Reporting And Analytics Quotes

Narrative Science has its sights set on far more than just the news industry. Quill is designed to be a general-purpose analytical and narrative-writing engine, capable of producing high-quality reports for both

Complex operations, in which agencies assume complementary roles and operate in close proximity-often with similar missions but conflicting mandates-accentuate these tensions. The tensions are evident in the processes of analyzing complex environments, planning for complex interventions, and implementing complex operations. Many reports and analyses forecast that these complex operations are precisely those that will demand our attention most in the indefinite future. As essayist Barton and O'Connell note, our intelligence and understanding of the root cause of conflict, multiplicity of motivations and grievances, and disposition of actors is often inadequate. Moreover, the problems that complex operations are intended and implemented to address are convoluted, and often inscrutable. They exhibit many if not all the characteristics of "wicked problems," as enumerated by Rittel and Webber in 1973: they defy definitive formulations; any proposed solution or intervention causes the problem to mutate, so there is no second chance at a solution; every situation is unique; each wicked problem can be considered a symptom of another problem. As a result, policy objectives are often compound and ambiguous. The requirements of stability, for example, in Afghanistan today, may conflict with the requirements for democratic governance. Efforts to establish an equitable social contract may well exacerbate inter-communal tensions that can lead to violence. The rule of law, as we understand it, may displace indigenous conflict management and stabilization systems. The law of unintended consequences may indeed be the only law of the land. The complexity of the challenges we face in the current global environment would suggest the obvious benefit of joint analysis - bringing to bear on any given problem the analytic tools of military, diplomatic and development analysts. Instead, efforts to analyze jointly are most often an afterthought, initiated long after a problem has escalated to a level of urgency that negates much of the utility of deliberate planning.

In short, many of these analyses, though conducted in the forties and fifties of the century, could just as well have been conducted in 1905 or thereabouts. It is not clear whether the analysts knowledge was static and fixated on that early date or whether they had the benefit of later theoritical knowledge ( and hence of all the progress made since that time ) but did not apply it dynamically. I do not maintain, of course, that if these rather anachronistic errors had been avoided in the previous analyses of the patients mentioned above they would all have been cured. Some were apparently unrecognized schizoid personalities, some where incapable of changing. But many, in fact most, could be helped when their pyshic masochism was put in the center. I have reporter in earlier books such seemingly hopeless but actually curable cases in which i was the second, third or fourth analyst. The decisive point, which cannot be stressed often enough, is that the patient has to be given an analytic chance. He is deprived of this chance when his deep masochistic conflict is neglected and his difficulties explained only in terms of superficial layers; he is simply not familiarized with his real unconscious problem. This does not mean that the patient will always use that unique chance, but that's his affair.

In a report released on the topic of IoT, Accenture notes that this technology “is driving innovation and new opportunities by bringing every object, consumer and activity into the digital realm”1. Being a tool that can only be used in physical environments, the IoT’s immense power means that this data-empowered category of technology can help swing the pendulum back towards brick-and-mortar shopping. As

Forensic DNA Expert Anil Gupta offer a variety of DNA forensic testing systems including STR, Y-STR, and mitochondrial DNA. The DNA Sample in Forensic Analysis can be collected from blood, saliva, perspiration, hair, teeth, mucus, finger nails, semon and these can be found almost anywhere at crime scence. Anil Gupta is here to help make sense of this complex scientific issue and to testify before the court on these issues when necessary. Initial Consultation is FREE – If you send us the report we will lend you our expertise to help you understand your situation. Written Reports and Affidavits Discovery Documents – free by request, all you need to obtain the entire laboratory case file Mike is a leading forensic DNA expert with considerable experience in forensic biology. He is a clear and balanced expert opinion highly qualified provider to help lawyers, attorneys and lawyers support their clients and the criminal justice system. He is a very experienced scientist, whose career has focused on developing the ability to DNA analysis, defining standards, interpreting results, explaining evidence and providing advice to help both the defense and Processing equipment. Mike has a great depth of technical knowledge. As the chief DNA scientist (head of discipline) with the former Forensic Science Service (FSS), he established technical standards for DNA analytical processes, staff competencies and training. He was head of the Specialist Unit at FSS DNA and led the creation of the first dedicated facility of ultra-clean low template DNA. He has led the validation and implementation of several important new DNA processes. Through audit and process review, it can provide an effective and risk-based quality assurance, as it has for many years to the FSS, to the National DNA Database and to the courts.

