Leading by Example

(This article originally ran on ANA.net)

Data has become essential to brand managers’ ability to develop more effective marketing campaigns and land bigger budgets. Yet despite the opportunities big data provides (most notably keener insights about consumer behavior), many marketers continue to get bogged down by the numbers and fail to connect the dots.

According to a survey released earlier this year by the CMO Council and RedPoint Global, just 7 percent of marketers said they deliver real-time, data-driven engagements across both physical and digital touchpoints. The survey, based on responses from 263 senior marketing executives, most from consumer-facing brands, found widespread consensus that CMOs should steer the company’s data-driven customer strategy.

The rub, of course, is turning the strategy into action throughout the entire enterprise. Indeed, 23 percent of survey respondents admitted that customer engagements were being developed ad hoc and by a multitude of disconnected teams, with little collaboration or connection across functional groups. Other roadblocks preventing companies from implementing a data-driven customer strategy include inadequate budget (54 percent), failure to fully embrace a customer-centric culture (43 percent), and no senior level support to spark change.

Without buy-in from the C-suite, trying to monetize data analytics is a nonstarter. But from an operational standpoint, experts say, it’s crucial that marketers recalibrate their teams to elevate people who can leverage data not just to improve analytics but also internal practices such as budgeting and resource allocation.

The winners of this year’s ANA Genius Awards, which recognize the most progressive organizations for their innovation and creativity in analytics, demonstrate how brands are starting to instill data-driven marketing and advanced analytics throughout the business to drive results and ROI. Here’s how two award recipients are leading the marketing industry by example.

Analytics Adoption: NASCAR

NASCAR has taken a sharp turn in its efforts to improve data analytics. For years the auto racing giant reported metrics for television viewership only, while tracking digital and social media audiences separately.

NASCAR realized it had to change the way it communicated the strength of its audience to media buyers and present a more holistic evaluation of how auto racing fans consume NASCAR across multiple channels.

So in 2016 NASCAR launched the Analytics & Insights Group. Based in Charlotte, N.C., the group includes 20 staffers encompassing the brand’s TV, digital, social media, fan engagement, and sponsorship programs.

“It’s a pretty progressive move,” says Norris Scott, VP of analytics and insights at NASCAR. “We have plenty of the ‘what’ about our data; now we need to explain the ‘why’ and [put the data] in meaningful context. The sports industry grew up successfully counting things like impressions and fans viewing signs. With the influx of data, there is so much more to be used, deeper analytics around behavior, engagement, purchase intent, brand loyalty, and predicate outcomes. We need to be at the forefront of this rapid change.”

One of the major elements of the new analytics program is NASCAR’s Fan Council, an online panel of 25,000 avid fans. The organization’s marketing team reaches out to council members on a weekly basis to gauge what they’re thinking and find ways to improve the customer experience.

The marketing group also collaborates with other NASCAR business units, such as brand marketing, partnership marketing, racing operations, social media, and digital teams, to develop questionnaires and distribute surveys to members to get a better sense of what makes auto racing fans tick.

Another key aspect of the effort is NASCAR’s Fan and Media Engagement Center (FMEC), a hub for social analytics on race day. As the races are happening, FMEC collects millions of social posts via Facebook, Instagram, and Twitter, and sifts through the data. “We’re able to talk to fans sitting on their couch in real time,” Scott says.



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The FMEC tracks social conversations around each race for its top three national series — Monster Energy NASCAR Cup Series, Xfinity Series, and Camping World Truck Series — totaling nearly 100 races throughout the 10-month season.

The center generates much more granular metrics that NASCAR, in turn, shares with media buyers. While still in its infancy, the Analytics & Insights Group has greatly improved NASCAR’s dialogue with fans, media buyers, and broadcast partners.

“We’re having more meaningful conversations and are slowly becoming a data culture,” Scott says. “We’re starting to see more people work with us on the front end [of marketing campaigns], rather than the back end. Also, having all of our media aligned helps us show what content is working and how it works best for buyers.”

Since the Analytics & Insights Group started, for example, video content running on NASCAR’s social channels has increased 40 percent. Ditto for user engagement when served video content.

Scott stresses that with the analytics group in tow, NASCAR has started to tame the data beast. “We’re getting closer,” he says. “The ability to gauge fan data is moving from reactive claimed data to retentive data — what we have now — to what very soon will be user predictive data.”

Analytics Impact: IBM

When Michelle Peluso was named IBM’s first CMO in late 2016, the tech giant’s marketing departments got a major shot in the arm. Having Peluso at the helm provided IBM’s 5,000 marketers across 170 countries with a much bigger opportunity to show the C-suite how marketing was adding to both the top and bottom lines.

“One objective in mind was to help [Peluso] make her case for marketing investment using an approach focused on ROI,” says Akesh Bhalla, managing consultant at IBM. “We wanted to show how we optimize [budgets] we have available and that we’re making sure every marketing dollar we’re spending is justifiable.”

But to cross that bridge IBM had to reconfigure its marketing analytics strategy. The overall goal was to unite IBM’s various business units under one goal: driving ROI.

With that in mind, IBM’s analytics and data team earlier this year created a new Cognitive Management System that integrates structured and unstructured data from 12 disconnected systems across marketing, sales, finance, operations, media, and other functions. The system also provides a clear view of marketing ROI that can be understood down to the individual campaign and individual tactic within each campaign.

The system tracks marketing spend throughout the duration of a campaign, including granular details such as paid media allocations, web page metrics, and user experience. Using machine learning, the system learns and recommends what’s providing the most encouraging returns on campaigns.

“The concept is, how much is the campaign contributing to the business and what’s the ROI investment?” Bhalla says. “Previously, every marketing team had its own processes but no unified view of marketing spend efficiency or performance.”

The Cognitive Management System simplifies the process under one transparent platform. For example, the CMO from one of IBM’s subsidiaries selling information security products and services is able to break down how the marketing dollars are performing for one of his campaigns versus other campaigns within the corporate portfolio.

“It shows how much the CMO is putting into, say, impressions or online traffic, and what that looks like top to bottom from a campaign level,” Bhalla says. “That makes it easier to fix something, so the campaign will have a bigger media impact.”

Results from IBM’s marketing campaigns are aggregated and analyzed by the corporate office; future marketing investments are based on those campaigns showing the most spending efficiencies.

“If we have marketing within the company held more accountable and driving real impact, business leaders are going to want to invest in marketing for product development or sales,” Bhalla says. “It would be difficult to find a marketing conversation in IBM today that doesn’t include data or insights from our platform.”

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Matthew Schwartz
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