capital, Redpoint Ventures, and U.S. Venture Partners. Founded by two PhD's in atomic physics, the company now has offices in Silicon Valley and New York, NY. Quantifind | 8 Homewood Place, Menlo Park, California 94025 | 650.561.4937 General Email: [email protected] | Media & Press: [email protected] www.quantifind.com 12 ©2016 Quantifind, All rights reserved.