What the C-suite should know about analytics
Five key areas – and how to prevent analysis paralysis
Kishore S. Swaminathan, Accenture

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Case study after case study confirms the value proposition for analytics across a wide range of business functions, including pricing, demand prediction, targeted marketing, supply chain optimization, customer relationship management and HR. In my view, analytics is something much more than a technology with an ROI; it’s a transformational phenomenon that will fundamentally change how business discourse will be conducted and decisions made. Here are five key areas to focus on:

1. High analytical literacy

Data is a double-edged sword. When properly used, it can lead to sound, well-informed decisions. When improperly used, the same data can lead not only to poor decisions but Analytics is something much more than a technology with an ROI; it’s a transformational phenomenon that will fundamentally change how business discourse will be conducted and decisions made. to poor decisions made with high confidence that, in turn, could lead to erroneous and expensive actions. Let’s consider some specific examples.

When one has access to real-time data, it’s tempting to make real-time decisions. For instance, if you are a retailer and you have real-time access to sales data from cash registers from all of your stores and the inventory in your warehouse, you could be tempted to run sales promotions on the fly and manage your supply chain in tandem to support your real-time promotions.

This is unlikely to work because three types of events – your decisions, the ensuing customer behavior and supply chain events – operate in different time frames, so making decisions faster than the slowest-moving event could be useless at best and dangerous at worst.

Another problem with data and analytics is that they give you very fine-grained visibility into your business processes, and you could be tempted to over-optimize the processes. Highly optimizedprocesses – just-in-time inventory as an example – are very fragile because circumstances beyond your control could arise, and there is little room for error.

A third problem is known as “oversteering,” or making a decision when none is needed. For example, your data could tell you that a project is behind schedule, which, in turn, may lead you to berate the project manager or tell your stakeholders that the project will be delayed. Neither of these actions may be necessary if the project has contingency built in, if the status update has a different frequency from your sampling frequency, or if perhaps the employees who are aware of the project delay will put in more work time to get the project back on schedule.

2. Volatility

Businesses thrive on stability and repeatability. Stable and repeatable processes justify large-scale capital expenses and large-scale employee training. That stability also reduces cognitive overhead because those processes and decisions do not change, hence their rationale does not have to be explained repeatedly. By contrast, an analytically based enterprise of the future will have to be designed around volatility rather than repeatability.

When you have fine-grained visibility into your processes, customers, suppliers and competitors, you have the ability to make fine-grained decisions. In fact, your decision rules can capture subtleties such as “stock more beer on Sunday nights in locations where the home football team is on a winning streak.” Such decisions are highly context-sensitive and can change as rapidly as the fortunes of the football team.

Volatility – or rapidly changing decisions that are context- and time-sensitive – will be a big challenge for enterprises. Decisions are no longer easily explainable and capital investments cannot be based on mass repeatability, but must cater to endemic volatility.

3. Integrated awareness

Today’s enterprises have more information than they can act upon because the information is siloed in so many ways: technologically (data in different systems that cannot be brought together), organizationally (data in different governance units that cannot be brought together) or by ownership (inside versus outside the enterprise). The enterprise of the future will be (or will be forced to be) conscious in the sense that it will know that it must integrate everything it has access to.

Three tips for sound decision making

• Provide a top-down commitment to analytics. Reinforce the good, tried and true, decision making processes while driving the quest for analytic maturity.
• Staff the right talent. Find the right people to unleash on the right data to uncover the value that is still untapped.
• Put the right tools to work. Analytical software is only one tool in the toolbox; the others include executive decision makers, analytics staff, data, strategy, etc. The key is using all of the tools effectively.

As an extreme example of integrated awareness, let’s consider the pharmaceutical industry, which has traditionally relied on clinical trials data to establish the efficacy and side effects of a drug.

A pharmaceutical company today can legally and morally claim immunity from adverse effects of a drug that were not revealed during clinical trials – in other words, any information that it did not explicitly collect as part of a clinical trial protocol. But in a world of blogs and social networks, where people share this information unprompted and in public, it will become both a responsibility and an obligation of pharmaceutical companies to monitor public sources and integrate the public information with their own clinical data. In the future, businesses will likely be run by managers and leaders who are no-nonsense empiricists; they won’t move a finger until all of the relevant data has been gathered and analyzed.

“I should have known” (either for regulatory or competitive reasons) will be the new normal, replacing the “I did not know” or “I could not have known” approach to awareness and information integration.

4. The end of analysis paralysis

In the future, businesses will likely be run by managers and leaders who are no-nonsense empiricists; they won’t move a finger until all of the relevant data has been gathered and analyzed. A recipe for organizational analysis paralysis? This is not an unreasonable fear. Though it may seem counterintuitive, an empirical enterprise with high analytical literacy is less likely to fall prey to this malady than today’s enterprises.

There are three very distinct ways that organizations can fall into the analysis-paralysis trap.

One is a managerial tendency to “over-fit the curve” – a statistical term that refers to the diminishing value of additional data once a pattern (or curve, in the graphic sense) has been found. Data collection has a price, inaction has a price, and an analytically literate organization will clearly understand the cost of over-fitting.

The second cause of analysis paralysis is waiting for data that simply does not exist, which reflects an inability to design experiments to generate the needed data. An analytically literate organization will be characterized by a clear understanding of data gaps and the value of experimentation to break the logjam.

The third cause of analysis paralysis is that most companies do not know their risk tolerance and are much more likely to penalize failed action than inaction. As a result, many managers do not act unless there is enough data to assure them of successful outcomes. An analytically literate organization will have a firm grasp of its risk tolerance. With guidelines and models for action under uncertainty, it will restore the symmetry between how it treats failed action and inaction.

5. Intuition’s new pulpit Empiricism and analytics sound a death knell for such vaunted business traits as intuition, gut feeling, killer instinct and so forth, right? Wrong. Science is purely empirical and dispassionate, but scientists are not. Science is objective and mechanical, but it also values scientists who are creative, intuitive and can take a leap of faith.

Data, by itself, can be interpreted in many ways. Imagine a physical or business phenomenon that produces the following sequence of data: 1, 2, 6, 24, 33. Perhaps it’s a factorial sequence with 33 as noise or a sequence where every fourth term is twice the multiple of the previous three. Or perhaps every fifth term is the sum of the previous four.

All are correct. To prove or disprove any theory, you need the next several terms of the sequence. A good scientist knows when there is enough data to warrant a theory, when there isn’t, what new data to gather and how to design an experiment to gather the right data.

