Big Data isn’t just the buzz word du jour; it’s here to stay. With the volume of data created by organizations and consumers growing vastly minute by minute, it’s imperative that companies their marketing teams understand how to effectively leverage this ever-growing volume of data in meaningful ways that can be used to drive business decision-making.
Just having more data isn’t enough to make an impact. Arming your company with the right tools, technologies, and partners to efficiently gather, analyze, and draw actionable marketing insights from your company’s data is critical to avoid getting lost in a sea of data. Numbers alone mean little. It’s how you use them that counts.
To help companies and marketing teams get a handle on the most effective ways to make use of the growing volume of data that exists today, we’ve compiled this comprehensive list of tidbits and tips on using Big Data for marketing decision-making. In addition to highlighting a few of the innovative ways companies are making use of data in all facets of business, from recruiting top-notch job candidates to making more efficient use of farm land (illustrating the myriad ways in which Big Data transforms business decision-making), we’ve compiled dozens of tips for improving data collection techniques, using the right data analytics tools to make sense of data, and deriving actionable insights to help you make smart decisions that drive business growth.
Benefits of Big Data and Predictive Analytics for Marketers
1. Big Data has game-changing effects. Companies that take advantage of new technologies can quickly gain an edge over competitors. “The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground.
“When the executive convened a group of senior leaders to dig into the competitor’s practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization— from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business.
“What this executive team had witnessed first hand was the gamechanging effects of big data. Of course, data characterized the information age from the start. It underpins processes that manage employees; it helps to track purchases and sales; and it offers clues about how customers will behave.” – Brad Brown, Michael Chui, and James Manyika, Are you ready for the era of ‘big data’?, McKinsey Quarterly; Twitter: @McKQuarterly
2. Gain a competitive edge in every facet of your business. “When Billy Beane, the subject of the 2004 book ‘Moneyball’, took over as the general manager of the Oakland Athletics in the late 1990s, he revolutionized the way baseball teams were run.
“At a time when other managers and scouts relied on their experience to identify promising new players, Beane successfully used ‘sabermetrics’ — the statistical analysis of baseball — to see value in players other teams had passed over, turning the Athletics into a team capable of competing with the biggest names in the sport.
“Now industry watchers say a similar statistics revolution is going on in the business world.
“Technological advances are giving rise to huge amounts of data — about consumers, supply chains and world events — that businesses can use to make better decisions and gain a competitive edge.” – Edwin Lane, for CNN, Moneyball: How businesses are using data to outsmart their rivals, CNN; Twitter: @CNN
3. Leaders in data-driven marketing report higher levels of customer engagement and market growth. “Organizations that are ‘leaders’ in data-driven marketing report far higher levels of customer engagement and market growth than their ‘laggard’ counterparts. In fact, leaders are three times more likely than laggards to say they have achieved competitive advantage in customer engagement/loyalty (74% vs. 24%) and almost three times more likely to have increased revenues (55% vs. 20%). This is the key finding from the just-released report from Forbes Insights and Turn, the marketing software and analytics platform, ‘Data Driven and Digitally Savvy: The Rise of the New Marketing Organization.‘ The report, based on a global survey of more than 300 executives, finds widespread agreement that data-driven marketing is crucial to success in a hyper-competitive global economy.” – Forbes Staff, New Report Shows Data-Driven Marketing Drives Customer Engagement & Market Growth, Forbes Corporate Communications; Twitter: @ForbesPR
4. Big Data can inform your messaging. In fact, it’s even being used to inform political campaigns. “Big data is now informing a number of political campaigns in order to find different, more strategic ways to engage the electorate. By gathering that data, it helps political parties, lobbyists and other political entities predict how to attract voters.
“For example, if you receive emails from President Obama, First Lady Michelle Obama and members of their staff, you might see subject lines like:
- ‘[Your Name], I need you’
- ‘This is in your hands’
- ‘This is actually really fun (and I’ll totes do it with you).’ This one was sent by White House staffer and sometimes actor Kal Penn. Way to stay hip, Kal!
“These emails are especially prevalent during election seasons on both sides of the aisle. After the 2012 election, Obama campaign email director Toby Fallsgraff revealed that a great deal of A/B testing went into finding which email subject lines garnered the Dems the most campaign donations. Between 20 writers and a powerful analytics system, they tested and re-tested several email drafts and subject lines along with numerous variations of those drafts, gathering data on their performance. The ones that tested the strongest were sent to subscribers, and then awaited those sweet, sweet donation checks.
“Here’s what they found: The subject line that garnered Obama’s team the most contributions was ‘I will be outspent.’ How much? $2,673,278 out of the $690 million raised came from that email. Their testing also found that while one subject line might work at first, it eventually lost traction and they had to test something new.” – Business.com Editorial Staff, How Big Data Is Driving Content Marketing Strategies, Business.com; Twitter: @businessdotcom
5. Big Data can drive substantial revenue growth, and SMBs shouldn’t shy away from upfront costs — the revenue growth often achieved by employing Big Data is more than worth it. “Those with a deep understanding of big data analytics never doubted that it would one day become the norm, but for those who are still on the fence, real-world examples of the paradigm’s success can prove motivating. An Entrepreneur infographic highlights several examples of businesses turning large quantities of data into actionable intelligence that directly affects their bottom lines.
“Big data collects data points across disparate systems during each interaction, whether with a customer, partner or internal resource. Only by combining and analyzing that data can real insights be found.
“One credit card company utilized big data to analyze customer behavior, incorporating such data points as their transaction data, social media behavior, mobile behavior and even entertainment choices to determine how to best target them. The result was a 25 percent increase in conversion, allowing the company to save $3.5 million in digital ad spend.
“Another company, this one specializing in digital games, utilized big data to track player engagement and rate new revenue initiatives. The results allowed the company to better target its revenue-generating player base and increase its overall revenue from $50 million to $600 million.” – Shawn Drew, The Results Are In: Big Data Analytics Is Driving Revenue, PivotPoint; Twitter: @IBMMSP
6. The abundance of Big Data tools make it possible for every business to tap into Big Data to drive better marketing decisions. “Big data, IoT and cloud are the hottest trends in technology, their applications numerous, from improving science and research to optimizing sports team performance and aiding law enforcement. In the business world, these technologies have become a primary driver of digital marketing initiatives. Massive data volumes, sourced from websites, apps and machines, enable marketers to develop highly targeted digital campaigns and promotions.” This July 2015 2nd Watch survey finds that 50% of respondents are likely to expand use of big data to support digital marketing. Another third say programs have been so successful, they plan to divert resources intended for other projects to support big data-based digital marketing programs. – 2nd Watch Survey: Big Data, IoT and Cloud Are Driving Digital Marketing, Market Wired; Twitter: @Marketwired, @2ndWatch
7. Data has reduced barriers to entry for startups and small businesses. “Thanks to data, startups and small businesses today face lower barriers to market entry than they have since the 1870s. The data-driven marketing revolution has eased access to advertising, and advertising-dependent capital. Products and publications that deliver value to users can be launched and grow at a pace unchecked by the need to find a large pool of customers willing to make immediate payment for the value they receive, because entrepreneurs and investors have confidence that customers or audience can be built incrementally, and when they build over time, they eventually have value to advertisers.” – “The Value of Data: Consequences for Insight, Innovation, and Efficiency in the U.S. Economy,” A Study Commissioned by DMA’s Data-Driven Marketing Institute (DDMI), October 14, 2013; @DMA_USA
8. Data is critical to not just one, but every marketing function within your organization. “Having a data strategy is by no means a new discipline for marketers. Even back in the 1960s, pioneers such as Robert Kestnbaum were outlining new and imaginative ways to collect and analyze customer data to deliver more relevant marketing campaigns.
“But while database marketing was for so long seen as a specific type of marketing in the subsequent years that followed Kestnbaums’ innovative work, the relatively recent advent of Big Data means that data analysis is now no longer just viewed as critical to one marketing function, but every marketing function.
“American professor of psychology Dan Ariely famously described Big Data as being ‘like teenage sex – everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it’ – a statement germane to what marketers were experiencing in the 2000s, when consumer data levels first truly exploded. However, in the last five years a seismic shift has occurred that makes this less and less representative.
“Marketing is now predominantly a data science operation, and what’s more, the technology is there to assist this – a fact that consumers are well aware of. 100% of marketers state that successful brands must use customer data to drive marketing decisions, while IBM research shows that 75% of consumers now ‘expect organizations to understand their individual needs’. Brands constantly referred to the need to turn their marketing operations from art to science. Subsequently, marketers must have a robust data-driven marketing strategy in place to ensure they not only capitalize on the Big Data opportunities, but also satisfy customer requirements that are frequently being made a larger part of their remit.” – Chris Ward, How to build a data-driven marketing strategy, MyCustomer.com; Twitter: @MyCustomer
9. Even beyond marketing, Big Data can benefit every facet of your organization, from marketing to HR. “There are tons of resources out there talking about the importance of organizing big data into lead generation chunks, and that is one of its most profitable uses. But data can also be used to analyze how existing customers interact with your digital content, where upsell opportunities exist, or what areas offer the most potential for creating an improved user experience.
“There are plenty of non-sales or UX related uses as well, such as evaluating the interest of potential employees in the hiring process. My favorite HR-related hypothetical involves the practical example of using your website data to find the ideal employee:
“Lets say you’re interviewing candidates for an important new job at your business. You narrow the list down to three equally impressive candidates. In addition to checking background information, references and social media snooping, you decide to use your marketing automation program to see how each prospect interacted with your web content. The first applicant visited your site for a total of ten minutes over two separate visits. The first visit, he looked through the careers section and quickly glanced at the About Us section. The second visit, he jumped around looking at a few employee bios, downloaded a white paper on one of your products, and spent most of his time looking at the employee benefits and recruiting pages. The sort of traffic you would expect. The second applicant spent an hour and forty-five minutes on your site over the course of five different visits. She read your company’s mission statement, the entire careers section, each executive’s bio and a dozen blog entries. She also downloaded four whitepapers relevant to the position she’s interviewing for, watched an hour-long recorded webinar, and even watched that goofy Christmas video your employees made last December. The third applicant’s only interaction with your web content was opening the email inviting him for an interview.
“In a situation such as this one, the first two applicants have proven themselves as serious about the company, its culture and potentially working there, while the third applicant may be more interested in using an offer from your firm as leverage at his current job, or only truly be half-interested in the position. Strategic insights like this can help find employees with the right work ethic and a genuine interest in your company.” – Chris Gaffney, Data Driven Marketing is the Future of B2C and B2B Digital Marketing, Volacci; Twitter: @gaffneych
10. Identify the key factors that set your customers apart. “Everyone has different customer types. Not all customers are created equal. Identify what key factor(s) set one apart from another and segment your users from one another. It could be geography; it could be specific products they buy or it could be a demographic detail. Once you understand that, you are better able to target messaging, develop product and drive value for both the customer and your business.” – Reid Carr, Red Door Interactive, as quoted by Scott Gerber, 9 ways of gathering meaningful data about your customers, The Next Web; Twitter: @TheNextWeb, @reddoor
11. Use data to make “next best offers” that customers can’t resist. “Advances in information technology, data gathering, and analytics are making it possible to deliver something like—or perhaps even better than—the proprietor’s advice. Using increasingly granular data, from detailed demographics and psychographics to consumers’ clickstreams on the web, businesses are starting to create highly customized offers that steer consumers to the ‘right’ merchandise or services—at the right moment, at the right price, and in the right channel. These are called ‘next best offers.’ Consider Microsoft’s success with e-mail offers for its search engine Bing. Those e‑mails are tailored to the recipient at the moment they’re opened. In 200 milliseconds—a lag imperceptible to the recipient—advanced analytics software assembles an offer based on real-time information about him or her: data including location, age, gender, and online activity both historical and immediately preceding, along with the most recent responses of other customers. These ads have lifted conversion rates by as much as 70%—dramatically more than similar but uncustomized marketing efforts.” – Thomas H. Davenport, Leandro Dalle Mule, John Lucker, Know What Your Customers Want Before They Do, Harvard Business Review; Twitter: @HarvardBiz
12. Ensure you’re collecting real customer insights by integrating your data sources. “To ensure you are collecting real customer insights, you need to broaden data collection practices by integrating social data, web and social analytics, and customer records to get more reliable and accurate data to make strategic marketing and business decisions.
