Marketing Analytics Quotes

If you are on social media, and you are not learning, not laughing, not being inspired or not networking, then you are using it wrong.

Tweet others the way you want to be tweeted.

Good decision-making is based on access to the correct information at the right time.

There is no better tool to bring you closer to your competitors than market research. So, keep your friends close and your competitors even closer with the help of market research.

Market research gives you enough time to learn from your competitors’ mistakes, take inspiration from their strengths, and exploit their weaknesses.

Good market research would have made you aware of the real picture at the right time, and maybe you would have been able to write a different future because of that.

When we master customer behavior and are able to offer them exactly what they want, we can achieve the biggest business opportunities.

Making the right decision at the right time is what sets a successful entrepreneur apart from the others.

Let market research be a permanent, ongoing part of your business strategy.

You don’t have to wait until you have a heart attack to understand the importance of a healthy lifestyle.

Keep learning more and more about your customers, your competitors, your brand, your target market, and business opportunities.

In current times, we have access to so much data. Having said that, data analysis can uncover so many hidden patterns about customer behavior and how they interact with various products.

Break down your problem as much as you can, but don’t do it on the basis of your guesses. If you don’t know exactly what is wrong, use market research to break down your problem further and further until you reach the very point of your trouble.

Without solid insights gained through market research, any kind of marketing you do is like throwing your pamphlets at Times Square and hoping somebody will pick them up, read them, become interested, and get the product. That happens… just not so often.

If you have no idea whether the problem is with your product, price, or something else, then it’s a good idea to start with a little research. It’s going to help you understand what the main problem is, or why people are not buying more of your products.

Use the data from the clients you already have to help you find new clients just like them.

The superior investor is mature, rational, analytical, objective and unemotional.

If we forgot our resentment, if we forgot revenge, if we acknowledged that we are all puppets in someone else's play, if we had not fought a war against each other, if some of us had not called ourselves nationalists or communists or capitalists or realists, if our bonzes had not incinerated themselves, if the Americans hadn't come to save us from ourselves, if we had not bought what they sold, if the Soviets had never called us comrades, if Mao had not sought to do the same, if the Japanese hadn't taught us the superiority of the yellow race, if the French had never sought to civilize us, if Ho Chi Minh had not been dialectical and Karl Marx not analytical, if the invisible hand of the market did not hold us by the scruffs of our necks, if the British had defeated the rebels of the new world, if the natives had simply said , Hell no, on first seeing the white man, if our emperors and mandarins had not clashed among themselves, if the Chinese had never ruled us for a thousand year, if they had used gunpowder for more than fireworks, if the Buddha had never lived, if the Bible had never been written and Jesus Christ never sacrificed, if you needed no more revisions, and if I saw no more of these visions, please, could you please just let me sleep?

Data Analytics is critical to wise investing, but so is good old fashioned understanding of business and markets.

Forward-thinking organizations seek hybrid professionals who are highly proficient writers, analytical, creative, and tech savvy, with strong competencies in business management, information technology (IT), and human behavior.

As a brand or branded individual, success means sending out interesting, authentic, relevant content on a regular basis.

On the other hand, data smart marketers look beyond data and do not go around chasing KPI. They focus on solving their customers’ problems, one at a time.

Google Analytics is the best friend of all SEO Specialist and Digital Marketer around the globe.

In an ever-more complex world, Mandelbrot argues, scientists need both tools: image as well as number, the geometric view as well as the analytic. The two should work together. Visual geometry is like an experienced doctor's savvy in reading a patient's complexion, charts, and X-rays. Precise analysis is like the medical test results-the raw numbers of blood pressure and chemistry. "A good doctor looks at both, the pictures and the numbers. Science needs to work that way too," he says.

How have people come to be taken in by The Phenomenon of Man? We must not underestimate the size of the market for works of this kind [pseudoscience/'woo'], for philosophy-fiction. Just as compulsory primary education created a market catered for by cheap dailies and weeklies, so the spread of secondary and latterly tertiary education has created a large population of people, often with well-developed literary and scholarly tastes, who have been educated far beyond their capacity to undertake analytical thought.

