Enterra Insights

The Digital Path to Purchase, Part 1

"The nature of the digital challenge is evolving rapidly and challenging traditional business models," write McKinsey & Company analysts Francesco Banfi, Paul-Louis Caylar, Ewan Duncan and Ken Kajii. "The initial wave that propelled the dramatic rise in consumer digital usage has already turned into a transformative surge - reshaping the ways consumers buy new products and services." ["E-journey: Digital marketing and the 'path to purchase'," McKinsey & Company Telecom, Media, and High Tech Extranet, 16 January 2013] If the authors are correct and the business landscape is being reshaped, businesses must also change if they are going to traverse the new terrain successfully. "The challenge today is much bigger than simply building an efficient online sales engine," the McKinsey analysts insist. "Instead, it involves creating brand engagement through digital media and platforms, turning that brand engagement into brand preference, and leveraging it to drive sales and loyalty."

What most people call the "digital path to purchase" (DP2P), the McKinsey analysts call "the consumer decision journey (CDJ)." Whatever you call it, it "describes the iterative and circular process shoppers go through today when selecting brands, products, and services." The analysts assert that the CDJ "has four phases: consideration, evaluation, purchase, and post-purchase." Frankly, there is nothing new about that conceptual framework. What's new, the authors report, is that digital media "has made this already complicated process even more complex." They explain:

"The traditional channels that typically influence consumers during the consideration phase include the 'big three' above-the-line (ATL) media - TV, radio, and print advertising - along with word-of-mouth recommendations from friends and family. With the advent of digital channels, the list expands to include brand Web sites, mobile apps, online advertising, social networks, price comparison engines, as well as blogs and forums. ... Pulling this tangled knot of complexity even tighter, digital touch points themselves have spread far beyond traditional personal computer platforms to include those behind smartphones, tablets, gaming devices, TVs, and even smart applications."

Dion Hinchcliffe agrees with the McKinsey analysts that complexity now characterizes the consumer's journey to purchase. "Businesses planning today to improve their connection to customers in digital channels," he writes, "are increasingly looking at the discipline of mapping out what's being called the 'customer journey'." ["The new digital customer journey: Cross-channel, mobile, social, self-service, and engaged," ZD Net, 19 May 2013] He continues:

"Over the last ten years, the fragmentation of customer engagement across dozens of channels has turned into both a highly vexing problem and an increasingly disruptive challenge to businesses that still keep doing what used to work, but are getting sharply falling off results from old touchpoints like TV, phone, and e-mail. This fragmentation of customer touchpoints cuts across marketing, sales, customer service, and even product development. In short, customers have moved to the digital world en masse, and companies have not kept up."

The McKinsey analysts note that not all "customer touch points" are equal. They concluded, "A brand's Web site has by far the highest touch point 'quality' - or the ability to positively influence a customer toward a particular brand." They claim that "mobile apps also show solid potential." What about social media? The analysts conclude that despite the "impressive growth and strong hype" they have received, "social networks do not appear to drive brand preference among consumers." To learn more about the importance of brand engagement, read my posts entitled Targeted Marketing and Brand Engagement, Part 1 and Part 2. Hinchcliffe agrees that now and in the future it is "imperative for companies to shift from transactions to engagement." Traditional marketing solutions, he believes, "have long been channel-centric, instead of customer-centric. While the majority of companies certainly do have modern call centers, social media marketing plans, e-mail campaigns, a mobile app strategy, an SEO policy, and so on, they are frequently unsynchronized and siloed. It's also highly likely these channels are also not monitored well and fall far short of the participation levels required to achieve ROI." Anytime you hear or read the word "silo" associated with business, you can be assured that alignment challenges are lurking nearby.

Keeping marketing strategies aligned across channels is important because consumers are likely to use several digital touch points during their path to purchase. Just as importantly, they are likely to jump in or depart the path to purchase at any time. As the McKinsey analysts conclude:

"The consumer decision journey is not a onetime process. Customers are continuously engaged and in various stages of multiple processes at any given point in time. For an individual brand, the importance here is its ability to keep customers coming back. Active loyalty plays a significant role in driving repeat purchase decisions - particularly in most developed markets. A broad shift in the way consumers are engaged online becomes apparent - even after the purchase. This can positively influence the repeat purchase decision."

Banfi, Caylar, Duncan and Kajii recommend that companies keep three key considerations in mind when establishing a digital path to purchase strategy. First, identify "the most critical touch points across the consumer decision journey for their own products and services." Second, "have a multichannel orientation and [don't] focus on one digital channel exclusively or on digital at the expense of traditional channels." Finally, balance activities between obtaining new customers and retaining old ones. "Since the consumer decision journey applies to both the retention and the acquisition phases," they write, "the relative focus on digital in either of these phases is organization-specific." Hinchcliffe points out that simply identifying critical touch points is not as easy as it may appear. "New digital channels," he asserts, "are accumulating faster than many companies can integrate them into their customer experience." He continues:

"New social networks, mobile devices, app stores, online touchpoints like fan sites, customer communities, Facebook pages, Twitter accounts, etc. seem to emerge on a weekly basis. If that wasn't enough, customers are more ready to interact than ever before, further creating challenges of scale: Even if a company could integrate a new digital engagement channel into their efforts, these new venues are far more two-way. Customers expect a meaningful response to their attempts to connect with the companies they are interested in or otherwise desire involvement. So this is the opportunity and the challenge combined: Engaged customers generate more revenue and stay more involved with the companies that respond in kind. Yet it's very challenging to meet their demands for engagement without fundamentally changing the rules of how companies connect with them."

Because there is such a dizzying array of touch points for consumers to use on their path to purchase, Hinchcliffe recommends that companies look at each of them with three things in mind. First, use them to "solve a problem. Make a pain point go away, such as seamlessly conveying the current status of orders in any desired channel, or providing an innovative new way to participate in the co-design of a new product or service." Second, "make life simpler. Remove the time, effort, and/or friction the customer has in engaging with you." Finally, "engage the customer. The core of the problem companies have in onboarding new digital channels such as social media, is that customers will then expect the company to respond and participate in conversations. And engagement at scale is one of the hardest things for companies to do as they are organized today." He concludes, "The one thing that is ultimately untenable is ignoring customer needs."

Hayden Sutherland believes that the traditional path to purchase is dead. "The path to purchase is dead," he writes, "long live the path to purchase." Just like royal succession, he knows that something is always ready in the wings to ensure that business moves on. ["The digital path to purchase," Press 2.0, 4 March 2013] He continues:

"Today's shoppers now have a number of tools at their fingertips to help them chose which products to buy and consumer technology has been the facilitator of this. Research from Google Analytics has found that over a two-day period customers now interact with a brand 4.3 times before making a purchase. Furthermore the average U.S. shopper now interfaces with a total of 10.4 traditional and online media sources prior to purchasing. That's a lot of browsing before buying (or looking before booking, if you're in an industry such as online travel). Furthermore, according to further research by Google, online customers are now making the majority of their purchasing decision before engaging with a sales representative. There's no doubt that the traditional way that shoppers purchase has now evolved and the marketing to these people has changed with it."

You might ask yourself, who are "these people" to whom Sutherland refers? That is the subject that I'll address in Part 2 of this series.

Smart Cities 37 Years On

"If you work for a young web company," writes Jasmine Gardner, "you probably think your office is pretty cool. Maybe it has a pool table or even a roof terrace. Pah! Give it 37 years and, according to engineering company Arup, our office blocks will contain working farms, produce their own energy, be linked together by suspended green walkways and sections of each floor will be removable, upgradable and replaceable." ["Smart cities: what urban life will be like in 2050," London Evening Standard, 4 March 2013] Sounds exciting doesn't it? Well, don't get too excited. Futurists tend to exaggerate how much things will change in the future and seldom are their predictions fulfilled. The attached 1925 postcard is a good example of two things. First, buildings last a long time. You can easily see recognizable New York City buildings that are standing today. Even though a few high profile buildings had been added to NYC skyline by 1962 (37 years after the postcard was printed), people traveling forward in time would still have been able to walk down the streets of New York and recognize where they were. The second point, is that the details are nearly always wrong. Aircraft never looked like those depicted; elevated trains never ran skyward through buildings; and so forth. Obviously, futurists don't get everything wrong; but, if they were given a letter grade for the bulk of their predictions they would probably get a "D."

Having said that, I really don't fault the futurists. Their job is to look at what's possible not really predict the probable. Everything that the Arup company predicted is possible. Buildings like those described probably will be built, but not a lot of them. I predict that if you travel forward in time 37 years to 2050, you would be able to walk down the street of any major city that exists today and find your way around using landmark buildings that are already in place. Cities change slowly -- at least established (i.e., brownfield) ones.

