Trending December 2023 # What Predictive Analytics Means For Businesses Today # Suggested January 2024 # Top 14 Popular

You are reading the article What Predictive Analytics Means For Businesses Today updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 What Predictive Analytics Means For Businesses Today

Businesses have more data from various sources at their disposal than they may think.

With predictive analytics, you can use past information to project future outcomes for your business.

Analytics help you identify future opportunities, serve customers better and make more informed business decisions over time.

This article is for business owners and managers who want to leverage data and predictive analytics to bring thoughtful change to their organization.

Applying predictive analytics to a business or organization requires specialized software. It’s offered by several vendors, including IBM, SAP and SAS. It crunches the collected data to determine the specific answers a business is looking for.

While each software offering has different capabilities and user interfaces, the premise is the same. They all work by first analyzing all the information a company collects. This includes sales and customer statistics, employee productivity, and social media data.

They then plug that data into predictive models. They can project future trends and problems based on that past behavior by using specially created algorithms.


Be sure to use relevant, accurate data with a considerable sample size. Otherwise, predictive analytics may not produce reliable insights.

These models can help businesses predict various consumer trends, as well as employee productivity shifts, to help drive supply and marketing decisions and improve efficiency.

While predictive analytics software used to be an option for larger organizations only, recent developments have made it more accessible to small businesses. This type of software, which is available from vendors such as Emanio and Angoss, is now sold at more affordable prices. It can be run from any personal computer instead of needing to be installed directly on a company’s server.

Examples of predictive analytics

Predictive analytics was originally used by large retailers and financial institutions. Today, businesses in every industry and of all sizes employ it to get a jump on the competition.

According to IBM, businesses can use predictive analytics in many different ways, such as these:

Uncovering hidden patterns and associations

Enhancing customer retention

Improving cross-selling opportunities through personalized offers and experiences

Maximizing productivity and profitability by aligning people, processes and assets

Reducing risk to minimize exposure and loss

Extending the useful life of equipment

Decreasing the number of equipment failures and maintenance costs

Focusing maintenance activities on high-value problems

Increasing customer satisfaction

For example, Sephora analyzes customers’ purchase histories and preferences to predict which products will most appeal to them. These tailored recommendations have led to 80% of its customers being completely loyal to the company. Similarly, Harley-Davidson uses predictive analytics to highlight potential high-value customers whom marketing agents and salespeople can target.

The popularity of predictive analytics with businesses has led to other types of organizations using the software. For example, healthcare firms use it to predict how certain drugs and therapies will be received by patients and help doctors better detect early warning signs for life-threatening diseases and illnesses.

Government bodies use predictive analytics software to help prevent crime, deliver social services and better serve residents. For example, more than two dozen U.S. cities use predictive analytics to determine where different crimes are most likely to occur. They then use this data to allocate resources appropriately, fighting crime while reducing costs.

Moving forward, businesses that don’t use predictive analytics software to drive their decisions will find themselves in the vast minority.

Pros and cons of predictive analytics

While predictive analytics holds vast potential, according to BDO Digital, just 19% of midsize companies are actively planning analytics initiatives. Part of that is because the technology comes with some potential downsides. Here’s a look at the benefits and drawbacks of predictive analytics today.


It provides actionable insights to help you get ahead of the competition.

It saves time that would otherwise be used for manual research and testing.

It can lower ongoing expenses through workflow optimization.

It may reduce wasted capital on ineffective marketing campaigns.

It becomes more reliable as time goes on.


It takes time to produce meaningful results.

It requires considerable data-gathering efforts and preparation upfront.

It may come with high upfront costs and initial disruptions.

Making the most of predictive analytics

Given the potential drawbacks, you need to apply predictive analytics correctly to experience its benefits. One of the most important considerations is to use reliable, clean data.

If these algorithms don’t have high-quality data, they won’t produce accurate results. Consequently, organizations believe that bad information is costing them $15 million a year in losses, according to research by Gartner. You can avoid this by collecting data from reliable sources and cleansing it before feeding it into predictive models. That includes verifying it against other sources, removing redundancies and standardizing its format.

As with any new technology, it’s also best to start small. You can minimize the initial expense and disruptions by applying predictive analytics to one area first, then slowly expanding it as your company learns to manage it. This will also help your employees understand how to work with these technologies more effectively.

Finally, you should regularly review your predictive analytics data to ensure it remains reliable. As situations change, algorithms will likely need tweaks and adjustments. Monitoring their performance can help your business experience the benefits without assuming too much risk.