The late Steve Jobs, Apple’s former CEO, was known to explicitly discount the value of surveys and focus groups for designing new products. How do you explain his apparent anti-empiricism?

One explanation is that, much like a creative scientist, people like Jobs recognize when there is not enough data or the right kind of data to form a theory. They recognize that, for completely new lines of products that will change a user’s experience or behavior, the only useful data is experiential data, not commentary and reactions from those who have never used the product.

Jobs and people like him are akin to scientists who recognize what type of data is needed to support a theory (in this case, whether a product will succeed), recognize that such data cannot be gathered through focus groups (one type of experiment) and boldly design new types of experiments (release the product and gather experiential data).

It should be noted that some products – in Apple’s case, it was the Newton – do not succeed and are terminated. Intuition, creative leaps and clever experimentation are not incompatible with empiricism; in fact, the value of these traits will be even better understood in the future enterprise by analogy to theoretical and experimental scientists.

The enterprise of the future, based on empiricism and analytical decision making, will indeed be considerably different from today’s enterprise.

10 characteristics of an analytic leader

1. Communicates well with others.
2. Sets the expectation that decisions will be based on data and analysis.
3. Hires smart people, and gives them credit for being smart.
4. Leads by example, using data and analysis in decision making.
5. Sets strategy and performance expectations.
6. Looks for incremental achievements.
7. Demonstrates persistence over time.
8. Builds an analytical ecosystem of industry leaders, external analytical suppliers and business partners.
9. Works along multiple fronts with a portfolio of projects.
10. Knows the limits of analytics.

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Embracing big data can add years to a CMO’s tenure: Four ways chief marketers can make an impact with big data and high-performance analytics
Wilson Raj, SAS

In a recent article for Chief Marketer, Wilson Raj, SAS Global Customer Intelligence Director, tackles this question and offers a solution: Evolve. Become a new breed of CMO – one who recognizes big data as the fundamental consequence of our new market landscape, and takes advantage of it with high-performance analytics.

Raj makes a strong case that CMOs who adopt an integrated marketing management strategy with big data can make a substantial impact in these four key areas:

1. Customer experience In the past, marketers analyzed customer feedback with minimal consideration of operational and financial data. Big data offers rich insight unachievable by examining customer feedback data alone. For instance, CMOs can use operational data in call centers (e.g., wait times or time to resolution) to improve the customer experience across channels. Operational data can also reveal training opportunities to enable front line staff to deliver better service.

2. Customer engagement To engage your customers successfully, you must know who they are, where they are, what they want and when they want it – across all channels. This is a huge challenge for marketers, but with big data analytics CMOs can exert tremendous influence on customer engagement. They can find out what needs to change to achieve positive customer engagements, and, better still, what customers want.

3. Customer retention and loyalty Big data lets marketers augment existing customer touch points and anticipate new ones to keep valuable customers loyal in a brand-fickle world. Further, big data analytics can help CMOs allocate resources to drive revenue through successful loyalty initiatives.

4. Marketing optimization/ performance As marketers shift budgets from traditional to digital marketing channels (email, social media, search engine optimization, display advertising and mobile), CMOs need to know the optimal marketing spend across multiple channels. With big data, CMOs can continuously optimize marketing programs through testing, measurement and analysis. With a test-and-learn approach, CMOs can deliver on the key determinant of longevity: return on investment. The bottom line: CMOs who capitalize on big data will reap big rewards, both personally and professionally.

​Big data lets marketers augment existing customer touch points and anticipate new ones to keep valuable customers loyal in a brand-fickle world.

With big data, CMOs can continuously optimize marketing programs through testing, measurement and analysis.

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Can a country’s online ‘mood’ predict unemployment spikes? SAS, UN discover that social media chatter can provide advanced warning of unemployment increases – and that’s just the tip of the iceberg
I-Sah Hsieh, SAS

“At a time when our need for policy agility has never been greater, our traditional 20th century tools for tracking international development cannot keep up. Too often, by the time we have evidence of what is happening at the household level, the harm has already been done… And the irony is that we are actually swimming in an ocean of real-time information.”

With that statement in a November 2011 address, United Nations Secretary-General Ban Ki-moon declared the need for more sophisticated methods to understand, in near-real time, what is happening in the world. United Nations Global Pulse was created to research ways analytics can make that possible.

The world has now reached the point where billions of digital comments are posted daily on public Web forums, blogs and social media sites like Facebook and Twitter. These public comments express sentiment about employment, transportation, cost of living and anything else that’s on people’s minds.

If we collect and analyze all of these public comments with respect to current events (financial crisis, natural disasters, sporting events), clear and common sentiments begin to emerge.

Social chatter precedes events

For example, a recent study on unemployment conducted by SAS and the UN Global Pulse (@unglobalpulse) revealed that increased chatter about postponing vacations, increasing use of public transportation and downgrading one’s automobile could, indeed, predict an unemployment spike in the US and Ireland. I-Sah Hsieh is the Global Manager of International Development at SAS Read more about this study sas.com/ci-smstudy Learn more about UN Global Pulse unglobalpulse.org While these relationships are not surprising, the analytics quantified the amount of time by which these conversations usually precede an event (e.g., chatter about delaying travel typically preceded unemployment spikes by five months in the US).

Could social media analysis replace the need for official statistics? Not in my opinion. Social media analysis is an excellent complement to official statistics, often bringing more insights. For example, official statistics may tell us that there is a decline in the number of children enrolled in school in a developing region, and the public chatter may tell us why children are being pulled out of schools (to work in the local markets for food, to protect the village from civil unrest, etc.).

And, as shown in the project with UN Global Pulse, the analysis can also determine the types of chatter that usually precede or follow an event captured by official statistics.

Endless possibilities

I believe this is just the tip of the iceberg of what we can do with social media and other nontraditional big data sources. For example, what if we took anonymized credit card data on spending habits or anonymized mobile phone data to see how people are moving before and after a crisis event?

Similar to how social media analysis enriches official statistics, these other data sources would also provide additional detail and perspectives to tell a clearer story.

Or how about also using satellite data to tell us how quickly things are growing throughout a country experiencing drought conditions, as a way to help understand migration patterns? The possibilities are endless. However, it all comes back to the UN secretary-general’s belief that big data is useless without analytics.