Jack's marketing books had been a part of her life for so long that she had ceased to register their presence, simply moving them from the couch to the coffee table, from the bed to the nightstand. How to Sell Everything to Anybody. Eight Great Habits of CEOs. They all seemed to involve numbers, as if you could simply count yourself to riches, like following sheep to sleep.

While I enjoy the work because of my love of mathematics, I luckily realized that this career path was simply designed to exploit inefficiencies in markets in order to extract profits from others. This financial realm known as trading is a zero-sum game where for every dollar you make, someone else loses a dollar, and I know I’m not destined to become such an obvious parasite on society. I only aspire to lead a meaningful, impactful life where I can apply my skills as an extremely analytical individual toward the benefit of humanity. I’m

My “10 Smart Market Diagnosis and Profiling Questions” What keeps them awake at night, indigestion boiling up their esophagus, eyes open, staring at the ceiling? What are they afraid of? What are they angry about? Who are they angry at? What are their top three daily frustrations? What trends are occurring and will occur in their businesses or lives? What do they secretly, ardently desire most? Is there a built-in bias to the way they make decisions? (Example: engineers = exceptionally analytical) Do they have their own language? Who else is selling something similar to their product, and how? Who else has tried selling them something similar, and how has that effort failed? So, Step 1 in our system is to analyze thoroughly, understand, and connect with the customer.

Unfortunately, the critics of economics have had a tendency to discuss the whole structure as a tissue of misconceptions. It is a critique that fails. The strength of economics is its considerable, if far from complete, understanding of the flows and comparative advantages that underlie trade, jobs, capital and incomes, and the logic of optimising behaviour, all backed by glittering accomplishment in mathematics. That makes it a powerful analytical instrument, so that just a few misconceptions – such as a failure to understand the informal economy or resource depletion – have leverage: like a baby monkey at the controls of a Ferrari, they can turn it into an instrument with extraordinarily destructive potential. If it were a tissue of errors, it would not be dangerous: it is its 90 percent brilliance which makes it so.

In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.

Marc Andreesen puts it, “Markets that don’t exist don’t care how smart you are.”[

Scaling is good if it brings in incremental revenue, but you have to watch for a decrease in engagement, a gradual saturation of the initial market, or a rising cost of customer acquisition. Changes in churn, segmented by channels, show whether you’re growing your most important asset — your customers — or hemorrhaging attention as you scale.

Recall Sergio Zyman’s definition of marketing (more stuff to more people for

Unfortunately, the truth is that in universities the world over, the tremendous analytical power and insights of market economics are unappreciated. For whatever political or ideological reasons, we have deprived generations of university students of a clear understanding of basic economic forces and their proper role in a free society.

He asked good questions and told educational stories. There’s nothing I like so much as learning, and I had never met anyone who thought about business in such a clear way. On that first day, he introduced me to an intriguing analytic exercise that he does. He’ll choose a year—say, 1970—and examine the ten highest market-capitalization companies from around then. Then he’ll go forward to 1990 and look at how those companies fared. His enthusiasm for the exercise was contagious.

LEADERSHIP ABILITIES Some competencies are relevant (though not sufficient) when evaluating senior manager candidates. While each job and organization is different, the best leaders have, in some measure, eight abilities. 1 STRATEGIC ORIENTATION The capacity to engage in broad, complex analytical and conceptual thinking 2 MARKET INSIGHT A strong understanding of the market and how it affects the business 3 RESULTS ORIENTATION A commitment to demonstrably improving key business metrics 4 CUSTOMER IMPACT A passion for serving the customer 5 COLLABORATION AND INFLUENCE An ability to work effectively with peers or partners, including those not in the line of command 6 ORGANIZATIONAL DEVELOPMENT A drive to improve the company by attracting and developing top talent 7 TEAM LEADERSHIP Success in focusing, aligning, and building effective groups 8 CHANGE LEADERSHIP The capacity to transform and align an organization around a new goal You should assess these abilities through interviews and reference checks, in the same way you would evaluate potential, aiming to confirm that the candidate has displayed them in the past, under similar circumstances.

capabilities to find trends and to understand and segment audiences based on user attributes, media consumption habits, and more. Many large corporations with complex segmentation needs,

sometimes what you need is a new market, not a new product,

In a startup, the purpose of analytics is to find your way to the right product and market before the money runs out.

