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How Does Big Data Affect Your Content Marketing Strategy?

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How Does Big Data Affect Your Content Marketing Strategy?


Content marketing has become increasingly common nowadays. Brands today understand that their consumers want something more from businesses – they want value and engagement. A great way to do that is by creating and publishing various forms of content.

But how can we be sure that the content we are creating will interest our viewers? The digital age today means that businesses have a diverse set of followers. That makes it impossible to manually collect and process such large amounts of data to gain valuable insights.

That is where Big Data comes in.

Big Data allows brands to develop data-driven content strategies designed to cater to their consumers’ needs and interests. Moreover, combining it with AI will enable companies to collect large sets of consumer behavior data and help them extract valuable insights from them.

Before we get the relationship between Big Data and marketing, we first need to understand the idea behind content marketing itself.

Content Marketing – Why is it so popular?

Content marketing, as the name suggests, is the practice of creating and spreading quality content. The purpose is to inform and engage the readers and bring them to your business by cementing yourself as an authority on the topic. The benefits of incorporating content into your marketing strategy include:

  • It allows brands to define and establish their audience and draw their attention to the company’s offerings.
  • It allows you to establish yourself as an authority that provides the content of value to its consumers. That helps boost your brand recognition and builds a relationship based on trust with the audience.
  • Compared to other marketing techniques, creating and publishing content is relatively cost-friendly. While it may take a little time to make an impact, once your content offerings expand, you can quickly attract a large audience.

A content marketing strategy plans to create your audience by creating and publishing consistent and informative content that solves the consumers’ problems.

The idea behind it is that a consumer that looks to you to solve their problems is far likelier to make a sale.

Conventional marketing practices tend to annoy people nowadays. For example, ads on TV or online are seen as intrusive and negatively affect the online viewing experience. However, by providing a novel way to market your services and products to enhance the experience, content marketing has made its mark as the go-to marketing technique in 2021.

Three Ways Big Data Can Benefit Marketers

There are three segments of data that are of use for a content marketer – customer data, financial data, and operational data. Obtained from different sources and stores in various locations, this data can be hard to analyze without the help of specialized tools. That is where Big Data comes into play.

It contains all the necessary information about your target audience. It includes the consumer names, email addresses, social media handles, purchase and browsing history, and other general information. Additionally, it also contains important insights such as audience interactions, time spent on-page, and information gathered via surveys and questionnaires.

It can help you evaluate your content performance and optimize your content strategy accordingly. Also, it includes data such as the number of sales, number of conversions, revenue generated vs. the cost of marketing, etcetera. It might also contain any publicly available financial data from your competition, such as their pricing plans.

That contains the operational metrics of your content. It includes factors such as your search engine rankings, click-through rates, and more. It helps you figure out how well your content performs on the web and make the required tweaks to optimize your strategy further.

Using this data, you can generate insights on developing a great marketing strategy and making any tweaks necessary along the way.

How Does Big Data Enhance Your Content Marketing Strategy?

Big Data is the name given to the large amounts of information and insights generated by users online. A click, a swipe, or even a hover is all part of the data used to gain insights on user behavior. Businesses worldwide can use those insights to adapt their services and offerings, as they tell us how consumers act and respond to a given scenario.

This data also consists of the consumer likes and dislikes, which can help target the right customers by the businesses. Using this information, brands can focus on creating content and optimizing their offerings for their ideal consumers. Let’s look at a few other ways Big Data can help us build a better content marketing strategy.

  • Define and Understand Your Marketing Goals

Before you start developing your content marketing strategy, you first need to define the goals of your marketing campaign. By specifying these goals, you’ll create a set of guidelines to help you develop the perfect marketing strategy.

To do that, you first need to understand the needs of your business. Next, you need to define each area that could use the marketing insights gathered from user data. Once you have determined these areas, the next step is to use Big Data analytics tools to gather the required information. That will help you pick the necessary insights to find the solutions to those improvements.

Big Data can expose the hidden patterns and trends in the data. As a result, it helps you develop more robust marketing plans for your business.

  • Understand Your Target Demographic

Developing your marketing strategy around your customers is what every business needs to do. Big Data can help you do that by providing relevant insights into the likes and dislikes of your target consumers.