There are new cities being built, however, especially in the developing world. How those cities are built will make a huge difference in the lives of millions of future citizens. It is in those cities where one might see the kinds of buildings predicted by Arup. Gardner continues:

"This is the building of the future, imagined in a report released [in March 2013] by Arup’s 'Foresight + Innovation' arm. It is just one example of the elements that will make up our 'smart cities' of the next age. 'Smart cities' is the buzz phrase of the moment. It refers to energy-efficient and spacially economical urban worlds in which we'll live in years to come — all thanks to technology. Smarter cities are now a focus of both big business, such as Shell and IBM, and small entrepreneurs and scientists, such as the Dutch microbiologists who have developed a self-healing concrete. Cracks in the buildings of the future will be filled by calcium carbonate, produced by a bacteria feeding on nutrients, both incorporated into the cement. The bacteria are only activated when rainwater gets into a crack."

Some older buildings will undoubtedly be retrofitted with smart technologies to make them more efficient and others will gain new life by housing urban farms or 3D manufacturing plants; but, only in newly constructed cities are you likely to find "from-the-ground-up" smart technologies built into every building. Nevertheless, it remains important for urban planners and engineers to keep developing concepts. You never know when a true breakthrough will occur that will help us cope with the 9 billion people who will inhabit the planet in 2050 (most of whom will live in cities). Rick Robinson, executive architect for the IBM's Smarter Cities initiative, told Gardner, "In the West we've become accustomed to building cities outwards around cars. If more people fall into that lifestyle we're going to exhaust the world's resources very, very quickly." Robinson recognizes that it is the new urban dweller coming to live in newly constructed cities that will in large measure hold the key to planet's future.

But city dwellers living in brownfield cities are also going to put pressure on resources and energy. Adam Newton, a project manager for the Strategy and Scenarios team at Shell, told Gardner, "By 2050, between 70 and 80 per cent of the world's population will live in cities. How and where people consume energy will be very important." Frankly, I'm much more concerned about the future of brownfield cities than those that are yet to be constructed. Here are a few reasons why. Old water pipes leak. Sanitation systems are being overwhelmed (if they exist at all). Transportation patterns are mostly established. Inefficient electric grids are already in place. Finally, many of the building that will be around in 2050 are already built. Smart people and enlightened politicians are going to have to work with private enterprise if we want things to change for the better in many of these brownfield cities. Gardner continues:

"For IBM, smarter cities mean ones that harness data. It is already creating space on the cloud to share information such as water flow and distribution. 'By managing pressure on a water distribution network, you can serve additional houses without needing to expand the system — allowing you to support a growing population without spending hundreds of millions of pounds on infrastructure,' explains Robinson. Intelligent traffic lights are also high on the agenda. 'In Singapore and California we've used technology that can make predictions that are 85 per cent accurate about how traffic is going to develop over the next hour,' he says. In future the light sequencing might change automatically. For Shell, it's all about energy efficiency. 'The low energy prices that drove cities to sprawl may not exist in 20 years. Fewer roads and better integrated public transport is likely to be the way forward,' says Newton."

But better and more integrated public transportation systems costs money (which cities don't have) and will only be sustainable if the cultural mindset of urbanites and suburbanites change significantly. Rick Robinson bluntly states, "No-one is going to pay cities to become Smarter." [Sustainable Cities Collective, 30 November 2012] He goes on to discuss "four ways in which money is already spent" and then recommends way "to harness that spending power to achieve the outcomes that cities need." He concludes, "We should not wait for new, large-scale sources of Smarter City funding to appear before we start to transform our cities – we cannot afford to; and it’s simply not going to happen. What we must do is look at the progress that is already being made by cities, entrepreneurs and communities across the world, and follow their example."

The economic sector that is most likely to see deep penetration of smart technologies is the electric utility sector. Gardner reports, for example, that the U.S. energy data analytics company Intelen has begun a project "which monitors energy usage in office blocks with smart meters and has created a social gaming application where employees compete for low-energy scores." That's a great initiative because it addresses both technology and culture. Gardner admits that "these don’t seem [like] outlandish developments," such as those described in the Arup report, and she wonders if its "futuristic building [is] just science fiction." Josef Hargrave, the author of Arup's report, acknowledges that some of the predictions made in the report are unrealistic (like the flying robots which remove and re-insert building sections), but he believes that smart electricity grid integration is very realistic. Having said that, Hargrave told Gardner that "all the ideas are based on current prototypes and development — flying robots are actually being developed in Zurich, Switzerland, and can build a six-metre tower out of foam bricks." Gardner concludes:

"Given the exponential rate of technological advancement, it's not hard to fathom that skyscrapers coated with photovoltaic paint will indeed come to pass — and sooner than you think. Indeed, the floors of Arup's building occupied by algae-filled biofuel pods are not unlike a current project by French biochemist Pierre Calleja. He is building algae street lamps that eat up CO2 in the atmosphere. Combine this with another algae lamp that produces its own light using energy created by photosynthesis and you get self-powered, anti-pollution street lamps. Researchers at the University of California Los Angeles (UCLA) just announced their creation of a graphene supercapacitor — essentially a battery but one that charges up to 1,000 times faster than the normal kind, and that can be composted. The future promises instant phone chargers and petrol stations with plugs that can charge cars faster than they currently fill up on unleaded. ... The real smart aspect may come down not to the technology which we know exists but to foresight and willingness to change. 'There have to be new models of collaboration for businesses and decision-makers in cities and government to have the positive impact we know the technology could support,' says [Shell's] Newton. The city that leads in this department may just end up the smartest in the class."

Cities will change over the next 37 years. Change is inevitable. Whether that change is good or bad remains to be seen. The one thing that worries me most is the fact that almost all Smart Cities initiatives address problems associated with connected citizens. Many of today's largest cities have large, unconnected ghettos associated with them. Individuals who live in those ghettos are survivors; but, getting them connected to grids and networks would make their lives much more productive and their futures much brighter. In all the talk about smart cities, we can't forget about them.

Numbers in the City, Part 2

In Part 1 of this series, I discussed an emerging movement called quantitative urbanism and how mathematics can be used to better understand life in city. The goal of the movement is to use this knowledge to make life better for people living in urban environments. The article from which many of the observations were drawn was written by Jerry Adler. ["Life in the City Is Essentially One Giant Math Problem," Smithsonian, May 2013] According to Adler, the movement can trace its origin to a collaboration between Geoffrey West, Luís Bettencourt, and José Lobo. West and Bettencourt head up the "Cities, Scaling, and Sustainability" initiative at the Santa Fe Institute. This "research effort is creating an interdisciplinary approach and quantitative synthesis of organizational and dynamical aspects of human social organizations, with an emphasis on cities. ... A particularly important focus of this research area is to develop theoretical insights about cities that can inform quantitative analyses of their long-term sustainability in terms of the interplay between innovation, resource appropriation, and consumption and the make up of their social and economic activity." The movement is catching on and they have been joined by others. Adler reports:

"If Bettencourt and West are building a theoretical science of urbanism, then Steven Koonin, the first director of New York University's newly created Center for Urban Science and Progress, intends to be in the forefront of applying it to real-world problems. Koonin, as it happens, is also a physicist, a former Cal Tech professor and assistant secretary of the Department of Energy. He describes his ideal student, when CUSP begins its first academic year this fall, as 'someone who helped find the Higgs boson and now wants to do something with her life that will make society better.' Koonin is a believer in what is sometimes called Big Data, the bigger the better. Only in the past decade has the ability to collect and analyze information about the movement of people begun to catch up to the size and complexity of the modern metropolis itself."

Bettencourt writes, "A science of cities, recognizable across the full spectrum of urban disciplines, from physics and biology to social psychology and sociology is starting to emerge." ["The Kind of Problem a City Is," Santa Fe Institute Working Paper, 2013-03-008] He concludes his Working Paper this way:

"Cities reveal at once the best and the worst aspects of humanity, in terms of our creativity and imagination but also in our tendencies for violence or discrimination. Because of this enormous potential for human development cities should not be seen as systems to be controlled or resisted, but encouraged to evolve spontaneously in the direction of achieving the best open-ended expressions of our collective nature. That then is our challenge. We are living the last few decades of the great urban transition and finally fulfilling our potential as the most social of all species to create something altogether new in Earth's history. We have within sight age-old human aspirations, such as to eliminate extreme poverty, to end most injustice, to gain access to good health for all, and to do all that in balance with the Earth's biosphere. All this will have to happen in cities and it can now happen very quickly. Bigger data and a more scientific approach to cities will certainly help. But the ultimate challenge for all of us involved in influencing and practicing urban planning is to translate, apply and further develop these new ideas to promote urban environments that can encourage and nurture the full potential of our social creativity, targeted at sustainable and open-ended human development."