In our experience, brands are aware of at least 90% of issues that the company could face. These issues could come from operational challenges, customer service complaints, product disputes, and so on.

Others can be very hard to solve — two notoriously hard examples are predicting extended weather conditions or stock-market performance.

Reach—Facebook breaks down reach into organic, paid, and viral. Organic reach is the number of people who have seen a post in the news feed, in the ticker, or on the page itself. Paid reach is the number of unique people who have seen an advertisement or a sponsored story. Viral reach is the number of unique people who have seen a story about a page published by a friend.

If you’re good at data analytics but you don’t have this feel for the business, you’ll make naïve decisions.

If you’re comfortable with the feel of the business but you never use analytics, you’re just leaving a lot of money on the table that your competitors

When you’re scaling, you know your product and your market. Your metrics are now focused on the health of your ecosystem, and your ability to enter new markets.

The private-equity approach can take the form of simple improvements, such as changing irrigation from antiquated dykes and canal networks to automatic spray systems: these are the equivalent of picking low-hanging fruit. Pricey robots can boost milk per cow by 10-15%. Using “big-data” analytics to plant and cultivate seeds can push crop yields up 5%. “This is an industry where the gap between the top and bottom quartile is greater than anywhere else,” says Detlef Schoen of Aquila Capital, an alternative-investment firm. And yet the 36 agriculture-focused funds, with $15 billion under management, pale in comparison to the 144 funds focused on infrastructure ($89 billion) and 473 targeting real estate ($163 billion), according to Preqin, a data provider. TIAA-CREF, an American financial group, is a market leader with $5 billion in farmland, from Australia to Brazil, and its own agricultural academic centre at the University of Illinois. Canadian pension funds and Britain’s Wellcome Trust are among those bolstering their farming savvy.

The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big-data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big-data project.

Question: Do the machines depicted in the economy of The Matrix produce value? The answer, of course, depends on what value means and how it differs from price. One definition of value is the price towards which the actual price tends under normal market conditions. Another derives from the idea that the value of things reflects the true costs of producing them. One thing is certain: just like love, poetry, porn and beauty, one knows value when one sees it, even if one finds it impossible to define it analytically.

If you want your fans to abandon you as quickly as possible, go ahead and delete their messages from your Facebook areas.

It’s SOCIAL Media.   You must engage. Period.

The factors that usually decide presidential elections—the economy, likability of the candidates, and so on—added up to a wash, and the outcome came down to a few key swing states. Mitt Romney’s campaign followed a conventional polling approach, grouping voters into broad categories and targeting each one or not. Neil Newhouse, Romney’s pollster, said that “if we can win independents in Ohio, we can win this race.” Romney won them by 7 percent but still lost the state and the election. In contrast, President Obama hired Rayid Ghani, a machine-learning expert, as chief scientist of his campaign, and Ghani proceeded to put together the greatest analytics operation in the history of politics. They consolidated all voter information into a single database; combined it with what they could get from social networking, marketing, and other sources; and set about predicting four things for each individual voter: how likely he or she was to support Obama, show up at the polls, respond to the campaign’s reminders to do so, and change his or her mind about the election based on a conversation about a specific issue. Based on these voter models, every night the campaign ran 66,000 simulations of the election and used the results to direct its army of volunteers: whom to call, which doors to knock on, what to say. In politics, as in business and war, there is nothing worse than seeing your opponent make moves that you don’t understand and don’t know what to do about until it’s too late. That’s what happened to the Romney campaign. They could see the other side buying ads in particular cable stations in particular towns but couldn’t tell why; their crystal ball was too fuzzy. In the end, Obama won every battleground state save North Carolina and by larger margins than even the most accurate pollsters had predicted. The most accurate pollsters, in turn, were the ones (like Nate Silver) who used the most sophisticated prediction techniques; they were less accurate than the Obama campaign because they had fewer resources. But they were a lot more accurate than the traditional pundits, whose predictions were based on their expertise. You might think the 2012 election was a fluke: most elections are not close enough for machine learning to be the deciding factor. But machine learning will cause more elections to be close in the future. In politics, as in everything, learning is an arms race. In the days of Karl Rove, a former direct marketer and data miner, the Republicans were ahead. By 2012, they’d fallen behind, but now they’re catching up again.