Listening to be heard: In a noisy market, Globe Telecom gets its message out

Mobile-telephone users all over the world switch providers. But in the Philippines, customers switchproviders on any given day, every single day. With the overwhelming popularity of prepaid calling plans, the country’s 100 million inhabitants keep more than 80 million active SIM cards at hand, poised to react to each price change in the market’s volatile rate war. After years of fast rollout growth accelerated by affordable pay-asyou- go offers, the mobile market in the Philippines – and in other developing countries – has peaked. Few unsigned customers remain. The prepaid model that gave rise to a crowded market now threatens the stability of providers that don’t know how to compete. Globe Telecom, the second largest telecommunications company in the At Globe, we’re now focused on keeping the customers we want to keep by understanding them well enough to know what they want. Philippines, knows it can’t continue its growth trend based on rates alone. Besides, notes Ernest Cu, Globe President and CEO, “Rates alone do not ensure customer loyalty. Products and services do.” Over the years, Globe has built a stable, loyal base of customers by knowing exactly which products and services they want.

“More subscribers does not mean more revenue, as it did in the past,” explains Cu. “We’ve been successful in getting more quality subscribers who are happy to stay with us. Our strong customer focus helps us understand our customers well enough to know what they want.”

A communications pioneer

Globe has lived up to its reputation as a leader in innovation for 83 years and prides itself in being first to market with new products. The first SMS or text messaging service in the Philippines was introduced by Globe, as well as the first mobile wallet (GCash) and the first personalized postpaid plans. Globe has pioneered communications in many ways, and each new beginning is a testament of the company’s commitment to better serving its customers.

Globe recognizes that its business is shaped by the bonds that tie Filipinos together and believes that communications is ultimately about relationships.

This drives the company to constantly innovate and find new ways to enhance its services to be up-to-speed and relevant to their customers.

Hearing its customers

By “listening” to what the data has to say on more than 30 million subscribers, Globe offers its customers a world of their favorite products and services. And by paying attention to all the information that subscribers share with Globe via its call centers, the Web, market research and other touch points, Globe makes better decisions that keep customers loyal and happy.

SAS is a vital partner in Globe Telecom’s efforts to listen to its customers. This partnership began in 2004 and has grown over the years. Globe Telecom’s analytic journey began with the creation of the Business Intelligence (BI) team. Together with SAS, the Globe BI team has progressed on the path to advanced analytic capabilities.

Competitive advantage through analytics

Analytics proved an effective tool and provided a clearer understanding of the market and consumer behavior, helping Globe to reduce monthly subscriber churn. Armed with the capability for sound decision management and superior insight, analytics became a requisite for continued growth and competitiveness.“Analytics aids us in understanding our customers better. Through analytics everyone makes faster, more informed decisions, whether it’s in the area of customer management, retail management or day-to-day operations of the organization,” says Raul Macatangay, Head of Globe Telecom’s Business Intelligence team.But as more players – including brands that aren’t even in the industry – enter the market, even well-established providers like Globe must constantly remind existing subscribers why they picked Globe in the first place.“It is critical that we are effective in reaching our customers with our message,” Cu says. “At Globe, that’s about understanding our customers. And that begins with understanding our data. Globe is a trusted brand because customers know that we deliver on our commitment. As a result, we deliver effective loyalty programs.”

At Globe, we’re now focused on keeping the customers we want to keep by understanding them well enough to know what they want. Ernest Cu, President and CEO, Globe Telecom

How to stop annoying your customers

Take a page from Globe’s book – know your customers so well that you only contact them with offers they want. Start by following these six steps:

1. Collect as much of the right data as possible. Start with the transactions and the channel systems and collect as much information as you possibly can.
2. Create a 360-degree customer view that includes data from every relevant source. Good data management tools make it possible to incorporate data from disparate source systems.
3. Build customer intelligence on top of the data warehouse. Without analytics, it’s impossible to build rules with the necessary level of specificity.
4. Automate inbound and outbound communications. You need to know the right offer to make while customers are talking to your call center, in your store, or on your website.
5. Add scope and analytic sophistication as you go. Continuously keep building: Add predictive analytics to segment customers on a more dynamic basis. Use text mining to understand unstructured data. Add constraint-based optimization.
6. Make analytic insights available at customer touch points across marketing, sales and service. Connect your business so that the service side is aware of what’s happening on the sales side, and vice versa.

Competitive, lucrative industry

Cu and his executive team understand that competitive advantage in the Philippine market has less to do with winning the rate war and more to do with improving customer experiences and forging strong relationships.

In the Philippines, every retailer – from modest street vendors to department stores – sells prepaid communication products. With its offerings displayed alongside the competition’s, Globe has no control over branding and customer experience issues in those settings.

So Globe remodeled its own retail stores to create interesting, consumer-friendly spaces that would attract consumers and capture their undivided attention. This is just one example of the many initiatives that are part of Globe Telecom’s business transformation program.

The transformation covers network and IT modernization, where SAS continues to be a vendor-partner. This program is aimed at significantly improving network quality and customer experience, increasing capacity and driving down costs, as well as preparing Globe Telecom’s network to meet its customers’ future needs.

Reaping the benefits

Informed decisions and strategies fuel Globe Telecom’s competitive advantage despite the noisy mobile communications market. According to Jay Beltran, Head of Customer Lifecycle Management (CLM), “knowing our customers is a key driver to our success. It is the goal of my organization to keep our customers happy and content with quality of service and other value-added services we provide.”

The core CLM strategy is micro-segmentation for highly targeted campaigns on an almost one-to-one basis to more than 30 million of Globe Telecom’s mobile subscribers, and to launch an umbrella rewards program for Globe Telecom’s ever-growing loyal subscribers.

“SAS took this daunting journey with us and we succeeded. To date, we have generated US$42 million in incremental revenues through micro-segmented campaigns and through the rewards program,” says Beltran. “What used to be a double digit churn rate is now best-in-class at a blended churn rate of 5 percent for prepaid and 1.65 percent for postpaid.”

Social media: Driving profits or just popularity? Five best practices for linking social media metrics to business results

Nothing has generated more buzz or brought more scrutiny than social media – and how to get more value out of it. It’s gratifying if consumers like you on Facebook. But do they buy? Do they recommend? Does all the awareness, website traffic and goodwill your company is working so hard to generate translate into dollars and profits?

If you have lots of online traffic, that’s a good indicator, right? No, says Katie Paine, CEO of KDPaine & Partners. “’Hits’ stands for ‘How Idiots Track Success.’ If all you’re doing is counting hits, you’re not tracking anything that is meaningful in today’s marketplace.”

