Predictive Analytics Quotes

Someday soon, say predictive analytics experts, it will be possible for companies to know our tastes and predict our habits better than we know ourselves.

An economist is an expert who will know tomorrow why the things he predicted yesterday didn’t happen. —Earl Wilson

Already law enforcement agencies make use of predictive analytic tools to identify suspects and direct investigations. It’s a short step from there to the world of Big Brother and thoughtcrime.

Having a good understanding of other people’s algorithms (their beliefs and needs) helps to predict their behavior. This gives you the opportunity to make interactions easier, more productive, and more fun.

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

All models are wrong, but some are useful.’ CHAPTER 6 Algorithms, Analytics and Prediction

The trouble with the world is that the stupid are cocksure and the intelligent are full of doubt. —Bertrand Russell

As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.

Predictive modeling generates the entire model from scratch. All the model’s math or weights or rules are created automatically by the computer.

No matter how intelligent and advanced the analytical tools we use, they will still process data from the past to predict the future.

Predictions have an expiry date. Action is needed before predictions expire.

We know the what, but we don’t know the why.6 When applying PA, we usually don’t know about causation, and we often don’t necessarily care. For many PA projects, the objective is more to predict than it is to understand the world and figure out what makes it tick.

As soon as an Analytical Engine exists, it will necessarily guide the future course of the science. Whenever any result is sought by its aid, the question will then arise — by what course of calculation can these results be arrived at by the machine in the shortest time?

And yet Simulmatics’ legacy endures in predictive analytics, what-if simulation, and behavioral data science: it lurks behind the screen of every device. Simulmatics, notwithstanding its own failure, helped invent the data-mad and near-totalitarian twenty-first century, in which the only knowledge that counts is prediction and, before and after the coming of the coronavirus, corporations extract wealth by way of the collection of data and the manipulation of attention and the profit of prophecy. In a final irony, Simulmatics, whose very past has been all but erased, helped invent a future obsessed with the future, and yet unable to improve it.

Since the neocortex is divided into two halves called hemispheres, it makes sense that we analyze and spend a lot of time thinking in duality: you know, good versus bad, right versus wrong, positive versus negative, male versus female, straight versus gay, Democrat versus Republican, past versus future, logic versus emotion, old versus new, head versus heart—you get the idea. And if we’re living in stress, the chemicals we’re pumping into our systems tend to drive the whole analytical process faster. We analyze even more in order to predict future outcomes so that we can protect ourselves from potential worst-case scenarios based on past experience. There

20th Century 21st Century Scale and Scope Speed and Fluidity Predictability Agility Rigid Organization Boundaries Fluid Organization Boundaries Command and Control Creative Empowerment Reactive and Risk Averse Intrapreneur Strategic Intent Profit and Purpose Competitive Advantage Comparative Advantage Data and Analytics Synthesizing Big Data

But all predictive models share the same objective: They consider the various factors of an individual in order to derive a single predictive score for that individual. This score is then used to drive an organizational decision, guiding which action to take. Before using a model, we’ve got to build it. Machine learning builds the predictive model:

Eventually, the performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning. The five main steps that are involved in training a machine learning algorithm can be summarized as follows: Selection of features. Choosing a performance metric. Choosing a classifier and optimization algorithm. Evaluating the performance of the model. Tuning the algorithm.

Military analysis is not an exact science. To return to the wisdom of Sun Tzu, and paraphrase the great Chinese political philosopher, it is at least as close to art. But many logical methods offer insight into military problems-even if solutions to those problems ultimately require the use of judgement and of broader political and strategic considerations as well. Military affairs may not be as amenable to quantification and formal methodological treatment as economics, for example. However, even if our main goal in analysis is generally to illuminate choices, bound problems, and rule out bad options - rather than arrive unambiguously at clear policy choices-the discipline of military analysis has a great deal to offer. Moreover, simple back-of-the envelope methodologies often provide substantial insight without requiring the churning of giant computer models or access to the classified data of official Pentagon studies, allowing generalities and outsiders to play important roles in defense analytical debates. We have seen all too often (in the broad course of history as well as in modern times) what happens when we make key defense policy decisions based solely on instinct, ideology, and impression. To avoid cavalier, careless, and agenda-driven decision-making, we therefore need to study the science of war as well-even as we also remember the cautions of Clausewitz and avoid hubris in our predictions about how any war or other major military endeavor will ultimately unfold.

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.

—General George S. Patton A kiss on the hand may be quite continental, but diamonds are a girl’s best friend.

They concentrated their efforts to persuade the group of Persuadables. They invested heavily in this strategy; apparently it paid off.