When I first joined Facebook, I was working with a team to answer the critical question of how best to grow our business. The conversations were getting heated, with many people arguing their own positions strongly. We ended the week without consensus. Dan Rose, leader of our deal team, spent the weekend gathering market data that allowed us to reframe the conversation in analytics. His effort broke the logjam. I then expanded Dan’s responsibilities to include product marketing. Taking initiative pays off. It is hard to visualize someone as a leader if she is always waiting to be told what to do.

The quality of students wasn’t an issue; Tsinghua and nearby Peking University attracted the highest-scoring students from each year’s national examinations. But the SEM’s curriculum and teaching methods were dated, and new faculty members were needed. To be a world-class school required world-class professors, but many instructors, holdovers from a bygone era, knew little about markets or modern business practices. The school’s teaching was largely confined to economic theory, which wasn’t very practical. China needed corporate leaders, not Marxist theoreticians, and Tsinghua’s curriculum placed too little emphasis on such critical areas as finance, marketing, strategy, and organization. The way I see it, a business education should be as much vocational as academic. Teaching business is like teaching medicine: theory is important, but hands-on practice is essential. Medical students learn from cadavers and hospital rounds; business students learn from case studies—a method pioneered more than a century ago by Harvard Business School that engages students in analyzing complex real-life dilemmas faced by actual companies and executives. Tsinghua’s method of instruction, like too much of China’s educational system, relied on rote learning—lectures, memorization, and written tests—and did not foster innovative, interactive approaches to problem solving. Students needed to know how to work as part of a team—a critical lesson in China, where getting people to work collaboratively can be difficult. At Harvard Business School we weren’t told the “right” or “wrong” answers but were encouraged to think for ourselves and defend our ideas before our peers and our at-times-intimidating professors. This helped hone my analytical skills and confidence, and I believed a similar approach would help Chinese students.

Here are several rules that worked for me as I grew from a wild amateur into an erratic semiprofessional and finally into a calm professional trader. You may change this list to suit your personality. Decide that you are in the market for the long haul—that is, you want to be a trader even 20 years from now. Learn as much as you can. Read and listen to experts, but keep a degree of healthy skepticism about everything. Ask questions, and do not accept experts at their word. Do not get greedy and rush to trade—take your time to learn. The markets will be there, offering more good opportunities in the months and years ahead. Develop a method for analyzing the market—that is, “If A happens, then B is likely to happen.” Markets have many dimensions—use several analytic methods to confirm trades. Test everything on historical data and then in the markets, using real money. Markets keep changing—you need different tools for trading bull and bear markets and transitional periods as well as a method for telling the difference (see the sections on technical analysis). Develop a money management plan. Your first goal must be long-term survival; your second goal, a steady growth of capital; and your third goal, making high profits. Most traders put the third goal first and are unaware that goals 1 and 2 exist (see Section 9, “Risk Management”). Be aware that a trader is the weakest link in any trading system. Go to a meeting of Alcoholics Anonymous to learn how to avoid losses or develop your own method for cutting out impulsive trades. Winners think, feel, and act differently than losers. You must look within yourself, strip away your illusions, and change your old ways of being, thinking, and acting. Change is hard, but if you want to be a professional trader, you have to work on changing and developing your personality.

In today’s media environment, invisibility is a fate much worse than failure for any business large or small.

Rachel is a smart marketer. How do we know this? She’s promoting her album on her cover photo, and also -- in the description.

People are sometimes shocked to hear that only 1 percent of their fans will see any of their posts unless they’re “boosted” (paid for).

even if you don't think of your company as a global entity, you must remember that everything on the Internet is seen everywhere.

We don’t subscribe to the “build it and they will come” philosophy because we’ve never seen it work.