Predictive analytics revolutionizing business

Predictive analytics has changed the way many businesses operate. Companies across virtually every industry have seen remarkable improvements after implementing this technology. It could become the norm as more people realize these benefits.

Like any technology, predictive analytics is not a cure-all. It won’t solve every problem a company faces, especially without careful planning and implementation, but it can offer substantial help. It will undoubtedly change the way business works.

Chad Brooks contributed to the writing and research in this article. 

You're reading What Predictive Analytics Means For Businesses Today

What The Apple Ios Source Code Leak Means For Iphone Users

What Happened?

So, let’s get the obvious out of the way: Something in iOS’ core operating system was leaked to GitHub by an unknown person, which led to a moderate amount of noise and a pretty large amount of panic.

Apple’s modus operandi usually involves trying to keep the code for its operating systems as locked down as possible, especially since it depends on a blend of hardware and software that could be reverse-engineered if anyone took a good enough look at it.

Unlike other components of iOS whose source code was released by Apple on occasion, the company took painstaking efforts to make sure that iBoot’s code never reached the wrong hands since it is a sort of “master key” that unlocks the ability to run iOS on other hardware in many instances.

Can Hackers Take Advantage of This?

Although iBoot’s code could be (and has been) reverse-engineered at any point in time, most hackers won’t be interested in some code that may or may not imitate Apple’s original stuff.

A good hacker could reverse-engineer something very similar to iBoot but could never reproduce the full product. For both counterfeiters and hackers, having an original copy is important.

At this point, there are certainly many people interested in poking through iBoot, looking for holes to exploit. Surely, both security researchers and hackers are hard at work on that as you’re reading this.

However, we must point out that the code that was leaked belongs to iOS 9, meaning that a good portion of it might be outdated. On the other hand, it could provide some valuable insight on how Apple’s pre-boot process works and allow counterfeiters to create their own platforms that run iOS, boosting the “iPhone copycat” market.

There’s also the fact that vulnerabilities found in iOS 9’s iBoot could still work perfectly fine on hardware running iOS 11. Although iPhone hardware changes frequently, things strictly related to bootup don’t often “need” to change along with it.

For those who are worried about a mass infection of Apple devices, it would take quite an effort to actually do damage by exploiting an iBoot vulnerability. The reason for this is that Apple has multiple layers of fail-safes in both its hardware and software that might make a full-blown infection difficult.

Miguel Leiva-Gomez

Miguel has been a business growth and technology expert for more than a decade and has written software for even longer. From his little castle in Romania, he presents cold and analytical perspectives to things that affect the tech world.

Subscribe to our newsletter!

Our latest tutorials delivered straight to your inbox

Sign up for all newsletters.

By signing up, you agree to our Privacy Policy and European users agree to the data transfer policy. We will not share your data and you can unsubscribe at any time.

What It Means To Be An Entrepreneur

Being an entrepreneur is more than being a business owner; it’s a perspective and a lifestyle.

Make sure you have a solid idea that you’re passionate about pursuing. 

Look to examples of successful entrepreneurs and testimonials for inspiration.

This article is for aspiring entrepreneurs and business owners. 

The road to entrepreneurship is often a treacherous one filled with unexpected detours, roadblocks and dead ends. There are lots of sleepless nights, plans that don’t work out, funding that doesn’t come through and customers that never materialize. It can be so challenging to launch a business that it may make you wonder why anyone willingly sets out on such a path.

Despite those hardships, every year thousands of people embark on an entrepreneurial journey, determined to bring their vision to fruition and fill a need they see in society. They open brick-and-mortar businesses, launch tech startups, or turn an idea into a new product or service. With the right motivation, inspiration and game plan, you can be a successful entrepreneur, too.

What entrepreneurs do

An entrepreneur identifies a need that no existing business addresses and determines a solution for that need. Entrepreneurial activity includes developing and starting a new business and implementing a business marketing plan, often with the end goal of selling the company to turn a profit.

An entrepreneur who regularly launches new businesses, sells them and then starts new companies is a serial entrepreneur. Whether a business owner should be considered an entrepreneur often depends on whether they created the business, and other legalities. That said, any founder of a successful household-name business began as an entrepreneur. 

If you want to become an entrepreneur yourself but you worry you don’t have the money for it, finances don’t have to stop you from achieving your career goals. Many entrepreneurs seek financing options that bypass traditional banks, like funding from angel investors that provide entrepreneurs with capital to cover startup costs (or, later, expansion costs). If you can demonstrate a high growth potential for your business, you can also turn to a venture capitalist, who offers capital in exchange for receiving equity in your company.