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

In the years before Julia joined the group, People Analytics had determined that Google needed to interview a job applicant only four times to predict, with 86 percent confidence, if they would be a good hire. The division had successfully pushed to increase paid maternity leave from twelve to eighteen weeks because computer models indicated that would reduce the frequency of new mothers quitting by 50 percent.

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.

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.

Big data is, in a nutshell, large amounts of data that can be gathered up and analyzed to determine whether any patterns emerge and to make better decisions.

When we receive information of any kind, it travels up the spinal cord toward the neural networks of the brain. The first part of the brain to get this information is the emotional center—before the analytical or interpretive parts. Predictably, this causes some problems in our daily life.

Sometimes I feel compelled to do something, but I can only guess later why it needed to done, and I question whether I am drawing connections where none really exist. Other times I see an event – in a dream or in a flash of “knowing” – and I feel compelled to work toward changing the outcome (if it’s a negative event) or ensuring it (when the event is positive). At the times I am able to work toward changing or ensuring the predicted event, sometimes this seems to make a difference, and sometimes it doesn’t seem to matter. Finally, and most often, throughout my life I have known mundane information before I should have known it. For example, one of my favourite games in school was to guess what numbers my math teacher would use to demonstrate a concept, or to guess the words on a vocabulary test before the test was given. I noticed I was not correct all the time, but I was correct enough to keep playing the game. Perhaps partially because of the usefulness of this mundane skill, I was an outstanding student, getting straight As and graduating from college with highest honours in neuroscience and a minor in computer science. I was a modest drinker even in college, but I found I could ace tests when I was hungover after a night of indulgence. Sometimes I think I even did better the less I paid attention to the test and the more I felt sick or spacey. It was like my unconscious mind could take over and put the correct information onto the page without interruption from my overly analytical conscious mind. At graduate school in neuroscience, I focused on trying to understand human experience by studying how the brain processes pain and stress. I wanted to know the answer to the question: what’s going on inside people’s heads when we suffer? Later, as I finished my PhD in psychoacoustics, which is all about the psychology of sound, I became fascinated with timing. How do we figure out the order of sounds, even when some sounds take longer to process than others? How can drummers learn to decode time differences of 1/1,000 of a second, when most people just can’t hear those kinds of subtle time differences? At this point, I was using my premonitions as just one of the tools in my day-to-day toolkit, but I wasn’t thinking about them scientifically. At least not consciously. Sure, every so often I’d dream of the slides that would be used by one of my professors the next day in class. Or I’d realize that the data I was recording in my experiments followed the curve of an equation I’d dreamed about a year before. But I thought that was just my quirky way of doing things – it was just my good student’s intuition and it didn’t have anything to do with my research interests or my life’s work. What was my life’s work again?

AI-powered analytics can provide predictive insights about employee turnover, helping HR to develop retention strategies proactively. Similarly, AI can support performance management by analyzing employee performance data and providing recommendations for improvement.

Daniel Roth of North Coast Container has established himself as an executive leader. Through his rich resume and vast experience, he inspires growth in large organizations, focusing on predictive data analytics, manufacturing, project management, and business development. Mr. Roth is the Senior Vice President of Sales and General Manager for Stavig Industries LLC. He holds his MBA from Portland State University and applies his business and leadership acumen to embracing opportunities for himself and his clients.

Psychologist Philip Tetlock once wrote: “We need to believe we live in a predictable, controllable world, so we turn to authoritative-sounding people who promise to satisfy that need.” Satisfying that need is a great way to put it. Wanting to believe we are in control is an emotional itch that needs to be scratched, rather than an analytical problem to be calculated and solved. The illusion of control is more persuasive than the reality of uncertainty. So we cling to stories about outcomes being in our control.

There are two distinct branches of data mining, predictive and descriptive/exploratory (Figure 1.2), that can turn raw data into actionable knowledge. Sometimes you hear these two categories called directed (predictive) and undirected (descriptive). Predictive models use known results to develop (or train or estimate) a model that can be used to predict values for different data. Descriptive models describe patterns in existing data that may be found in new data. With descriptive models, there is no target variable for which you are striving to predict the value. Most of the big payoff has been in predictive modeling when the models are operationalized in a real-world setting.

predictive Analytics enabled the Big Data to deliver the actual usage and value to the businesses by putting the processed information to a real use.

PA’s mission is to engineer solutions. As for the data employed and the insights gained, the tactic in play is: “Whatever works.” And yet even hard-nosed scientists fight the urge to overexplain.