“’The quest for a single tool/source to answer all your questions will ensure that your business will end up in a ditch, and additionally ensure that your career (from the Analyst to the web CMO) will be short-lived,’ says Avinash Kaushik, author of Web Analytics 2.0, to inc.com, making it very important to not only use multiple sources of data to gain customer insights, but to also use the right sources.” – Jillian Nason, Are Your Customers Who You Think They Are?, LoginRadius; Twitter: @LoginRadius
13. Meet customers at different touch points throughout the buyer’s journey. “The CEO of a major supplier to the telecom industry was frustrated. An initiative to increase sales volumes and shift the company’s product mix to higher-value components was stalling, and not for lack of effort. With support from a marketing campaign that emphasized a slew of new product features, frontline sales managers had stepped up calls to their purchasing contacts at OEM customers. Yet they reported that buyers weren’t buying. Impediments appeared to include tough new requirements from chief purchasing officers, negative chatter on social media about post-sales support, and skeptical questions on a product-rating site about an offering’s fully loaded costs.
“Welcome to the new dynamics of B2B sales. Decision-making authority for purchases is slipping away from individuals in familiar roles—often those with whom B2B sales teams have long-standing relationships. Just as the digital revolution has transformed once-predictable consumer purchasing paths into a more circular pattern of touch points, so too business-to-business selling has become less linear as customers research, evaluate, select, and share experiences about products. More people within (and, thanks to digital engagement, even outside) the organization are playing pivotal roles in sizing up offerings, so the path to closing sales has become more complicated.
“The best response is to embrace the new environment. Sellers who are ready to meet customers at different points on their journeys will exploit digital tools more fully, allocate sales and marketing resources more successfully, and stimulate collaboration between these two functions, thereby helping to win over reluctant buyers. Our experience with upward of 100 B2B sales organizations suggests that while the change required is significant, so are the benefits: an up to 20 percent increase in customer leads, 10 percent growth in first-time customers, and a speedup of as much as 20 percent in the time that elapses between qualifying a lead and closing a deal.” – Oskar Lingqvist, Candace Lun Plotkin, and Jennifer Stanley, Do you really understand how your business customers buy?, McKinsey & Company, McKinsey Quarterly; Twitter: @McKQuarterly
14. Use predictive analytics to pinpoint trends, understand customers, improve business performance, and more. “According to TDWI Research, the top five reasons why companies want to use predictive analytics are to predict trends, understand customers, improve business performance, drive strategic decision-making, and predict behavior. Cox Communications decided to use predictive analytics to identify business drivers for growth and then pinpoint existing and prospective customers to cultivate new offerings. They wanted to be able to answer difficult questions like why customers chose them instead of their competitors and what type of customer is likely to buy a specific product. With the predictive analytics tools in place, the company was able to put more campaigns into the field, as well as measure the effectiveness of different offers and marketing techniques to different customer segments. Recent campaigns have generated an 18 percent increase in customers responding to the promotion.” – Chandi Vyas, Why You Should be Using Predictive Analytics, Forbes; Twitter: @ForbesInsights
15. Learn your competitors’ weaknesses before they do with predictive analytics. “Get a unique competitive advantage and learn competitors’ weaknesses before they do. Using a predictive model generated by your data, you have a proprietary source of business intelligence to help you generate sales and retain customers. This also enables you to identify microsegments of customers who choose your company compared to your competitors, letting you know where the competition falls short.” – Michael Brenner, Predictive Analysis: 7 Reasons You Need It Today, Digitalist by SAP; Twitter: @digitalistmag
16. Few companies are exploiting predictive analytics to its fullest potential. Those that do realize substantial increases in ROI and other competitive advantages. “Few companies today are fully exploiting predictive analytics, despite its obvious potential to improve decision-making and bolster competitive advantage. For the most part, key executives don’t truly understand what can be achieved with predictive analytics, raising the risk that these firms will be left behind by rivals. Indeed, the faster businesses can move on fact-based decisions and the more accurately businesses can predict trends and decision impacts, the greater the chances of them outpacing their competitors.
“But aspirations on predictive analytics often collide with the struggle to contain and cut IT budgets. This lands squarely in the lap of the CIO, who needs to sell the merits of predictive analytics to the business while also working out how to deliver on it within tightening constraints. These aren’t the only challenges CIOs face here. For one, they’re often considered only an implementation partner, rather than a trusted business advisor.
“Second, many IT functions lack the skills and capabilities to really understand the business questions being posed and answer with the relevant data. Finally, other executives often control the data that CIOs need to access to make predictive analytics truly work.” – Predictive Analytics, The CIO’s Key to the Boardroom, Ernst & Young; Twitter: @EYnews
17. Healthcare organizations are using Big Data and analytics in powerful, innovative ways to transform healthcare delivery and patient outcomes. “Dartmouth-Hitchcock has brought together a team of experts spanning industries from medicine to retail, entertainment, publishing and hospitality to pilot a new health solution called ImagineCare. Powered by Microsoft’s new Cortana Analytics Suite and Microsoft Dynamics, ImagineCare is a coordinated, personalized solution that encompasses physical, mental and emotional health.
“ImagineCare is fed with data from wearable and home sensors, health assessments and sensing and ongoing health risk assessments. The connected devices include blood pressure cuffs, pulse oximeter devices and activity trackers like Microsoft Band. The collected data is transmitted to the Azure Cloud via smartphone, where it’s pulled into a Cortana Analytics Suite dashboard at a contact center.
“Clinical care is paired with advanced analytics and machine learning to create adaptive protocols that take into account electronic health records (EHRs), claims, environmental, socio-economic and other data sets. At the data center, registered nurses with access to each patient’s personalized care plan continually monitor patients’ health status and potentially serious trends. If there’s a problem, a nurse receives an alert and immediately reaches out to the patient and authorized family members via phone call, video chat or secure text — often before the patient realizes there’s a problem.” – Thor Olavsrud, How predictive analytics will revolutionize healthcare, CIO; Twitter: @CIOonline
18. Even book publishers are reaching broader audiences through data-driven marketing. “Marriage of data and an intuitive knowledge of your audience can lead to sales, drive traffic to websites, increase engagement on Facebook, LinkedIn, Twitter and Pinterest, gather reader preferences, and target click-throughs for advertising. With the right data, you can make smarter marketing decisions and maximize your budget, engage readers and drive book sales. Data can indicate reach, reputation, following, influence and brand awareness. And that’s not even scratching the surface of what data can do for all of us in the publishing industry.
“When publishers use data they can create better campaigns and communities. They can be more responsive to the needs of their readers, and can quickly assess which type of marketing produces results and which does not. Publishers can use data to determine the right customized plan for each author. True enough, but how do we trust the data that we have? I put the question to Tom Thompson, Vice President, Group Director of Verso Advertising. ‘It’s important to work with trusted partners as close to the source of the data as possible. The fewer middlemen the better chance that the data you are collecting is accurate.'” – Fauzia Burke, Founder of FSB Associates, Data Driven Marketing for Books, The Huffington Post; Twitter: @FauziaBurke
19. Marketers who use data-driven insights gain a competitive advantage in customer loyalty. “Marketers who use data insights to drive marketing campaigns are three times more likely to report competitive advantage in customer loyalty than those who don’t, according to new research.
“The study, from Turn in conjunction with Forbes Insights, indicates that while many marketers agree on the use of data in targeting, almost half of marketers consider themselves lagging behind in data-driven marketing strategies.
“Other key points from the report include:
- Many marketers who consider themselves ‘laggards’ in data-driven marketing blame siloes across the business
- 74% of marketers considered ‘leaders’ in data-driven marketing achieve the advantage in customer loyalty
- 55% of marketers using a data-driven campaign see increased revenue
- The travel industry and the retail industry are leaders in data-driven marketing” – Data-driven marketing drives customer loyalty, NetImperative; Twitter: @_netimperative_
20. Big Data proves effective, leading more companies to increase resource allocation to Big Data initiatives. “Digital marketing programs are driving adoption of big data, Internet of Things (IoT) and cloud technology, research shared today by 2nd Watch reveals.
“During the first half of July, 2nd Watch surveyed 500 IT and marketing professionals of mid-size and large organizations. It said the results show that big data, cloud and IoT are a “primary driver” for digital marketing strategies as teams use high volumes of data from applications, websites and machines for their campaigns.
“Fifty percent of survey participants said they will likely increase their use of big data to supplement their digital marketing. Meanwhile, a third of respondents said they will allocate resources meant for other marketing programs to big data-based ones because of the success of previous programs.” – Scharon Harding, Digital marketing driving big data, cloud, IoT – survey, Channelnomics; Twitter: @Channelnomics
21. Big Data provides more effective alternatives to top-down ad campaign planning. “Historically, the most sophisticated marketers have relied on top-down ad campaign planning. They develop econometric models by looking at the distribution of the whole advertising budget. They analyze changes in allocation and one-time promotions and see how those changes affect their key performance indicators (KPIs), which may be making an in-store purchase, or opening a new account.
“That paradigm is flipped in the digital world. Marketers rely on digital scoring of actions, starting from the bottom up with the KPI. ‘You try to work backwards to see the touch points along the consumers digital journey,’ says Kim Riedell, senior vice president of product and marketing at Digilant, a customized programmatic media solutions company in Boston. Thanks to technologies such as cookies and browser pixels, marketers can now tell exactly where a specific buyer saw their ads. The data even shows how long that buyer watched a video or lingered on a page carrying the ad. It’s all found by backwards tracking from the point of sale of the product the person ultimately bought.” – Driving Marketing Results with Big Data, MIT Technology Review; Twitter: @techreview
22. Big Data brings marketing and finance together. “When Raja Rajamannar became CMO of MasterCard Worldwide in 2013, he moved quickly to transform how the credit card giant measures marketing. His artillery: Advanced Big Data analytics. MasterCard had always been a data-driven organization. But the real power and full potential of data was not being fully realized by marketing.
“Rajamannar involved finance early. To spearhead analytic efforts, he assigned a finance person – who was already embedded in marketing – to create an ROI evaluation framework and integrated her deeper into the marketing function. With a better understanding of the marketing context, she has brought a new level of financial discipline and rigor to the marketing team. This has reframed the conversation to balance the interests of both sides.” – Wes Nichols, How Big Data Brings Marketing and Finance Together, Harvard Business Review; Twitter: @HarvardBiz
23. Industries such as the auto insurance industry are making use of innovative methods of gathering data to drive decisions such as consumer pricing models based on risk behaviors, as well as to implement automatic response mechanisms to improve safety. “Auto insurers in Europe and the United States are testing these waters with offers to install sensors in customers’ vehicles. The result is new pricing models that base charges for risk on driving behavior rather than on a driver’s demographic characteristics. Luxury-auto manufacturers are equipping vehicles with networked sensors that can automatically take evasive action when accidents are about to happen. In medicine, sensors embedded in or worn by patients continuously report changes in health conditions to physicians, who can adjust treatments when necessary. Sensors in manufacturing lines for products as diverse as computer chips and pulp and paper take detailed readings on process conditions and automatically make adjustments to reduce waste, downtime, and costly human interventions.” – Jacques Bughin, Michael Chui, and James Manyika, Clouds, big data, and smart assets: Ten tech-enabled business trends to watch, McKinsey Quarterly; Twitter: @McKQuarterly
24. Big Data is revolutionizing supply chain management. “Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.
“Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.
“Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems.” – Louis Columbus, Ten Ways Big Data Is Revolutionizing Supply Chain Management, Forbes; Twitter: @LouisColumbus
25. Much like marketing organizations use Big Data to determine how likely customers are to make a purchase and remain loyal, higher education institutions are making use of data to determine which students are most likely to succeed and graduate — thus, driving admissions decisions. “Applicants for this year’s freshman class at Ithaca College didn’t have to send their standardized test scores. If they did, the scores were considered, but so were some surprising other factors — how many friends and photos they had on social media, for instance.
“The same big data techniques that are transforming other industries are seeping into the college and university admissions process to help predict whether students will succeed and graduate.