Koonin agrees with Bettencourt. He told Adler, "We have acquired the technology to know virtually anything that goes on in an urban society, so the question is, how can we leverage that to do good? [How can we] make the city run better, enhance security and safety and promote the private sector?" Driving all of this potential is big data analytics. As we now know, the data comes from all sorts of sources: mobile phones, CCTV systems, the Internet, and so forth. Increasingly, data will come from machine-to-machine networks that help our systems run more efficiently and effectively.

Adler goes on to point out how mathematics can help us understand other things about how a city grows. For example, Glen Whitney, who founded the Museum of Mathematics in New York City, has a theory about the height of skyscrapers in cities (i.e., the better the Gross Regional Product the taller the buildings). Whitney admits that "building heights are constrained by engineering , while there's no limit to how big a pile you can make out of money, so there are two very rich cities whose tallest towers are lower than the formula would predict. They are New York and Tokyo. Also, his equation has no term for 'national pride,' so there are a few outliers in the other direction, cities whose reach toward the sky exceeds their grasp of GDP: Dubai, Kuala Lumpur." Adler notes, "Deep mathematical principles underlie even such seemingly random and historically contingent facts as the distribution of the sizes of cities within a country. There is, typically, one largest city, whose population is twice that of the second-largest, and three times the third-largest, and increasing numbers of smaller cities whose sizes also fall into a predictable pattern. This principle is known as Zipf’s law, which applies across a wide range of phenomena." He concludes:

"As West and his colleagues are well aware, this research takes place against the background of a huge demographic shift, the predicted movement of literally billions of people to cities in the developing world over the next half century. Many of them are going to end up in slums — a word that describes, without judgment, informal settlements on the outskirts of cities, generally inhabited by squatters with limited or no government services. 'No one has done a serious scientific study of these communities,' West says. 'How many people live in how many structures of how many square feet? What is their economy? The data we do have, from governments, is often worthless. In the first set we got from China, they reported no murders. So you throw that out, but what are you left with?”

The challenge, of course, is the fact that slums are generally considered to be places that are "off the grid." The ubiquity of mobile phones is making this less true every day; nevertheless, getting data about slums remains difficult. Adler reports that "the Santa Fe Institute, with backing from the Gates Foundation, has begun a partnership with Slum Dwellers International, a network of community organizations based in Cape Town, South Africa," to address challenges associated with off the grid communities. "The plan is to analyze the data gathered from 7,000 settlements in cities such as Mumbai, Nairobi and Bangalore, and begin the work of developing a mathematical model for these places, and a path toward integrating them into the modern economy." Lobo told Adler, "For a long time, policy makers have assumed it's a bad thing for cities to keep getting larger. You hear things like, 'Mexico City has grown like a cancer.' A lot of money and effort has been devoted to stemming this, and by and large it has failed miserably. Mexico City is bigger than it was ten years ago. So we think policy makers should worry instead about making those cities more livable. Without glorifying the conditions in these places, we think they're here to stay and we think they hold opportunities for the people who live there." To read a little more about slums, see my post entitled: Will Cities Save Us?

Adler writes that he hopes Lobo is correct because Michael Batty, who runs the Centre for Advanced Spatial Analysis at University College London, predicts "that by the end of the century, practically the entire population of the world will live in what amounts to 'a completely global entity ... in which it will be impossible to consider any individual city separately from its neighbors ... indeed perhaps from any other city.'" Although I doubt that prediction will come completely true, I certainly believe that urbanization is going to continue unabated. Bettencourt told Adler that we are seeing "the last big wave of urbanization that we will experience on Earth." Adler believes that it will be the math men, like West, Koonin, Batty and their colleagues, that will unlock the formulae that will point us down the road to a better urban future.

Showrooming Remains a Problem (or Does It?)

"Physical retail stores have long been considered both places to research and to purchase products," write McKinsey & Company analysts Francesco Banfi, Paul-Louis Caylar, Ewan Duncan and Ken Kajii. ["E-journey: Digital marketing and the 'path to purchase'," McKinsey & Company Telecom, Media, and High Tech Extranet, 16 January 2013] But with retail stores finding it hard to compete with online prices, today's stores continue to wrestle with a practice known as "showrooming." Showrooming involves consumers using a brick-and-mortar store to physically check out items but then purchasing those items online. How prevalent (and worrisome) this practice is remains the subject of debate. The editorial staff at Consumer Goods Technology writes, "Brick and mortar retailers are painfully aware of the impact of showrooming — the act of consumers using their mobile phones in-store to compare prices with competing retailers — on their bottom line." The staff reports that just before last year's holiday season, Edgell Knowledge Network (EKN), in partnership with eBay Local, conducted a survey of retailers. One of the survey's findings was that "80 percent of retailers expect[ed] to be impacted by showrooming; they expect[ed] the average loss of sales to be 5 percent — a significant number, given the low margins in many retail segments." ["80% of Retailers to be Affected by Showrooming," 20 November 2012]

Gaurav Pant, research director at EKN, told the editorial staff, "Showrooming is a phenomenon that's here to stay. One in four shoppers used their mobile phones to compare prices while in the store during the 2011 holiday season, and those numbers will only grow." Henry Helgeson, CEO and co-founder of Merchant Warehouse, agrees with Pant that showrooming is here to stay, but claims the percentage of shoppers that check prices using their mobile devices is much higher than Pan reports. He writes, "A recent study noted that, while shopping in a brick-and-mortar store, 67 percent of people check their smartphones to see if there's a better price elsewhere, and the majority (62 percent) will leave a store and buy online for a lower price." ["The Best Weapon That Merchants Have To Combat 'Showrooming'," Business Insider, 28 April 2013] It turns out that the 62 percent of shoppers who leave a store to buy elsewhere for a lower price do have turning point for making that decision. Chuck Martin reports that a nationwide study, conducted by Anderson Robbins Research for Linkable Networks, concluded, "The majority (62%) of shoppers would leave a store and buy online for a price up to 20 percent below the one in the store. For a discount of 5% and 10%, a store would lose almost a quarter (23%) of its price-checking shoppers and for a discount of 15% and 20%, another 39%." ["Showrooming & the 20% Mobile Deal Factor," MobileShopTalk, 5 April 2013] The study concluded that only 13 percent of hardcore shoppers would leave a store regardless of how small the savings found elsewhere.

In other words, brick-and-mortar stores have some wiggle room to negotiate with consumers who comparison shop using their mobile phones. Working with customers to get them the best in-store deal may have benefits beyond the initial sale. The McKinsey analysts note that stores play a significant role in retaining customers. They write:

"In today's digital era, the role of stores has evolved significantly. When analyzing customer decision journeys [CDJ] for retention versus acquisition activities, it becomes apparent that stores are essentially 'retention machines'. In other words, while their influence on non-customers is rather low due to very low penetration rates (in the 10 to 20 percent range), they retain strong influence over current customers considering purchasing the same brand again. Such customers visit stores more reliably, with penetration rates in the 25 to 45 percent range."

Monica Ho reports that "just 22 percent of mobile users complete a purchase directly via their smartphone or tablet." ["Smartphone vs. Tablet Commerce: 3 Essential Behaviors You May Be Overlooking," Search Engine Watch, 22 May 2013] In fact, she reports that 68 percent of smartphone users (those most likely to be "showroomers") still make their purchases in-person. She continues:

"If a retail chain understands that the majority of their smartphone customers will actually make a purchase in-person, they can modify their targeting to focus on users within the vicinity of their brick-and-mortar stores instead of a broad run of network strategy to drive direct mcommerce. Ad creative would then focus on an offline call to action relevant to the various brick and mortar locations, such as a coupon or limited time offer redeemable on premises. By considering mobile consumers' preferred method of conversion, you can make simple yet powerful changes in strategy and creative that can amount to big results."

The point is, brick-and-mortar stores needn't throw their up hands in despair over the practice of showrooming. Even the first generation of so-called "digital natives" still enjoys shopping in traditional stores. "Despite being far more tech-savvy than previous generations, Generation Y, the 80-million strong cohort of Americans between the ages of 18 and 35, has not forsaken shopping in stores for online purchasing — as long as retailers keep their offerings 'fresh' and interesting, says a new report from the Urban Land Institute (ULI)." ["New Gen Y Shopping Preferences Revealed," Consumer Goods Technology, 20 May 2013] In other words, retailers should take heart but don't get complacent. The article continues:

"The study found that 37 percent of Gen Yers love shopping and 48 percent enjoy it. Half of the men surveyed and 70 percent of the women consider shopping a form of entertainment and something to share with friends and family. The appeal of shopping is particularly strong among Gen Yers who are Hispanic and African American. Gen Yers tend to spread their dollars around generously, the study found, with more than half visiting a variety of retail centers at least once a month, including discount department stores (the retail type most frequently visited by Gen Y), community shopping centers, enclosed malls, department stores, big-box power centers, chain apparel stores, and neighborhood business districts. At the same time, 91 percent of respondents said that they had made online purchases over the previous six months, with 45 percent spending more than an hour a day looking at retail-oriented web sites."