Even the most elite of engineers commits the most mundane and costly of errors. In late 1998, NASA launched the Mars Climate Orbiter on a daunting nine-month trip to Mars, a mission that fewer than half the world’s launched probes headed for that destination have completed successfully. This $327.6 million calamity crashed and burned indeed, due not to the flip of fate’s coin, but rather a simple snafu. The spacecraft came too close to Mars and disintegrated in its atmosphere. The source of the navigational bungle? One system expected to receive information in metric units (newton-seconds), but a computer programmer for another system had it speak in English imperial units (pound-seconds). Oops.

The emotions aren’t always immediately subject to reason, but they are always immediately subject to action. —William James

The Internet of free platforms, free services, and free content is wholly subsidized by targeted advertising, the efficacy (and thus profitability) of which relies on collecting and mining user data. —Alexander Furnas, writer for The Atlantic

The dilemma is, as it is often said, correlation does not imply causation.5 The discovery of a predictive relationship between A and B does not mean one causes the other, not even indirectly. No way, nohow.

A crime risk model dehumanizes the prior offender by paring him or her down to the extremely limited view captured by a small number of characteristics (variables input to a predictive model). But, if the integration of PA promises to lower the overall crime rate—as well as the expense of unnecessary incarceration—is this within the acceptable realm of compromises to civil liberties one endures when incarcerated in the first place?

As Mike Loukides, a vice president at the innovation publisher O’Reilly, once put it, “Data science is like porn—you know it when you see it.

The alternative [to thinking ahead] would be to think backwards . . . and that’s just remembering. —Sheldon, the theoretical physicist on The Big Bang Theory

What is the great revolution of science in the last 10, 15 years? It is the movement from the search for universals to the understanding of variability. Now in medical science we don’t want to know . . . just how cancer works; we want to know how your cancer is different from my cancer.

key is to focus on the big decisions for which if you had better data, if you had better predictive ability, if you had a better ability to optimize, you’d make more money.

Proponents like to say that predictive analytics is actionable. Its output directly informs actions, commanding the organization about what to do next. But with this use of vocabulary, industry insiders have stolen the word actionable, which originally has meant worthy of legal action (i.e., “sue-able”), and morphed it. This verbal assault comes about because people are so tired of seeing sharp-looking reports that provide only a vague, unsure sense of direction. With this word’s new meaning established, “Your fly is unzipped” is actionable (it is clear what to do—you can and should take action to remedy), but “You’re going bald” is not (there’s no cure; nothing to be done). Better yet, “I predict you will buy these button-fly jeans and this snazzy hat” is actionable, to a salesperson.

Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere! —John Elder

As NASA put it in 1965 when defending the idea of sending humans into space, “Man is the lowest-cost, 150-pound, nonlinear, all-purpose computer system which can be mass-produced by unskilled labor.” But, for some tasks, we don’t have to pretend anymore. Everything changed in 1997 when IBM’s Deep Blue computer defeated then world chess champion Garry Kasparov. Predictive modeling was key. No matter how fast the computer, perfection at chess is impossible, since there are too many possible scenarios to explore. Various estimates agree there are more chess games than atoms in the universe, a result of the nature of exponential growth. So the computer can look ahead only a limited number of moves, after which it needs to stop enumerating scenarios and evaluate game states (boards with pieces in set positions), predicting whether each state will end up being more or less advantageous.

data, models, transformation. Data is the creative use of internal and external data to give you a broader view on what is happening to your operations or your customer. Modeling is all about using that data to get workable models that can either help you predict better or allow you to optimize better in terms of your business.

Backtesting against historical data, all indications whispered confident promises for what this thing could do once set in motion. As John puts it, “A slight pattern emerged from the overwhelming noise; we had stumbled across a persistent pricing inefficiency in a corner of the market, a small edge over the average investor, which appeared repeatable.” Inefficiencies are what traders live for. A perfectly efficient market can’t be played, but if you can identify the right imperfection, it’s payday.

Predictive analytics (PA)—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.

Predictive model—A mechanism that predicts a behavior of an individual, such as click, buy, lie, or die. It takes characteristics of the individual as input, and provides a predictive score as output. The higher the score, the more likely it is that the individual will exhibit the predicted behavior.

Nobody questions the importance of knowing what happens outside, but such knowledge should be preceded by knowing what happens inside. Generally, benchmarking will not help resolve incorrectly formulated questions or questions that are misdirected. Externalism can harm human capital management as much as self-centeredness can. An appropriate balance between looking outside and knowing the inside seems to be the obvious solution to this crisis.