the Telegraph group noted that four key skills for their reporters would be ‘social, video, analytics and search engine optimisation’. What about ‘journalism

If you find yourself in that situation, we recommend doing an analytics audit and reassessing what data points you’re recording—removing the irrelevant ones—before using the data to make decisions. The next part is how we group metrics together so that we can spot trends and opportunities. There are three key ways: segmentation, cohort analysis, and funnels. Analytics track every customer equally and report the average behavior. For example, a new customer will use an app’s first-use tutorial—some might skip it—but a returning customer won’t even see the first-use tutorial. If you simply looked at how often a customer views the tutorial out of how many times people use the app, it’d look like very few people use the tutorial overall. It’s up to you to segment your data, which means grouping it by common characteristics.

A task force of the American Society for Cell Biology (ASCB) distinguished four kinds of reproducibility: analytic reproducibility, which tries to duplicate original conclusions by reanalyzing original data; direct reproducibility, which tries to get the same experimental results using the same experimental conditions as in the original report; systematic reproducibility, which tries to get the same results as the original study under different experimental conditions than the original ones; and conceptual reproducibility, which uses new experimental approaches and aims “to demonstrate the validity of a concept or finding using a different paradigm.

In particular, there is strong social pressure from peers, colleagues and clients to boost near-term performance. Even if one has developed the analytical skills to spot the winner, the psychological disposition necessary to own shares for prolonged periods is not easily come by. J.K. Galbraith observed that: “nothing is so admirable in politics as a short-term memory.” Why should politics have a monopoly on sloppy thinking? Which makes us think that long-term investing works not because it is more difficult, but because there is less competition out there for the really valuable bits of information.

Bruce Shi, a recent USC graduate with a B.S. in Finance and an impressive 3.9 GPA, is making strides in the finance field. At Holmes Financial Sales, Bruce's data analysis and reporting skills resulted in substantial cost savings of over $200,000. As an intern at Analytic, Inc., he received accolades for his contributions. Bruce's altruistic side shines through his volunteer work, where he helped clients collectively save $25,000. With meticulous attention to detail and a passion for financial analysis, Bruce is poised for a promising career in finance.

SUMMARY A vast array of additional statistical methods exists. In this concluding chapter, we summarized some of these methods (path analysis, survival analysis, and factor analysis) and briefly mentioned other related techniques. This chapter can help managers and analysts become familiar with these additional techniques and increase their access to research literature in which these techniques are used. Managers and analysts who would like more information about these techniques will likely consult other texts or on-line sources. In many instances, managers will need only simple approaches to calculate the means of their variables, produce a few good graphs that tell the story, make simple forecasts, and test for significant differences among a few groups. Why, then, bother with these more advanced techniques? They are part of the analytical world in which managers operate. Through research and consulting, managers cannot help but come in contact with them. It is hoped that this chapter whets the appetite and provides a useful reference for managers and students alike. KEY TERMS   Endogenous variables Exogenous variables Factor analysis Indirect effects Loading Path analysis Recursive models Survival analysis Notes 1. Two types of feedback loops are illustrated as follows: 2. When feedback loops are present, error terms for the different models will be correlated with exogenous variables, violating an error term assumption for such models. Then, alternative estimation methodologies are necessary, such as two-stage least squares and others discussed later in this chapter. 3. Some models may show double-headed arrows among error terms. These show the correlation between error terms, which is of no importance in estimating the beta coefficients. 4. In SPSS, survival analysis is available through the add-on module in SPSS Advanced Models. 5. The functions used to estimate probabilities are rather complex. They are so-called Weibull distributions, which are defined as h(t) = αλ(λt)a–1, where a and 1 are chosen to best fit the data. 6. Hence, the SSL is greater than the squared loadings reported. For example, because the loadings of variables in groups B and C are not shown for factor 1, the SSL of shown loadings is 3.27 rather than the reported 4.084. If one assumes the other loadings are each .25, then the SSL of the not reported loadings is [12*.252 =] .75, bringing the SSL of factor 1 to [3.27 + .75 =] 4.02, which is very close to the 4.084 value reported in the table. 7. Readers who are interested in multinomial logistic regression can consult on-line sources or the SPSS manual, Regression Models 10.0 or higher. The statistics of discriminant analysis are very dissimilar from those of logistic regression, and readers are advised to consult a separate text on that topic. Discriminant analysis is not often used in public

Business intelligence is a broad set of Information Technology (IT) solutions that includes tools for gathering, analyzing, and reporting information to the users about performance of the organization and its environment.