“’This is the kind of stuff that savvy parents, students and college counselors know about,’ said Bruce Poch, dean of admission and executive director of college counseling at the Chadwick School, a private school in Southern California, and former dean of admissions at Pomona College.
“The point is simple: to increase graduation rates by using big data to identify the kinds of students who, experience has proven, are most likely to stick around.” – Emmanuel Felton, The Hechinger Report, The new tool colleges are using in admissions decisions: big data, PBS News Hour; Twitter: @NewsHour
26. Big Data can help you make more efficient use of your resources. In fact, Big Data is being used to help farmers determine how efficiently they’re using their land, allowing them to increase crop production and quality. “To feed the world, farmers need to double their production by 2050. That’s not easy, and it means they have to take advantage of everything that computing has to offer in order to do it.
“FarmLogs is trying to help by enlisting big data and real-time analytics that tells farmers how efficiently they’re using their land, said Jesse Vollmar, chief executive of FarmLogs, speaking at the Intel Developer Forum in San Francisco.
“Volmar said that his company helps farmers make use of every inch of crop land, and FarmLogs is already used to help grow 25 percent of row crops in the U.S. FarmLogs makes use of data from sensors on machinery as well as data coming in from satellite imagery.” – Dean Takahashi, How farmers are using big data to grow better crops, VentureBeat; Twitter: @VentureBeat
27. Get to market faster with products and services that are well-aligned with consumer demands. “Big data is changing the competitive landscape. Those who are in a position to take advantage of it often get to market faster with products and services that are better aligned with customer needs and desires. A 2014 Gartner survey found that 73% of respondents had invested in — or planned to invest in — big data in the next 24 months, up from 64% in 2013. Improving customer experience and process efficiencies are the top two priorities cited by respondents.
“Customer experience improvements are happening online and offline, with data being collected from smartphones, mobile apps, POS systems, and e-commerce sites. With the ability to collect and analyze more data, and more types of data, than ever before, businesses are in an unprecedented position to quantify what works, what doesn’t work, and why. And, the ones that are the most agile are adjusting their business strategies as necessary to increase or maintain marketshare. When executed well, customer experience improvements can help boost customer loyalty and revenue growth. On the other hand, if a company chooses to disregard what the data is indicating, it may well lose customers and deals to a more agile, data-savvy competitor.” – Lisa Morgan, Big Data: 6 Real-Life Business Cases, InformationWeek; Twitter: @InformationWeek
How Marketers Can Use Big Data and Predictive Analytics
28. Determine the right media mix using big data analytics. “The promise of big data analytics is that marketers can analyze thousands of points of information about the digital activity of the purchaser—stripped of personally identifiable information—and combine it with their knowledge of television, radio, billboard, and print campaigns to tailor marketing messages and, ultimately, improve return on investment (ROI). With analysis, the numbers show how much lift each data point provided for each ad in each channel. With that data, marketers can make better decisions about how to allocate their ad budgets. Indeed, the analytics themselves will identify the smart choices.” – Driving Marketing Results with Big Data, MIT Technology Review; Twitter: @techreview
29. Data analytics drives business value for both enterprises and SMBs, but it must be digestible to be meaningful. “It turns out that enterprises and SMBs alike are driving real business value from implementing analytics and are planning to increase their spend in 2015. Ninety-seven percent of our survey respondents reported that using marketing analytics has made their business more successful, and 87 percent said they plan to increase their marketing analytics investments in the coming year.
“Conducted in partnership with market research firm RedShift, the survey outlines a growing need for marketers to embed analytics into all of their campaigns in order to keep pace with the competition. A majority 65 percent of respondents claim they have just the right amount of data to be useful. Conversely, for marketers big data still remains largely elusive, with 77 percent noting that it remains a promise, not a reality. Taking into account these two opposing stats, it seems marketers are relying more on bite-sized, manageable chunks of ‘small data’ rather than trying to tap into the big data movement.” – Tricia Heinrich, Senior Director of Strategic Communications at ON24, New Survey Finds B2B Marketers are Driving Real Business Results from Analytics; ON24; Twitter: @ON24
30. Use insights to drive growth of your existing business streams. “While cost reduction approaches could ‘keep you in the game’, they don’t bring the differentiation that most companies need to thrive in highly competitive markets. Our study suggests that a significant proportion of organizations know that they need to do more. Around 61% of respondents state that big data is becoming a driver of current revenues in its own right. Insights from big data are being used to enhance existing market offers through better understanding of customers and consumers and of the effectiveness of marketing and sales activities. For many industries, this means developing a much more granular understanding of their customers by aggregating and analyzing all the relevant data per customer (from inside and outside the company, including social media) to achieve an accurate 360-degree customer view. Using the power of deep customer analytics and behavioral modeling, organizations can then create both innovative and relevant service offerings that customers actually want, all delivered through an integrated and seamless digital customer experience.” – Big & Fast Data: The Rise of Insight-Driven Business, Capgemini; Twitter: @Capgemini
31. Segment your email marketing campaigns for relevant, personalized customer engagement. “Do you segment your email list by what your subscribers are interested in? This will help you tailor your email communications to subscribers, so that you deliver what they want. Most email marketing programs today have some way to segment subscriber interest. Some programs can even ‘learn’ what recipients are interested in based on which emails they’ve opened and what they’ve clicked on in the past, making it easy to segment.
“It’s getting harder to get readers’ attention. People are overwhelmed, and many email inbox programs do a lot of filtering before people even see your messages. It pays to be as relevant as possible today so that they seek out your messages, rather than become annoyed with irrelevant ones. Only send people what they want, rather than one-size-fits-all mailings.” – Anita Campbell, CEO of Small Business Trends, 5 Ways to Use Big Data, Inc.com; Twitter: @smallbiztrends, @Inc
32. Use analytics to inform your editorial calendar and drive your content marketing strategy. “At the heart of any content marketing strategy sits the editorial calendar, and this document should be driving the data that you choose to study. Everything in that editorial calendar should be subject to data interrogation — from types of content to blog titles, publication days/times, content categorization, authors, and more.
“If you already have a calendar, then you have a great starting point for analyzing what data tells you about your content marketing strategy. If you don’t, the analysis process remains the same, though you may have less structured data with which to work.
“Are you part of a larger enterprise? You may be lucky enough to have a data analyst or team of developers in-house. If so, request that they extract your blog’s content data in a structured way — ideally as a table showing each published post and its key info, along with your chosen key performance indicators (KPIs), such as site visits (e.g., from Google Analytics), engagement (social shares), and conversions (also from Google Analytics).” – Ben Harper, How to Use Data to Improve Your Content Marketing Strategy, Content Marketing Institute; Twitter: @CMIContent
33. Data-driven segmentation and profiling is an integral step for customer acquisition, brand awareness, and customer retention. “In our award-winning, annual Digital Marketer Report we discuss using a data-driven segmentation and profiling strategy. This is an integral step for any marketer looking to build a brand, acquire new customers or retain the most loyal fans. By using consumer data to find insights into the way target customers think, feel and behave, marketers will be able to make (and justify) smarter decisions about messaging and campaign execution.” – John Fetto, How to enable real decisions about your marketing strategy, Experian; Twitter: @ExperianMkt
34. Set clear goals and stick to them. “Sophisticated analytics tools have meant that we have access to a huge amount of information about our customers, also known as – warning, buzzword coming up! – ‘big data‘. The sheer amount of things we now know has meant we need to adapt systems and methodology to cope. This has presented a lot of companies with a basic problem: where do we start? And more importantly – where do we stop?
“Before you dive head-first into spreadsheets, spend some time thinking about the information you’d like to extract from your data set. For example, say you’re sending out an email to lapsed customers to encourage them to engage with you again. The data you’re looking for is the date of their last interaction with your business. Their middle name and phone number is probably of little interest to you. Don’t become overwhelmed by data: set clear goals at the start, and stick to them.” – How to Make Data-driven Marketing Decisions, Clever Marketing; Twitter: @clevermUK
35. Predictors are the foundation of predictive analytics. “The driving behavioral characteristics within Predictive Analytics are known as predictors. These can be any behavioral characteristics which determine the likelihood of someone converting, for example; length of visit, location, etc. These predictors are then used within a model (a formula) which allows you to calculate the profitability of a specific type of user. Predictors should be combined within a model and the more predictors you use, the more accurate your analysis will be.
“For example, let’s say your data shows you that proximity plays a role in conversions; i.e. the closer a user is to your premises the more likely they are to convert. Additionally, the longer a user spends on your website, the more likely they are to convert. In this scenario, you would use the formula;
“Location + Visit Length = Conversion Probability
“You would assign higher values to locations closer to your premise and then focus your marketing efforts on user types with a higher conversion probability.” – Gemma Holloway, Why Your Business Needs Predictive Analytics, Koozai; Twitter: @Koozai
36. You need good data for predictive analytics to work (well). “Lack of good data is the most common barrier to organizations seeking to employ predictive analytics. To make predictions about what customers will buy in the future, for example, you need to have good data on who they are buying (which may require a loyalty program, or at least a lot of analysis of their credit cards), what they have bought in the past, the attributes of those products (attribute-based predictions are often more accurate than the ‘people who buy this also buy this’ type of model), and perhaps some demographic attributes of the customer (age, gender, residential location, socioeconomic status, etc.). If you have multiple channels or customer touchpoints, you need to make sure that they capture data on customer purchases in the same way your previous channels did.
“All in all, it’s a fairly tough job to create a single customer data warehouse with unique customer IDs on everyone, and all past purchases customers have made through all channels. If you’ve already done that, you’ve got an incredible asset for predictive customer analytics.” – Tom Davenport, A Predictive Analytics Primer, Harvard Business Review; Twitter: @HarvardBiz
37. Big Data and predictive analytics are increasingly used to refine sports, such as for football teams, to improve strategy and boost wins — in much the same way as they are in business. “One big lesson from this year’s MIT Sloan Sports Analytics Conference was that sports analytics has now fully grown up from its blogger-in-his-pajamas phase and is now big business. Major, global consulting firms that do most of their work for billion-dollar-budget federal agencies are advertising their services to teams and leagues. Much of the growth in the field is in player tracking, both in terms of physiological measurement and in digitizing player location information during games.
“I have no doubt that both of those approaches can offer teams insights and benefits, but it remains to be seen just how big those benefits might be and how costly and difficult it will be to get them. I get the sense that there will be an avalanche of data and it will require large and expensive efforts to gain marginal benefits above what conventional methods offer.
“That’s why I remain convinced that the most direct, most demonstrable, and most actionable analytic approach in football is in-game decision support.
“By direct I mean that benefits of the analysis lead immediately to the thing that matters most–winning. Better physiological data could lead to healthier athletes, which would then lead to winning. But it’s murky how healthier we can make players and how much the improvement could mean to a team’s win total.
“By demonstrable I mean that the analysis can directly quantify and verify its own impact. When a coach is faced with two alternatives and the traditional choice offers .05 points of win probability less than the choice the analysis recommends, we can credit the analysis with having that impact on the game.
“By actionable I mean that the analysis doesn’t exist for its own sake. There is a high-stakes decision to be made that is directly informed by the analysis. In short, it matters.
“Perhaps the most important aspect of game strategy analysis is that it’s the most cost-effective way to increase a team’s win total, by far.” – Brian Burke, The Value of a Good Analytics Program, Advanced Football Analytics; Twitter: @BBurkeESPN
38. Predictive analytics is paving the way for tremendous advances in healthcare. “Predictive analytics is not reinventing the wheel. It’s applying what doctors have been doing on a larger scale. What’s changed is our ability to better measure, aggregate, and make sense of previously hard-to-obtain or non-existent behavioral, psychosocial, and biometric data.
“Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in enhanced reengineering of clinical pathways and truly personalized care.” – Vinnie Ramesh, Chief Technology Officer, Co-founder of Wellframe, as quoted in The Future of Personalized Healthcare: Predictive Analytics, authored by Malay Gandhi, Managing Director, and Teresa Wang, Strategy Manager at Rock Health; Twitter: @myvinnie
39. Big Data can help you uncover new stories for your company to tell. “One of the most exciting aspects of big data is that the information captured uncovers new stories for companies to tell, says Kerry Ruggieri, senior vice president of Ketchum Sports and Entertainment.