Bizarre and Surprising Insights—Consumer Behavior Insight Organization Suggested Explanation7 Guys literally drool over sports cars. Male college student subjects produce measurably more saliva when presented with images of sports cars or money. Northwestern University Kellogg School of Management Consumer impulses are physiological cousins of hunger. If you buy diapers, you are more likely to also buy beer. A pharmacy chain found this across 90 days of evening shopping across dozens of outlets (urban myth to some, but based on reported results). Osco Drug Daddy needs a beer. Dolls and candy bars. Sixty percent of customers who buy a Barbie doll buy one of three types of candy bars. Walmart Kids come along for errands. Pop-Tarts before a hurricane. Prehurricane, Strawberry Pop-Tart sales increased about sevenfold. Walmart In preparation before an act of nature, people stock up on comfort or nonperishable foods. Staplers reveal hires. The purchase of a stapler often accompanies the purchase of paper, waste baskets, scissors, paper clips, folders, and so on. A large retailer Stapler purchases are often a part of a complete office kit for a new employee. Higher crime, more Uber rides. In San Francisco, the areas with the most prostitution, alcohol, theft, and burglary are most positively correlated with Uber trips. Uber “We hypothesized that crime should be a proxy for nonresidential population.…Uber riders are not causing more crime. Right, guys?” Mac users book more expensive hotels. Orbitz users on an Apple Mac spend up to 30 percent more than Windows users when booking a hotel reservation. Orbitz applies this insight, altering displayed options according to your operating system. Orbitz Macs are often more expensive than Windows computers, so Mac users may on average have greater financial resources. Your inclination to buy varies by time of day. For retail websites, the peak is 8:00 PM; for dating, late at night; for finance, around 1:00 PM; for travel, just after 10:00 AM. This is not the amount of website traffic, but the propensity to buy of those who are already on the website. Survey of websites The impetus to complete certain kinds of transactions is higher during certain times of day. Your e-mail address reveals your level of commitment. Customers who register for a free account with an Earthlink.com e-mail address are almost five times more likely to convert to a paid, premium-level membership than those with a Hotmail.com e-mail address. An online dating website Disclosing permanent or primary e-mail accounts reveals a longer-term intention. Banner ads affect you more than you think. Although you may feel you've learned to ignore them, people who see a merchant's banner ad are 61 percent more likely to subsequently perform a related search, and this drives a 249 percent increase in clicks on the merchant's paid textual ads in the search results. Yahoo! Advertising exerts a subconscious effect. Companies win by not prompting customers to think. Contacting actively engaged customers can backfire—direct mailing financial service customers who have already opened several accounts decreases the chances they will open more accounts (more details in Chapter 7).

Data matters. It’s the very essence of what we care about. Personal data is not equivalent to a real person—it’s much better. It takes no space, costs almost nothing to maintain, lasts forever, and is far easier to replicate and transport. Data is worth more than its weight in gold—certainly so, since data weighs nothing; it has no mass. Data about a person is not as valuable as the person, but since the data is so much cheaper to manage, it’s a far better investment. Alexis Madrigal, senior editor at The Atlantic, points out that a user’s data can be purchased for about half a cent, but the average user’s value to the Internet advertising ecosystem is estimated at $1,200 per year. Data’s value—its power, its meaning—is the very thing that also makes it sensitive. The more data, the more power. The more powerful the data, the more sensitive. So the tension we’re feeling is unavoidable.

Predicting better than pure guesswork, even if not accurately, delivers real value. A hazy view of what’s to come outperforms complete darkness by a landslide. The Prediction Effect: A little prediction goes a long way.

People . . . operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage. —Michael Lewis, Moneyball: The Art of Winning an Unfair Game

PA is the process by which an organization learns from the experience it has collectively gained across its team members and computer systems. In fact, an organization that doesn’t leverage its data in this way is like a person with a photographic memory who never bothers to think.

Security is often at odds with civil liberties. The act of balancing between the two gets even trickier with predictive technology at play. PA threatens to attain too much authority. Like an enchanted child with a Magic 8 Ball toy (originated in 1950), which is designed to pop up a random answer to a yes/no question, insightful human decision makers could place a great deal of confidence in the recommendations of a system they do not deeply understand. What may render judges better informed could also sway them toward less active observation and thought, tempting them to defer to the technology as a kind of crutch and grant it undue credence. It’s important for users of PA—the judges and parole board members—to keep well in mind that it bases predictions on a much more limited range of factors than are available to a person.

There are four main predictive modeling techniques detailed in this book as important upstream O&G data-driven analytic methodologies: Decision trees Regression Linear regression Logistic regression Neural networks Artificial neural networks Self-organizing maps (SOMs) K-means clustering

Predictive modeling generates the entire model from scratch. All the model’s math or weights or rules are created automatically by the computer. The machine learning process is designed to accomplish this task, to mechanically develop new capabilities from data. This automation is the means by which PA builds its predictive power.