Understanding your consumers is necessary for a business to ensure that their content is related to their interests. There are many ways to use Big Data to your benefit for this purpose, including:

  • It can help you identify the various segments of our target audience. Moreover, it can provide you details about their interests and preferences, helping you create content suited to their interests.
  • By analyzing the user interaction data for your content, you will identify and improve the user experience issues within your content.
  • It also allows you to identify trends and develop projections for the future through user interactions, which is necessary for a good marketing strategy.
  • Boosting Conversion Rate

Content marketing aims to guide a consumer through the sales funnel and convert them into a customer. To help you improve your conversion rate, Big Data can help you identify the correct content types. That will help you create content targeted for different purposes, from topics meant to educate to those intended to convert.

A few insights that could be of use here include:

  • Logging a user’s interaction with your content
  • Logging the amount of time each user spends on your content
  • At what point does the user convert

This data can help you optimize your content and increase your conversion rate. By understanding the consumer’s behavior, you will identify the factors that lead to conversion and implement them within the various copywriting examples you produce.

What you need to understand is that conversion depends entirely on the quality and relevance of your content. Big Data can help you ensure that all your content is appropriate and relevant to its purpose in the sales funnel.

  • Improves Customer Retention

A content marketing strategy doesn’t just improve your conversion rate. It also helps you retain existing customers by providing quality content that interests your readers.

However, finding suitable topics to engage and retain existing customers might not be so easy. Choosing content that interests your existing and potential customers requires that you go through large amounts of data. That is something that is exactly what Big Data was made for.

Big Data can help you retain customers by:

  • Processing important customer metrics like click-through rates, time spent viewing the content, the number of returning customers, and more. That can help you understand the topics and types of content that interest your users, which could go a long way into helping you retain your existing customers.
  • Moreover, it can help you identify the needs of your customers’ satisfied and unsatisfied segments. That will help you create better-targeted content to satisfy the needs of all your customers.
  • Enhances Your Account Visibility

Content marketing serves another vital purpose besides attracting and retaining your customers. It also helps to establish your presence in the market by creating content that boosts your visibility.

For your content to do that, it needs to be entertaining, informative, SEO optimized, and most importantly, of interest to your readers. Using Big Data, you can:

  • Analyze your competition to understand what interests your potential customers and which distribution channels would yield the best results.
  • Evaluate each channel to identify what type of content would suit it the best
  • Identify where your links might have the best chance of reaching your potential customers, using big data analytics to assess your competition.

Conclusion

The bottom line of this entire article is that Big Data is necessary for your content marketing strategy today. Helping you analyze the gathered data allows you to make informed decisions to improve your content’s impact.

Creating content for your customers is quite tricky today. First, businesses have to create content that speaks to several customer demographics. Big Data provides a little clarity to that process by generating valuable insights to help you attract and retain your customers.

Now you know how Big Data can benefit and improve the content marketing process. That means that you are now ready to use that knowledge to create a strategy that helps you stand out and establish yourself as an authority in your industry.

Image Credit: provided by the author; thank you!

Amtul Rafay

Amtul Rafay is a content marketer by profession. She loves to explore futuristic trends in the tech industry while believing in the influential power of research-backed opinions. Being passionate about blogging, she writes on a variety of topics including digital marketing, graphic designing, branding, and social media. Besides that, she is also fond of traveling & an avid reader by heart.

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How Augmented Reality Continues to Transform Customer Experience?

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Impact of AR


Augmented reality (AR) is continuously paving the way for more engaging interactions between businesses and their target customers. In more ways than one, AR-based customer experience can ensure increased customer satisfaction and business conversion.

According to a study, a whopping 71% of customers have very favorable opinions about AR in the shopping experience. They say it would urge them to buy from the store more often.

Augmented Reality (AR), which works as an overlaying aspect over the appearance of the physical objects and the world at large, can enhance the perception of the real world and thus can revolutionize the customer and shopping experience.

Source: retailperceptions

With the present mobile support for a three-dimensional view of the objects, delivering AR-powered shopping and customer experience across online stores and mobile stores has become more accessible.

According to a Tractica report, by 2022, the number of monthly active users for mobile augmented reality apps is likely to reach 1.9 billion.

Augmented reality-based customer experience promises a digital shopping and browsing experience transforming the entire customer journey with either immersive attributes or more meaningful interactions or more layers of information. Businesses that employ a developer to build an AR app equipped with AR experience should insist on making things easier for the customers.