Shoppers are obviously looking for fair prices and good deals; but, as I've noted in previous posts, they are also looking for great in-store experiences (read, for example, my post entitled Retailing Faces a Brave New World). M. Leanne Lachman, president of real estate consulting firm Lachman Associates LLC and executive-in-residence at Columbia University's Graduate Business School, told the CGT staff, "Contrary to what some retailers have feared, we found that Gen Y still does most of its purchasing in stores. Gen Yers use the Internet to research products, compare prices, envision how clothing or accessories might look on them, or respond to flash sales or coupon offers, as well as to purchase items; they are definitely multi-channel shoppers." Lachman pinpointed the most important thing that today's large retailers need to keep in mind -- shoppers are using multiple channels on their journey to purchase.

Evan Schuman believes that showrooming has been overblown. He even calls the phenomenon a "myth." ["The Myth Of Showrooming Takes Another Hit," StorefrontBacktalk, 15 May 2013] He reports that a recent Bizrate survey of shoppers who made purchases on-line found "an overwhelming 78.15 percent of those online purchasers had not looked at those products in any physical store." While that figure does shoot holes in the notion "that tons of shoppers are flooding stores to only use them as a physical showroom," it doesn't address the situation where consumers do engage in comparison shopping while in stores. Not to worry, Schuman writes, he reports that the survey also looked at the "remaining 21.85 percent of shoppers who had looked instore before buying online" (i.e., the showroomers) and found that "most of them (54 percent) ended up buying from the same chain. In other words, they were in a Target or a Best Buy and then purchased from Target.com or BestBuy.com." That strengthens Lachman's point that multi-channel activities are becoming more important for retailers.

Although convenience plays a role in the showrooming arena (i.e., it's more convenient to buy a bulky item online and have it delivered than to wrestle it home from the store yourself), the bottom line is product price. Ann Zimmerman writes, "Lower prices are one of the main reasons people pick Amazon and other Internet-only emporiums over traditional retailers. If brick-and-mortar stores can't compete on price, it is unclear how successful they can be with tweaks to merchandising and customer service." ["Can Retailers Halt 'Showrooming'?" Wall Street Journal, 11 April 2012] Anne Zybowski, director of retail insights for Kantar, told Zimmerman, "Offering people personalized prices through their mobile device may be the most effective way to beat showrooming." These targeted marketing schemes are much more effective when made while the consumer is actually in-store because they have the advantages of time and location. For large retailers with no counter-strategy, showrooming could remain a problem. Innovative retailers, however, can still find ways to make the in-store experience a preferred path to purchase.

Targeted Marketing and Brand Engagement, Part 2

In Part 1 of this two-part series, I discussed how marketing is getting more personal. I also noted that today's successful marketers are providing consumers with "calls to action" and great experiences. Grant Shippey insists that you need to know your customers "up close" if you are going to motivate them. He writes, "The global view risks overgeneralising the customer base and therefore failing to properly accommodate localised needs and wants." ["Nice from far, but far from nice: big data can help retail bring everyone ‘up close’," memeburn, 21 May 2013] In the past, he writes, the inability of businesses to get close to customers was "largely a legacy of the relatively high cost of data in the past, coupled with data scarcity, leaving us with a very dim light from which to garner insights." In the age of big data, those impediments no longer exist. In fact, Shippey insists that today many companies have a data surplus.

By analyzing this data, companies can now engage in what David Armano, Managing Director of Edelman Digital Chicago, calls "Responsive Marketing," Logic + Emotion, 19 May 2013] Even though companies CAN engage in responsive marketing, he writes, many (if not most) don't. He explains:

"Despite the pervasive nature of all manifestations of digital, including social and mobile, much of the marketing emphasis remains dedicated to reaching people in mass, following a tried and true formula for advertising designed to build off consumer insights and craft compelling messages which could be distributed across a myriad of channels (including digital). The approach is designed for the broadcast industrial machine including print, radio and television, which, despite rumors of its demise is likely to stay with us for some time. The problem it poses however is that it is an approach that ... is neither nimble nor flexible and isn't built for rapid change nor does it adapt well beyond the dominant media it was designed for."

Companies that have discovered the importance of responsive marketing, he says, "understand that their customers value content, consume it, create it, and share it." In other words, they respond to calls for action and, as a result, have a great online experience. Because traditional media outlets aren't going away any time soon, Armano believes that companies should take an evolutionary, hybrid approach to marketing. "The solution to the content question," he writes, "lies somewhere between acknowledging that a brand must support both a traditional, linear marketing model in addition to a newer, cyclical construct which is constantly in tune with the current environment and operates in consolidated time frames. Responsive marketing sits at the core of the content evolution." Jim Bampos, Vice President of quality at EMC, also believes that big analytics lie at the heart of providing customers with great experiences. ["Data-Driven Customer Experience, Direct Marketing News, 1 February 2013] He writes:

"Many different types of data elements are captured, analyzed, and turned into information that internal businesses can use to make decisions that are focused on continued improvements in the customer experience. From analyzing unstructured data via sentiment and suggestions in open-ended comments, to analyzing structured data via customer metrics, Big Data analytic technologies and tools are only part of the equation; people and processes are also essential to improving the customer experience and business results."

Steve Goldner, a Senior Director of Social Marketing for Ryan Partnership, reaffirms what others have said, namely, simply having a presence on social media isn't enough to engage consumers. "There is no value added for brands in their social presence," he writes. "That is, unless they have a unique offering for their audience with their content, creativity, and presence on social channels." ["The New Customer Demands New Marketing," Social Steve's Blog, 11 February 2013] He continues:

"Today brand image and style remain extremely important. But more and more consumers feel that they are manipulated by advertising. Many feel that the ads are merely a façade for brands and what they stand for. The new consumer wants to feel that brands stand for something that they themselves value. The new consumer is looking for greater brand transparency and truthfulness. And if a consumer thinks a brand has done an injustice, they will call them out in public space. The consumer opinion and influence is stronger than ever and they are pickier than ever because they now know they hold power. It is tougher and tougher for brands to capture consumer preference. The new consumer is powerful and thus a new marketing approach is required."

The new marketing approach recommended by Goldner clearly relies on big data analytics. He asserts that this approach must have the following elements:

• Complete understanding and empathy for their target audience.


• Brand commitment to their target audience to deliver not only an outstanding product/service but also a strong and engaging ongoing user experience.


• A publisher mentality – brand delivery of valuable content to their audience to reinforce who they are, what they stand for, while having complete understanding of the audience’s wants, needs, and desires.


• Creativity that allows the brand to stand out in saturated marketing channels.


• Listening – the new consumer is very social. Listening to them allows brands to shape their products/services closer to user-perceived perfection. Listening also allows brands to address problems immediately before a PR nightmare occurs. It also provides an opportunity to amplify positive feedback.

Goldner insists that new marketing approaches must "be completely driven by the consumer." He concludes:

"Understand how your competition is attempting to win customers. Use the channels where your audience goes and produce a most dramatic presence in a creative way. Be bold and stick out way beyond commodized marketing."

The marketing firm Urban Airship believes that future of targeting marketing and customer engagement is going to be driven by mobile devices. The following Urban Airship video offers some compelling reasons why they believe this.

Even if Urban Airship is correct and the future belongs to mobile, more traditional Internet activities aren't going disappear. Websites will continue to be an important outreach medium. There, too, targeted marketed (or whatever you prefer to call it) plays an important online role. Ruben Corbo prefers the term behavioral targeting. The objective of this approach remains the same -- consumer engagement. "Behavioral targeting is a means of creating more efficient marketing strategies," Corbo writes. "It's conducted through use of specific apps and programs that monitor the activity of the site. It also allows visitors to pick and choose their site subscriptions preferences to their liking." ["Why Behavioral Targeting Can Determine Your Website's Niche," Web Hosting Secret Revealed, 22 April 2013] Corbo continues:

"Behavioral targeting is conducted through creating a specific profiling pattern on each customized visitor's page, allowing them the choice to browse topics that are of interest to them. They are also able to receive recommendations about topics, products, or services that would appeal to their tastes. ... Behavioral targeting is best used on sites with high amounts of traffic and activity, as these sites offer the best content and services available. The range of advertising is also increased yet centered on areas that a specific user or client may seem to show a specific interest in, thereby increasing the chance of views which is by far a better alternative to just random ads. ... Nobody likes to receive advertisements that aren't relevant to them – especially in this over-saturated web where everything commands our attention. The benefit of behavioral targeting is it only suggests products or services to the customers based on their unique needs."