“For example, a Ketchum client used analytics to track how far tennis players ran throughout the course of a match – a statistic that had never been shared with fans. ‘Data opened up a whole new avenue for us for storytelling,’ she says.” – Using data to drive marketing, UNC Kenan-Flagler Business School; Twitter: @kenanflagler
40. The sheer volume of activities and audiences that your company’s marketing dollars must support call for a more sophisticated approach to Big Data analytics. “The diverse activities and audiences that marketing dollars typically support and the variety of investment time horizons call for a more sophisticated approach. In our experience, the best way for business leaders to improve marketing effectiveness is to integrate MROI options in a way that takes advantage of the best assets of each. The benefits can be enormous: our review of more than 400 diverse client engagements from the past eight years, across industries and regions, found that an integrated analytics approach can free up some 15 to 20 percent of marketing spending. Worldwide, that equates to as much as $200 billion that can be reinvested by companies or drop straight to the bottom line.
“Here’s one example. A property-and-casualty insurance company in the United States increased marketing productivity by more than 15 percent each year from 2009 to 2012. The company was able to keep marketing spending flat over this period, even as related spending across the industry grew by 62 percent. As the chief marketing officer put it, ‘Marketing analytics have allowed us to make every decision we made before, better.'” – Rishi Bhandari, Marc Singer, and Hiek van der Scheer, Using marketing analytics to drive superior growth, McKinsey & Company; Twitter: @McKinsey
41. Data management platforms (DMPs) can help you make sense of and take action on the vast amounts of data generated by your company and its consumers. “As marketers drown in the piles of customer data they’ve collected, they’re turning to marketing technology to make sense and act on the information collected. Data management platforms (DMPs) are one option for doing this.
“Senior marketers polled worldwide in May 2015 Econsultancy in association with Oracle Marketing Cloud reported a wide range of data-related benefits to using DMPs. The majority cited centralized control and standardization of existing first-party data as a major benefit. Just over half said the same about the ability to use existing data for better personalization across several channels. Developing actionable data sets across sources, attribution models for better analysis and better cross-channel marketing effectiveness were also among the leading benefits.
“Other research points to benefits related to personalization and targeting as a result of data gleaned from DMPs. In June 2015 research by VB Insight, DMPs ranked as the No. 1 customer database for personalization purposes among marketers worldwide. And according to January 2015 research by Forrester Consulting commissioned by Adroit Digital, 57% of US digital marketers and customer insights professionals said their digital marketing teams leveraged technology like a DMP that allowed them to use driven audience targeting online.” – DMPs Drive Data-Driven Marketing Success, eMarketer; Twitter: @eMarketer
42. Take advantage of data-gathering opportunities. “Every interaction with your subscriber base is an opportunity to exchange value for data. Use your email messages to gather inferred preferences via clicks to navigation bars and other content. Use explicit surveys to collect preference information and better understand consumer intent. To take data gathering to the next level, integrate more of a dialogue approach into the email program by asking subscribers for input on their experience, interests and sentiment, or use email to “crowd source” input on upcoming promotions, catalog covers or other things that you can customize based on consumer input.” – Catherine Magoffin, Data Must Drive Your 2015 Digital Marketing Planning, ClickZ; Twitter: @ClickZ
43. Data can help you predict where customers are in the purchase funnel and engage them at the right moment. “Timing is everything when it comes to getting in front of your audience.
“In a survey from comScore, 39% of respondents claim to search and buy online, while 21% search in-store and buy in-store, and 13% search online and buy in-store.
“Additionally, a study by Ipsos reveals that 51% of consumers gain inspiration in-store compared to 42% on computers and 55% split across tablets and smartphones.
“In regard to research, 50% of respondents research on computers compared to 48% in-store, 34% smartphones, and 25% tablets. Ipsos data also showed that a majority (67%) of people still purchase in-store compared to 31% on computers and 21% and 18% on smartphones and tablets.
“All of these stats suggest that there are many paths to purchase. Using available search trends and channel insights, marketers have an opportunity to predict where their customers are in the purchase funnel and influence them at the right moment.” – James Green, How Marketers Can Drive Seasonal Sales With Intent Data, Marketing Land; Twitter: @JamesANGreen, @Marketingland
44. The most effective marketers use data-driven insights to drive content strategy. “Marketing leaders are more likely to use number-based insights to decide on content strategies than underperformers. They’re more likely to consider metrics around past performance (42 percent) and market research (35 percent) than the laggards (33 percent and 27 percent of whom rely on those insights, respectively).
“The leaders also have a stronger appreciation of the nuances of content analytics. While they’re more likely to rely on these figures, stronger marketers say analyzing data is a greater challenge. A full 31 percent of top marketers know there’s room for improvement in this area, while just 21 percent of underperformers recognize the need to beef up their data learning processes.” – Katherine Griwert, Survey says: Diverse formats & data analysis drive content marketing success, Brafton; Twitter: @kgriwert, @Brafton
45. Sophisticated algorithms that make use of data to inform intelligent marketing decision-making offer tremendous value. “Most of the big data investment focus to date has been on the underlying infrastructure, while development of the applications that make use of that infrastructure – and that deliver actual business value – has lagged.
“Marketing organizations in particular are eager to use big data to drive better results. Today’s marketers want to be data-driven, yet in most cases they don’t have the time or tools to analyze the increasingly large volumes of data that they collect. For this they have rushed to assemble teams of data scientists, with the hope that if they understand each customer and how he or she behaves they can then execute more personalized interactions that impact key metrics, such as revenue, churn rates, consumption, promoter scores, etc.
“The value that data scientists deliver in developing sophisticated algorithms that enable intelligent marketing decisions is tremendous.” – Dr. Olly Downs of Globys, Why Marketing Organizations Need More than a Team of Data Scientists, Inside Big Data; Twitter: @insideBigData
46. Data can be used to validate gut choices, resulting in a data-informed marketing strategy. “Instead of focusing on data alone, data-informed marketing considers data as just one factor in making decisions. We then combine relevant data, past experiences, intuition, and qualitative input to make the best decisions we can.
“Instead of poring over data hoping to find answers, we develop a theory and a hypothesis first, then test it out. We force ourselves to make more gut calls, but we validate those choices with data wherever possible so that our gut gets smarter with time.” – Ezra Fishman, The Dangers of Data-Driven Marketing, Wistia; Twitter: @wistia
47. An RFM analysis (Recency, Frequency, and Monetary) can help tailor marketing efforts to reach the right consumers with the right messaging. “Marketing campaigns can often be expensive, time consuming, and worst of all, ineffective. Too many businesses waste valuable resources advertising to customers who are unlikely to respond. If it is true that 80% of sales come from 20% of customers, then how can marketing efforts be tailored to most effectively reach the right audience?
“One of the most popular ways to achieve this is through RFM analysis. This technique segments customers based on three factors: Recency, Frequency, and Monetary value. Ordered by their importance, recency is the most significant predictor of whether a customer will return. The more recently they have made a purchase, the more likely they will again. Frequency is also very important. Customers who purchase from you often will likely continue to do so is satisfied. Finally, the amount a customer spends on each transaction can predict future purchases, too. Those who spend more are more likely to return than those who spend less.” – OroCRM, Driving Better Marketing Results with RFM Analysis, DemacMedia; Twitter: @demacmedia
48. Don’t ignore your “small data.” “The sheer volume of big data available makes it difficult for those monitoring development projects to distinguish between what is important and what isn’t. Producing meaningful insights from these datasets alone can, at best, be difficult, and at worst, misleading. For example, the Belgian researchers still needed reliable household survey data in order to establish the relationship between call detail records (CDR) and food security indicators. “Small data” is still essential.
“Similarly, while the boom in mobile phone usage across developing countries means that much big data is being generated in previously underserved areas, the characteristics of mobile phone users may not be applicable to the population of a country as whole. Researchers from the UK and US found that in Rwanda and Kenya mobile phone owners are not representative of the general population. They are disproportionately male, educated and from larger households. If we relied solely on mobile phone data we risk getting a skewed view of progress towards the SDGs.” – Paul Jasper, Why we shouldn’t get too excited about using big data for development, The Guardian; Twitter: @guardian
49. Predict what your customers want before they ask for it. “Remember when that shopkeeper had your loaf of bread all wrapped up and ready to go before you even told her that’s what you wanted? Providing that same service for online shoppers based on their past behavior is exactly how companies are using big data to increase customer satisfaction — and increase purchases.
“Companies gather a ton of data on customers, not only what they’ve purchased but also what websites they visit, where they live, when they’ve contacted customer service, and if they interact with their brand on social media. It’s an overwhelming amount of seemingly unrelated data (that’s why it’s called big data), but companies that can properly mine this to offer a more personalized touch. To properly predict the future, companies must promote the right products to the right customers on the right channel.
“Amazon long ago mastered the recommendation of books, toys, or kitchen utensils that their customers might be interested in. Other companies have followed suit, such as recommending music on Spotify, movies on Netflix, or Pins on Pinterest.” – 5 ways companies are using big data to help their customers, VentureBeat; Twitter: @VentureBeat
50. Identify opportunities for growth, new channels, and even new verticals. “The use of Big Data is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value. Indeed, we found early examples of such use of data in every sector we examined. In healthcare, data pioneers are analyzing the health outcomes of pharmaceuticals when they were widely prescribed, and discovering benefits and risks that were not evident during necessarily more limited clinical trials. Other early adopters of Big Data are using data from sensors embedded in products from children’s toys to industrial goods to determine how these products are actually used in the real world. Such knoiwledge then informs the creation of new service offerings and the design of future products
“Big Data will help to create new growth opportunities and entirely new categories of companies, such as those that aggregate and analyse industry data. Many of these will be companies that sit in the middle of large information flows where data about products and services, buyers and suppliers, consumer preferences and intent can be captured and analysed. Forward-thinking leaders across sectors should begin aggressively to build their organisations’ Big Data capabilities.
“In addition to the sheer scale of Big Data, the real-time and high-frequency nature of the data are also important. For example, ‘nowcasting,’ the ability to estimate metrics such as consumer confidence, immediately, something which previously could only be done retrospectively, is becoming more extensively used, adding considerable power to prediction. Similarly, the high frequency of data allows users to test theories in near real-time and to a level never before possible.” – Tim McGuire, James Manyika, Michael Chui, James Manyika, and Michael Chui, Why Big Data is the new competitive advantage, Ivey Business Journal; Twitter: @IveyBusiness
51. Use dynamic pricing models, but use them wisely. Uber’s ‘surge pricing’ model proved not as efficient as originally thought when algorithms drove prices through the roof on New Year’s Eve. “These algorithms monitor traffic conditions and journey times in real-time, meaning prices can be adjusted as demand for rides changes, and traffic conditions mean journeys are likely to take longer. This encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low. The company has applied for a patent on this method of Big Data-informed pricing, which is calls ‘surge pricing’.
“This algorithm-based approach with little human oversight has occasionally caused problems – it was reported that fares were pushed up sevenfold by traffic conditions in New York on New Year’s Eve 2011, with a journey of one mile rising in price from $27 to $135 over the course of the night.
“This is an implementation of ‘dynamic pricing’ – similar to that used by hotel chains and airlines to adjust price to meet demand – although rather than simply increasing prices at weekends or during public holidays, it uses predictive modelling to estimate demand in real time.” – Bernard Marr, The Amazing Ways Uber Is Using Big Data Analytics, LinkedIn; Twitter: @BernardMarr
Using Big Data and Analytics to Better Understand (and Target) Your Customers
52. By leveraging your data in smart ways, you can better engage your customers and increase ROI. “In one of many case studies found in Big Data, Meet Dumb Data, author Neil Ungerleider highlights how Vail Resorts devised a smarter way of leveraging their data sets to better engage with their customers — and increase ROI.
“By unifying disparate silos of data from hotels, ski hills, and ski schools into a single analytics platform, Vail Resorts created a data driven marketing campaign that connected all the dots on Vail’s customer touch points. The company could then create a smart marketing campaign driven by users that resulted in more than 35 million social impressions across Twitter and Facebook.” – William Chadwick, Big data, meet dumb data: How CMOs are driving value from more (and less) data, VentureBeat; Twitter: @VentureBeat
53. Deliver a tailored experience across all marketing channels. “No wonder that harnessing the potential of big data is on the agenda of chief marketing officers in almost every large company. When and how should they tap into big data sets and what should they do with it? What is the best approach to realize the benefits? What are the opportunities and challenges? In particular, marketing leaders want to know how to monetize the big data.