Since AR, for improving the customer experience, offers so many promises, it is essential to look at the key ways AR can transform customer experience altogether.

Removes All Pre-purchase Uncertainty and Confusion

Pre-purchase

Despite the rapid growth story of e-commerce and mobile commerce stores across all niches and categories, the confusion and uncertainty about making purchases are still dominant for most customers buying online.

Since while buying online, people cannot try the products in person, the rates of product return and abandoned carts are considerably high compared to the brick and mortar stores.

This is exactly where AR can play an essential role by bridging the gap between the physical shopping experience and the online ones. Thanks to AR, online stores and mobile commerce stores can deliver a truly feel-real impression of the purchased products.

Already many stores have started to incorporate AR for the express purpose of offering an immersive shopping experience. The IKEA furniture store right on our smartphone allows you to see each furniture piece in your room.

Similarly, several garment and fashion accessory brands enable you to visualise the items on your body. For example, Gucci offers an AR trial feature so that customers can see their sneakers adorning their feet just by capturing the feet with the smartphone camera.

Delivering a Detailed Layer of Product Information

Augmented Reality (AR), besides offering an immersive and engaging way of trying the products before purchases, also ensures feting very detailed product information to the customers instantly. The best thing is this ability made AR-powered shopping popular for offline stores as well.

No wonder that in Japan, a whopping 66% of customers now expect regular brick and mortar stores to provide AR experiences. As far as understanding the production cost of the app with AR experience is concerned, this can ensure better business conversion and hence is worth the cost.

AR-based shopping and customer service can offer a lot of information instantly and guide customers navigating in a commercial space or a destination. No wonder travel companies are now utilizing AR to deliver guided tours to their customers.

While booking bus tickets or booking tickets for a sports arena or booking tickets of flights, now AR-based displays can show you the details of every seat along with the facilities to make highly informed purchases.

Already sprawling museum premises and large indoor sites in many parts of the world are helping the incoming visitors to navigate their ways with augmented reality-based interactive maps. Airliners can also guide their customers to navigate to the right gates in quick time.

Adding More Value Through Interactive Packaging

In a multitude of ways, augmented reality (AR) is making a positive impact on customer service, starting from pre-sale orders to the actual purchase experience. Interactive packaging is the latest example of this impact.

When scanning the product packages with their smartphone camera, customers can see very detailed and multilayered visuals with a lot of additional and helpful information. This results in more purchase decisions for the customers.

Heinz uses AR-based packaging to allow customers to get a lot of helpful information when purchasing tomato ketchup, an excellent example of this AR-powered interactive packaging.

There is no dearth of brands that allow customers to scan the QR code to get more detailed information about the products they are going to purchase. In business-to-business (B2B), customer communication can be highly useful. Instead of carrying brochures, visiting cards, or business presentations, you can allow your audience to scan a code to get the details.

Futuristic AR Based Dining Experience

For food and restaurant chains to receive a continuous flow of new customers and retain old customers, providing a smooth and frictionless experience became the established norm. Augmented reality (AR) in the food and beverage industry has already proved to boost customer engagement and improve brand loyalty.

There are already AR-based menus that are transforming food ordering for customers. Besides offering interactive 360-degree visuals of the food items, the latest AR-based food menu provides multiple layers of information and interactive overlays for the customers to customize before placing the orders.

Thanks to AR scanning, the food package labels have also become more accessible than ever before.

AR Powered Travel and Hospitality

If one industry we need to name has received the most significant impact of AR technology, none other than the travel and hospitality industry. AR-powered information overlays or navigational guidance appearing right on the smartphone screens completely revolutionized the traveler experience.

The Interactive virtual tours of travel sites, hotels, and restaurants offering an immersive, 360 degrees interactive visual of the sites and ambiance of premises added value to the travel experience in a never-before manner.

AR is also ensuring a highly reliable travel guide offering route and navigation guidance in real-time. Last but not least, modern translation apps providing a real-time translation of displayed signs is another excellent example of AR’s impact on the travel experience.

AR to Revolutionize Post Sales Support

Source: techseedotme

In today’s competitive business environment, every brand needs to establish its reliability through robust and uncompromising post-sales support. Such support refers to all the activities after a product is sold or a service is offered to the customers.