Once you develop a model, don’t pat yourself on the back just yet. Predictions don’t help unless you do something about them. They’re just thoughts, just ideas. They may be astute, brilliant gems that glimmer like the most polished of crystal balls, but hanging

Calmness—a lack of anxiety—empowers you with the freedom to do as you please.

We cannot solve our problems with the same thinking we used when we created them. —Albert Einstein

Summarizing documents: Reducing a document to its most import features or concepts can give the reader quick overview, saving time.

A Data Scientists strength lies solely in their tenacity.

Target isn’t alone in its desire to predict consumers’ habits. Almost every major retailer, including Amazon.com, Best Buy, Kroger supermarkets, 1-800-Flowers, Olive Garden, Anheuser-Busch, the U.S. Postal Service, Fidelity Investments, Hewlett-Packard, Bank of America, Capital One, and hundreds of others, have “predictive analytics” departments devoted to figuring out consumers’ preferences. “But Target has always been one of the smartest at this,” said Eric Siegel, who runs a conference called Predictive Analytics World. “The data doesn’t mean anything on its own. Target’s good at figuring out the really clever questions.

Sports are among the increasingly rare moments of totally unscripted television. The human element informs everything, in confounding and inconsistent ways. And since these are only games, and since all games are ultimately exhibitions, the stakes are always low. Any opinion is viable. Any argument can be made. It’s a free, unreal reality. Yet everything about the trajectory of analytics pushes us away from this. The goal of analytics is to quantify the non-negotiable value of every player and to mathematically dictate which strategic decisions present the highest likelihood of success; the ultimate goal, it seems, would be to predict the exact score of every game before it happens and to never be surprised by anything. I don’t see this as an improvement. The problem with sports analytics is not that they are flawed; the problem is that they are accurate, to the benefit of almost no one. It’s being right for the sake of being right, in a context where there was never any downside to being wrong. The fact that my twelve-year-old self would have loved this only strengthens my point.

For example, you could build many companies based on applying the cutting edge predictive analytics and data mining techniques commonly used at consumer web startups, quantitative hedge funds, etc., to less advanced industries.

The company says it “weighs dozens of factors—ranging from a patient’s age, geography, and marital status, to prior prescription records and physician’s profile—to determine with 94 percent accuracy whether that patient will take his medication as prescribed. By comparison when asked, patients themselves correctly predict their own future nonadherence just 10 percent of the time.”3 It also analyzes speech patterns in its call-center traffic to identify follow-up needs. Other PBMs have similar “predictive analytics” capabilities.4

This final point may ironically be the real key to unlocking other people—making sure we understand ourselves at a bare minimum before we turn our analytical gaze outward. If you’re unaware of how you may be projecting your own needs, fears, assumptions, and biases onto others, your observations and conclusions about others will not amount to much. In fact, you may have simply discovered a roundabout way of learning about yourself and the cognitive and emotional baggage you’re bringing to the table.

In my opinion, harnessing data analytics in negotiation empowers the formulation of incisive, strategically sound decisions through the meticulous examination of market trajectories, historical precedents, and competitive intelligence. It aids in discerning pivotal leverage points, refining propositions, and attenuating risks via predictive modeling and scenario analysis. For instance, in a critical procurement negotiation, analyzing supplier pricing patterns and industry benchmarks allows you to construct a persuasive, data-informed counterproposal that not only aligns with fiscal objectives but also resonates with prevailing market conditions, thereby fortifying your negotiating stance.

This is not to say that the race question is any nearer resolution in Brazil than anywhere else - simply that racist ideology faces a more difficult task in Brazil on account of the racial confusion and the range of race mixtures that exist there. Discrimination confronts a web of racial lines as unpredictable as the lines of the human palm. This invalidation of racism by virtue of the scattering of its object is far more subtle and effective than ideological struggle, whose ambiguity invariably revives the very problem it seeks to resolve. Racism will never end so long as it is combated frontally in terms of rational rebuttal. It can be defeated only through an ironic give-and-take founded precisely on racial differences: not at all through the legitimation of differences by legal means, but through an ultimately violent interaction grounded in seduction and voracity. One thinks of the Bishop of Pernambuco; one thinks of the words 'How good he was, my little Frenchman!' He is very good-looking, so he is sanctified - and eaten. He is granted something greater than the right to exist: the prestige of dying. If racism is a violent abreaction in response to the Other's seductive power (rather than to the Other's difference), it can surely be defused only by an increase in seductiveness itself. So many other cultures enjoy a more original situation than ours. For us everything is predictable: we have extraordinary analytical means but no situation to analyse. We live theoretically well beyond our own events: hence our deep melancholy. For others destiny still flickers: they live it, but it remains for them, in life as in death, something forever indecipherable. As for us, we have abolished 'elsewhere' . Cultures stranger than ours live in prostration (before the heavens, before destiny); we live in consternation (at the absence of destiny). Nothing can come from anywhere except from us. This is, in a way, the most absolute misfortune.