“Sophisticated analytics solutions for big data provide new approaches to addressing some of the key marketing imperatives and delivering impressive results. These solutions can transform traditional marketing roles and improve how to execute essential marketing functions. Marketers are collecting the data produced from a variety of live customer touch-points to paint a complete picture of each customer’s behavior. Analyzing this large amount of data in motion enables marketers to fine-tune customer segmentation models and apply the insights to develop customer engagement strategies and improve the value of customer interactions.
“As the number of customer channels increases, marketers need to ensure that they are delivering a tailored experience across all channels. All of these efforts help provide a highly personalized experience while maximizing the return on the marketing investment. In the longer term, marketers can feed these new, real-time insights back into the organization to influence product development and product pricing as well.” – Michael Svilar, Arnab Chakraborty and Athina Kanioura, Big data analytics in marketing, Informs; Twitter: @INFORMS
54. Make your customers a part of the decision-making process. This is a practice Tesla has employed through the use of advanced analytics, and it’s paid off handsomely. “Far from being an annoyance, customers are a critical part of Tesla’s decision-making process. In addition, with no dealer network, Tesla interacts with customers directly and not through proxy. (In Jaguar’s case, most of the complaints I see aren’t being sourced at the dealer but at the Jaguar corporate office, which, in North America at least, appears poorly run.) Since the Jaguar F-Type is a highly marketed halo car designed to change people’s minds about Jaguar for the better, the fact that so many loyal Jaguar customers are now expressing disloyalty suggests that Jaguar management is either incompetent or uninformed. (I’d guess the latter.)
“Tesla’s use of sensor data, customer contact and analytics appears to scale even better than Apple’s. Loyalty at Apple appears to be dropping as the company’s human ambassadors, its Genus Bar employees, cycle to other jobs and are replaced by folks who are increasingly less passionate about Apple’s ever-more-common products and less tolerant of hearing the same stupid questions over and over again. Machines actually like the same question over and over again and deal with them better than people do.
“What really showcases the benefits of applied analytics at Tesla is that the company exists at all. The electric charging infrastructure is pathetic compared to gas, the fully loaded car exceeds $100,000 and it represents a massive risk to anyone who buys it. Rather than go under, though, Tesla has grown faster than the rest of the market, its customer loyalty is far higher, and its car has been rated the best in the world – ahead of cars and firms that have been in the car business for more than a century.
“Tesla itself runs the most active forums on its own car, which gives it a running sense of what excites and annoys customers and, in turn, gives Tesla a massive advantage over firms that don’t host or monitor forums on their cars. I frankly wonder if most auto firms have discovered the Internet.” – Rob Enderle, Why Analytics Makes Tesla Better Than Jaguar, CIO; Twitter: @CIOonline
55. Put customer intelligence at the fingertips of highly trained customer representatives to optimize the customer experience. “If we were still in the era of simple landline phones and voice mail, maybe telecom customer care jobs would be in decline.
“But in the age of smartphones and a dizzying array of app and network technology, customers expect call center pros to step up and help them solve tough problems.
“In fact, the technical expertise of many customer care pros today puts them in the league of ‘Computer Support Specialists’, a $48,500 a year job the U.S. Department of Labor says will grow an above-average 17% in the next decade.
“But technocrats undervalue the role of humans in the customer experience mix. The truth is that full, end-to-end care automation is neither affordable nor wise. The best strategy is a semi-automated one that puts great intelligence at the fingertips of highly trained reps.” – Interview with Brian Jurutka, Driving Customer Care Results & Cost Savings from Big Data Facts, Black Swan Telecom Journal; Twitter: @BlackSwanTel
56. Build a holistic view of the customer. “Cutting-edge marketing organizations increasingly rely on technology: CRM systems, big data analytics, marketing optimization tools, and a host of other specialized software. Historically, the IT function has housed and managed most of this technology, and central analytics groups have mined and processed the data. But that’s changing rapidly.
“Look, for example, at how the marketing function at Nordstrom is evolving. The company’s overarching strategic goal is still to improve the customer experience every year. But in the past its central marketing organization played the role of order taker, executing the plans of merchants in each category. Moreover, individual teams focused on the department or the product line for which they were responsible, while no one was building a holistic view of the customer.
“Today the company faces new challenges, such as tracking and engaging customers across all four of its sales channels (Nordstrom, Nordstrom Rack, Nordstrom.com, and HauteLook). It has begun to address these challenges with far more sophisticated analytic and testing capabilities than were previously available. It has learned, for example, that customers who buy from multiple channels typically have a higher lifetime value than single-channel shoppers. It has learned that customers who spend $100 in some key categories often have a higher lifetime value than those who spend the same amount in other categories. That kind of analysis allows Nordstrom’s marketers to put customers, rather than categories or brands, at the center of their efforts. Brian Dennehy, the company’s new CMO, has embraced an aggressive test-and-learn philosophy and is gauging success along such metrics as customer acquisition, customer migration across channels, and customer lifetime value.” – Aditya Joshi and Eduardo Giménez, Decision-Driven Marketing, Harvard Business Review; Twitter: @HarvardBiz
57. Use data to better define your Ideal Customer Profiles (ICPs). “Use heaps of analytics to learn more about your target buyers than you’ve ever known before.
“Whereas in years past, marketers would make educated guesses at the age, demographics, and work profile of their target buyer, modern marketers have vats of data intelligence to prove their intuitions, and shed light on a more granular level of detail, such as: which web sites a user frequents most often, which social media profiles they have and use, and even which buttons they click on a given website.
“ICP (or Ideal Customer Profiles) can be extremely targeted, while also data-backed.
“For instance, in an Avis Budget case study, Tim Doolittle, vice president of CRM and marketing science, said adding Big Data to understand their customer profile ‘…increased the effectiveness of our contact strategy, in many cases above 30% over control.'” – Jean Spencer, 5 Ways Marketers Can Actually Use Big Data, Salesforce; Twitter: @salesforce
58. Tap into social data to find out what customers want and need, and create targeted, personalized campaigns. “As a marketer, you’re always searching for ways to better communicate with consumers and be more relevant, personalized, and targeted. And never before have companies been able to ‘listen in’ on what consumers are saying until now. Social media data can help reveal important trends to craft more targeted, personalized campaigns. For example, Amazon uses social platform data to better target consumers. The online retail giant tracks customers’ social habits to make future product recommendations.” – Make More Informed Decisions using Social Media Data, Hoover’s; Twitter: @Hoovers
59. Treat your network traffic like a gold mine, and make use of that data to better understand your customers. “Treat Your Network Traffic as a ‘Gold Mine’… and Mine that Gold! Your network contains a wealth of Data in Motion that many companies don’t take advantage of. Harvesting this valuable information is the first step in truly understanding your customers’ experience.” – Mike Dickey, Cloudmeter, 10 Ways to Use Big Data to Get to Know Your Customers Better, Wired; Twitter: @WIRED
60. Tap into relationship-oriented data to create truly personalized experiences. “Marketing solutions provider Yesmail Interactive and market research firm Gleanster surveyed 100 senior-level marketers at mid-size and large business-to-consumer (B2C) companies to gain insight into the businesses’ customer engagement. The report revealed that most marketers simply don’t have enough information to send the kind of personalized, targeted campaigns that would keep customers returning to those brands. The key to earning market share, says the report, is building customer relationships. The best way to do that is to harness ‘relationship-oriented data,’ such as information on social media use and online behavior. Less than half of the survey respondents base their marketing campaigns on these types of data, which provide far better targeting than basic transactional data or demographic information like gender and age.” – Nicole Fallon, 7 Ways to (Really) Know Your Customers, Business News Daily; Twitter: @BNDarticles
61. Manage your leads better with more robust data. “Use improved sales data to figure out how to manage your leads better. Then you can comb the data to see how you’re doing in retention and with suspects and prospects – and craft your marketing messages accordingly.
“In my case, I was able to drill down to who was downloading our product resources—and also what search terms they used to get to those pages. After following that path, I was able to see who was looking for terms that described our product mission—rather than our product features or benefits.
“Synchrono champions demand-driven, Pull-based manufacturing philosophies because we believe using these will help manufacturers succeed. We had been crafting many of our messages around that ‘brand purpose’ – which defines for your customer why you are doing what you’re doing before they know what you are selling. The ‘heart’ of your mission, if you will.
“According to Jim Stengel, former CMO of P&G, companies that transmit their brand purpose effectively are three times more profitable than those that don’t. I found that our potential and current customers were on board with our brand purpose—they needed to know how to start to apply these principles to their own environments. Once I knew we were hitting the mark with our brand purpose, all of our marketing resources and tactics became aligned behind it.” – Pam Bednar, Three Ways to Use Big Data to Understand Your Customers, Synchrono; Twitter: @Synchrono_News
62. Turn your data into action. “if you have the right data collection and storage tools, you are armed with the insight to understand who your customers are – what they like, how they engage, and much more. This type of information gives you the power to foster more purpose-driven consumer actions, build authentic customer relationships and predict future behaviors.
“One highly efficient strategy is to group consumers into segments based on similar characteristics or behaviors to reach them with relevance at scale. Triggering communications based on specific events – whether they be life events like birthdays or on-site actions like abandoning cart – is another key tactic for building personal customer relationships.
“One of the strongest ways to turn your data into action is to deliver more personalized user experiences. From greeting customers by name to recommending specific products, opportunities to personalize consumers’ on-site experiences are endless. By tying all on-site activity like socially shared and favorited recipes to a single user profile, McCormick is able to provide each user with a unique ‘Flavor Print,’ or taste profile, which is then used to recommend recipes she may like.” – Rachel Serpa, 3 Strategies for Understanding and Implementing Customer Data, Gigya; Twitter: @Gigya
63. Don’t overlook the value of real-world observations and interactions. “In the early days of developing a new product or service, seek depth over breadth. Move beyond large surveys and customer data sets, and have long, open-ended conversations with 10 current or potential customers about their experiences with your product and competitors’ products. Explore: What problem are they really trying to solve by using your product? Where do various products delight or disappoint them? What’s the biggest hassle they tolerate when using your products — perhaps without even realizing it? What must they give up to use your offering? If you can answer these types of questions, you can unlock innovation opportunities that data alone will miss.” – Carter Cast and David Schonthal, Kellogg, Your Customers Aren’t Data — They’re People, Forbes; Twitter: @KelloggSchool
64. Combining multiple predictors helps you better rank and target customers with relevant messaging. “It turns out you can do even better by using more than one predictor at a time, combining them with a model. Creating this model is the very purpose of predictive analytics.
“One way to combine two predictors is with a formula, such as simply adding them together. If bothrecency and personal income influence the chance that a customer will respond to a mailing, a good predictor may be:
recency + personal income
“Voilà, a new, improved predictor. If recency is twice as important, give it twice the weight:
2 x recency + personal income
“A scheme such as this that combines predictors is called a model — in the case of the summation above, a linear model. For this reason, predictive analytics is also called predictive modeling.
“Other predictive models are business intelligence rules, such as:
“If the customer is rural, and her monthly usage is high,
then the customer will probably renew.
“If you discover that urban customers who spend more time exploring new service features are at a greater risk to cancel, expand this rule-based model with a second rule:
“If the customer is urban, and new feature exploration is high,
then the customer will probably not renew.
“The right combination of predictors will perform better prediction by considering multiple aspects of the customer and her behavior. To match the complexity of customer decisions, a predictive model must usually be much richer and more complex than the above examples, combining dozens of predictors.” – Eric Siegel, Ph.D., Predictive Analytics with Data Mining: How It Works, Prediction Impact; Twitter: @PredictAnalytic
65. By knowing your customers better, you can capitalize on intelligence to create more relevant experiences. “Decent content isn’t all that hard to find. Much rarer is a strategic plan for relationship-building with prospects and customers. For marketers to demonstrate a return on digital relevance (i.e., the measurable business value of content), the customer connections it initiates must last. Sustainable relationships aren’t built by short-term programs. Accordingly, Ardath advises marketers to kick time-bound campaigns to the curb.