Some of the crucial post-sales support mechanisms that matter for branding and customer appreciation include installation, upgrades, warranties, repairs, troubleshooting, and answering customer queries.

In this respect, remote augmented reality-based customer support has already proved to be a significant way to push changes. It helped increase call resolution at the first instance by at least 20%, and the AR-based support also helped reduce the rate of dispatching technicians by at least 17%.

AR is transforming post-sales support for brands in two significant ways through AR-powered self-service and AR-powered technical support.

Business brands are increasingly using AR to help customers with self-service support. On their smartphones, customers can get guidance through highly interactive visuals and media, screen overlays on technical aspects and quick FAQ answers.

  • AR-based Technical Support

The AR-based visual support can also help customers understand the technically demanding aspects of various products, parts, the ways they function, and the particular measures of troubleshooting and problem-solving. An AR-based screen overlay can give details of all parts along with the model number, manufacturing details, versions and the problem resolution timing.

All those bulky user manuals written in multiple languages are already on their way to exit. They are being replaced by interactive AR-powered user manuals showing minute details of every component and how to operate a device with switches, buttons and other controls. For instance, a new car owner can take interactive visual guidance on different systems and car mechanisms after purchasing the vehicle.

Conclusion

Quite convincingly, AR has transformed the customer experience across e-commerce stores, brick and mortar stores and all other B2C and B2B interactions. AR has proved to be the most critical value addition impacting the customer experience.

Wasim Charoliya

Wasim Charoliya

Digital Marketing Strategist

Wasim Charoliya is a digital marketing consultant and strategist. He is passionate about helping startups, enterprises, B2B and SaaS businesses to establish thought leadership in their industry with actionable content strategy.

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The Hitchhiker’s Guide To Survival Analysis

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Benedict Timmerman


Survival analysis is the best thing in the world since sliced bread! However, in most machine learning circles, it’s pretty much synonymous with an “# it’scomplicated” relationship status.

Survival Analysis is an Extremely Valuable Branch of Statistics.

We want our guide to better serve you as a straightforward go-to/how-to, eliminating any confusion. The guide provides a valuable resource on how survival analysis, which can be applied to — well, almost anything.

However, survival analysis is wrought with misunderstanding and misuse.

What else should I know about survival analysis?

Also referred to as “time-to-event” analysis, simply put, it’s what we find when we analyze the time it takes for something like buying a new home (an event) to happen after getting a promotion -which we call “an exposure.

Basically, it’s modeling or a set of statistical stratagems which measure the time as mentioned above to an event. Literally how long it takes for something of interest to happen. Depending on what you are studying, observing, researching, or just finding interesting- you want to know, and we can now actionably determine how long it takes to happen.

To get started, first and foremost, you need to set and formulate your research question aptly to perform a survival analysis approach.

Often, researchers will simply use ‘when’ and/or ‘whether’ terminology. But, first, the information is given — as a prediction of when and/or whether something will happen.

Then the conclusion is in a yes, or no determination. Finally, the conclusion is an analysis about how long it takes before what we want to see (the subject of interest being examined) will happen, and whether what we’re looking for will happen or not.

When you’re analyzing how long it takes for an event to happen and whether it will happen at all, it is imperative that what you want to eventually see and find is the same (equal) for all the subjects you examine.

In other words, you don’t want a sample with elements that have no chance of experiencing the event. It just won’t work.

Exposure is the point when we’re off to the races and start the proverbial research clock in order to analyze any time-to-event.

The event itself, in this case buying a new home, means simply the time needed to process and develop from the exposure which is getting that promotionthe moment when we stop the “clock.”

The time elapsed between these two points is the focus of interest which we call “the survival time.”

Survival analysis is a game-changer for a diverse variety of disciplines and areas of research.

Most experts, however, mistakenly consider survival analysis a tool solely applied to study death and disease, an accurate method to measure relapse of a medical condition, the potential hospitalization of a patient, and the mortality rate in medicine and biomedical disciplines.

Survival analysis application has thankfully spread to serve a variety of fields and disciplines, including engineering, social and behavioral sciences, even professional sports.

In engineering, this process is known as “failure-time analysis” and is mostly applied to test the durability and quality of products.