Examples of successful entrepreneurs

Many people whose names no one knew decades ago exemplify entrepreneurial success today. Here are just a few examples:

Steve Jobs: The late tech leader started Apple in a garage and grew it into the dominant company it is today. Jobs even faltered partway through his career, leaving Apple for more than a decade before returning to the company and taking it to new heights

Elon Musk: He founded SpaceX and has since become known for putting the billions of dollars his company has earned him toward some benevolent projects, including providing clean water to Flint, Michigan, and donating FDA-approved ventilators to hospitals fighting COVID-19.

Bill Gates: The Microsoft co-founder has often been listed as the world’s wealthiest individual and has become an internationally renowned leader on pandemics and how to handle them. The Bill & Melinda Gates Foundation, shared with his former wife, focuses on combating poverty, inequity and disease globally.

Jeff Bezos: The founder and creator of chúng tôi originally started the enterprise as an online book retailer. The internet marketplace has since become one of the most valued companies in the world, selling nearly every product imaginable.

Mark Zuckerberg: As a college student, he helped shape the future of social media by co-founding the social networking platform Facebook. Initially launched for only select college campuses, the service quickly expanded to the broader public. Its success turned Zuckerberg into one of the youngest self-made billionaires in America.

Sara Blakely: She took $5,000 and turned it into a $1 billion company with an invention known today as Spanx. The idea was born out of Blakely’s frustration with the pantyhose she had to wear for prior jobs. She had no fashion experience but researched everything from patents to fabrics. 

Key Takeaway

Many of the biggest businesses had humble beginnings and evolved into highly successful enterprises over time. Don’t be afraid to change your initial idea to fit shifts in your market.

The value of entrepreneurship

Being a successful entrepreneur isn’t an easy path. It can often take far more hard work, ingenuity and perseverance than the typical 9-to-5 job and yet still not pan out in the long run. However, succeeding as an entrepreneur can be one of the most rewarding experiences, because you’re doing so on your own terms and affecting society at the same time. For many, those rewards are invaluable. There’s never a guarantee that an idea will succeed, but you’ll still see many people starting their own businesses anyway. After all, failure is just as uncertain.

Isaiah Atkins and Paula Fernandes contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article. 

Core Web Vitals Means Deciding What Add

Google’s Martin Splitt discussed the value of being flexible enough to replacing a plugin or add-on if it cannot be adapted to scoring better in core web vitals.

“If someone is in a situation where they’re using various different tools, add-ons, apps and plugins to make their user experience “better” or upsell the user or whatever it is and those tools aren’t making the changes… and they can’t implement them differently… should they be looking at different solutions?”

Martin Splitt answered:

“I guess looking at different solutions is definitely a good idea.”

Martin next made the analogy of owning a car with very good gas mileage that had a flaw in that it tended to crash every couple of months.

Screenshot of Loren Baker, a Founder of Search Engine Journal

Loren asked:

“The Tesla analogy?”

Martin Splitt paused a second then responded:


Screenshot of Martin Splitt Playfully Responding With a No

They both chuckled about the joke and moved on with the discussion.

Martin continued:

“If it gives you more stuff that potentially is great but it has these implications… you have to judge for your specific case if you’re okay with the implications that it has or if you’re like nah, I’ll try to see if we have something else that does that without the problems.”

So in other words, if a publisher’s site is slowed down because of a web page feature and the feature cannot be optimized then maybe it’s time to look for another way to accomplish what that feature was doing or simply do without the feature if it’s not really going to be missed.

These web page features can be plugins and apps like chatbots or contact forms that are slowing down rendering.

“It’s a good chance to look through all of these legacy tools that people have utilized over the years and put together a nice SWOT analysis.

What are the pros and cons? Such and such ups conversion rates or whatever by X percent…

Core Web Vitals May Require Website Technical Analysis

Part of the battle of optimizing is measuring how much impact any plugin or addon has on a site. Lighthouse and PageSpeed Insight give an idea of what the troubling files are.

But at a certain point, optimizing for speed might make publishers think hard about how necessary some addons are to a site because the slower page speed could be working against conversions and rankings.

And that means tossing out everything that’s acting like a dead weight to the site and slowing it down.


Watch Martin Splitt at 38:15 minute mark

Understanding The Predictive Analysis Framework For Ticketing Systems

Ticketing systems are one of the top tools for collecting and tracking customer data. They manage all the customer problems in one place and act as a base to make future business decisions.

It can be a great source of predictive analysis that measures the anticipated risks and outcomes related to customer issues.