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.

One other problem is that too many people—and vendors in particular—are already using big data to mean any use of analytics, or in extreme cases even reporting and conventional business intelligence.

What makes a good metric What vanity metrics are and how to avoid them The difference between qualitative and quantitative metrics, between exploratory and reporting metrics, between leading and lagging metrics, and between correlated and causal metrics What A/B testing is, and why multivariate testing is more common The difference between segments and cohorts

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).

The analytical process starts with the receipt of a report, continues with the collection of additional related information, goes through different forms of analysis, and ends with either a detailed file concerning a money-laundering (or financing of terrorism) case that is forwarded to the law-enforcement authorities or prosecutors or the reaching of a conclusion that no suspicious activity was found. After the analysis is performed, the primary report that triggered it may represent a small part of the file.

Although the position of the executives may be high level, the levels of questions and quality of the conversations I have found are variable at best. Many of these meetings consist of an exchange of summary findings, reports, analytic data, rapid-fire question–answer segments, and perhaps a brief brainstorming session to decide on a solution. Sometimes, there is debate about the analysis, but this inevitably concludes with recommendations for a future course of action. Rarely do I encounter meaningful critical dialogue.

Our focus, however, should not be on the differences among our sectors, but rather on supporting and promoting the entire innovation economy — from tech to bio to clean energy to health care and beyond. In fact, in its recent Impact 2020 report, the Massachusetts Biotechnology Council highlighted the interrelationship of these vital sectors working together, combining cutting-edge biomedical research with new information technology tools for capturing and integrating data, conducting sophisticated analytics, and enhancing personal connectivity. Massachusetts is a national and world leader in the growing field of life science information technology.

Good teams engage directly with end users and customers every week, to better understand their customers, and to see the customer's response to their latest ideas. Bad teams think they are the customer. Good teams know that many of their favorite ideas won't end up working for customers, and even the ones that could will need several iterations to get to the point where they provide the desired outcome. Bad teams just build what's on the roadmap, and are satisfied with meeting dates and ensuring quality. Good teams understand the need for speed and how rapid iteration is the key to innovation, and they understand this speed comes from the right techniques and not forced labor. Bad teams complain they are slow because their colleagues are not working hard enough. Good teams make high‐integrity commitments after they've evaluated the request and ensured they have a viable solution that will work for the customer and the business. Bad teams complain about being a sales‐driven company. Good teams instrument their work so they can immediately understand how their product is being used and make adjustments based on the data. Bad teams consider analytics and reporting a nice to have.

Using this technique, Baum et al constructed a forest that contained 1,000 decision trees and looked at 84 co-variates that may have been influencing patients' response or lack of response to the intensive lifestyle modifications program. These variables included a family history of diabetes, muscle cramps in legs and feet, a history of emphysema, kidney disease, amputation, dry skin, loud snoring, marital status, social functioning, hemoglobin A1c, self-reported health, and numerous other characteristics that researchers rarely if ever consider when doing a subgroup analysis. The random forest analysis also allowed the investigators to look at how numerous variables *interact* in multiple combinations to impact clinical outcomes. The Look AHEAD subgroup analyses looked at only 3 possible variables and only one at a time. In the final analysis, Baum et al. discovered that intensive lifestyle modification averted cardiovascular events for two subgroups, patients with HbA1c 6.8% or higher (poorly managed diabetes) and patients with well-controlled diabetes (Hba1c < 6.8%) and good self-reported health. That finding applied to 85% of the entire patient population studied. On the other hand, the remaining 15% who had controlled diabetes but poor self-reported general health responded negatively to the lifestyle modification regimen. The negative and positive responders cancelled each other out in the initial statistical analysis, falsely concluding that lifestyle modification was useless. The Baum et al. re-analysis lends further support to the belief that a one-size-fits-all approach to medicine is inadequate to address all the individualistic responses that patients have to treatment. 

interest that was surrounding the sightings. Could this be why the FBI professed no interest in the Colorado helicopter encounters? [The MJ-12 Documents: An Analytical Report, William L. Moore & Jaime H. Shandera, Fair Witness Project, 1990.] Moving on from the mutilations

A data warehouse is an organized store of data from all over the organization, specially designed to help make management decisions. Data can be extracted from operational database to answer a particular set of queries. This data, combined with other data, can be rolled up to a consistent granularity and uploaded to a separate data store called the data warehouse. Therefore, the data warehouse is a simpler version of the operational data base, with the purpose of addressing reporting and decision-making needs only.