If your neocortex is the home of your conscious awareness and it’s where you construct thoughts, use analytical reasoning, exercise intellect, and demonstrate rational processes, then you’ll have to move your consciousness beyond (or out of) your neocortex in order to meditate. Your consciousness would have to essentially move from your thinking brain into your limbic brain and the subconscious regions. In other words, in order for you to dial down your neocortex and all the neural activity that it performs on a daily basis, you’d have to stop thinking analytically and vacate the faculties of reason, logic, intellectualizing, forecasting, predicting, and rationalizing—at least temporarily.

Even PayPal’s millions of dollars in bad transactions could be justified for the extensive data set they generated. “Losing a lot of money to fraud was a necessary byproduct in gathering the data needed to understand the problem and build good predictive models,” Greenfield later wrote on a personal blog. “With millions of transactions and tens of thousands of fraudulent transactions, our fraud analytics team could find subtler patterns and detect fraud more accurately.” Taken together, PayPal turned fraud from an existential threat to one of the company’s defining triumphs. It also had the unexpected benefit of thinning out the competition. “As the Russian mobsters got better and better,” Thiel said, “they got better and better at destroying all our competitors.” Thieves forced to work ever harder to fleece PayPal customers moved on to easier prey. “We’d also find that fraudsters were kind of lazy, right? They want to do just the least amount of work… So we just kind of hoped to push them off onto [our competitors],” Miller observed.

Regression: This is a well-understood technique from the field of statistics. The goal is to find a best fitting curve through the many data points. The best fitting curve is that which minimizes the (error) distance between the actual data points and the values predicted by the curve.  Regression models can be projected into the future for prediction and forecasting purposes.

When we develop predictive analytics systems, we are not merely automating a human’s decision by using software to specify the rules for when to say yes or no; we are even leaving the rules themselves to be inferred from data. However, the patterns learned by these systems are opaque: even if there is some correlation in the data, we may not know why. If there is a systematic bias in the input to an algorithm, the system will most likely learn and amplify that bias in its output

Internet services have made it much easier to amass huge amounts of sensitive information without meaningful consent, and to use it at massive scale without users understanding what is happening to their private data. Data as assets and power Since behavioral data is a byproduct of users interacting with a service, it is sometimes called “data exhaust”—suggesting that the data is worthless waste material. Viewed this way, behavioral and predictive analytics can be seen as a form of recycling that extracts value from data that would have otherwise been thrown away. More correct would be to view it the other way round: from an economic point of view, if targeted advertising is what pays for a service, then behavioral data about people is the service’s core asset.

CRM (Customer Relationship Management) is a marketing strategy that focuses on managing interactions and relationships with customers. CRM enables businesses to improve customer satisfaction, loyalty, and retention by providing personalized experiences that meet their needs. CRM is an essential aspect of modern marketing as it enables businesses to understand their customers' behavior, preferences, and needs and develop targeted marketing campaigns that resonate with them. In Go High Level, CRM (Customer Relationship Management) is a core component of the platform. The CRM functionality in Go High Level enables businesses to manage their customer interactions and relationships more effectively, improving customer satisfaction, loyalty, and retention. The CRM functionality in Go High Level includes a range of features and tools designed to help businesses automate and streamline their customer-facing processes, as well as provide them with insights into their customers' behavior, preferences, and needs. In essence, CRM is a set of practices, technologies, and strategies that businesses use to manage their customer interactions and relationships. The goal of CRM is to build stronger, more meaningful relationships with customers by providing them with personalized experiences and tailored solutions. CRM in marketing can be divided into three main categories: operational CRM, analytical CRM, and collaborative CRM. Operational CRM focuses on automating and streamlining customer-facing processes, such as sales, marketing, and customer service. This type of CRM is designed to improve efficiency and productivity by automating repetitive tasks and providing a centralized database of customer information. Operational CRM includes features such as sales pipeline management, lead nurturing, and customer service management. Analytical CRM focuses on analyzing customer data to gain insights into their behavior, preferences, and needs. This type of CRM enables businesses to make data-driven decisions by providing them with a better understanding of their customers' needs and preferences. Analytical CRM includes features such as customer segmentation, data mining, and predictive analytics. Collaborative CRM focuses on enabling businesses to collaborate and share customer information across different departments and functions. This type of CRM helps to break down silos within organizations and improve communication and collaboration between different teams. Collaborative CRM includes features such as customer feedback management, social media monitoring, and knowledge management. CRM is important for marketing because it enables businesses to build stronger, more meaningful relationships with customers. By understanding their customers' behavior, preferences, and needs, businesses can develop targeted marketing campaigns that resonate with them. This results in higher customer satisfaction, loyalty, and retention. CRM can also help businesses to improve their sales and marketing processes by providing them with better visibility into their sales pipeline and enabling them to track and analyze their marketing campaigns' effectiveness. This enables businesses to make data-driven decisions to improve their sales and marketing strategies, resulting in increased revenue and growth. Another benefit of CRM in marketing is that it enables businesses to personalize their marketing campaigns. Personalization is essential in modern marketing as it enables businesses to tailor their marketing messages and solutions to meet their customers' specific needs and preferences. This results in higher engagement and conversion rates, as customers are more likely to respond to marketing messages that resonate with them. Lead Generation: Go High Level provides businesses with a range of tools to generate leads, including customizable landing pages, web forms, and social media integrations.