Every time you change a campaign, your relevance ebbs. The introduction of a new theme requires that you reestablish relevance. The switch in topics and focus allows your prospects an opportunity to decide your content is no longer relevant to them.
“The traditional campaign mindset creates the illusion that marketers have exclusive control of their brand, which simply isn’t true. Modern marketers must accept that what others say about their brand is what defines it and, as Ardath counsels, learn to “work competently with less direct control.” A good way to accomplish this shift is to focus on knowing customers better and using that intelligence to move the relevance needle.” – Shelly Lucas, including a quote from Ardath Albee (@ardath421), Want Digital Relevance? Make Your Marketing Relationship-savvy, Dun & Bradstreet Connectors; Twitter: @pisarose
66. You must understand your company’s various buyer personas in order to create effective, engaging content that drives your marketing campaigns forward. “When starting content marketing, one of the first things you need to do is know your audience. If you don’t understand your audience and what they need, it’s all too likely that you’re going to write meaningless content wasting the reader’s time. The solution is to write your content for your specific audience and not just for your brand. You need your readers to interact with the content by sharing and providing community.
“One key to this is to understand your user persona. Rather than trying to write for thousands of people, think of one single person that is your customer and write your content for them. Clearly define the characteristics for this person, the more details you have, the more likely your success.” – Mike Kamo, Content Marketing and CRM: Synergies That Drive Results, Stride ; Twitter: @ProsperWorks
Getting the Most Out of Your Data
67. Pay close attention to the quality of your data sources and collection systems. In fact, you should verify the correct tagging, filters, and analytics configuration before relying on any data set or report. “Take search as an example. Brands are investing millions in search, but when it comes to correct tagging of URLs and websites, most brands fall short. I can’t count the number of times we looked at search data and discovered that the URLs could not handle paid search tags, or that we had missing or duplicate analytics tags in place. Marketers optimize against conversion data coming from the analytics platform, but how often is it that the agency that buys the media also controls the tagging? This begs the question: why do so many brands invest millions in media, but still shy away from investing in data quality and control?
“I have seen many brand-agency relationships suffer from issues caused by bad data. In one instance, the client was double tagging some of their landing pages, resulting in inflated page views and visits. When the brand launched a new site with improved tagging, traffic and engagement dropped dramatically.” – Benjamin Spiegel, Chief Executive Officer at MMI Agency, Big Data: Inspect What You Expect, ClickZ; Twitter: @nxfxcom
68. Robust analytics combined with effective customer feedback mechanisms are the recipe for success for modern enterprises. “Companies that don’t have a robust analytics capability are at a huge disadvantage in today’s marketing ecosystem, and businesses that fail to fully understand customer feedback are also at risk.
“One reason companies fail to find opportunities when they look through feedback for ways to increase efficiency is that they too often rely on a single department’s account of the customer experience: sales. The sales teams’ perspective is incredibly valuable, but now it’s possible to gain customer feedback during the sales process and analyze technology breadcrumbs for a more accurate account of what’s happening in real time.
“Another important component of success, demand generation is singularly well-suited for analytics for the simple reason that if you can figure out how customers hear about your company, you can more precisely target marketing efforts and advertising dollars to the most fruitful channels. But it’s important to keep in mind that data quality is the key to success. For effective demand generation, companies need access to complete data, or they’ll be making a decision based on only a portion of the relevant factors.
“The same is true of customer satisfaction. Customer feedback mechanisms must be well-designed and comprehensive to deliver actionable data. Many companies still rely on annual surveys to measure customer satisfaction.
“Though the data generated from a yearly survey can bolster the case for an executive bonus or provide a fig leaf to managers who insist that they care what customers think, annual surveys are virtually useless as a tool for measuring and improving customer satisfaction. To be effective, feedback should be solicited in a timely fashion (ideally at the point of the customer interaction) and acted upon immediately.
“Both analytics and customer feedback can provide a rich portrait of customers and potential customers. In fact, companies that develop an advanced data framework convert feedback into data that can be folded into broader analytics.” – Giles House, Find the Right Balance Between Big Data and Human Feedback, MarketingProfs; Twitter: @housegiles
69. Teams must work together, often across business lines, to glean the most value from company data. “While IT is building out the capabilities, and in many instances taking ownership for the actual systems, the survey confirms that executive leadership, marketing, business analysts, and finance are playing an instrumental role in driving organizations to embrace big data solutions. And, when asked how organizations are leveraging or planning to leverage big data, respondents cite enabling business intelligence and analytics (76 percent), business strategy and direction (55 percent), as well as data discovery and exploration (44 percent) as primary objectives.
“‘The importance of partnering with lines of business when building a big data environment is essential, especially with so many different users and uses,’ says Robin Reddick, solutions marketing manager at Houston-based BMC Software, a leading provider of IT automation solutions. ‘Developing high-value big data analytics requires these teams to work together. There are a lot of options when it comes to collecting and analyzing data, so IT and application development need to work closely with the line of business or these projects become nothing more than IT experiments.'” – Big Data, Big Opportunity: Survey results yield checklist for turning big data into bankable results, CIO; Twitter: @CIOonline
70. It’s not how much data you have, but whether you have the right data, that matters. “With the advancement of technology, it is a lot easier and cheaper to generate a lot of data in different types and formats, such as pictures and videos from the smart phones, blogs and comments from social media as well as the sensor (machine generated) data. At the same time, data storage and computing costs have shrunk dramatically, therefore it has become very affordable to store and process a lot of data.
“A lot of people are saying – data is the new oil. That could be true but it doesn’t mean the more, the better, because that “new” oil still needs refineries so that companies can profit from it. The volume of data really doesn’t matter, the key questions should be –do you have the “right” data? Do you have the right people and processes to convert the data to business values?
“It is about getting a deeper understanding of your customer and learning how to create more value. Before we talk about the new possibilities and opportunities, there are a few key underlying problems that we need to address:
“1) With the advancement of technology, it is a lot easier and costs a lot less to track and collect data, however, most of the data collected is in siloed systems. Data in siloed systems are not about to tell us new insights into our customers.
“2) Analytics is about making decisions and taking actions to create more values for the customers. No one is interested in analytics output unless it is ‘actionable’, so the question should be – what actions we can take? How fast can we take action? And is it possible to automate and act on the results in real-time (or close to real-time)?” – Joni Ngai, Senior Business Optimization Consultant, The myths of big data and analytics, Connect the Experience; Twitter: @joningai
71. Consider the scale. The right sample volume will provide greater accuracy in testing. “The test you ran for a population of 1,000 users may deliver completely different results with 10,000 users — or 1,000,000. There are many ways to estimate the right scale for your test, but here’s an easy formula for a population of any size that gives you a 95 percent confidence level with standard deviation of 0.5 and a plus or minus 5 percent margin of error:
sample = population / (1 + (population*0.0025))
“For example, if we were looking at a population of 10,000, we’d aim for a sample of 385 individuals:
sample = 10,000 / (1 + (10,000*0.0025)) = 384.6
“If your sample volume is too low and you can’t easily reach the scale you need, just extend the amount of time allocated to testing. Yes, it will take longer, but the improvements in accuracy will be invaluable.” – Neil Coleman, Managing Director, AdRoll New York, 6 tips for making better marketing decisions with data, iMedia Connection; Twitter: @AdRoll, @iMediaTweet
72. Pinpoint precise points of conversion or failure to optimize the sales funnel. “From discovery to purchase, the sales cycle has a long funnel with a lot of bottlenecks. The kind of detailed website analytics we have nowadays can provide information about exact points of conversion or for that matter the point of exit. For instance, if some people are clicking on adverts and visiting a website but not making a purchase, we can always see at what point they are leaving the website. For instance, if a lot of people are adding items to the cart but not completing the purchase, this little piece of data can tell you that there is probably a problem in your payment system that needs to be corrected.” – Blair Strasser, 6 Techniques to Use Data to Make Marketing Decisions, Small Biz Triage; Twitter: @SmallBizTriage
73. Review your data map in conjunction with short-term and long-term strategy to identify gaps. “Set aside some time with your team and review your data map in conjunction with your short-term and long-term strategy to determine where the gaps are. Keep it simple and focus on delivering actionable insight.
“Here are three quick considerations:
- Link to Strategy: Always consider the link to your business and marketing strategy as you review your data portfolio. Everything we do must contribute to or complement our strategy. Identifying that link and validating its relevance are critical.
- Identify Key Data Sources: This sounds simple, but you may be surprised how complicated or confusing it can get—even for those data sources, which you may take for granted. Which data sources are missing? How critical are they? What value do they provide?
- Narrow the List: Once you have the list as part of your data map that includes the underlying detail, begin to review each one. Assess:
- Feasibility: Is it possible? Can you capture the data?
- Resources: What’s needed? Do you have the right resources?
- Timeline: How long will it take? Can you do it in a timely manner?” – Kaan Turnali, Global Senior Director, Business Intelligence (BI), for SAP’s Global Customer Operations (GCO) Reporting & Analytics Platform, Using Customer Data for Informed Marketing Decisions, iAcquire; Twitter: @iAcquire
74. Test, test, test: Experimentation is the gold standard of causation. “Correlation is not causation. Every data scientist worth their salt will tell you this. But as marketers, it’s usually causation that we’re after — we want to know what we can do that willcause more customers to do more business with us. So what do we do when data shows a correlation that may reveal such a cause? We run a controlled experiment. Keep all other variables constant (as much as is practically feasible) and test the alternatives to prove or disprove our hypothesis. Google runs over 10,000 such experiments every year. It’s the most powerful data you can generate, which is why big testing will be bigger than big data.” – Scott Brinker, 14 rules for data-driven, not data-deluded, marketing, Chief Martec; Twitter: @chiefmartec
75. Integrating data sources remains a challenge for marketers in some parts of the world. “In spite of data availability, businesses are failing to make an informed decision, says a market research survey. The survey, with a sample size of over 2,700 marketing professionals across the Asia Pacific region, including 533 from the country, has found that the volume and variety of data are obscuring valuable insights, thus making it harder for businesses to use them to their advantage.
“Indian businesses are investing more in data-driven digital platforms and tracking systems to help them understand the challenging online landscape. Much of this data come into the marketing department, with one in three marketers (37%) now managing real-time data as part of their role, says a TNS Marketing Monitor survey. However, the survey has found that 70% of marketers in the country admit that they find it difficult to integrate data from different sources.
“With so much data available, marketers know they should be able to make decisions in real time, but many are struggling to integrate traditional and digital measurements.” – TNS Marketing Monitor research, Businesses fail to use data for better marketing decisions: Survey, ET BrandEquity; Twitter: @EconomicTimes
76. Increase efficiency and reduce costs through process innovation. “Data analysis can increase efficiency and reduce costs through what can be called ‘process innovation.’ Logistics companies are using data to improve efficiency. UPS, for example, is using data analytics to determine the optimal routes for its drivers. The Internet of Things can reveal new correlations that lead to insight and innovation.” – Corrine Sandler, How Every Business Can Use Big Data to Make Better Decisions, ProfitGuide; Twitter: @CB_PROFIT
77. Having the right management system in place is crucial. “A McKinsey study which explored the attitudes of C-level executives found that investing in analytics was seen as the best way to help create value that leads to competitive advantage, scoring higher than the other two key trends in digital business: digital and social media and cloud computing and mobility.
“Having the right management system in place is crucial. It requires a combination of technology, processes and people, something we at Mu Sigma like to call the ‘man-machine ecosystem’. To generate a successful analytics effort, you need a seamlessly integrated ecosystem that can scale and sustain the use of analytics. That means the following foundations must come together:
78. Increasing your spend on data analytics or increasing your volume of data isn’t enough. You have to know how good your insights are — and how good your business partners are at making your insights actionable. “There’s more marketing data available than ever, and that’s exactly why it’s so challenging to truly make sense of it all. While cloud-based data platforms have accelerated the availability and access of marketing data, it hasn’t made the marketer’s job any easier. It’s just the opposite. ‘Mo’ data, mo’ problems.’
“VB Insight’s new research on analytics shows that brands plan to increase their spending on the category by a whopping 73 percent over the next 3 years. For big market cap B2C companies, it’s closer to a 100 percent increase.