“Organizations realize the benefits of social media as it relates to awareness, but now the question is how to link this to tangible value in the company in a way that starts to justify the investment,” says John Bastone, Global Customer and Media Intelligence Manager at SAS.

So how do you prove the bottom-line value of this ephemeral new media? In a recent webcast, Paine and Bastone shared these five best practices for getting the most from social media measurement:

1. Consider all the ways social media can drive profits. “Social media as a channel tends to be most strongly aligned with marketing or marketing communications,” said Bastone, “but its impact is reverberating across the enterprise. Many different groups have a vested interest.”

The most obvious business functions that can benefit from social media tracking include:

• Online media analysis. Where are consumers talking about you? How is volume trending? Who are the most influential sources? Which sites are more positive? Negative?
• Brand and market tracking. What do consumers say about your brand, your products and your competition? What is the impact of these discussions? Who are the influencers?
• Public relations and reputation tracking. What are online journalists and bloggers saying about your organization? What is the threat to your reputation? Where are the opportunities to build advocacy?
• Customer feedback management. How do perceptions voiced on social media compare to direct customer feedback from other sources? Are there issues that require response or resolution?

The key is to create business processes whereby information from social media is translated into action. Customer complaints should be funneled to a customer care center. An identified need can be routed to a sales contact. An influential blogger can be referred to the public relations department as a potential new media contact.

2. Know what you want out of social media.

Define the R in your ROI. To be able to prove the ROI, you have to have a tangible business goal to begin with, says Paine. What is the return that you’re hoping to deliver? Why are you doing this? What is the problem you are trying to solve?

Define the audience. Who are you really trying to reach? It’s one thing to go out there and reach 57 million people, but that’s not very meaningful if those 57 million people are not really your target market.

Establish benchmarks. “Everyone tears their hair out and says, ‘There are no benchmarks in social media.’ But that’s not really true. There are. There’s always your competition – your peers – that you can benchmark against,” says Paine.

Define your Kick Butt Index. “What do your bosses define as ‘kicking butt?’” asked Paine. “Find out what causes them to say ‘Congratulations, you’re really kicking butt out there,’ or, ‘Hey, we’re really getting our butt kicked.’ What are those metrics? If executives agree to this up front, you have a tangible way of proving the value later,” says Paine.

3. Make it a two-way conversation. “Marketers are having to make an adjustment to account for the fact that the ‘social’ part of social media demands a give and take,” says Bastone. “As online conversations about your business are happening, you need to not just talk about yourself, but engage people in a two-way conversation.”

Paine agrees: “The first thing you have got to understand is this: It’s not about you. Too many marketers think social media is basically just another way to get the word out, when in fact it’s a very different entity. The entity is the conversation.”

The first thing you have got to understand is this: It’s not about you. Too many marketers think social media is basically just another way to get the word out, when in fact it’s a very different entity. The entity is the conversation. Katie Delahaye Paine, CEO of KDPaine & Partners

There are lots of ways to use social media not just as a way to get the word out there, but to touch customers and interact in ways that you couldn’t do before, says Paine. Savvy marketers will use social media to engage customers with the brand on a personal level, conduct customer meetings, gather feedback through surveys and focus groups, and identify opportunities for business development.

When Paine posted on Facebook that she was planning to build a brick walkway that weekend, she received a comment from Home Depot with a link to an instructional video about how to build brick walkways. By offering to help as a trusted adviser – rather than making an overt sales pitch – Home Depot ended up scoring the bricks-andmortar sale – literally.

4. Forget about impressions and hits. “For too long, we have been focused on counting eyeballs, and there is no way to count eyeballs effectively, consistently or accurately in social media,” said Paine. “So just give it up.”

Paine defined a five-level hierarchy of measurements, with each tier offering progressively more engagement – a more meaningful measure of how well you’re doing with social media.

At the lowest level are the simple, descriptive facts comparable to impressions in traditional media: how many followers, friends, likes, visitors, hits, comments, etc. “Impressions are a zero level of engagement,” said Paine. “You don’t care how many eyeballs you reach; you care what those eyeballs have done,” which brings us to the next level.

“Did they go to your site? Did they click through from the link you gave them? I classify likes as a Level 1, because it’s so easy to hit that like button,” said Paine. “My metric is not how many likes there are, but how many likes there are relative to how many people actually engaged in conversations on the site. Maybe 98,000 are likes, but if only 20 or 30 people are actually engaging in conversations, that’s not exactly a high level of engagement.”

Most organizations are measuring at the more participatory Level 2 or 3, said Paine. “If you are really good at getting engagement levels up there, you’ll get people who retweet, repeat comments and share posts. That’s a very high level of engagement. Ultimately (Level 4) you want their identity; you want them to register in some form, say nice things about your brand, and (Level 5) make a purchase and recommend you to others.”

As you move up the levels, the numbers will likely be small for now. It’s important to set management expectations appropriately. The absolute numbers – how many click-throughs or visitors – are not nearly as important as what percentage of people are moving up the levels. From month to month, as social media followers and friends move from Level 1 to Level 2 and up, you’ll know how well you are doing in getting people to engage with the brand.

5. Blend social media data with internal data. “Social media is a hot channel for understanding and interpreting online conversations, but it isn’t the only source of conversations,” Bastone noted. “If you really want to evaluate sentiment, conversations, topics, what people care about or don’t care about, it doesn’t make sense to analyze social conversations in one silo and other customer communications in a completely different silo.

“It gets really interesting when you start to blend social media data with internal data, such as the sentiment captured from call centers, surveys, customer service records, behavioral data, online chat and customer emails.” Supplement with external customer research, brand research and Web analytics to create an even richer view of the customer.

“Bringing this all together gives you a common lens to understand customer conversations and sentiment – and a much better handle on leading indicators,” said Bastone. “If an uptick happens across channels, that is a more reliable insight” to use as the basis for forecasting and other business decisions

 

Social media: next steps

What do you need to do next? Find out how you can integrate, archive, analyze and act on online conversations with SAS® Social Media Analytics.

Key benefits:

• Analyze conversation data.
• Identify advocates of, and threats to, corporate reputation and brand.
• Quantify interaction among traditional media campaigns and social media activity.
• Establish a platform for social CRM strategy.

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At the speed of marketing: How to make the right marketing decisions in real time. Lori Bieda, SAS

Dear Retailer,

If I can receive your special offer by text, quickly scan details on my iPhone,® later view comparable offers and social commentary about your product on my iPad,® and visit your website for other, more suitable product options, why can’t you recognize me when I call to speak to you? Or provide me with relevant up-to-date information when I visit your store?