But when our egos are out of balance due to a barrage of stress hormones, our analytical minds go into high gear and become overstimulated. That’s when the analytical mind is no longer working for us, but against us. We get overanalytical. And the ego becomes highly selfish by making sure that we come first, because that’s its job. It thinks and feels as though it needs to be in control to protect the identity. It tries to have power over outcomes; it predicts what it needs to do to create a certainly safe situation; it clings to the familiar and won’t let go—so it holds grudges, feels pain and suffers, or can’t get beyond its victimhood. It will always avoid the unknown condition and view it as potentially dangerous, because to the ego, the unknown is not to be trusted. And the ego will do anything to empower itself for the rush of addictive emotions. It wants what it wants, and it will do whatever it takes to get there first, by pushing its way to the front of the line. It can be cunning, manipulative, competitive, and deceptive in its protection. So the more stressful your situation, the more your analytical mind is driven to analyze your life within the emotion you’re experiencing at that particular time. When this happens, you’re actually moving your consciousness further away from the operating system of the subconscious mind, where true change can occur. You’re then analyzing your life from your emotional past, although the answers to your problems aren’t within those emotions, which are causing you to think harder within a limited, familiar chemical state. You’re thinking in the box.

Being an intelligence agent didn’t mean that one has overpowered the fear of the unknown. What it meant was, they had learned to use that fear to their advantage by sharpening their analytical, observant and predictive skills.

Mook, always attentive to cash flow, knew that it was much more costly to try to persuade undecided voters to back Hillary than it was to register her supporters or to make sure they went to the polls. The analytics team could also conduct less expensive surveys than the pollsters to get a snapshot of the horse race in a given state. Separate from the three scores, the analytics experts would do quick surveys with a small universe of voters and then extrapolate how many other voters with similar demographic profiles were likely to vote and for whom they would cast their ballots. The same methods had been used in the primaries, when adjustments could be made based on the outcome of a string of contests. The general election was different, in part, because there was only one Election Day. The analytics were also thought to be more precise at predicting general-election outcomes in each state than primary outcomes because the exact shape of the electorate could be harder to project in lower-turnout contests. But in both cases, Mook relied heavily on the data to figure out where the campaign could get the most bang for its buck. Like a baseball executive in the Moneyball era, Mook looked at the data as the means for taking the least costly route to victory.

For the prediction of football matches, it is possible to use Bet9ja vip, that is, to provide a data analysis program with as much information as possible and variables that allow a prediction to be made that is closest to the actual result. They are bookmakers, sports television channels, sports newspapers, sections of this area of printed and digital newspapers, and the same soccer teams, who make predictions of football matches and tournaments using Bet9ja vip and analytical programs, through the use of a predictive mathematics that is based on a very extensive menu of data that is processed once obtained. The data used are the variables that combine to define possible outcomes: team history, evaluation and soccer background of each player, statistics of wins and losses, results of teams as visitors and locals, technical, mental and emotional evaluation of each player, figures of results with teams that a team will face, strategies and tactics with which it has won and lost, climatic variables of the places where it is played, characteristics of each stadium including the behaviour of the people, political and economic variables of the countries where a team will play (in case of international games), among others. The combination of these variables makes it possible to predict football matches and tournaments, in particular of a football world cup where 32 teams face each other and where it is possible to apply the stated variables with a margin of error of approximately 20%; that is to say, that the use of Bet9ja vip to predict a Football Tournament has between 70% and 80% probability of hitting. All in all, the variables of a match and an international soccer tournament, the most important on the planet, that is, a World Cup, are so wide and diverse that we are only in conditions -from Bet9ja vip, analysis programs and even Machine Learning- to partially predict them. So to the question: is it possible to predict who will be the World Cup champion? we can answer that not absolutely and safely, and yes in a tendential and approximate manner; that is, if we use the Bet9ja vip correctly to predict each of the matches of the Tournament and predict who will be the champion of the same, we have between 70% and 80% margin to avoid mistakes. Therefore, when placing your bets, even when you rely on Bet9ja vip to perform them, bear in mind that there are variables that cannot be predicted, so there is no science that predicts with complete certainty their behaviour; finally human actions, in particular a game like soccer, are full of surprises and contingencies that we cannot control or predict yet.