“The trouble is most marketing organizations are lukewarm on both how good their own insights are and how good their business partners are at making their insights actionable.” – Jon Cifuentes, Research Analyst, VB Insight, Tracking, seeing, understanding: How top marketers make data-driven decisions, BrightTALK; Twitter: @BrightTALK
79. Understand the Decision-Making Unit (DMU) and how each individual plays a role in buying decisions. “Do remember that in many situations, it is not just one person that makes the decision to buy your products or services. In many markets, more than one person is involved in the buying decision. Children influence their parents, for example, and in business-to-business markets, the bigger the value of the purchase, the more people are involved in the decision. We call this the decision-making unit or DMU.
“There are six possible roles in a DMU and are listed below in no particular order. It is worth noting that some individuals in a DMU may have more than one functional role in the buying decision.
“DMUs may be individuals or groups of individuals and have the following roles in the buying process – and let us use an example of your organisation considering the purchase of a new telephone system:
- Users of the product or service – as the name suggests, these people may use the product or service and may be closely involved in after-sales service yet not necessarily close to the process of deciding which supplier to use or placing the order. For example, your office staff may be using any new telephone system but not involved in early discussions with system suppliers; they would be involved when being trained in how to use the new handsets.
- Influencers – these people have an effect on the decision-making process yet my never actually use the product or service; they may even be outside your organisation when placing the order. They may be technical advisors, journalists, or perhaps budget holders who influence how much is spent on the new system.
- Deciders – these people take all the opinions and ideas from the rest of the DMU to reach the final decision. In larger organisations the decision may fall to IT departments, for example, who make recommendations to others in the DMU. It may be a single individual or a team.
- Approvers – often the budget holders or management team, these are ‘signing-off’ the decision to buy the new system.
- Buyers – are involved in placing the order and dealing with the suppliers to ensure that the new system meets specification, is delivered on time, and installed to schedule.
- Gatekeepers – control access to the rest of the DMU and may be such as secretaries who control whether suppliers can get to speak or meet any of the individuals above.
“Before the new telephone system is purchased, everyone in the DMU must be through the buying process to ‘decision’ and ‘action’ at the same time otherwise the purchase cannot be agreed. Good CRM systems include information on the DMU and where each individual or group is in the buying process.” – Mac Mackay of Duncan Alexander & Wilmshurst, Using existing data to understand your customers, Marketing Donut; @MarketingDonut
80. Understand the different types of data and how they’re valuable to you as a marketer. “A good place to start is to understand the differences between data types and their associated value to marketers.
“Identity data can be divided into three main categories:
- Anonymous—The information that holds the least value is anonymous data. It may include a name and an email address, but there is no way to know who that person really is, what he or she is interested in, and how we can tailor products and services to this person. The profile only allows us to send general marketing offers with an ROI that can be hard to quantify. Marketing list providers that ascribe dollar amounts to data assign a cost of only $.10 to $.18 to these records.
- Inferred—Today, we can gather more information about customers than ever as they interact with our brand over websites or mobile devices. We can track purchase histories and buying preferences, such as whether a customer prefers to purchase over mobile or Web, whether he or she likes in-store pick-up or direct shipping, and many other observed behaviors. From this data, we can piece together clues and infer attributes like age and life stage. A mother with two children will buy distinctly different products than a bachelor, for example. Based on these inferences, marketers can tailor campaigns and coupon offers to encourage cross-sell and up-sell.But things can go awry with inferred data. Creating marketing programs based on assumptions sometimes results in misguided (and potentially embarrassing) communications. Sending an irrelevant promotional email to a customer based on a gift purchased for someone else is not only annoying; the customer may consider it a privacy violation. Though inferred data is certainly more valuable than anonymous data, it does have drawbacks.
- Deterministic—Deterministic data (specifically preference, privacy and consent data that comes directly from the customer) offers the most value. If a profile includes information about customer preferences, marketing list providers will raise the price per record by 545 times the price of anonymous data. This is a considerable cost increase—and for good reason. Deterministic data takes out any guesswork and provides marketers with clear instructions on how to engage with a customer. It often includes the updates, offers, and information a customer wants to receive, including the products and services that interest him or her the most. Plus, it can encompass consent and privacy choices, important information to have as companies comply with stronger privacy and security regulations.” – Steve Shoaff, Understanding the Real Value of Your Customer Data, MarketingProfs; Twitter: @MarketingProfs
81. Use Big Data to understand small moments of truth. “Multiple functions line up to influence and serve customers in the pre-shop, active shopping and post-shop consumption phases. Their actions all often happen apart one another with little right time coordination. This, and a lack of common understanding, greatly contribute to the challenges these industries have with sales growth and differentiation.
“Decades of technology investments in silos and the disruption caused by online channels, mobile and social media have created a cobbled-together infrastructure ill-suited to a harmonized view of the customer experience. When faced with the reality that pushing product messages, promotions and transaction-centric communications on your customers are ineffective, it’s difficult to imagine how to develop the right insight necessary to change course.
“As Brain also points out, ‘Customers aren’t following the customer journey you designed because they’re too busy hacking it.’ The moment of truth is in constant motion and varies by person.
“Understanding the many small moments that individual consumers face when approaching a purchase decision is a good place to start – no matter if you work in marketing, sales, customer service, supply chain or manufacturing. Everyone has a mandate to execute against such an understanding.” – Gib Bassett, Using Big Data to Understand Small Moments of Truth, BrianSolis.com; Twitter: @gibbassett, @briansolis
82. A decision requirements model can help you get the most from predictive analytics. “A decision requirements model describes the structure of the decision making involved in a decision you make over and over again like approving claims, pricing deals, making marketing offers or picking suppliers. It breaks down the decision-making and identifies the information needed to make the decision as well as the knowledge, the know-how, you need to make it effectively and correctly. It connects these elements – decisions, information and knowledge – into a requirements network.
“A decision requirements model helps you succeed with predictive analytics by:
- Enabling business owners to describe their decision making and so identify exactly where in that decision making predictive analytics would help – “if only we knew which customers would churn we could make this bit of the decision more accurately”.
- DMN is a standard modeling notation that is coming to the Business Analyst Body of Knowledge or BABOK in v3 and has been shown to be usable by business analysts. This allows teams to build these models without needing scarce data science resources initially – they only need to be brought in once the problem is well defined.
- Because it’s a well defined approach with a simple notation requirements can be specified quickly and accurately, saving time and resources.
- Decision requirements models can be linked to KPIs and metrics so it is clear which metrics will improve if the decision making does. This helps establish how the predictive analytics will add value and shows how to track and measure their ROI later.
- By clearly showing how the proposed analytic will impact decision-making they make it easier to plan and execute on model deployment and ensure that business users will use analytic models because they can see clearly where they fit and why they will help.
- Finally using a common notation across all analytic projects allows projects to be prioritized relative to each other and allows an organization to gradually build up the kind of decision inventory they will need over time (see this article in the WSJ by Tom Davenport for instance).” – James Taylor, Using Decision Modeling to Make Predictive Analytics More Pervasive, SmartDataCollective; Twitter: @SmartDataCo
83. Your data science and engineering teams must know how to work well together. “The best place to start is at the source. If you are collecting the data internally, you can work with your engineers to devise better ways of collecting and organizing data into a system that will require less post-processing. As Ryan Orban, Chief Data Wrangler at Zipfian stated, it’s essential that your data science and engineering teams know how to work together. Each team should have at least a base understanding of what the other does so that they understand, and can help mitigate, the pain points of the other.” – Sally Hadidi, Data is Ugly – Tales of Data Cleaning, KDnuggets; Twitter: @Sally_Hadidi
84. Be aware of potential ethical flaws and conflicts arising from your data. “Of course, marketers have always targeted racially defined customer-bases—typically to adjust price ranges along socio-economic lines. But with ever more data becoming available, the risk of ethical error becomes harder to avoid. In the digital age, customers are defined by where they click and by the information they register. It’s one thing to be labeled as more likely to buy a pair of ski gloves, but quite another to be thought of as less likely to pay a credit card bill just because you have an ethnic-sounding name.
“There is very little regulation to protect consumers from data misuse in this regard, and little guidance for companies as to how data can be segmented and sold to third parties in ethical ways. As Frank Pasquale writes in his new book, The Black Box Society, some data-broker customer-targeting lists include such ethically questionable consumer categories as ‘probably bipolar,’ and ‘gullible elderly.’
“Ultimately, the owner of a supermarket doesn’t really care who shops there or what they buy—only about the total value of the basket. If he or she identifies, for example, that people who buy premium ice cream often also buy other premium items, such as high-margin wine and or gourmet snack foods, it would be a good strategy to discount the ice cream to increase the overall basket value.
“But if an email marketing campaign for that same brand of ice cream goes out to a list of 1,000 customers, chosen by an algorithm, and none are from ethnic minorities, is the algorithm racist?” – Steve Jones, Global vice president of big data, Capgemini, The ethical blindness of algorithms, Quartz; Twitter: @mosesjones, @qz
85. You must account for human influence, even in the most sophisticated data analytics systems. “Companies are constantly facing this challenge: how to manage the tension between predictive analytics and human nature. It’s why, on a gut level, we all have some suspicion about an Artificial Intelligence future. If you think about it, big data, business intelligence, and predictive analytics are all just pinpoints on a spectrum that stretches from human intelligence to artificial intelligence. And as much as the data scientists would like to convince us otherwise, the relationship between human intelligence and AI can’t be plotted on the same circle. A straight line, maybe. Possibly a Venn diagram.
“There’s an economic corollary to the big data-messy human challenge, which is the concept of irrelevancy and its importance in human economic behavior. Accounting for “irrelevancy” when creating economic models is essential for predicting accurate outcomes.
“In the same way, companies that rely heavily on data and analytics must account for human influence on even the most elegant systems. Those systems have to be like children’s furniture: rigorously tested to withstand a beating from the hard-to-predict humans that sometimes operate based on data that seems irrelevant to predictive algorithmic modeling.
“I think we can safely predict that big data will revolutionize the future, but the degree to which data will determine the future is entirely dependent on how well we account for messy human behavior in our data models. As Michael Scott in The Office once showed us: the machines are not always right.” – Carlos Melendez, In the real world, big data sometimes bumps up against Big Ideas, InfoWorld; Twitter: @infoworld
86. If your competitors aren’t using predictive analytics, it presents a great opportunity for you to get ahead. “From recommending additional purchases based on the items that customers place in online shopping carts to pinpointing hospital patients who have a greater risk of readmission, the use of predictive analytics tools and techniques is enabling organizations to tap their collections of data to predict future business outcomes — if the process is managed properly.
“Predictive analytics has become an increasingly hot topic in analytics circles as more people realize that predictive modeling of customer behavior and business scenarios is “the big way to get big value out of data,” said Mike Gualtieri, an analyst at Forrester Research Inc. As a result, predictive analytics deployments are gaining momentum, according to Gualtieri, who said that he has seen an increase in adoption levels from about 20% in 2012 to ‘the mid- to high-30% range’ now.
“That’s still relatively low — which creates even bigger potential business benefits for organizations that have invested in predictive analytics software. If a company’s competitors aren’t doing predictive analytics, it has ‘a great opportunity to get ahead,’ Gualtieri said.” – Corlyn Voorhees, Predictive analytics tools point to better business actions, TechTarget, SearchBusiness Analytics; Twitter: @BizAnalyticsTT
87. Set goals, and use predictive analytics to create a strategic roadmap for achieving them. “With simulation capabilities, you can plug your actual goals into your future and see what other corresponding metrics need to follow suit.
“For example, your goal might be 100 transactions from Facebook CPC traffic. Simply test “100” as your metric for CPC transactions and see how it affects other factors. You might find out that in order to get 100 transactions, your ad clickthrough rate needs to be at least 4%, your budget needs to go up $20/day, and you need to bid higher on average for your keywords.” – Chuck Reynolds, 15 Reasons Why Analytics Prediction Will Make You a Better Marketer, Convince & Convert; Twitter: @convince
88. Have a plan of attack and use a reliable framework to efficiently mine and analyze data for actionable insights. “Here’s the framework we will follow:
- Review objectives and goals
- Identify key questions
- Pull the analytics data
- Prepare the data for analysis
- Probe the data to address our key questions
- Plan next steps and action items
“What we’re trying to do here, especially as we get to the last steps, is to get as many actionable recommendations as we can as efficiently as possible.