Signed,

Frustrated customers everywhere

With the prevalence of social media, the explosion of marketing mediums, and rapid changes in the way people consume information, organizations are struggling to cobble together an increasingly fragmented view of the customer. The result? Frustrated customers, dwindling response rates, smaller prospect pools, and burgeoning “do not solicit” lists.

Traditional business models are no longer working. What’s needed is a new level of customer intelligence, marketing nimbleness and a more strategic way of managing the client experience. Business models should include an integrated marketing ecosystem that supports the fluidity of today’s consumer and allows marketers to capitalize on every client interaction.

Whether you’re a financial services firm, retail store, telecom provider, insurer, hotel chain, entertainment outlet, health plan or utilities provider, the average customer interacts with your brand dozens – sometimes hundreds – of times per year. From making purchases, browsing online, reviewing statements, calling in, emailing inquiries, Googling products and services, to proclaiming likes and dislikes on Facebook – the consumer’s voice is prominent.

What marketers do in the seconds leading up to each customer interaction, and during the live interaction itself, directly shapes the client experience; it also contains business risk and drives corporate profitability. In order to make the optimal decision and truly maximize the opportunity at the right moment, marketers must bring to bear the power of everything they know about a customer – combined with the nimbleness to adjust their course of action based on crucial new intelligence.

The marketing ecosystem

Today’s consumers – with their tablets, iPods® and all things mobile – demand fluidity and speed across all channels and geographies. Their worlds have become borderless and loyalty is now earned by the millisecond. All marketers, whether mass, direct, online or social, need to step out of their predefined channel strategies and reshape their approach based on the shifting patterns of customers and prospects. But to do so requires a customer information strategy and infrastructure that delivers rich information across touch points. That strategy must also support a dynamic, real-time flow of data across on- and offline channels. This is more than multichannel marketing. It’s not about scoring 2 million customer records with a series of product cross-sell models where the highest score wins and the identical offer gets appended to every possible channel. It’s not about sending out a static offer based on customer history. It’s about initiating and being prepared for a dialogue in real time. It’s about integrated, optimal and dynamic decision making with SAS® for integrated marketing management. Integrated because all channels operate in sync with one another – both online and offline. Each has the ability to receive and act on new information while using existing insight. For example, if a customer is on your site viewing line of credit product information when you’ve just emailed them a credit card offer, you recognize the relevance of that action. So when they call to inquire, your customer service representative is armed with real-time information that helps facilitate an unsecured line of credit application, based on the customer’s needs, up-to-date credit profile and value.

Dynamic and optimal because it’s possible to act in real time to incoming information – however it’s received – and take prompt action. Optimal because a marketer can isolate the best offer across all possible products, services and price points through best-in-class SAS® Marketing Optimization.

All marketers, whether mass, direct, online or social, need to step out of their predefined channel strategies and reshape their approach based on the shifting patterns of customers and prospects.

Advice for real-time marketing

David Meerman Scott, author and marketing strategist, shares tips for developing a real-time mindset:

• Act before the window of opportunity vanishes.
• Revise plans as the market changes.
• Measure results today.
• Execute based on what’s happening now.
• Implement strategies and tactics based on breaking news.
• Empower your people to act.
• Move when the time is right.
• Encourage people to make wise decisions quickly, alone if necessary.
• Quickly evaluate the alternatives and choose a course of action.
• Respond to customers on their time frames.

They can use all existing analytical models and business rules, as well as recognize and act on new and emerging patterns in the data. So, as business changes occur, business rules don’t need to be rewritten; models don’t need to be created or rebuilt. Rather than managing a library of hundreds of propensity models and business rules, marketers can rely on dynamic, analytical intelligence to find emerging patterns in data that they’ve not yet seen.

In action

What does integrated, optimal and dynamic marketing decision making look like in real life? If you’re a retailer, and a customer visits your website to comment on your new line of tween fashions and ask a question about a recent order, you know who the customer is, her social influence, number of followers and purchase history. You reply to her question, address the issue and follow up with an offer commensurate to her value and influence.

If you’re a telco and your customers come to your website or visit your branches to inquire about their mobile phone rate plans, you can detect the emerging pattern, recognize that a competitor has recently launched a promotion, and quickly counter with a retention offer to stave off impending churn.

Supporting this vision requires a marketing ecosystem that binds together not only what we know about customers (data and analytic insight), but also what we do with what we know – and how fast we act on it. Just as customers flow freely between channels, products, services and competitors, so too should marketers as they create a system that can keep up with those customers, know them whenever and wherever they show up – and make the most of every interaction.

eBay’s competitive edge in the Netherlands Marktplaats.nl, the Dutch arm of eBay, capitalizes on market and customer insights

In the Netherlands, fashionistas looking for the perfect pair of snakeskin pumps, collectors searching for hard to find items, and people just looking for a good deal go online to Marktplaats.nl, the Dutch arm of eBay.

Over the past 13 years Marktplaats.nl (“marketplace” in Dutch) has grown from a simple, brilliant idea to the leading online commerce platform for goods and services in the Netherlands.

So it seemed a natural fit when the Amsterdam-based company was acquired by eBay in 2004. Now the site has more than 6.5 million unique visitors per month and is the third largest European business of eBay Marketplaces.

Every day, more than 300,000 new listings are posted on the site for new and secondhand goods, by and for private and business users. In line with the mission of eBay Inc., Marktplaats.nl lets consumers buy anything, anywhere, and helps all kinds of sellers compete in today’s online commerce.

With an incredibly diverse range of offerings – from clothing and collectibles, to cars and services – and loads of data on the buying and selling patterns of 70 percent of Dutch Internet users, the company applies analytics to help its customers buy and sell their goods. Since analytics is deeply ingrained in the company’s DNA, its leaders use predictive analytics to make swift and agile decisions throughout the organization.

Analyzing data on 6 million monthly visitors

In 1999, Marktplaats.nl was one of the first websites in the Netherlands to focus exclusively on classified ads. To fulfill the company mission “to connect buyers and sellers,” the site relies on sound data management, accessible analysis and reliable forecasts of its 6 million monthly visitors’ buying and selling behavior.

“SAS® helps us gain insight and target our customers. It helps us stay customer-focused so we can offer the best possible service and experience,” says Dr. Natasha Zharinova, Finance Director at Marktplaats.