It's clear that targeted marketing is discussed under a number of monikers including response marketing, behavioral targeting, and content marketing; but, the desired results remain constant -- engage customers and strengthen brands. Providing consumers with a worthwhile experience is quickly becoming the best way to achieve those goals.

Targeted Marketing and Brand Engagement, Part 1

As businesses have created more ways to reach out to customers, deciding how to reach out has become increasingly complicated. In the days before newspapers, proprietors simply hung a sign on the front of their shop indicating what you could expect to buy inside. Unfortunately, only people passing in front of the shop were able to see the message. When newspapers came along, so did advertising and the ability to reach more people. The ads, of course, were aimed at drawing customers to the shop. As magazines, radio, and television were added to the mix, the ability to reach ever larger audiences increased as did the complexity of the decision of how to spend marketing dollars. The age of personal computers opened up the entire world to businesses and companies that are now wrestling with how best to divide marketing dollars to support multi-channel sales.

Until recently, the larger the audience that a business wanted to reach the more impersonal advertising became. Advertising campaigns had to have broad appeal since they were simply being scattergunned into the marketplace. Perhaps one of the most ironic things that has occurred in recent years is the fact that businesses now have the opportunity to advertise in a more personal way than has been available since the end of the door-to-door salesman era. What allows businesses to get personal with consumers is the collection and analysis of data. Unfortunately, targeted marketing wasn't possible in the early era of personal computers and consumers were blasted with an infinite array of advertisements (mostly in the form of unsolicited emails) that quickly became labeled "spam" or "junk mail." Almost as quickly, spam filters were created to keep annoying solicitations from ever reaching computer screens. To overcome the challenge of getting through junk mail filters without annoying consumers, reputable businesses eventually engaged in permission-based advertising.

Today, big data analytics can help businesses stay engaged with consumers by ensuring that the ads they send them are relevant to their lifestyles and preferences. Because this kind of brand engagement is so critical, Navin Nagiah, President and CEO of DotNetNuke, believes that companies should be keenly aware of the data that is being used to create targeted marketing campaigns. ["In The Age of Big Data - Data Is Power," Huffington Post Blog, 16 May 2013] He writes:

"It is imperative that you and your company understand which side of your bread is buttered and keep what's important (the data) closer to home. To build a successful company, you must own the customer and the prospect. You must own the engagement. You must control or manage the conversation and where it happens. It is critical that you own every aspect of the data as it relates to your prospects, possible buyers and customers, and that you are able to mine this for your own benefit, as you wish. ... If you do that, you will collect data that will you give leverage & power in the marketplace. If you settle for anything else, you are likely frittering away your competitive advantage as both the online world and technology changes around you. To keep the advantage, to stay competitive, you need to own the data."

Although I'm not convinced that you "need to own the data," I do believe that you must understand where you obtain data and ensure that it is the kind of data from which actionable insights can be gained. Although Nagiah focuses on a company's website as being its most important asset for gathering data, many companies believe that if the customer won't come to them they must go to the customer. One place they know that customers hang out is on Facebook. A recent MIT study noted that the types of corporate posts that were "liked" on Facebook "were often also shared." ["How to Create Brand Engagement on Facebook," by Arvind Malhotra, Claudia Kubowicz Malhotra and Alan See, MIT Sloan Management Review, 18 December 2012] Both "liking" and "sharing" are desirable types of consumer engagement. This kind of engagement is often prompted by what Laurent Faracci, the U.S. chief strategy and marketing officer for packaged-goods giant Reckitt Benckiser, labels "calls to action." He states that if he had his way, "100% of our digital media would have a call to action." ["Years After Ditching the Click, CPG Marketers Embrace Web Ads With 'Calls to Action'," by Jack Neff, Ad Age, 25 February 2013] Although a "call to action" might sound like a significant activity, it can be as simple as getting someone to enter a contest, answer a question, download a coupon, and so on.

Malhotra, Malhotra, and See report that lengthy a Facebook wall post "decreases the likelihood of [it] being 'liked'." Surprisingly, however, "length did not influence whether a post was shared." They continue:

"Additionally, there were no negative effects on 'shares' for posts announcing contests, advocating social causes or asking questions. There were two types of posts that drove 'shares' more than 'likes.' First, while posts with video content were not 'liked' more, they were shared significantly more than non-video posts. Second, whereas posts announcing deals did not receive high 'like' totals, these posts were shared much more often. It seems as if consumers chose to share attractive deal information rather than indicating appreciation. Regardless of whether a Facebook fan 'likes,' comments, shares or does some combination thereof, engagement through Facebook is becoming a critical element of any organization's marketing strategy. An opportunity exists to leverage wall posts more effectively to generate greater propagation and richer conversation — and to convert more consumers into brand advocates. In the highly networked social world we live in today, such brand advocates are priceless."

Jason Acidre believes that "the perceived value" that customers get through online engagement helps build trust and, as a result, stronger branding. ["How to Increase Conversions through Brand Strengthening," Kaiser the Sage, 20 May 2013] He writes:

"These processes involve content and strong brand indicators that a site builds to promote itself such as highly-informative blog posts, design/presentation/ experience, reviews and mentions from other people/sites, and a lot more. ... The truth is, it's easier to sell things when people (especially your target consumers) are already aware and fascinated by what your brand is putting out on the web. ... Basically, by building a strong online brand presence, you'll be able to acquire more qualified traffic that can easily be converted into leads and/or sales."

Acidre goes on to discuss a number of ways that brands can be strengthened through engagement, including: awareness, differentiation, and perceived value. He writes:

"Awareness -- Letting your target consumers know how your brand can help solve their problems – through education – is an important aspect of online branding, as it can influence buying decisions and brings them a step closer to your goals.

"Differentiation -- Brand attributes such as unique value proposition and pricing, can also increase a brand's chances of getting more interested users/consumers than its competitors. Highlight how your brand is unique or different in your space through your branding activities. ...

"Perceived value -- The way how people see or think about your brand or the quality of its products/services can extremely affect your site's conversions. It also scales your brand's marketing process, given that it naturally generates endorsements from satisfied users/customers, which then raises your brand's credibility."

Acidre is a proponent of getting your message out through as many channels as possible, such as, "brand mentions, links, social shares, endorsements, brand impressions, traffic – which all can help build trust, authority and conversions." He agrees with the MIT researchers that social media can be a powerful way to engage with consumers. He concludes:

"Offering the best experience possible for your users/visitors is imperative in this age of online marketing. Aside from helping your site perform better in search (as most of Google's algorithmic updates are based on usage), it can also help make your site become more remarkable to its users. It's easier to get endorsements when people are sticking around and getting remarkable experience from your site. Areas of your site's pages that you can continuously test to improve experience and visitor engagement are:

• Call to action
• Readability
• Content structure
• Thematic internal linking
• Design
• Accuracy of headlines and meta tags

"Improving your site's engagement rate can definitely increase its conversions, since people are more willing to take the next necessary action."

Marketing technologist Scott Brinker agrees with Acidre that providing the consumer with the right experience is important. Brinker believes that in the era of big data engaging with consumers should provide a big experience. ["The big data bubble in marketing -- but a bigger future," Chief Marketing Technologist, 21 January 2013] All of the pundits discussed above agree on one thing, engaging with the consumer is essential in today's marketplace.

Big Data Analytics: Good Questions Result in Better Answers

We've all been in classrooms or meetings when the person in charge (be it a teacher or facilitator) has stated, "There are no dumb questions." Of course there are! We've all seen the lists of supposedly real questions asked by trial lawyers in court; such as: "Now doctor, isn't it true that when a person dies in his sleep, in most cases he just passes quietly away and doesn't know anything about it until the next morning?" Comedian Bill Engvall believes that people who ask dumb questions should have to wear a sign that says, “I'm Stupid.” For example, he says: "Last time I had a flat tire, I pulled my truck into one of those side-of-the-road gas stations. The attendant walks out, looks at my truck, looks at me, and I SWEAR he said, 'Tire go flat?' I couldn't resist. I said, 'Nope. I was driving around and those other three just swelled right up on me. Here’s your sign."

Don't get me wrong. If you don't know something, you should ask a question. I believe that. But I also believe that you shouldn't ask dumb questions. Even if a question isn't a dumb one, there might be a better way to phrase it to obtain a clearer or more creative answer. Asking dumb questions is easy. Asking good questions is a lot harder. World-class researchers achieve the best results because they know how to ask the best questions. If you want big data analytic platforms to provide you with the best actionable intelligence, you need to ensure that the question you want the platform to analyze is framed in the best possible way. Good questions result in better answers.