Unveiling London E-commerce Triumph: Decoding Data with WooCommerce Analytics In the bustling realm of London e-commerce, navigating the digital landscape requires not just intuition but informed decision-making backed by data. This is where the marriage of WooCommerce and analytics becomes a game-changer. In this exploration, we delve into the nuances of leveraging WooCommerce Analytics for e-commerce success in London. As we embark on this journey, the expertise of a dedicated woocommerce development in london adds a unique perspective, unraveling the potential of data decoding in the heart of the e-commerce landscape. Understanding the London E-commerce Scene This section emphasizes the importance of understanding the unique characteristics of the London e-commerce landscape. It underscores the need for businesses to be attuned to local market trends, consumer preferences, and the digital sophistication of the London audience to effectively leverage WooCommerce Analytics. The Role of WooCommerce Agency in London E-commerce Analytics 1. Proactive Data Strategy: Setting the Foundation This point explains the proactive role of a WooCommerce agency in London in establishing a robust data strategy. It involves setting up analytics tools, defining KPIs, and aligning data collection with the specific goals of London e-commerce businesses. 2. Tailoring Analytics to London Market Trends Here, the focus is on tailoring analytics solutions to capture and interpret data that is directly relevant to the ever-evolving market trends of London. A WooCommerce agency in London customizes analytics approaches to provide actionable insights for businesses in the local market. Key Metrics and KPIs for London E-commerce Success 3. Conversion Rate Optimization (CRO): Turning Clicks into Transactions This point explores the pivotal role of Conversion Rate Optimization (CRO) in London e-commerce. It delves into how a WooCommerce agency in London optimizes the conversion rate by refining the checkout process, analyzing user journeys, and enhancing the overall user experience to maximize sales. 4. Customer Lifetime Value (CLV): Fostering Long-Term Relationships The focus here is on the importance of Customer Lifetime Value (CLV) analytics. It explains how a WooCommerce agency in London helps businesses identify high-value customers, tailor marketing strategies, and foster long-term relationships for sustained success. WooCommerce Analytics Tools and Implementations 5. Google Analytics Integration for Comprehensive Insights This point delves into the integration of Google Analytics with WooCommerce. It explains how a WooCommerce agency in London guides businesses through the integration process, utilizing Google Analytics to gain comprehensive insights into user behavior, traffic sources, and website performance. 6. Custom Reports and Dashboards: Tailoring Insights for London Businesses Here, the emphasis is on the creation of custom reports and dashboards by a WooCommerce agency in London. These tailored insights provide businesses with specific information relevant to their products, target audience, and market trends, enhancing decision-making accuracy. Analyzing User Behavior for Enhanced User Experience 7. Heatmaps and User Flow Analysis: Optimizing the Customer Journey This point explores the use of heatmaps and user flow analysis to optimize the customer journey in London e-commerce. A WooCommerce agency in London employs these tools to uncover patterns, identify bottlenecks, and make strategic adjustments for a seamless user experience. 8. Abandoned Cart Analysis: Recovering Lost Opportunities This section discusses the significance of abandoned cart analysis. It explains how a WooCommerce agency in London utilizes analytics to understand the reasons behind cart abandonment and implements targeted strategies to recover potentially lost sales through personalized retargeting campaigns.

There are those who find it soothing to say that the analytic statements of the second class reduce to those of the first class, the logical truths, by definition; 'bachelor,' for example, is defined as 'unmarried man.' But how do we find that 'bachelor' is defined as 'unmarried man'? Who defined it thus, and when? Are we to appeal to the nearest dictionary, and accept the lexicographer's formulation as law? Clearly this would be to put the cart before the horse. The lexicographer is an empirical scientist, whose business is the recording of antecedent facts; and if he glosses 'bachelor' as 'unmarried man' it is because of his belief that there is a relation of synonymy between these forms, implicit in general or preferred usage prior to his own work. The notion of synonymy presupposed here has still to be clarified, presumably in terms relating to linguistic behavior. Certainly the 'definition' which is the lexicographer's report of an observed synonymy cannot be taken as the ground of the synonymy.