What I want to encourage you to do is use a tool like Iconosquare to determine the best times of the day to post for your target demographic. You will be able to see how engaged your audience is and specifically when that engagement is occurring. The app records your posting times and shows your audience engagement over a 24-hour period. You can see which days are important and which times of the day are best for posting. If you have your Facebook page attached to your Instagram page you will get analytics directly from your Instagram page, but I still like to use Iconosquare

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.

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The extension of trust is an articulation of our assessment of a situation to deliver an expected outcome.

Systems thinking is a set of synergistic analytic skills used to improve the capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects. These skills work together as a system.

From one test session to the next, the interference patterns tended to differ because of slight variations in ambient temperature and vibration. So for the sake of simplicity I based the formal statistical analysis not on a change in the precise shape of the interference pattern, but rather on a decrease in the average illumination level over the entire camera image during the concentration or “mental blocking” condition as compared to the relaxed or “mental passing” condition. To test the design and analytical procedures for possible problems, I also included control runs to allow the system to record interference patterns automatically without anyone being present in the laboratory or paying attention to the interferometer. Data from those control sessions were analyzed in the same way as in the experimental sessions. Results I was fortunate to recruit five meditators, four of whom had many decades of daily meditative practice. Those five contributed nine test sessions. Five other individuals with no meditation experience, or less than two years of practice, contributed nine additional sessions. I referred to the latter group as nonmeditators. I predicted an overall negative score for each experimental session (illustrated by the idealized negative curve shown in Figure 15). The combined results were in fact significantly negative, with odds against chance of 500 to 1. The identical analysis across all the control sessions resulted in odds against chance of close to 1 to 1, indicating that the experimental results were not due to procedural or analytical biases. Figure 16 shows the cumulative score (in terms of standard normal deviates, or z-scores) for the nine sessions contributed by experienced meditators and nine other sessions involving nonmeditators. The experienced meditators resulted in a combined odds against chance of 107,000 to 1, and the nonmeditators obtained results close to chance expectation. This supported my conjecture that meditators would be better at this task than nonmeditators. Figure 16. Experienced meditators (more than two years of daily practice) obtained combined odds against chance of 107,000 to 1. Nonmeditators obtained results close to chance.

People Get Sick and Die I’m not afraid of death; I just don’t want to be there when it happens.

Very few companies know how to exploit the data already embedded in their core operating systems. THE SOLUTION Evidence-based, data-driven decision making provides the answer, but it requires a big cultural shift and four changes in how operations are managed. Who Benefits from Big Data? 496 words Big data is big business. The IT research firm Gartner estimates that total software, social media, and IT services spending related to big data and analytics topped $28 billion worldwide in 2012. All estimates predict rapid growth. In addition to vendors, at least three types of organizations are harvesting value from big data.

Data analytics involves analyzing and interpreting data to gain insights that inform smarter decisions. It consists of four key types: Descriptive Analytics, which looks at past data to explain trends; Diagnostic analytics, which digs deeper to uncover the reasons behind those trends; Predictive analytics, which anticipates future events; and Prescriptive analytics, which offers recommendations for the best course of action. These types help businesses optimize performance, reduce risks, and discover new growth opportunities. Data Analytics Courses in Bangalore offers certification courses both online & offline, take the next step in your learning journey and enroll now in the Training Institute in Bangalore.

questions had to be seen in American terms for the first time. When the Labour Party lost for the second time in the 1959 election, people were predicting that the Conservatives would be in power for a hundred years (thankfully they weren’t, but it looked endless). The leader of the Labour Party, trying to explain what had gone wrong to the 1959 Labour conference, reached into his analytic tool bag and blamed the telly, and the fridge, and the secondhand motorcar, and the women’s magazines, and the disappearance of the working-class cloth cap, and the fact that people didn’t go to the whippets anymore. The breakdown of cultural life explained what had gone wrong! Consequently, the next question that had to be faced was