Vijay Vijayasankar reports that, in the past, "a key principle for a good BI [business intelligence] system design" was discovering "the questions a user would ask the system, and then designing a solution around that." ["Big Data Solutions – Do Questions Matter?" Vijay's thoughts on all things big and small, 23 April 2013] While that approach may optimize obtaining "fast and accurate responses to predefined questions," it "also curtails our ability to change our mind and ask different questions." Flexibility is important because businesses may discover they are asking the wrong questions or need to tweak their questions if initial results don't provide all that was desired. Vijayasankar concludes, "I think we should use the big data momentum to make BI systems more intelligent than the rudimentary things it is capable of doing today."

One of the great things about analytic systems driven by artificial intelligence is that such systems can learn as they go. Over time the system itself may discover interesting questions to explore on its own. To be of the most use, Vijayasankar believes that analytic systems should permit natural language questions to be asked. He writes:

"Some training might be needed for the system and for the users to understand the restrictions – but no user should be constrained with the need to know how things work behind the scenes. A minority of people should have the skills to educate the computer – the rest of us should not be burdened with that. Instead, the computers should be smart enough to tell them answers to what questions users ask."

Navin Nagiah, President and CEO, DotNetNuke, notes that the world's most "powerful" companies "are powerful because they have information about you ... your background, your likes, your conversations, your actions, your location, your interests, your friends, your travel schedule, your profession, your hobbies, your kids. ... It is mind-blowing how much information these companies have about you." ["In The Age of Big Data - Data Is Power," Huffington Post Blog, 16 May 2013] I don't believe the age of big data revises the adage that "knowledge is power." Data that lies fallow in storage is no more powerful than gasoline that sits unused in an automobile. Power is only achieved when the data (or gasoline) is used to fuel a machine that does something with it. Mai Bruun Poulsen agrees that it is knowledge (and not data) that makes companies powerful. She believes that many "Companies Are Data-Rich and Insight-Poor." [Mindjumpers, 24 January 2013] "The amount of customer data can be overwhelming," she writes, "and some companies therefore fail to turn the data into useful information and insight." Nagiah implies as much. He writes:

"As science and technology evolves and as there is progress in behavioral economics, data mining, psychology and sociology in the connected world, these different fields will come together and companies that have this data will be able to derive intelligence. It is expected that in at least some scenarios they can predict your actions, and perhaps even know more about you, than you do yourself."

In other words, it is the analysis of data that provides the power -- and analysis is driven by questions. Of course, the questions you ask often depend on the position you occupy. Marketers will ask very different questions than factory managers or supply chain professionals. McKinsey & Company director David Court states, "If you look at the advantages people get from using data and analytics — in terms of what they can do in pricing, what they can do in customer care, what they can do in segmentation, what they can do in inventory management — it's not a little bit of a difference anymore. It's a significant difference." ["Putting big data and advanced analytics to work," McKinsey & Company Insights & Publications, September 2012]

Marketing, of course, is one area where big data analytics is becoming extremely important. Michael Brenner, Vice President of Global Marketing for SAP, believes that questions are particularly important in this area. "Big Data for Marketing," he writes, is more about the questions than the answers." ["Big Data For Marketing? It’s All About The Questions," B2B Marketing Insider, 14 May 2013] He also writes, "[It's] more about the insights than the technology." Gary Lockwood calls the ability to ask good questions a "magic performance tool." ["How to Ask Intelligent Questions With Impact," BizSuccess] He continues:

"The entire medical profession uses this technique. Educators, business leaders, salespeople, researchers, scientists and even business coaches use this method. ... There are thousands of examples to prove that asking good questions is one of the most important success secrets. ... Years ago, as a business consultant, I took pride in always having an answer. Now I realize that in our fast-moving, mile-a-minute world, answers have a very short shelf-life. Having the right questions is more important and more valuable."

Shane Snow agrees with Lockwood. He writes, "Good questions can move your business, organization, or career forward." ["The One Conversational Tool that Will Make You Better at Absolutely Everything," Fast Company, 17 December 2012] Snow shouldn't have limited his headline to "conversations." Good questions are important in any setting where good answers are sought. The point of this post is that good questions will make your business analysis better in every area. Snow doesn't assert there are no dumb questions; but, he does believe, "The worst kind of question is the one left unasked."

Numbers in the City, Part 1

You've probably heard the expression, "I'm tired of being treated like a number." In the past, "being treated like a number" has been a bad thing. That may change if experts in the emerging field of quantitative urbanism have their way. They "believe that many aspects of modern cities can be reduced to mathematical formulas" and want to use those formulas to make urban life richer and more efficient. ["Life in the City Is Essentially One Giant Math Problem," by Jerry Adler, Smithsonian, May 2013] Ben Hecht argues that, thanks to big data analytics, being treated like a number will make things more, not less, personal in the future. ["Big Data Gets Personal in U.S. Cities," Huffington Post TED Weekends, 8 February 2013] He writes:

"Much has already been said about how big data is dramatically changing the way that organizations make decisions. Today, more data is being created from more places than ever before. Blogs, Facebook, YouTube videos, retailer loyalty cards, mobile phones, and sensors on buildings are producing tons of data daily. Private sector companies, in their real-time data warehouses, are storing, analyzing, and harnessing it to help them to better understand their customers, dynamically alter pricing based on real-time demand, and even change their business models. And, increasingly government is putting the wealth of data that it generates to work to increase efficiency, save dollars, and create more proactive policy. But, as Deb Roy highlights in his TEDTalk, the true promise is where the numbers and patterns from this data connect and become personal -- enabling us to understand and to respond to humanity and the world in ways previously unimaginable."

Deb Roy's TEDTalk begins with the story of how big data analyzed his son's journey to an "adult" vocabulary and ends with how big data can be used to demonstrate important connections in society. If nothing else, the visualizations Roy uses in his presentation are stunning. Hecht asserts, "Already, in U.S. cities, we are seeing many promising signs of the transformative personal application of Big Data." Adler calls mathematical principles "the highest product of the human intellect." And even though he believes that "many aspects of modern cities can be reduced to mathematical formulas," he doesn't believe that means that all cities are alike. "Cities are particular," he writes. "You would never mistake a favela in Rio de Janeiro for downtown Los Angeles." Having made that point, he nevertheless asserts:

"Cities are also, at a deep level, universal: the products of social, economic and physical principles that transcend space and time. A new science — so new it doesn’t have its own journal, or even an agreed-upon name — is exploring these laws. We will call it 'quantitative urbanism.' It's an effort to reduce to mathematical formulas the chaotic, exuberant, extravagant nature of one of humanity's oldest and most important inventions, the city."

What makes cities chaotic, exuberant, and extravagant, are not the structures, the climate, or the location as much as the people. That's why big data analytics are so important in ensuring that our cities get smarter in the decades ahead. Big data analytics help us get personal. Adler explains:

"The systematic study of cities dates back at least to the Greek historian Herodotus. In the early 20th century, scientific disciplines emerged around specific aspects of urban development: zoning theory, public health and sanitation, transit and traffic engineering. By the 1960s, the urban-planning writers Jane Jacobs and William H. Whyte used New York as their laboratory to study the street life of neighborhoods, the walking patterns of Midtown pedestrians, the way people gathered and sat in open spaces. But their judgments were generally aesthetic and intuitive (although Whyte, photographing the plaza of the Seagram Building, derived the seat-of-the-pants formula for bench space in public spaces: one linear foot per 30 square feet of open area). 'They had fascinating ideas,' says Luís Bettencourt, a researcher at the Santa Fe Institute, a think tank better known for its contributions to theoretical physics, 'but where is the science? What is the empirical basis for deciding what kind of cities we want?' Bettencourt, a physicist, practices a discipline that shares a deep affinity with quantitative urbanism. Both require understanding complex interactions among large numbers of entities: the 20 million people in the New York metropolitan area, or the countless subatomic particles in a nuclear reaction."

Adler reports that "the birth of this new field can be dated to 2003, when researchers at SFI convened a workshop on ways to 'model' — in the scientific sense of reducing to equations — aspects of human society." The John D. and Catherine T. MacArthur Foundation believe this new field is so important that it provided a half-million grant to the Computation Institute (CI), a joint initiative between the University of Chicago and Argonne National Laboratory, in order to fund "a new Chicago-based research center using advanced computational methods to understand the rapid growth of cities. ... The funds help launch the Urban Center for Computation and Data (UrbanCCD), an initiative ... dedicated to data-driven urban research, planning and design." ["MacArthur Foundation grant supports Urban Center for Computation and Data," Green Car Congress, 20 January 2013] The MacArthur grant complements a $600,000-grant previously provided by the National Science Foundation.