“Another way is to just look at the numbers and and try to find patterns like some kind of marketing John Nash, but that would require us to have lots of time to burn (and to be slightly insane).” – David Fallarme, Make Smart Marketing Decisions With This 6-Step Framework, Growth Hero; Twitter: @davelocity
89. Don’t collect data just because; collect the data that will provide your business with meaningful insights to drive decision-making. “Does the data you’re collecting inform your decision making, or improve your customer’s experience with you? If not, then don’t collect it just because one day it might come in handy. Presenting a prospective customer or client with a giant form is not the best way to start a relationship. Collect the core data first, and fill in any “nice to have” gaps throughout their customer journey with you. You can justifiably spend a lot collecting, managing and interpreting data. Make it worth your while (and avoid creeping your customers out!).
“Here’s an example of a no-no: a prospective client had 17 fields they were asking for a simple e-newsletter subscriber, including some pretty personal questions and a mailing address as a mandatory field. They weren’t tailoring their e-newsletter with the data, or sending their subscribers any mail. Arguably the location information could have been useful but not essential, but a simple postcode would have sufficed.” – Turning big data into actionable data for smarter marketing, Traction Digital; Twitter: @TractionD
90. A ‘what if’ analysis can prove useful if you lack ample data. “Precise response data like leads, click-through rates, conversions and sales is valuable in making decisions about future marketing efforts.
“But what do you do to if you don’t have data? How do you make marketing decisions when you have only partial information or just estimates?
“That’s when a ‘What if…’ analysis can be extremely useful.
“A ‘What if…’ analysis uses existing data, along with estimated data points, to calculate a range of probable outcomes. This information allows a marketer to make more informed decisions.
“For example, suppose you are planning a new product launch and need to project the profit margin for the product. You know what the fixed costs will be, but variable costs depend on several factors that are not defined. ‘What if…’ analysis takes the known factors (in this case, fixed price) and estimates of unknown factors (variable costs such as raw material costs, product pricing and sales) and determines a range of probable profit margins. This information can be immensely helpful in deciding if a new product will likely provide enough margin to justify further investment.
“’What if…’ analysis has been used for years by many major corporations such as General Motors, Proctor and Gamble and Eli Lilly.
“While a ‘What if…’ analysis doesn’t predict a specific outcome, it does provide a precise range of probable outcomes. By varying the input data, decision makers can see how those changes impact the probability of desired outcomes.” – Jeff Ewald, Getting Unstuck: Making Smart Marketing Decisions When Data is Scarce, Optimization Group; Twitter: @optimizationgrp
91. Convert emotional intelligence and insights into actionable objectives that drive business results. “Is there a science to emotions that brands can tap into? How can your brand build a base of emotionally-connected consumers? And which emotions are best for your category and brand?
“For brands to be successful with an emotion-based marketing strategy, a critical step is the ability to convert emotional intelligence and insights into specific, actionable initiatives that drive business results. Business results are critical, such as spending or usage; increased visits; increased recommendations or referrals, and increased number of products purchased.” – Michal Clements, Glad, Sad or Mad? Driving Business Results with Emotional Marketing Strategies, The Market Strategist via ChicagoNow; Twitter: @MichalClements
92. Create a clear process structure that carefully monitors relevant data. “The key to any successful marketing strategy is to measure the results of a campaign to identify trends and improve ROI. It is important to start by setting measurable goals that hold teams accountable for meeting objectives and understanding the significance behind the data.
“By creating a process structure that carefully monitors data, brands can adjust campaigns based on obstacles and opportunities discovered through analysis and improve conversion rates.” – Thomas Stern, Measure Me: The 10 Must-Track Data Points for Digital Marketing ROI, Business.com; Twitter: @businessdotcom
93. Data cooperatives can offer unique access to data not used previously. “In the commissioned study conducted by Forrester Consulting on behalf of Adroit Digital,“Leverage a Data Cooperative for Deeper Customer Insights and Better Business Outcomes,” 54% of respondents currently using a co-op state it offers unique access to valuable data not used before. Further, 71% of respondents agree that implementing a digital data cooperative will increase revenue, while 76% report that this type of data sharing lowers marketing expenses. The study, conducted by Forrester Consulting in January, involved in-depth surveys with 103 customer insights professionals and 100 digital marketers from US companies that gauged their use of and sentiments toward data cooperatives.” – To Drive Better Marketing Performance and ROI, Marketers Are Increasingly Using Data Cooperatives, Says Adroit Digital and Independent Study, Reuters; Twitter: @Reuters
94. The right technology is critical for getting results from your data. “There is a gap between marketing technology, data, strategy and results. For marketers who are in the trenches marketing brands every day, this statement isn’t surprising. It’s simply a statement of fact. Yes, there are tons of tools available to integrate technology into marketing strategy, leverage massive amounts of data, and drive big results. However, actually using those tools to attain those big results is a hurdle that most marketers are still far from surmounting. A new report from CMO Council and Tealium, Quantify How Well You Unify, shares input from 150 senior marketers in North America who were surveyed during the third quarter of 2014, and the results are very disappointing. While 67% of senior marketers believe that technology is critical to their businesses, only 44% have formal marketing technology (martech) strategies in place and only 16% reported that their martech strategies are well aligned with their business strategies.” – Susan Gunelius, The Marketing Technology, Data, Strategy and Results Gap, CorporateEye; Twitter: @lucynixon
95. The Internet of Things (IoT) has a major impact on the Big Data landscape. The most successful companies will stay ahead of the game and learn how to navigate the new landscape through efficient data collection and analysis. “The Internet of Things (IoT) has been a major influence on the Big Data landscape. The main idea behind the IoT revolution is that almost every object or device will be having an IP address and will be connected to each other. Now, considering the fact that millions of devices will be connected and will be generating enormous volumes of data, the efficiency of data collection mechanism is going to be challenged.
“First, companies need to employ highly efficient data collection mechanisms.
“Second, companies are going to face unprecedented security issues which are probably not going to be addressed with traditional security mechanisms.
“Third, not all data generated by the devices will be useful. Companies need to distinguish between useful and redundant data. So, they face huge challenges to improve their data and analysis capabilities. In this context, tools like Hadoop are going to receive a lot of attention.
“Last, IoT Big Data is going to change our day-to-day lives at a fundamental level.” – Kaushik Pal, Impact of IoT on Big Data Landscape, KDnuggets; Twitter: @kaushikpal
96. The right technology partners allow you to effectively analyze and make use of your data. “You know there’s a lot of good stuff in big data, but how can you sort out what’s relevant to you from all that white noise? Plus, who has time for all that?
“The reality is that ‘big data’ is nothing new, although it is a new-ish buzzword. It’s simply information that’s useless when it’s not used–and a goldmine when you analyze is correctly.
“However, you’re not going to get very far using an Excel spreadsheet. Data needs to be curated, organized, managed, and then put into actionable use. If you miss even one of these steps, you’re wasting your time. Fortunately, there are a few businesses out there making this process much easier. Leave the big data to the experts, so you can sit back and reap the benefits.” – Drew Hendricks, 6 Companies Using Big Data to Change Business, Inc.com; Twitter: @DrewAHendricks
97. With the right algorithms, you can make decisions thousands of times faster than you would under ordinary conditions. “Data now stream from daily life: from phones and credit cards and televisions and computers; from the infrastructure of cities; from sensor-equipped buildings, trains, buses, planes, bridges, and factories. The data flow so fast that the total accumulation of the past two years—a zettabyte—dwarfs the prior record of human civilization. ‘There is a big data revolution,’ says Weatherhead University Professor Gary King. But it is not the quantity of data that is revolutionary. ‘The big data revolution is that now we can do something with the data.’
“The revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity, King explains. The doubling of computing power every 18 months (Moore’s Law) ‘is nothing compared to a big algorithm’—a set of rules that can be used to solve a problem a thousand times faster than conventional computational methods could. One colleague, faced with a mountain of data, figured out that he would need a $2-million computer to analyze it. Instead, King and his graduate students came up with an algorithm within two hours that would do the same thing in 20 minutes—on a laptop: a simple example, but illustrative.” – Jonathan Shaw, Why “Big Data” Is a Big Deal, Harvard Magazine; Twitter: @HarvardMagazine
98. Turn data into profits with smart, data-driven pricing models. “The key to better pricing is understanding fully the data now at a company’s disposal. It requires not zooming out but zooming in. As Tom O’Brien, group vice president and general manager for marketing and sales at Sasol, said of this approach, ‘The [sales] teams knew their pricing, they may have known their volumes, but this was something more: extremely granular data, literally from each and every invoice, by product, by customer, by packaging.’
“In fact, some of the most exciting examples of using big data in a B2B context actually transcend pricing and touch on other aspects of a company’s commercial engine. For example, ‘dynamic deal scoring’ provides price guidance at the level of individual deals, decision-escalation points, incentives, performance scoring, and more, based on a set of similar win/loss deals. Using smaller, relevant deal samples is essential, as the factors tied to any one deal will vary, rendering an overarching set of deals useless as a benchmark. We’ve seen this applied in the technology sector with great success—yielding increases of four to eight percentage points in return on sales (versus same-company control groups).” – Walter Baker, Dieter Kiewell, and Georg Winkler, Using big data to make better pricing decisions, McKinsey & Company; Twitter: @McKinsey
99. Use data-driven strategies to identify and recruit job candidates with highly desirable skill sets. “Other companies are also using data to find potential employees with the right skills. LinkedIn offers tools and advice on how to use its trove of data to find job candidates. And Patrick Gillooly, Monster’s social media and content director, told me recently that Monster is ‘focused on the passive [job] seeker.’ Using tools that comb through online data and web activities, Monster tries to match job seekers with employers.
“‘For instance, say I’m Patrick and I’m on Twitter,’ Gillooly explains. ‘Maybe I’ve never said publicly on Twitter that I know how to use [programming language] C# [pronounced as see sharp] but I have a resume on Monster that says I know how to use it really well…I could get an ad on Twitter from a customer of ours through Monster about the [related] job.’
“Of course, job seekers would be foolish to depend on employers to make the first move in contacting them. Job seekers should continue to proactively network and respond to job ads. However, these examples underscore the fact that nearly everything we do online can be tracked and analyzed.
“And as the recent spate of data breaches have shown, very little information remains anonymous. It also suggests that employers could have access to an overwhelming amount of data about potential job candidates and how they use it remains to be seen.” – Judith Aquino, Using Big Data to Find Your Next Employee, 1to1Media; Twitter: @1to1Media
100. Use Big Data to improve the development of the next generation of products and services. “There are five broad ways in which using big data can create value. First, big data can unlock significant value by making information transparent and usable at much higher frequency. Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time. Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Fourth, sophisticated analytics can substantially improve decision-making. Finally, big data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed).” – James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers, Big data: The next frontier for innovation, competition, and productivity, McKinsey & Company; Twitter: @McKinsey
101. Using the right tools, you can leverage all of your available data to make smarter marketing decisions. “Big Data is a philosophy indeed, especially since data volumes grow and evolve over time. Data is hardly finite or stagnant, and there is no point at which you can define Big Data or claim, ‘Here starts Big Data.’
“Rather, the philosophy around Big Data should focus on the way we approach the analytics and the usage of Big Data. This is philosophy is based on just two basic principles:
- Data volume is more valuable than an excellent data model.
- Trends are more important than individual events or isolated data values.
“With traditional Big Data analytics, the philosophy has always been about data quality. The priority has been defining a sample and a representative data set in order to analyse and compute a model that returns values that indicate the likeliness that something might occur. Now, however, we know that we can do more with Big Data.
“When we want to predict what is going to happen in a certain situation, we can leverage all of our available data, because chances are this situation has occurred before and that a pattern already exists in our data. Looking at a current reality can serve as a much more valuable analytic data model, and detecting trends offer far greater insights than viewing exact values.” – Nathan Chai, How businesses are actually using Big Data today, ITProPortal; Twitter: @Chaizard