When it comes to choosing a customer relationship management (CRM) tool, businesses have plenty of options to choose from. Two of the most popular options are Go High Level and Active Campaign. While both tools offer similar features and benefits, there are some key differences that may make one a better fit for your business than the other. Go High Level: Overview and Features Go High Level is an all-in-one sales and marketing platform designed specifically for businesses that want to streamline their customer management processes. The platform offers a wide range of features, including: 1. Sales Automation: Go High Level offers a range of sales automation features, including lead capture forms, appointment scheduling, and automated follow-up emails. 2. Marketing Automation: The platform also offers a range of marketing automation tools, including email marketing campaigns, SMS marketing, and social media marketing. 3. CRM: Go High Level provides a comprehensive CRM solution, with features that include lead management, contact management, and deal tracking. 4. Analytics: The platform also offers detailed analytics and reporting tools, allowing businesses to track the success of their sales and marketing efforts. Active Campaign: Overview and Features Active Campaign is another popular CRM tool that offers a wide range of features and benefits. Some of the key features of Active Campaign include: 1. Email Marketing: Active Campaign is primarily known for its email marketing capabilities, offering a range of tools for creating and managing email campaigns. 2. Marketing Automation: The platform also offers marketing automation tools, including lead capture forms, automated emails, and CRM integration. 3. CRM: Active Campaign provides a comprehensive CRM solution, with features that include lead management, contact management, and deal tracking. 4. E-commerce: Active Campaign offers e-commerce integrations that allow businesses to track customer behavior and make personalized product recommendations. Go High Level vs. Active Campaign: Comparison While both Go High Level and Active Campaign offer similar features and benefits, there are some key differences between the two platforms that businesses should be aware of. 1. Sales and Marketing Automation: While both platforms offer sales and marketing automation features, Go High Level offers a more comprehensive set of tools. This includes appointment scheduling, SMS marketing, and social media marketing. Active Campaign is primarily focused on email marketing, although it does offer some automation features. 2. Ease of Use: Both Go High Level and Active Campaign are user-friendly platforms, but Go High Level is known for its simplicity and ease of use. This makes it a good choice for businesses that are new to CRM tools and want to get up and running quickly. 3. Pricing: Pricing is an important consideration when choosing a CRM tool, both Go High Level and Active Campaign offer competitive pricing. However, Go High Level offers more flexible pricing options, including a pay-as-you-go plan that allows businesses to only pay for the features they need. 4. E-commerce Integration: While both platforms offer e-commerce integrations, Active Campaign is known for its strong e-commerce capabilities. This includes features like abandoned cart tracking, product recommendations, and personalized product recommendations based on customer behavior. 5. Customization: Go High Level offers more customization options than Active Campaign. This includes the ability to create custom workflows and integrations with third-party apps. Which One to Choose? Choosing between Go High Level and Active If you're looking for a simple and easy-to-use platform with a comprehensive set of sales and marketing automation features, Go High Level may be the right choice for you.

