Artificial intelligence and machine learning are among the top marketing buzzwords we come across in the field of digital marketing. These technologies have already become an integral part of digital marketing and are being leveraged to make campaigns more personable and efficient.
For instance, artificial intelligence can make personalization easy and quick by creating accurate buyer personas. These personas are auto-generated to deliver a holistic audience segmentation, thereby improving the effectiveness of the campaigns. In addition, Netflix, Google, Uber, Spotify, Pinterest, and other apps use machine learning to personalize individual accounts and make relevant recommendations to their users.
The ever-improving algorithms and the exponential growth of data are encouraging business leaders and marketers to use AI, in the form of machine learning, natural language processing (NLP), deep learning, and other technologies. These technologies are helping them improve customer experience and conversions.
A Gartner survey shows that 37% of organizations are applying AI in some form or the other to boost their digital performance.
This post highlights how AI and ML are proving to be game-changers in the digital marketing realm.
1. Offer a Better Understanding of the Audience
Great content starts with knowing the audience well. When a business knows its target audience, the connection feels more natural and relevant. That genuine connection goes a long way in building lasting relationships with customers.
In recent years, AI and ML have opened up a whole new world of possibilities for understanding audience behavior. AI tools and data-driven insights are helping businesses understand who they are reaching, what the customers want and need, when to communicate, and where to reach them.
Artificial intelligence helps marketers instantly define buyer personas. Then, platforms like Socialbakers auto-generate these personas to deliver more holistic audience segmentation in the form of actionable insights. These insights help content marketers share inspiring stories that convert.
Keeping your audience at the center of your online strategies is critical to business success. AI can help by offering unique audience insights, enabling businesses to deliver an integrated brand experience through relevant content. It also helps in selecting the most trustworthy and effective influencers for the brand.
2. Help with Lead Management
Big data, predictive analytics, and machine learning are being increasingly used in business intelligence these days. Machine learning, with its ability to bring out valuable hidden insights from large data sets, can create tangible value for businesses.
Leads are the driving force for businesses. They are the ones who will soon contribute to the organizational revenue. Hence, business leaders spend a significant amount of time in lead management. ML can be leveraged to improve and scale a firm’s approach to lead management, thereby boosting the bottom line. It helps firms generate better leads, qualify and nurture them, and ultimately monetize them effectively.
For instance, ML can help you create an ideal customer profile (ICP) to reach the best customers. ICP takes a structured look at the demographics and psychographics of an individual and determines their purchase intent and the content that matters to them. Thus, ICP can be used for lead scoring, allowing marketers to prioritize targeted accounts.
ML can also help firms generate more qualified leads from the traffic already coming to the site. For example, check out how Drift, a revenue acceleration platform, uses conversational AI to recognize quality from noise, learn from the conversations, and automatically qualify or disqualify website visitors. These qualifiers help the sales team focus on leads that are ready for conversions.
3. Curate and Create Better Content
AI is changing the game for content marketers. The technology is being used to automatically generate content for simple stories like sports news or stock market updates. AI also allows social channels to customize user new feeds.
But one content field where AI is increasingly applied is content curation. AI algorithms make it easier to collect target audience data to create relevant content at each stage of the marketing funnel.
For instance, the algorithms collect data on what the audience prefers to read, the questions they want answers to, or any specific concerns. Using this data, content marketers can curate and create relevant content that boosts customer experience and ultimately leads to conversions.
The North Face uses an AI-powered technology like IBM Watson that recreates shopping experiences. The AI tool uses cognitive computing that brings the online and in-store experiences closer together.
Besides, machine learning feeds content strategies by discovering fresh research-based content ideas, identifying the top-performing topic clusters, showing the most relevant keywords in a specific niche.
For instance, Google Analytics and SEMrush operate on machine-learning algorithms that are useful in keyword research and discovery, and content distribution. In addition, these tools can discover industry trends and show you ways to rank higher in SERP.
AI and ML-enabled tools improve the overall reception and performance of online content. In addition, the tools allow marketers to offer relevant and personalized digital experiences that positively influence engagement.
4. Help with Competitive Search Engine Ranking
Search engines are already using AI-enabled algorithms to deliver the most relevant SERP results. These algorithms rely on AI to understand the context of the content and spot irrelevant keywords. No wonder SEOs are constantly striving to understand these algorithms and coming up with strategies to create contextual, conceptual, and accurate content.
The placement of your business in the SERPs can make or break your online reputation and performance. AI technologies make it easier to create compelling content that answers the target audience’s queries, keywords, and phrases.
SEO isn’t a day’s job. It’s challenging, and the results of one’s efforts can only be seen after months. Fortunately, AI-based SEO tools help alleviate this stress. SEO optimization tools like Moz, WooRank, BrightEdge, and MarketMuse heavily rely on AI to offer SEO solutions like:
- Keyword research
- Search terms to make the content more relevant
- Link-building opportunities
- Trending topics
- Optimum content length
- User intent and more.