Companies and analysts usually extract and refine the data from these ticketing systems to identify similar customer patterns and find opportunities to make the business more profitable.

Effective use of ticketing system data to conduct predictive analysis also plays a vital role in reducing churn rate, improving retention rate, defining prices, and creating marketing campaigns as per customer and business needs.

Before diving into predictive analysis by leveraging the ticket system, you should thoroughly discuss the topic. This article will help you understand the predictive analytics framework for a ticketing system to perform future business actions.

Let’s begin!

What is a Ticketing System?

A ticketing system is a customer issue collection tool that automates and organizes all the customer data in a single dashboard.

Using a ticketing system in your business allows customers to submit their queries and requests via various channels, including email, social media, phone, or website.

It is specifically designed to make the customer interaction process smoother. This system helps build strong customer relationships and understand their requirements in detail.

Here are the features of a ticketing system:

Lets you keep track of all the current requests

Helps improve the customer-business relationship

Provides a personal touch to the customer requests

Ensures that no query is neglected

Helps understand customer behavior to create marketing campaigns

Integrates all the customer contacts in a single place

What is Predictive Analytics?

Predictive analysis refers to using historical business data and statistics to predict future events and outcomes.

Using this analysis as a base to perform future courses of action in a business helps to develop new products and even make investing options.

Predicting future events helps the companies to be prepared and work on the SWOT areas of the firm. It reduces the risk and operational efficiencies.

Types of Predictive Analysis Models For Ticketing Systems

Here are the top models of predictive analysis for the ticketing systems:

Clustering Model:

This includes categorizing the customer request based on their categories, priorities, and other essential attributes. These categories also include the interests, purchasing capacity, and demographics of a particular set of customers.


Forecast Model:

This model includes predicting a particular event or course of action based on past trends and data.


Neural Networks:

This model combines AI and

human intelligence

to predict future events for complex customer data.

Time-Series Model:

This model uses specific time intervals such as weekly, monthly, and yearly to make futuristic decisions for the betterment of the business.

Top Use Cases of Predictive Analysis For Ticketing System

Here are the top areas where predicting analysis can be conducted by leveraging customer data from the ticketing system:

Product Pricing:

Predictive analysis helps decide the final product with the help of customer feedback and data. You can experiment with different prices and get an idea of what best suits the target customers. One of the key reasons that restrict your sales margins could be higher product prices. Gathering abandoned cart data and knowing why customers are leaving between their buying journeys through the ticketing system helps you put a suitable price tag on your products.

Quality Assurance:

Providing high-quality products and experience to customers significantly impacts your sales margins and customer retention rate. Identifying and evaluating customer behavior through ticketing systems helps control the quality of the products and services, not only improving revenues but also helping in reducing additional problems such as customer costs, warranty issues, and extra repairs. An efficient predictive analysis will provide insights into possible quality issues and how to reduce them.

Sentiment Analysis:

Ticketing system data capture what customers say about your brand and your market reputation among the target audience. You can analyze this customer feedback to give insights and recommendations for improving your brand rapport.

Customer Segregation:

Predictive analysis with the help of ticketing systems helps in identifying your target audience. It helps you curate the product line and

make data-driven decisions

based on accurate customer data.

Volume Prediction:

Predictive analysis through ticketing systems works on a pattern that depends on various factors, including customer service and grievance redressal systems. In these automated systems, you get real-time customer data that helps you be well-equipped with the resources and get an estimate of future product sales.


In this digital data-driven era, you need to pay more attention to your customer data, and ignoring their feedback is a massive mistake for businesses of all kinds.

Hence, it becomes essential for businesses to automate and capture all customer data. The data should also include the queries and problems they face while encountering your brand.

It helps understand the target audience better and curate an improved experience for them.

Putting this data into analysis requires extra effort and resources but pays off as it helps make better-informed decisions while amending the ongoing processes.

What The ‘Hottest Day On Record’ Really Means

It seems like every couple of days now there is a new record being broken—just today Northern Ireland hit its highest ever temperature recorded as other parts of Europe deal with flooding and rain in rare quantities. 

Some records are a little trickier to define than others, though. Take Death Valley in California as an example. This is the hottest, driest, and lowest national park in the US and is no stranger to intense heat. High temperature averages are 100 degrees Fahrenheit and above for at least a third of the year, and just last week produced the highest daily average temperature observed on the planet at 118.1 degrees. On Friday, July 9, the high temperature reached a scorching 130 degrees Fahrenheit, one of the highest recorded temperatures of all time.