Incorporating survival analysis in engineering is valuable. For example, we see a manufacturer wants to test how long it takes for light bulbs to burn out, how often the company’s computers crash, even predict when a mechanical part like an engine head gasket will crack.

In social sciences, survival analysis is known as “time-to-event” analysis. This is because there have been scientific studies to answer queries such as how long it takes for one to get married, get a first tattoo, buy a first home, or to graduate.

Medicine and biomedical research

In addition to medicine and biomedical research, JADBio can definitively perform survival analysis on several out-of-the-box and even what one may consider ‘weird’ cases, including:

Health – Obviously, when analyzing health disciplines, we can actionably determine values such as the time to: death, device failure such as a heart pump, or simply the readmission rate of a specific subset of patients.

Market – We use survival analysis more and more in marketplace areas of research such as manufacturing or sales when we want to determine the time to: a component failure in machines, whether a certain device becomes obsolete, and how long it will take to obtain a certain patent for example.

Finance – A valuable tool in the evermore elusive waters of finance, survival analysis can be applied to calculate the time to predict when a hospital may turn a profit or report loss, calculate costs, and how often staff present burnout or should be promoted.

Social Sciences – Especially helpful in social sciences, where experts now can analyze the time to: divorce, new couples having their first or second child, and how long it will take new families to buy their first home.

Government and Social Services – Helps determine the time to: child welfare and to match children with appropriate foster parents. Used to optimize the length of stay of children in the program, to estimate participation time in various social programs, and to estimate the time it takes for various policies to take effect.

Law Enforcement – Predicts the time to: estimate the likelihood of recidivism in criminal offenders.

Marketing Operations – Performed to assess the time to: the length of participation in loyalty programs.

Sports – Sports is a field where survival analysis can really be your golden goose. Sports — that’s right. In professional sports, survival analysis will change the game when it comes to delivering results like time to: mechanical failure of race car engines, or tires in F1, and the time it takes athletes to be substituted in team sports like football.

A coach can know the best time to switch out a soccer player. Team doctors and government health authorities can accurately evaluate and certainly limit the rate of Chronic Traumatic Encephalopathy (CTE) –a degenerative brain disease observed in professional athletes, military veterans, and anyone with a history of repetitive brain trauma.

In essence, there is no differentiation to survival analysis being used as a tool whether we consider health disciplines, the global market, social and behavioral issues, or professional sports.

When researching for survival analysis — survival time is the main driving interest.

We perform survival analysis on subjects that present a delayed onset of events where our goal is to observe that specific timeframe, how long it takes for the event to happen.

It is irrelevant whether there is a positive or negative correlation attributed to the event. The event may very well be death (negative), yet it can also be a new promotion (positive).

Although initially developed in the biomedical sciences to analyze time to death either of patients or of laboratory animals, survival analysis is now widely used in engineering, economics, finance, healthcare, marketing, and public policy. Survival analysis can be used to predict when a patient will expire; when cancer will metastasize, or on anything you are trying to predict time-wise.

Our Secret Special Sauce

At the core of this work is JADBio. JADBio systematically compares the performance and stability of a selection of machine learning algorithms and feature selection methods that are suitable for high-dimensional, heterogeneous, censored, clinical and other forms of data. The data set is used in the context of providing specific, accurate, and actionable predictions.

Leveraging the advances in modern data collection techniques will produce ever-larger clinical and other large data sets. It’s imperative to identify methods that can be used to analyze high-dimensional, heterogeneous, survival data.

JADBio has a world-class team and constructs a range of machine learning algorithms capable of analyzing vast types of data providing clients with the power to make decisions and steer their respective objectives in the direction of success.

Definitions of standard terms in survival analysis:

  • Event: Death, disease occurrence, disease recurrence, recovery, or other experience of interest.
  • Time: The time from the beginning of an observation period (such as surgery or beginning treatment) to (i) an event, or (ii) end of the study, or (iii) loss of contact or withdrawal from the study.

Image Credit: Provided by the author from The Hitchhikers Guide to Survival Analysis; thank you!

Benedict Timmerman

Benedict Timmerman is a Senior IT Experience Analyst supporting Digital Giraffe’s clients operating within the AI industry. Benedict covers data and machine learning solutions, providing quantitative and qualitative analysis on the available practices, people and markets. Benedict also spearheads the company’s lead generation process for its clients designing outreach campaigns.