People choose Marktplaats.nl because it is highly accessible and allows ads to be placed in 36 different product groups. Increasingly, new products (nearly 30 percent of total goods offered) are being advertised on the site. The social aspects of the platform, combined with the heavy traffic, make Markplaats. nl attractive for service providers as well.

“Our visitors and customers are critical to our success,” emphasizes Zharinova. “Our relationship with them is truly based on what we can offer to one another. User experience is at the heart of everything we do. There has to be real value for our users in each new feature and tool.”

With minimal effort, we have access to graphical and visual reports, plus answers to ad hoc questions and specific what-if questions.Dr. Natasha Zharinova, Finance Director, Marktplaats.nl

Deep insights into customer behavior

The site’s simple navigation and ease of use are possible because of advanced monitoring and analysis of visitors’ surfing, browsing, buying and selling behavior. Through targeting techniques, Marktplaats. nl uses the data it collects to deliver the right ads to the right people, at the right time, through the proper channels – including mobile devices and social media, such as Facebook.

“With SAS, we unlock vast amounts of structured and unstructured data from both internal and external environments for statistical analysis, reporting and forecasting,” says Zharinova.

“Because we are able to drill down through multiple levels of data, we can actually increase our understanding of our visitors’ behavior and respond to it. We look at both buying and selling patterns. For example, we can group customers searching for products in categories with children’s clothing in the segment ‘parents.’ Based on the clothing sizes they seek, we estimate their child’s age. With this information, we can create a better fit with the customer’s needs by offering advertising based on their interests.

“A nice example is our Marktplaats Junior program, where we provide our users with relevant content and advertising, from pregnancy until the child is five years old. First we offer convenient baby products, then the ability to sell these products – while also providing an overview of children’s clothing advertisements in the next suitable size range.

“This proactive targeting greatly enhances customer satisfaction,” notes Zharinova. “The insights also help us to decide the direction in which we would like to grow and how we can further improve our services to our customers and advertisers.”

Helping small businesses succeed

To address the needs of business customers, the company uses a customer lifecycle management program. Admarkt is Marktplaats’ self-service, performance-based advertising product for small and medium enterprises (SME). It was launched in 2007 and has grown into a solid component of the company’s B2C offering.

“We have focused on data-driven programs that empower our SME customers to run a successful business through Marktplaats.nl,” says Zharinova. “We use SAS to define customer segments with varying needs and to implement an automated customer lifecycle program that supports customers through various points of their operations – from getting started, to improving performance and visibility, to reactivating customers whose usage has been declining.

“We also put our customers in the spotlight during the annual Admarkt Success Stories award. Last year’s winner was a children’s furniture shop – Zomerzoen – that doubled its revenues in one year by using our products. Ninety percent of its showroom visitors come via Marktplaats.nl, and 40 percent of the total revenues are attributable to Marktplaats.nl ads, meaning we’ve obtained our most important goal – happy and successful customers.”

Our ultimate challenge is to instantly offer visitors exactly the information and products they seek. With SAS, we have insight into customer data and trading behavior, and we are able to see connections that help us target our users with the right actions, at the right time. Dr. Natasha Zharinova, Finance Director, Marktplaats.nl

Analytics in action: PREDICTING SALES

By analyzing the time series and correlating it with all sorts of external variables including weather, holidays and special events, Marktplaats.nl is able to predict how high sales will be, when exactly, and for which products. The site also uses predictive analytics to develop its own services and to see how various elements of its services and offerings are influencing each other. Through cannibalization analysis, Marktplaats.nl can predict if new features will have a positive or negative effect on the bottom line.

Analytics in action: TARGETED MARKETING

Analytics enables Marktplaats.nl to be more focused in its behavioral targeting. Since people between the ages of 18-25 looking for a car in the €2,000 – 4,000 price range are often first-time buyers, Marktplaats.nl knows to show them advertisements by the Automobile Association for insurance and memberships. Other members selling DVDs or books get tailored offerings by DHL or TNT, the postal and express services.

One version of the truth

According to Zharinova, Marktplaats finds SAS really valuable for its ability to make the information gathered from huge amounts of data and data sources more accessible and useful.

“We hardly lose any time collecting data, which allows us more time to focus on making the best possible use of valuable insights and customer data,” says Zharinova. “With minimal effort, we have access to graphical and visual reports, plus answers to ad hoc questions and specific what-if questions.”

The reports are available instantly to the different departments – and they are easy to use and distribute in any format, including PowerPoint, Excel and Word. As a result, Marktplaats.nl has a uniform companywide view of its current business so that all decisions are based on facts.

“This new view has leveled the playing field when it comes to agile and fact-based decision making throughout the organization, empowering our employees, and creating a great place to work that has an impact on business and society,” says Zharinova.

With SAS, Marktplaats.nl is able to predict revenue streams in detail and immediately respond to any changes in customer behavior. “We know how trade is affected by things like the time and date of purchase, school holidays, and even weather conditions; we can make accurate predictions based on this information,” says Zharinova. “A major advantage is also the ability to trace data sources. If something doesn’t go as expected, we dive into the data to find a detailed explanation.” 

Customized experiences

Due to continuous efforts to optimize visitor experience, Marktplaats.nl has held on to its No. 1 position in a highly competitive industry for more than 12 years. “Our knowledge of the market and the visitors, and our adherence to the principle of constant improvement helps us remain No. 1,” says Zharinova. “We use the analytical power of SAS to answer complex questions – which form the basis for segmentation and customer lifecycle programs – and to enhance our marketing campaigns. We are also better able to predict the effects of changes to the site, as well as new products and services we introduce.

“Our ultimate challenge is to instantly offer visitors exactly the information and products they seek,” she says. “With SAS, we have insight into customer data and trading behavior, and we are able to see connections that help us target our users with the right actions, at the right time – which contributes to a positive customer experience.”

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Do marketers really need real-time analytics? Wilson Raj shares four tips for applying real-time approaches. Wilson Raj, SAS

The term “real-time analytics” is resurging as another huge buzzword — like “big data” — and has many dimensions and implications, such as real-time marketing, real-time data integration, real-time customer intelligence, real-time reporting, and so on.

Real-time analytics goes well beyond “realtime web analytics.” So, it’s important to have a grounded view so that businesses can establish a more comprehensive and realistic approach that is suited to their objectives and capabilities.

Real-time analytics is the use of (or the capacity to use) all available customer and enterprise data, processes and technologies to serve customers and the business itself Real-time analytics is the use of (or the capacity to use) all available customer and enterprise data, processes and technologies to serve customers and the business itself at “the point of need” — or, put another way, “the right time.” at “the point of need”— or, put another way, “the right time.”