Woobox

● Pursuing online courses with pre-recorded videos? ● Not able to communicate with the instructor while in an online lecture? ● Online lectures seem boring and disengaging? Not anymore. Technology has been able to advance an already transformative concept. Online learning has made its way into almost every professional’s career life. However, there is a new concept which not many people are aware of - LIVE & interactive learning. As the name suggests, it’s just like traditional classroom learning but entirely online. Let’s see what it is, how it works, and how it can benefit your career. LIVE Learning: The Better, More Interactive Learning Method LIVE & interactive learning entails experienced tutors and instructors delivering lectures via LIVE online learning platforms that are built with features to aid in engaging educational learnings. Furthermore, Online Courses are delivered in a similar format that is found in a traditional classroom. With interactivity, teachers can not only deliver lectures, take LIVE questions, and respond, but also the students can interact with one another - just like they would in a brick and mortar classroom. Taking Online Courses Up a Notch Instead of sitting through a pre-recorded lecture, you can now attend the session LIVE. And the best part about this type of learning is that both tutors and students can interact with each other, so query resolution is instant, students can voice out their thoughts, collaboration becomes easy, and the face-to-face interaction definitely makes it more interactive. Reasons Why LIVE & Interactive Learning is Taking the Lead ● Comfortable Learning Pace Students pursuing LIVE & interactive online courses get the opportunity to learn at their own pace. They can discuss their questions in LIVE lectures and interact with the faculty as well. ● Focus on Tougher Modules In a regular classroom, the teacher always decides which modules require special focus. However, with LIVE & interactive learning, you can choose how much time you want to spend on a particular module. ● Extensive Study Materials Another added benefit of LIVE & interactive online courses is that you have access to study material 24*7 and from anywhere. This gives you control and ample time to go through the material more than once or as required. ● Opportunity for More Interaction Ranging from Online Data Analytics Courses to finance, marketing, and sales, online courses allow students to involve themselves in class discussions and chat with more ease. This is just not possible in regular face-to-face interactions where teachers can ask questions and embarrass you in front of the entire class if you are wrong or don’t know the answer. It’s Not a Roadblock, Rather an Accelerant to Your Career The best part - you don’t have to leave your current job to pursue a degree program. Passion to gain knowledge and upskill and a search engine that will take you the right online course is all you need. So whether you are scouting for online data analytics courses, machine learning courses, or digital marketing, LIVE & interactive learning can help you gain the education you deserve.

4). LuckyOrange This is an awesome analytics plugin that shows not only where your users come from, but it also shows what they do on your site, where they click, and how far they scroll down. It’s an amazing plugin at an incredible price.

La herramienta de análisis más utilizada: Google Analytics

Risk aversion is an essential element in investing. People’s aversion to loss causes them to police the markets. Because most people are averse to risk: they approach investing with caution, they perform careful analysis when considering investments, and especially risky ones, they incorporate conservative assumptions and appropriate skepticism into their analysis, they demand greater margins of safety on risky investments to protect against analytical errors and unpleasant surprises, they insist on healthy risk premiums—the expectation of incremental returns—if they’re going to undertake risky investments, and they refuse to invest in deals that make no sense.

Deep work is not some nostalgic affectation of writers and early-twentieth-century philosophers. It’s instead a skill that has great value today. There are two reasons for this value. The first has to do with learning. We have an information economy that’s dependent on complex systems that change rapidly. Some of the computer languages Benn learned, for example, didn’t exist ten years ago and will likely be outdated ten years from now. Similarly, someone coming up in the field of marketing in the 1990s probably had no idea that today they’d need to master digital analytics. To remain valuable in our economy, therefore, you must master the art of quickly learning complicated things. This task requires deep work. If you don’t cultivate this ability, you’re likely to fall behind as technology advances.