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Expensive Coding Boot Camps are Limiting the Tech Talent Pool

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A Labor Shortage Could Be Our Economy's Biggest Downfall - ReadWrite


Coding boot camps have soared in popularity since their inception in the early 2010s. Course Report, an organization that conducts yearly market studies on boot camps, reports that nearly 25,000 coding boot camp graduates entered the job market in 2020 — up 39% from the previous year.

With the growth in popularity, however, has come an increase in price. Course Report also reports that the average coding boot camp costs more than $14,000. As costs increase, new opportunities for students to defer payments have surfaced, such as income share agreements, or ISAs, in which students don’t pay tuition until after they land jobs.

Rising Costs Mean Greater Risk

But many payback models come with risks. For example, a recent lawsuit filed against a coding boot camp is based on claims of false advertising around job placement rates that directly impacted students’ ISAs. And throughout history, there have been many predatory educational programs that have sold lies to bring in more revenue. Such instances resulted in rulings and regulations to protect students. However, it’s still true that tech training programs in the U.S. from for-profit enterprises face a complex balance of wanting to help students access better careers but needing to generate profits and returns for investors.

The bottom line is that the tech industry needs to look to new models for teaching coding skills that reduce students’ risk and financial burden.

Workforce initiatives and skilling pathways need to be more accessible to all Americans. While traditional college or university career pathways are an excellent option for some, there’s a large and growing pool of people in the U.S. for whom earning a four-year degree is unfeasible. The cost and risk of taking out student loans is a huge barrier, and many students can’t commit the necessary time while they work other jobs or care for families.

It’s Time to Create Realistic Opportunities

Workers today are interested in reskilling for new opportunities, and companies need more skilled tech workers.

But the time and money it takes to get a degree or go through a for-profit boot camp is often not an option for many.

For similar reasons, the same individuals who can’t take the traditional higher education pathways are still being left on the sidelines by boot camps.

It’s time to make opportunities more accessible and realistic for all. Exploring the following strategies can help the tech industry reduce the risk and financial burden of gaining new skills:

1. Create and support accessible, accelerated skilling pathways.

Coding boot camps do create a great talent pool. Still, to widen that pool for tech companies and create more accessible opportunities for job seekers, the tech industry needs to support other skilling pathways, such as free and accelerated digital job training courses, that open doors to individuals often shut out of other options.

Some options are not only free or affordable, but they also offer opportunities to learn skills part-time.

Because this format breaks down barriers presented by traditional education pathways and for-profit boot camps, it’s more accessible to those looking for career changes. It can produce a more diverse talent pool for tech companies.

Plus, getting more career changers into tech means bringing a wide and diverse set of transferrable soft skills into the industry.

2. Formalize apprenticeship programs.

Apprenticeship programs are great models for opening the door to more people. They allow entry-level workers to gain the specific skills they need to fill roles at a company while on the job and earning a wage. In this way, it minimizes risks for both employees and companies.

As employees learn precisely the skills they need while on the job, they can be sure they’re not risking time, money, and effort to learn potentially irrelevant skills or skills that could become irrelevant in the near future.

Companies benefit because they can mold apprentices to whatever skill sets they need. Instead of hoping candidates’ past experiences and education will serve them well in filling open roles, companies ensure candidates can do exactly what they need to fulfill current or future job responsibilities.

3. Upskill existing workers.

Even people who are already employed with a company might be interested in educational opportunities to learn new skills or sharpen their existing skill sets. Companies looking to fill tech roles can benefit from looking within their companies first to see whether anyone desires to learn new skills and move into a more technical career path.

When companies provide upskilling opportunities to current employees, they retain the talent they already have, provide accessible opportunities for employees to develop their careers, and contribute to a talent pool that will be able to fill future skills gaps.

Conclusion

Pursuing a career in technology has long been a hefty financial commitment for students — whether they’re following traditional university pathways or paying for coding boot camps.

Meanwhile, the tech industry has struggled for years to fill its skills gap and find adequate workers. Closing the gap will require the tech industry to support more accessible, financially viable opportunities for all.

Image Credit: pepi stojanovski; unsplash; thank you!

Jeff Mazur

Executive Director for LaunchCode

Jeff Mazur is the executive director for LaunchCode, a nonprofit aiming to fill the gap in tech talent by matching companies with trained individuals.

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