Differentiate between the customer-facing processes and operational marketing processes so that you can appropriately determine how real-time analytics can add tangible value to each aspect.

With the customer-facing processes, businesses can determine how they are executing on their marketing strategies and tactics. Here, real-time analytics essentially allows marketers to create value for their customers. With marketing operations, business can tell how well they are running their marketing and how well they are optimizing all of the available resources at hand — people, budgets, technologies, content assets, etc.). Here, real-time analytics give businesses an instant edge for improving marketing performance and reducing costs.

Real-time customer facing processes

With customer-facing processes, real-time analytics can be applied to a number of scenarios that create value for customers. The biggest one is real-time offer management where businesses can instantly queue up next best actions.

For instance, if a customer calls to cancel a cable TV subscription, an attrition or risk or even customer profitability score can be calculated with real-time analytics. The service rep can then present pre-emptive offers or take pre-determined actions to induce the customer to stay.

Another example is real-time customer response and support. With real-time social media analytics, businesses can listen, manage, and respond to customer feedback almost instantly. Another customer-facing scenario is real-time decision and interaction management. For instance, marketers can use real-time analytics in campaigns to determine customer eligibility and the likelihood to respond, and then surface a set of relevant offers or next best actions, and then present the best option to the customer.

Real-time marketing operations With marketing operations processes, “point of need” scenarios can dramatically improve marketing performance and reduce costs. A key example is real-time insight development. As customer data from multiple channels get refreshed, real-time analytics can re-score, re-segment, and re-evaluate customer profitability so that marketers can respond with timely, relevant campaigns and touch-points.

Another example involves real-time digital marketing. For instance, marketers can use real-time bidding (RTB) and analytics to create, buy, and serve digital ads that are customized and dynamic. Yet another example is real-time customer intelligence in terms of real-time visualization where data is instantly turned into useful and actionable analysis instantly — a far cry from the routine, transactional reports that comprise many business dashboards today.

So, how can you assure that real-time analytics enhances your customer and marketing operations? Here are some tips in applying real-time approaches:

1. Identify and prioritize where you want to implement real-time analytics. Do you want to create customer value through customer-facing processes? Or, do you want to improve marketing performance and efficiencies through marketing operations?
2. Assess the cost-benefit of your real-time analytics implementations. For many enterprises, it’s not merely a technology solution. You have to factor in culture implications, infrastructure needs, processes and people.
3. Understand the entire ecosystem of your customer and your part in it. Before embarking on a “real-time” marketing and analytics binge, take time to understand the various touch-points in your customers’ journey. Are there partners or other entities involved? What are the interactions you can directly control and impact? What are the hand-off points during customer interactions?
4. Do a self-assessment on the predictive and analytic capabilities you have before embarking on “real-time analytics.” Look at the extent of predictive, scoring, and interaction management models you have in-house and bridge gaps through talent acquisitions, partners, and vendors.

Intelligent advertising: German ad network Quarter Media serves up ads 24/7 for some of the world’s biggest brands

The Big Data dilemma shows that sifting through the influx of customer information is a tough task to tackle. When you establish a definition of real-time analysis as it is applied to customer-facing and marketing-facing processes, then companies can take practical, discrete steps to remain relevant in the “real” world of overwhelming data.

BMW, McDonald’s and Gore-Tex all use Quarter Media, a premium ad network based in Germany, to serve billions of impressions monthly to the online inventory of the company’s 250 publisher partners, including the Discovery Network and DMAX.

The ability to run ads in the most intelligent way for its customers 24 hours a day, 7 days a week is fundamental to Quarter Media. In the words of CEO Tibor Gaddum, “If we are not serving ads continuously in the most efficient way, we are leaking revenue. Any disruption to our operations means our publishers also lose revenue and our advertising customers don’t see results for their campaigns – it’s simply not an option.”

If we are not serving ads continuously in the most efficient way, we are leaking revenue. Tibor Gaddum, CEO, Quarter Media

A seamless migration

Quarter Media wanted to move to a new ad serving platform, but needed to be sure that the migration to a new platform would be risk-free and would not affect its customers: All of the network’s publisher customers had installed unique “ad tags” of code that enabled Quarter Media to serve ads to fill its inventory. Quarter Media was concerned that if it had to recode the ad tags, it would also need customers to do the same thing: a time-consuming process that would cause customer dissatisfaction.

“It quickly became clear that working with SAS and adopting its intelligent advertising platform would ensure a successful migration with minimal impact on our customers,” explains Gaddum. “SAS’ offer was unique: The team built a bespoke translator tool to ensure that publishers would not need to change the coding of their ad tags, and took the burden of process away from us, whilst providing us with enhanced functionality and productivity via the new platform.

Thousands and thousands in savings

“Without this offer, we would have needed to run a translation tool ourselves, at a significant financial and time cost,” adds Gaddum. “No other provider was able to offer this service. If we had translated the ad tags ourselves, we would have had to continue to run our previous platform alongside SAS® Intelligent Advertising for Publishers during the two or three month translation period to ensure that all publishers were delivered ads, whether their ad tags had been recoded yet or not. This would have shouldered us with an additional €75,700 (US$95,400) in costs and a very complex task that would impact our operational performance.”

Putting Advertising Intelligence to work

There are a variety of ways to achieve advertising intelligence, and different methods will suit different kinds of publishers. Those who want to use an intelligent approach should:

Implement a future-focused, data-rich platform. When choosing an ad-serving and delivery platform, publishers should look for technology that’s innovative, scalable and engineered with the future in mind. Publishers have massive amounts of data, with new types of data sets available all the time (such as social media sources). Publishers who use this information efficiently will, by default, be selling and managing inventory effectively.

Focus on incorporation versus integration. Demand a comprehensive solution that has all of the tools incorporated into one – a guarantee that every facet of a system can work interdependently, sharing data and insight. Publishers don’t just need facts – they need analysis and holistic recommendations based on those findings.

Bringing advanced analytics to digital advertising

Last year, $32 billion was spent on online advertising in the United States, and the market is projected to reach $50 billion by 2015. Despite its size, the digital ad market is fragmented and lacks purpose-built analytical applications, making it difficult for publishers to effectively manage ad inventory and optimize profitability. As the digital ad market continues to shift toward real-time bidding, the ability to process and reallocate ad inventory in real time, whether via video, mobile, display or other digital media, will be critical for publishers. With the recent acquisition of aiMatch, SAS can provide publishers with a complete solution to help manage, forecast, optimize and measure ad inventory to maximize ad revenue.