Adler reports that Geoffrey West, a theoretical physicist, along with Bettencourt and José Lobo, an economist at Arizona State University, were the trio that founded the quantitative urbanism movement. They "produced the seminal paper in the field: 'Growth, Innovation, Scaling, and the Pace of Life in Cities.'" The abstract of that paper reads:

"Humanity has just crossed a major landmark in its history with the majority of people now living in cities. Cities have long been known to be society’s predominant engine of innovation and wealth creation, yet they are also its main source of crime, pollution, and disease. The inexorable trend toward urbanization world- wide presents an urgent challenge for developing a predictive, quantitative theory of urban organization and sustainable development."

Lobo told Adler, "What people do in cities — create wealth, or murder each other — shows a relationship to the size of the city, one that isn't tied just to one era or nation." Adler goes on to explain some of the math described in the paper:

"The relationship is captured by an equation in which a given parameter — employment, say — varies exponentially with population. In some cases, the exponent is 1, meaning whatever is being measured increases linearly, at the same rate as population. Household water or electrical use, for example, shows this pattern; as a city grows bigger its residents don't use their appliances more. Some exponents are greater than 1, a relationship described as 'superlinear scaling.' Most measures of economic activity fall into this category; among the highest exponents the scholars found were for 'private [research and development] employment,' 1.34; 'new patents,' 1.27; and gross domestic product, in a range of 1.13 to 1.26. If the population of a city doubles over time, or comparing one big city with two cities each half the size, gross domestic product more than doubles. Each individual becomes, on average, 15 percent more productive. Bettencourt describes the effect as 'slightly magical,' although he and his colleagues are beginning to understand the synergies that make it possible."

Adler reports that not only positive effects scale superlinearly. Some diseases, like AIDS, and serious crime also demonstrate superlinearity. On the other hand, he reports, "some measures show an exponent of less than 1, meaning they increase more slowly than population. These are typically measures of infrastructure, characterized by economies of scale that result from increasing size and density." The important point about these calculations is that "this phenomenon applies to cities all over the world, of different sizes, regardless of their particular history, culture or geography." Adler continues:

"One implication is that ... 'big cities are not just bigger small cities,' says Michael Batty, who runs the Centre for Advanced Spatial Analysis at University College London. 'If you think of cities in terms of potential interactions [among individuals], as they get bigger you get more opportunities for that, which amounts to a qualitative change.' ... There is a point, Whitney says, at which a system — a market, or a city — undergoes a phase shift and reorganizes itself in a more efficient and productive way."

That is exactly what the smart cities movement is trying to do as more of the world becomes urbanized. Adler goes on to discuss how mathematics can help people move through lines more quickly and how communications networks can be made more efficient using "a branch of mathematics called queuing theory." He discusses how mathematics can help people save money or logisticians run a transit system. In the final installment of this series, I'll continue the discussion of how quantitative urbanism can help make cities more efficient and livable.

Forecasting the Future with Big Data

All of us are curious about what lies ahead of us. We get nervous when our vision is limited. In such circumstances, we proceed cautiously, at best, and fearfully, at worst. It's why at night we carry flashlights and drive with our headlamps on. It's no different in the business world. The reason that companies get involved in forecasting and engage in "what if" scenario planning is that they don't want to be blindsided by the future. Big data is playing a larger role in these processes because such data can be analyzed to discover patterns that make predictions more accurate and timely. You might be surprised at the number of ways that companies are using big data to improve their forecasts about the future.

For example, take the world of entertainment. When Netflix decided to produce a series of its own, it "chose House of Cards as its first big project. Based on a BBC series, the show stars Kevin Spacey and is directed by David Fincher, and it has quickly become the most watched series ever on Netflix." ["The 'Big Data' Revolution: How Number Crunchers Can Predict Our Lives," KRWG, 13 April 2013] In the series, Spacey is a scheming politician from South Carolina hoping to plot his way into the White House. The article continues:

"The success of House of Cards is no accident. Netflix executives knew exactly what their millions of customers were watching; they knew precisely how popular the works of Fincher were, and how many of their customers were fans of Kevin Spacey, and how many people were streaming the British House of Cards. Sifting through that mountain of data, Netflix executives were able to predict that House of Cards would be just what Netflix viewers would want to watch. That kind of decision-making is an example of Big Data: the decade-long explosion of digital information, much of it personal, that has become available to companies and governments."

The article notes, "This trend in predictions and decisions is the topic of a new book, Big Data: A Revolution That Will Transform How We Live, Work and Think." Read my review of the book by clicking this link. One of the stories used in that book involves the retail chain Target. Using algorithms Target was able to figure out which of its customers were pregnant even if the women hadn't revealed that specific tidbit to the company or anyone else. ["How Companies Learn Your Secrets," by Charles Duhigg, New York Times Magazine, 16 February 2012] Duhigg relates the following story:

"A man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation. 'My daughter got this in the mail!' he said. 'She's still in high school, and you're sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?' The manager didn't have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man's daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. 'I had a talk with my daughter,' he said. 'It turns out there's been some activities in my house I haven't been completely aware of. She's due in August. I owe you an apology.'"

Target's objective, of course, was to sell products not reveal secrets; but, the story underscores how analytics can be used to make predictions with great accuracy. Laura Hazard Owen penned an article about a software program developed by Eric Horvitz, of Microsoft Research, and Kira Radinsky, of the Technion-Israel Institute, "that analyzes 22 years of New York Times archives, Wikipedia and about 90 other web resources to predict future disease outbreaks, riots and deaths." ["How two scientists are using the New York Times archives to predict the future," Gigaom, 1 February 2013] Although their work sounds ominous, Owen reports that their aim is to prevent potentially disastrous events from happening. She continues:

"The new research is the latest in a number of similar initiatives that seek to mine web data to predict all kinds of events. Recorded Future, for instance, analyzes news, blogs and social media to 'help identify predictive signals' for a variety of industries, including financial services and defense. Researchers are also using Twitter and Google to track flu outbreaks."

You can download a copy of Horvitz' and Radinsky's research paper by clicking on this link. In the paper, they describe how news about natural disasters, like storms and droughts, can be used to predict (and hopefully prevent) outbreaks of cholera that inevitably occur after such disasters in the developing world. Armed with data about such relationships, governments and non-governmental organizations can help educate the public about the dangers involved and how to mitigate them. Owen reports that Horvitz and Radinsky "outline the advantages that software has over humans in this area." They include:

• "Learning: Software 'has the ability to learn patterns from large amounts of data, can monitor numerous information sources, can learn new probabilistic associations over time, and can continue to do real-time monitoring, prediction, and alerting on increases in the likelihoods of forthcoming concerning events.'


• "Tireless researching: Software, with its 'long tentacles into historical corpora and real-time feeds,' can dig up data that humans might never find because they're too focused on 'knowledge that is easily discovered in studies or available from experts.'


• "Lack of bias: Software can assist 'when inferences from data run counter to expert expectations,' or when 'there is a significantly lower likelihood of an event than expected by experts based on the large set of observations and feeds being considered in an automated manner.'


• "Greater access to news: 'A system monitoring likelihoods of concerning future events typically will have faster and more comprehensive access to news stories that may seem less important on the surface (e.g., a story about a funeral published in a local newspaper that does not reach the main headlines), but that might provide valuable evidence in the evolution of larger, more important stories (e.g., massive riots).'

Owen reports, "One of the problems that the researchers faced in developing their software model is the fact that tragic events in poor African countries are often not widely reported." Lack of data is not generally a problem for businesses. Yet, all of the arguments used by Horvitz and Radinsky to convince us that big data analytics can offer significant insights about the future apply in the business setting as well as in the natural disaster setting. Michael Fitzgerald, for example, reports that "combining sensors with analytics allows companies to spot potential equipment problems before they occur." ["Sensing the Future Before It Occurs," MIT Sloan Management Review, 20 December 2012]

In an interview with William Ruh, vice president and general corporate officer of General Electric's global software headquarters, Fitzgerald discussed "a version of the idea of the Internet of things in which industrial machines connect to other machines and share data that will help companies improve operations." Ruh calls this the "Industrial Internet." He told Fitzgerald that GE is interested in the Industrial Internet because it can help the company make machines and systems more intelligent and help its customers run their equipment more effectively and efficiently. Ruh notes, for example, that companies use GE "gas turbines to generate 25 percent of the world's electricity. A one percent savings in fuel is worth $4.5 billion. A one percent change they can take to the bank and to shareholders." The collection and analysis of real-time data is one way that GE can help find that 1 percent improvement. Ruh concludes, "Real-time, no SQL, Hadoop and other kinds of big data technologies are actually very early on. They give us hope that we can manage this data differently than we did before. So the Industrial Internet is economical and feasible at a time when productivity needs to be a focus." As Fitzgerald noted earlier, one of the real advantages of real-time data analysis is the ability to predict an imminent failure. Failures not only shutdown processes they often result in collateral damage that compounds the severity of the failure. Ruh asserts that there are so many sensors being embedded into today's equipment that "the capability and complexity are off the charts, they are the stuff of science fiction, things you see in movies like 'Minority Report'."