There are five ways technology can boost marketing practices: Make more informed decisions based on big data. The greatest side product of digitalization is big data. In the digital context, every customer touchpoint—transaction, call center inquiry, and email exchange—is recorded. Moreover, customers leave footprints every time they browse the Internet and post something on social media. Privacy concerns aside, those are mountains of insights to extract. With such a rich source of information, marketers can now profile the customers at a granular and individual level, allowing one-to-one marketing at scale. Predict outcomes of marketing strategies and tactics. No marketing investment is a sure bet. But the idea of calculating the return on every marketing action makes marketing more accountable. With artificial intelligence–powered analytics, it is now possible for marketers to predict the outcome before launching new products or releasing new campaigns. The predictive model aims to discover patterns from previous marketing endeavors and understand what works, and based on the learning, recommend the optimized design for future campaigns. It allows marketers to stay ahead of the curve without jeopardizing the brands from possible failures. Bring the contextual digital experience to the physical world. The tracking of Internet users enables digital marketers to provide highly contextual experiences, such as personalized landing pages, relevant ads, and custom-made content. It gives digital-native companies a significant advantage over their brick-and-mortar counterparts. Today, the connected devices and sensors—the Internet of Things—empowers businesses to bring contextual touchpoints to the physical space, leveling the playing field while facilitating seamless omnichannel experience. Sensors enable marketers to identify who is coming to the stores and provide personalized treatment. Augment frontline marketers’ capacity to deliver value. Instead of being drawn into the machine-versus-human debate, marketers can focus on building an optimized symbiosis between themselves and digital technologies. AI, along with NLP, can improve the productivity of customer-facing operations by taking over lower-value tasks and empowering frontline personnel to tailor their approach. Chatbots can handle simple, high-volume conversations with an instant response. AR and VR help companies deliver engaging products with minimum human involvement. Thus, frontline marketers can concentrate on delivering highly coveted social interactions only when they need to. Speed up marketing execution. The preferences of always-on customers constantly change, putting pressure on businesses to profit from a shorter window of opportunity. To cope with such a challenge, companies can draw inspiration from the agile practices of lean startups. These startups rely heavily on technology to perform rapid market experiments and real-time validation.

Now you see why there are so few truly great marketers out there. You’ve got to be sufficiently analytical to organize and build a mountain of analysis, and you’ve got to be creative and courageous enough to leap off that mountain in the direction your intuition tells you to. Strong left brain, strong right brain and the courage and boldness required to pull it all off. Great marketing isn’t for the faint of heart.

Some people argue that economics is an exception to this general story. Economics, they say, provides a much more analytically precise and tightly integrated body of theory—a theory that is explicitly linked to a small set of generally accepted assumptions about human beings’ motivations and decision-making procedures, and that has been rigorously tested against quantified empirical evidence. Among all the social sciences, economics alone, these boosters contend, has a defensible claim to true scientific status. Economics certainly deserves to be regarded as the queen of the social sciences; unlike the others, it has unquestionably produced useful knowledge on a wide range of issues that affect our daily lives. Yet we should be suspicious of its bold claims to scientific status. Modern neoclassical economic theory is firmly grounded in the kind of mechanistic worldview (described in “Complexities”) that sees the economy as a machine, and to explain the operation of this machine it imports many of the concepts of nineteenth-century classical physics. So it stresses the natural tendency of the economy to find a stable equilibrium and the possibility of isolating the effect of changes in different economic factors (like changes in interest rates) on economic performance.25 As well, to achieve its simplicity and elegance, the theory focuses on the behavior of independent individuals operating in a market—individuals who are atomized, rational, similar in preferences, and stripped of any social attributes. But this makes the theory largely asocial and ahistorical: there’s generally no place in it for large-scale historical, cultural, and political forces that sometimes have a huge impact on our economies—forces like the emancipation of women, rising environmental consciousness, or democratization in poor countries. Because it’s insensitive to broad social forces, modern economic theory is also surprisingly insensitive to its own tight relationship with capitalism. Nevertheless, it’s clearly a product of capitalism—a specific, historically rooted economic system—and it only makes sense in the context of capitalism.26

There are three essential approaches for analytic marketing: (1) propensity models predict likelihood to purchase, (2) market basket analysis provides actionable association rules (answering questions such as customers who buy this product also buy what else?), and (3) decision trees enable hypersegmentation based on events and other customer characteristics.