No risk, lots of rewards 

Minimizing the possible impact of a migration on customers was a priority for Quarter Media. To ensure that the migration would be as risk- free as possible, the SAS team did a trial run of the entire process, allowing Quarter Media to try out the new platform. SAS built a translation tool and converted the rich media code for ad tags. This trial process allowed SAS to react to Quarter Media’s evolving needs, and to mitigate potential risks as far as possible ahead of migration.

The migration itself involved two steps: first, recoding the Quarter Media site and associated translation processes; second, transferring all the campaigns from the old system to the new one.

“The migration was very smooth, and the SAS team was in the office with us as we made the switch to the new system,” remarked Gaddum. “SAS did a tremendous job: I have never received such a professional and efficient service from a supplier. The team at SAS was always available to help and to allay our concerns.”

Plugging revenue leaks

The SAS intelligent advertising platform is used by Quarter Media’s ad operations team to monitor ad campaigns currently running and to set up new campaigns. Gaddum commented on the improvements from using SAS: “We are now able to offer our advertising clients direct access into the SAS platform to book new campaigns, which negates the need for us to receive booking calls and improves efficiency; previously this would have been impossible.”

Since moving to SAS Intelligent Advertising for Publishers, Quarter Media has unlocked a series of new functionalities and improvements, including better reporting capabilities. Now, it can spot underperforming campaigns rapidly and make corrections. As impressions generated directly translate into revenue for the business, identifying and addressing underperformance improves the bottom line.

As Gaddum describes through an example, “For a campaign with a €50 cost per thousand impressions, and a target of 1 million impressions which we underdelivered by 10 percent (100,000 impressions), we could expect to leak approximately €5,000 in revenue.”

For Quarter Media, avoiding revenue leakage is all about a powerful solution that can report on and present data in a clearly understood way. According to Gaddum, “Reporting is more automated than was previously the case, allowing for a faster and more comprehensive overview of how campaigns are performing. We can ensure that campaigns are consistently delivering the full number of impressions during the time allocated; if they are underperforming, we can adjust the campaign in a single screen. This is much faster to do in the new system, increasing productivity and preventing revenue leakage.”

He added, “We haven’t yet quantified the complete financial impact of reduced leakage, but we expect it to be very significant.”

Last year, $32 billion was spent on online advertising in the US, and the market is projected to reach $50 billion by 2015.

Many online advertisers are wasting their money on ads no one sees. In a recent study, up to 31 percent of 1.7 billion online ad impressions were never in view.

Marketers flunk the big data test: Research shows that the vast majority of marketers rely too much on intuition

The big data explosion is driving a shift away from gut-based decision making. Marketing in particular is feeling the pressure to embrace new data-driven customer intelligence capabilities. No wonder a strong appetite for data is one of the most sought-after qualities in new marketers. And yet, a recent CEB study of nearly 800 marketers at Fortune 1000 companies found the vast majority of marketers still rely too much on intuition – while the few who do use data aggressively for the most part do it badly. Here are our key findings:

Most rely too much on gut

On average, marketers depend on data for just 11 percent of all customer-related decisions. In fact, when we asked marketers to think about the information they used With consumer behaviors in flux, once-valid assumptions (e.g., “older consumers don’t use Facebook or send text messages”) can quickly become outdated. Download special report: Customer Intelligence Tames the Big Data Challenge sas.com/ci-hbr to make a recent decision, they said that more than half of the information came from their previous experience or their intuition about customers. They put data last on their list – trailing conversations with managers and colleagues, expert advice and one-off customer interactions.

But in today’s volatile business environment, judgment built from past experience is increasingly unreliable. With consumer behaviors in flux, once-valid assumptions (e.g., “older consumers don’t use Facebook or send text messages”) can quickly become outdated.

A majority struggle with statistics

When we tested marketers’ statistical aptitude with five questions ranging from basic to intermediate, almost half (44 percent) got four or more questions wrong and a mere 6percent got all five right. So it didn’t surprise us that just 5 percent of marketers own a statistics textbook.

Some are dangerously distracted by data

While most marketers underuse data, a small fraction (11 percent in this study) just can’t get enough. These data hounds consult dashboards daily, and base most decisions on data. They have a “plugged in” personality type and thrive on external stimulation – so they love data and all forms of feedback including data on marketing effectiveness, input from managers or peers, and frequent interaction with others.

We call these marketers “Connectors,” and they’re exactly what most CMOs are looking for. But these types of marketers are actually severe underperformers; they receive much lower performance ratings from their managers than average marketers do.

The problem is, they don’t have the statistical aptitude or judgment required to use data effectively. Every time they see a blip on the dashboard, they adjust – and end up changing direction so often that they lose sight of end goals. In management positions, these people can wreak havoc by creating endless fire drills and preventing anyone from sticking with projects long enough to achieve the best results.

Worse, many marketing disciplines (especially direct, digital and loyalty marketing) unwittingly encourage these behaviors and endup magnifying the problem. That’s because dashboards often capture response-based metrics such as clicks and open rates that aren’t tied to more important measures such as customer loyalty or lifetime value – and yet, marketers are rewarded for improving the response metrics.

The best focus on goals and filter out noise

Today’s top-performing marketers as rated by the managers (a profile we call “Focusers“) have three key qualities: comfort with ambiguity, ability to ask strategic questions based on data, and narrow focus on higher-order goals. Together, these traits help them filter out noise and apply only the insights or data points that truly matter for long-term success. As marketers get better access to raw numbers and big data keeps growing, the importance of this filtering ability will only intensify.

The bad news for marketing leaders is that ability to filter out noise is rare (only about 10 percent of marketers excel here) and hard to teach. The good news is that a well-guided team environment can protect noise chasers from themselves – by providing blinkers that keep “bright shiny objects” out of view.

To drive effective data use, the best marketing leaders reiterate critical business goals constantly (to keep them front-of-mind despite distractions), teach marketers to put data front and center in their decision making, and sensitize marketers to common data interpretation mistakes. This enables even the most distractible data lovers to overachieve.

With consumer behaviors in flux, once-valid assumptions (e.g., “older consumers don’t use Facebook or send text messages”) can quickly become outdated.

Patrick Spenner is a Managing Director and Anna Bird is a Senior Researcher in the Corporate Executive Board’s Sales, Marketing and Communications practice.