When it comes to choosing a customer relationship management (CRM) tool, businesses have plenty of options to choose from. Two of the most popular options are Go High Level and Active Campaign. While both tools offer similar features and benefits, there are some key differences that may make one a better fit for your business than the other. Go High Level: Overview and Features Go High Level is an all-in-one sales and marketing platform designed specifically for businesses that want to streamline their customer management processes. The platform offers a wide range of features, including: 1. Sales Automation: Go High Level offers a range of sales automation features, including lead capture forms, appointment scheduling, and automated follow-up emails. 2. Marketing Automation: The platform also offers a range of marketing automation tools, including email marketing campaigns, SMS marketing, and social media marketing. 3. CRM: Go High Level provides a comprehensive CRM solution, with features that include lead management, contact management, and deal tracking. 4. Analytics: The platform also offers detailed analytics and reporting tools, allowing businesses to track the success of their sales and marketing efforts. Active Campaign: Overview and Features Active Campaign is another popular CRM tool that offers a wide range of features and benefits. Some of the key features of Active Campaign include: 1. Email Marketing: Active Campaign is primarily known for its email marketing capabilities, offering a range of tools for creating and managing email campaigns. 2. Marketing Automation: The platform also offers marketing automation tools, including lead capture forms, automated emails, and CRM integration. 3. CRM: Active Campaign provides a comprehensive CRM solution, with features that include lead management, contact management, and deal tracking. 4. E-commerce: Active Campaign offers e-commerce integrations that allow businesses to track customer behavior and make personalized product recommendations. Go High Level vs. Active Campaign: Comparison While both Go High Level and Active Campaign offer similar features and benefits, there are some key differences between the two platforms that businesses should be aware of. 1. Sales and Marketing Automation: While both platforms offer sales and marketing automation features, Go High Level offers a more comprehensive set of tools. This includes appointment scheduling, SMS marketing, and social media marketing. Active Campaign is primarily focused on email marketing, although it does offer some automation features. 2. Ease of Use: Both Go High Level and Active Campaign are user-friendly platforms, but Go High Level is known for its simplicity and ease of use. This makes it a good choice for businesses that are new to CRM tools and want to get up and running quickly. 3. Pricing: Pricing is an important consideration when choosing a CRM tool, both Go High Level and Active Campaign offer competitive pricing. However, Go High Level offers more flexible pricing options, including a pay-as-you-go plan that allows businesses to only pay for the features they need. 4. E-commerce Integration: While both platforms offer e-commerce integrations, Active Campaign is known for its strong e-commerce capabilities. This includes features like abandoned cart tracking, product recommendations, and personalized product recommendations based on customer behavior. 5. Customization: Go High Level offers more customization options than Active Campaign. This includes the ability to create custom workflows and integrations with third-party apps. Which One to Choose? Choosing between Go High Level and Active Campaign ultimately comes down to your business needs and preferences. If you're looking for a simple and easy-to-use platform with a comprehensive set of sales and marketing automation features, Go High Level may be the right choice for you.

See Statement by Admiral David Jeremiah (USN, ret.), Press Conference, CIA Headquarters, 2 Jun 1998, for a suggestion that failures by senior managers to make key decisions had been an important factor in the CIA’s failure to warn of an impending Indian nuclear test. (The subject was the "Jeremiah Report" on the 1998 Indian nuclear

the 9/11 Commission and the SSCI report on Iraqi WMD hardly appear to be convincing root causes of these recent intelligence failures.

need‐based segmentation is to understand the needs of customers and why they choose a company, whereas value‐based segmentation has to do with which specific customers a company must focus on to retain their earnings.

The yearly survey is essential for strategic decision support, as it shows the overall strengths and weaknesses of the company, and overall customer satisfaction scores are often used as a strategic target.

If a business, for instance, has built its market position on being the cheapest, it stands to reason that intense focus will be on the optimization of internal processes.

The Brotherhood’s campaign to intimidate and silence officials in Washington who link Islam and terrorism is working brilliantly. The 9/11 Commission Report used the word jihad 126 times, Muslim 145 times, and Islam 322 times. A decade later, they have been virtually banished from official U.S. government documents. The FBI’s Counterterrorism Analytical Lexicon and the 2009 National Intelligence Strategy have zero mentions of jihad, Muslim, or Islam. Instead, they refer to “violent extremism” in general.

In the court of the mind, skepticism makes a great grand vizier, but a lousy lord, and a worse bard. As readers and interpreters, I believe we must move gingerly when dealing with reports of extraordinary experiences, which can be convulsive to experiencers, but strangely delicate things in the analytic afterglow. While I do want to analyze the experiences ahead, as well as the written accounts, I am far more interested in providing close readings of them than in explaining them away. I want to provide maps of their influences, resonances, and structural dynamics rather than unravel their ultimate meaning or origin or cause. We need to give these enigmas and strange loops room to breathe and to be—to enjoy what Bruno Latour, a philosopher and sociologist who will accompany us throughout our work, calls their own “ontological pasture.

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Instead of housing the analytics or data in a separate system, our reporting data and its infrastructure were considered part of the product itself and were owned by the product development team. The reports were available on our website, accessible to anyone with an employee account.

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