Tools like Alli AI can instantly optimize your website regardless of the CMS and your web development expertise. The platform performs a site-wide content and SEO audit, automatically optimizes the content, and resolves duplicate content issues. All this makes it easier for content creators to avoid poor-performing content and boost their online ranking.
5. Improve Page Speed
Google has put an exact value on fast user experience by including page speed as one of its ranking signals. That’s why boosting page speed is one of the top priorities for all businesses, especially ecommerce firms. As a result, Webmasters take all sorts of measures to improve page speed.
For instance, WordPress site owners may speed up WordPress by optimizing background processes, keeping the WP site updated, using a content delivery network (CDN), or using faster plugins. Of course, they also use various tools like Page Speed Insights, load time testers, and CMS plugins for the purpose. But now, there’s another ML-powered solution available for boosting the page speed – the Page Forecasting Model.
This model predicts user behavior using machine learning and predicts the next page visitors will click on in real-time. This allows Webmasters to preload the page in the background, thus improving the overall experience.
The algorithm is trained with historical data from Google Analytics.
For instance, user patterns like going from home page to category page or product page to the shopping cart are recognized, understood, and included in update algorithms. If the user behaves similarly, the algorithm is automatically prepared with the next page.
However, the prediction accuracy is dependent on the amount of data available to train the algorithm and the website structure. So, the models will vary according to these factors. For instance, if yours is an ecommerce website that combines industry news with product pages, it’s better to use two or more models that can predict the behavior per section.
6. Automate Website Analytics Process
Web analytics isn’t new. Businesses have been assessing user behavior and tracking key performance metrics since the mid-’90s. But thanks to AI and machine learning, web analytics tools now have robust capabilities that allow businesses to automate the process. These tools can offer auto-generated reports and on-demand insights that feed marketing strategies.
Within a single visit to a webpage, each user generates hundreds of data points like the time spent on a page, the browser details, its location, and others. It is practically impossible to analyze all this data manually. AI and ML make such analysis faster and accurate by speeding up the data processing.
AI-based tools can help you track each visitor’s online behavior, understand user journeys, and how customers move through the marketing funnel. They also point out issues, if any.
Let’s say you have a blog post that gets a lot of traffic, but visitors just read the post and leave without taking action like subscribing to your newsletter or sharing your post on social media. AI-based tools can flag such issues, allowing you to take the necessary corrective action like adding internal links or improving your CTA.
Google Analytics (insights section), Adobe Analytics, and Kissmetrics are among the top web analytics tools that help firms see patterns in customer behavior and predict future trends.
7. Improve Site Navigation
Site navigation is another critical area in digital performance where AI and ML can help is site navigation. Though it may sound negligible, the importance of having organized and easy-to-follow navigation cannot be ignored. Well-planned navigation improves the visit duration, reduces the bounce rate, and boosts user experience. It also enhances the overall aesthetic appeal of the website design.
AI can help Webmasters create a user-friendly website structure that’s easy to navigate. AI-powered chatbots can guide users through the pages and help them find what they are looking for within the first few clicks. This significantly improves the user experience and sends good signals to search engines, indicating that your content is useful and relevant.
Thus, Google and other search engines will rank your page higher than any other website offering similar content.
8. Design Better Websites
AI applications can improve the usability and experience of a website by enhancing the site’s appearance, strengthening its search abilities, managing inventory better, and improving interaction with website visitors. No wonder a growing number of designers and developers are moving towards AI-based design practices.
AI is slowly becoming an indispensable part of modern web design and development. Take the field of artificial design intelligence (ADI) systems, for instance. ADI has triggered a sudden shift in the way web designing is done. It allows designers to combine applications into the website for better user experience and functionality.
Check out The Grid website platform that automatically adapts its design to highlight the content. The platform uses ML and constraint-based design and flow-based programming to dynamically adapt the website design to the content.
Today, we have several entrants in this space that are taking AI in web design to a whole new level. Brands like Adobe, Firedrop, Bookmark, Wix, Tailor Brands, and many others are leading the segment and leveraging the capabilities of AI in web design. In addition, most of these ADI platforms can learn and offer suggestions for optimizing the website for better user experience and SEO performance.
The Way Forward
Artificial intelligence and machine learning are proving to be awesome technologies when it comes to improving a firm’s digital performance. However, it is essential to remember that these ML models are only as good as the data that’s used to train them. Therefore, it’s critical to ensure that your marketing team has access to high-quality and accurate data.
So, before applying these technologies to your digital efforts, there are specific steps that you need to take.
- Set up tags to track and capture on-site user behavior.
- House all the data from different sources in one central place like Google BigQuery, a Big Data analytics platform.
- Invest in data deduplication to eliminate duplicate copies of repeating data from multiple sources.
Once your data is in place, you will be in a great position to start deploying AI and ML for boosting your digital performance. In addition, the information shared above will prove to be useful as you start building machine learning solutions for improving your business’s online presence.
How Augmented Reality Continues to Transform Customer Experience?
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.
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
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
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.
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.
The Hitchhiker’s Guide To Survival Analysis
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 promotion– the 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!
Expensive Coding Boot Camps are Limiting the Tech Talent Pool
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
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.
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!