Harvard's Doc Searls believes that people need to have the same capability to monitor data about their body's health as GE's clients have for monitoring their machines' health. If you're really connected, he writes, you might be gathering "data produced by your Withings scale, your Zeo sleep manager, your Nike+ sportwatch, your Omron blood pressure monitor, your Fitbit Flex wristband, your Moves smartphone app, your Sportline heart rate monitor, your MoodScope log, your Accu-Check blood glucose meter and your workout machine data from the gym." ["What can people do with data that companies alone can’t?" Doc Searls Weblog, 19 May 2013] The problem, Searls writes, is that data "are silo'd by the companies supplying those devices." He continues:

"If you had that data, you could correlate weight loss or maintenance to specific workout routines, moods or dietary practices. You could present that data to your insurance company or health care provider to get better rates and services from both. The list goes on, and can get very long."

My suspicion is that one of the things on that long list would be the ability to predict a potentially serious health problem. As we all know, prevention is better than cure. That is the power of data and analytics and why we are likely to see an increasing array of products aimed at helping individuals, governments, and organizations forecast the future with greater accuracy by analyzing it. Martin U. Müller puts it this way: "Forget Big Brother. Companies and countries are discovering that algorithms programmed to scour vast quantities of data can be much more powerful. They can predict your next purchase, forecast car thefts and maybe even help cure cancer." ["Living by the Numbers: Big Data Knows What Your Future Holds," ABC News, 18 May 2013] He believes that big analytics could, in some arenas, actually lead to "the end of chance." We'll just have to wait and see.

Can Machine Intelligence Emerge from a Single Algorithm?

Daniela Hernandez entices us into her Wired article about Stanford computer science professor Andrew Ng with this statement, "There's a theory that human intelligence stems from a single algorithm." ["The man behind the Google Brain: Andrew Ng and the new AI," 8 May 2013] If the theory is true, then scientists could be closer than we think to creating a deep-learning machine that eventually develops sentience. Hernandez writes:

"The idea arises from experiments suggesting that the portion of your brain dedicated to processing sound from your ears could also handle sight for your eyes. This is possible only while your brain is in the earliest stages of development, but it implies that the brain is -- at its core -- a general-purpose machine that can be tuned to specific tasks. About seven years ago, Stanford computer science professor Andrew Ng stumbled across this theory, and it changed the course of his career, reigniting a passion for artificial intelligence, or AI. 'For the first time in my life,' Ng says, 'it made me feel like it might be possible to make some progress on a small part of the AI dream within our lifetime'."

What Hernandez and Ng are referring to is often described as artificial general intelligence (AGI) in order to differentiate it from AI systems that can learn and think but only in limited ways to perform specific functions. AGI is the Holy Grail for many scientists working the field. Hernandez' article received a lot of attention in the blogosphere. One reason is because people get excited when new, easy to understand possibilities are presented to them. Ng explained to Hernandez that traditional approaches to AGI have generally assumed that the greatest challenge to achieving it would be dealing with the complexity of the mind. Hernandez writes:

"In the early days of artificial intelligence, Ng says, the prevailing opinion was that human intelligence derived from thousands of simple agents working in concert, what MIT's Marvin Minsky called 'The Society of Mind.' To achieve AI, engineers believed, they would have to build and combine thousands of individual computing modules. One agent, or algorithm, would mimic language. Another would handle speech. And so on. It seemed an insurmountable feat."

If the theory is true that a single algorithm that promotes deep learning can produce the complexity required to achieve what Ray Kurzweil calls "the singularity," the feat doesn't appear to be quite so insurmountable. That is what is getting people excited. Researchers have known for some time that simple algorithmic instructions can lead to complex computer behaviors in limited experiments. More recent research demonstrates that these kinds of simple instructions can be used to deal with a much broader spectrum of challenges than previously thought. For example, Alexander Wissner-Gross, a physicist at Harvard University and a fellow at the Massachusetts Institute of Technology Media Lab, and his colleague Cameron Freer, a post-doctoral fellow at MIT's Computer Science and Artificial Intelligence Laboratory, have developed a software program called Entropica, which they label "Sapient Software." George Dvorsky believes, "Wissner-Gross’s work has serious implications for AI. And in fact, he says it turns conventional notions of a world-dominating artificial intelligence on its head." ["How Skynet Might Emerge From Simple Physics," Sentient Developments, 11 May 2013] Dvorsky's "Skynet" reference is to the Terminator movie series in which an AGI system (i.e., Skynet) eventually gets so smart it takes over control of the world and starts killing humans. Wissner-Gross' work implies that the desire for control actually leads to intelligence and not the other way around. As Dvorsky writes, "From the rather simple thermodynamic process of trying to seize control of as many potential future histories as possible, intelligent behavior may fall out immediately." To learn more about this work, read my post entitled Intelligence from Chaos. The bottom line is that complexity has been shown to emerge from simplicity. Returning to Hernandez' article on Professor Ng, she writes:

"Ng now leads a new field of computer science research known as Deep Learning, which seeks to build machines that can process data in much the same way the brain does, and this movement has extended well beyond academia, into big-name corporations like Google and Apple. In tandem with other researchers at Google, Ng is building one of the most ambitious artificial-intelligence systems to date, the so-called Google Brain. This movement seeks to meld computer science with neuroscience -- something that never quite happened in the world of artificial intelligence. 'I've seen a surprisingly large gulf between the engineers and the scientists,' Ng says. Engineers wanted to build AI systems that just worked, he says, but scientists were still struggling to understand the intricacies of the brain. For a long time, neuroscience just didn't have the information needed to help improve the intelligent machines engineers wanted to build. What's more, scientists often felt they 'owned' the brain, so there was little collaboration with researchers in other fields, says Bruno Olshausen, a computational neuroscientist and the director of the Redwood Centre for Theoretical Neuroscience at the University of California, Berkeley. The end result is that engineers started building AI systems that didn't necessarily mimic the way the brain operated. They focused on building pseudo-smart systems that turned out to be more like a Roomba vacuum cleaner than Rosie the robot maid from the Jetsons. But, now, thanks to Ng and others, this is starting to change. 'There is a sense from many places that whoever figures out how the brain computes will come up with the next generation of computers,' says Thomas Insel, the director of the National Institute of Mental Health."

Frankly, the debate between engineers and scientists may be a red herring. If deep learning does lead to AGI, whatever machine intelligence does emerge will be something unique and different. Hernandez notes that, wherever this journey may lead, deep learning is the first step. The so-called "Google Brain" project received a lift (at least in publicity) when Ray Kurzweil joined the Google team. As noted above, Kurzweil is long-time, true believer in the singularity. Not everyone, however, is a believer. "Miguel Nicolelis, a top neuroscientist at Duke University, says computers will never replicate the human brain and that the technological Singularity is 'a bunch of hot air'." ["The Brain Is Not Computable," by Antonio Regalado, MIT Tech Review, 18 February 2013] "The brain is not computable," he told Regalado, "and no engineering can reproduce it." Nicolelis calls Kurzweil's vision of recreating the human brain "sheer bunk."

True or not, there is still a lot to be gained from deep learning machines. Advances in both mathematics and computing power have "normally cautious AI researchers hopeful that intelligent machines may finally escape the pages of science fiction." ["Ten Breakthrough Technologies 2013," by Robert D. Hof, MIT Tech Review, 23 April 2013] Hof continues:

"Indeed, machine intelligence is starting to transform everything from communications and computing to medicine, manufacturing, and transportation. The possibilities are apparent in IBM's Jeopardy!-winning Watson computer, which uses some deep-learning techniques and is now being trained to help doctors make better decisions. Microsoft has deployed deep learning in its Windows Phone and Bing voice search. Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power. And we probably won’t see machines we all agree can think for themselves for years, perhaps decades—if ever. But for now, says Peter Lee, head of Microsoft Research USA, 'deep learning has reignited some of the grand challenges in artificial intelligence'."

The most important characteristic of deep learning Ng told Hernandez is that the software learns on its own. "With Deep Learning, Ng says, you just give the system a lot of data 'so it can discover by itself what some of the concepts in the world are'." We have all experienced the thrill of self-discovery. Although I'm not sure that machines will ever "feel" that thrill, they are certainly becoming more capable of learning on their own. The "single algorithm" theory may not lead to AGI, but it should help get us closer to that goal.