But every time Death Valley has a wildly high temperature, a weird historical artifact pops up—the one time in 1913 that the temperature was recorded as 134 degrees Fahrenheit. Over a century ago, US Weather Bureau observer Oscar Denton measured a week-long string of suspiciously high temperatures in Death Valley’s Greenland Ranch. If you look at the other temperatures recorded nearby, this recording stood out like a sore thumb—temperatures creeping into the high 120’s didn’t start reappearing until the late 1990s when the IPCC had begun to recognize global warming. 

While that reading is still technically listed as the world’s hottest temperature ever recorded, some argue that this month’s high is likely a more realistic record. 

“If Friday’s observation passes an investigation (instrument calibration, etc.) then, yes, this is a new reliably measured global extreme heat record,” weather records expert Christopher Burt told Yale Climate Connections. That’s because it was shockingly tricky to get accurate temperature readings in the past, which makes it difficult to know which temperatures are legitimate and which aren’t.

How we record temperatures

Like many modern-day data capturing methods, temperatures (as well as precipitation, cloud cover, wind speed and pressure) have been collected automatically in Automated Surface Observing Systems since the mid-90s, explains Jon Erdman, a senior meteorologist at the Weather Channel. There are over 900 of these systems throughout the US, and on average report weather data around every 20 minutes. 

But the thermometer that records temperatures all day every day from stations around the world isn’t exactly like the ones you might have outside your house, Erdman adds. Official weather-taking thermometers need to be shielded from the sun and wind, and have what looks like little lampshades on them so that they can measure the actual surface temperature without being influenced by other sources. 

[Related: Heat is the silent killer we should all be worried about.]

“If you don’t shield the thermometer from direct sunlight, it’s going to heat up more,” he says. So if you’re ever wondering why your personal thermometer might show a bit more heat than the Weather Channel is showing, that could be a reason why. 

Scientists have been shielding their thermometers since the late 1800s, starting with the Stevenson screen, which looked a bit like a little white birdhouse or cabinet and held a variety of weather-investigating tools. Like today’s setup, this contraption protected the thermometer from the elements to just get surface temperature.

In the 21st century, the setup hasn’t changed much—the key difference is just how involved humans used to be. “What you’ll see in these [historical] measurements is literally hand-written measures of every single day, going and seeing what that thermometer says,” says Karen McKinnon, a professor of statistics and sustainability at UCLA.

So, occasional errors are completely within expectation. But how could a person record such a high temperature by accident? From a technological standpoint, in a place as toasty as Death Valley, even a little bit of direct sunlight or scorching hot sand brushing against the meter could rack up some extra degrees, Erdman says. 

McKinnon has also found in her research that people often round up to preferentially chosen numbers rather than be uncertain about the exact temperature. Then, when it comes to the Global Historical Climate Network Daily database, everything gets rounded up to the closest 0.1 degree Celsius, despite the level of precision it was initially recorded. In her findings, 63 percent of temperature samples were biased by this double-rounding. 

[Related: Are ‘heat days’ the new snow days?]

In a lengthy 2023 take-down of the record, geographer William T. Reid and weather historian Christopher Burt also raised concerns about the observers’ credibility to accurately record temperatures, exaggerating the temperature beyond what anyone could explain meteorologically. 

“Things can always go wrong,” McKinnon says. “People can misrecord temperatures, which is probably the most likely situation”

Why does any of this matter?

This isn’t the first time a global heat record has been challenged. Back in 2012, a reading of 136.4 degrees Fahrenheit in El Azizia, Libya on Sept. 13, 1922, was removed from its record-holding spot after an international team found five major concerns with the recording, from unreliable equipment to an inexperienced observer. And there’s certainly a chance that the 1913 Death Valley record could come into reassessment under the National Climate Extremes Committee. 

And even though our systems are much more accurate nowadays, with automated readings and no deciphering of hand-written notes, there’s still a chance that our modern systems may mess up. “You can certainly have problems at individual sites,” Erdman says, “But those are pretty rare.” Just take some of Tampa’s 2023 record highs that ended up being inaccurate due to being too closely located to toasty parking lots and highways, picking up too-high temperatures. 

Just like every anomaly, there’s a chance that the old record was truly a one-in-a-million occurrence. But whether it stands or not, what really matters is that these records are being broken more and more often in a pattern, we cannot ignore—temperatures are rising worldwide, and the entire planet is now experiencing the wrath of intensified weather. 

“What you’re gonna see is that there’s an upward trend,” McKinnon says. “And you can see breaking records with a greater probability.”  

Update the detailed information about What Predictive Analytics Means For Businesses Today on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!