In today’s competitive professional landscape, a personal brand is more than just a buzzword—it’s your unique digital fingerprint. Establishing a strong personal brand is crucial for professionals aiming to stand out, build credibility, and invite meaningful opportunities.

You can enhance your professional identity and career trajectory by strategically sharing your skills, values, and voice. A personal brand lets you clearly communicate who you are, your distinctive strengths, and the value you bring to your field.

Whether you’re new to personal branding or looking to refine your current public-facing presentation, this guide will equip you with the knowledge to effectively showcase what makes you uniquely and authentically you.

Table of Contents

• What is a personal brand?
• Key elements of a personal brand
• Step-by-step guide to creating a personal brand
• Building your personal brand online
• Growing and maintaining your personal brand
• Common mistakes to avoid in personal branding

What is a personal brand?

A personal brand is your unique professional identity. From how you communicate to your skills, values, and relationships, a personal brand is your reputation. While social media is a major channel to express your brand, a personal brand is far more than a LinkedIn profile or Instagram feed.

Why every professional needs a personal brand

Developing a personal brand is essential for professionals across all industries. Here’s why it’s valuable:

• Career growth: A well-crafted brand can open doors to new roles, promotions, and partnerships by showcasing your value and strengths.
• Networking opportunities: A personal brand connects you with people who share your values, industry interests, or work goals, enabling you to build a network of valuable contacts and mentors.
• Credibility and trust: With a strong brand that’s transparent and consistent, you’re more likely to become a trusted authority in your field. Others are more likely to engage with and support someone whose expertise they recognize and respect.

Key elements of a personal brand

Your unique value proposition

Your unique value proposition (UVP) is the foundation of your personal brand. It’s what differentiates you from the hundreds or thousands of others in your field and highlights the distinctive strengths, skills, experiences, and passions you personally bring to the table. To identify your UVP, ask yourself questions like:

• What skills do I excel in, and how do I demonstrate them?
• What aspects of my background make my perspective unique?
• How do my personal passions and values align with my work?

Reflecting on these questions will clarify what makes you memorable, what resonates with others, and what aligns with your career goals. Think of your UVP as a cornerstone that supports everything else in your brand. If you need guidance to get started on your UVP, you can reference this detailed overview.

Your personal brand story

A powerful brand involves storytelling that shows who you are beyond a list of job titles and workplaces. An effective story doesn’t just list achievements; it shares the motivations, struggles, and key milestones that define your career journey.

For example, consider the challenges you’ve overcome and how these experiences shaped your skills and goals. Did a particular experience ignite your passion for your field? Was there a mentor who influenced your approach? How have your skills and goals changed as your personal story entered new chapters?

Consider integrating these elements and your unique career arc into your personal bio. Sharing these moments allows others to connect with your journey emotionally and adds authenticity to your brand.

Your brand’s core values

Values are the guiding principles that define your brand’s mission and influence how others perceive you. For example, if your values include innovation and integrity, these should be evident in your actions and interactions.

Take the time to outline a few key values that align with your mission and professional goals. Whether you prioritize creativity, integrity, collaboration, or continuous learning, these values help create a consistent and authentic voice that guides your actions and decisions.

Your brand’s visual identity

Visual consistency reinforces your brand and makes it more recognizable. From your best headshots to your logo, color schemes, and fonts you might use on your LinkedIn and personal website, each visual element should express a cohesive brand personality that aligns with your professional goals.

For instance, if your brand emphasizes creativity, opt for visuals that are bold and expressive while also clearly communicating your unique authority in your industry. Your visual identity should be cohesive across all platforms, creating a professional and trustworthy impression.

Step-by-step guide to creating a personal brand

1 Identify your target audience

An effective personal brand speaks to a specific audience. Start by defining who you want to reach—potential employers, clients, peers, or industry influencers—and research what they value. How do you see your values aligning? Where do you see your unique perspective offering something to expand or refine these shared values?

Knowing your audience allows you to shape your brand’s message to resonate with their needs and preferences. For instance, if your audience is tech entrepreneurs, your brand should emphasize innovation and problem-solving, showcasing skills and qualities that cater to their needs while ensuring that your unique approach offers something that won’t be found anywhere else.

2 Define your unique value proposition

Figure out what makes you unique. What sets you apart from your colleagues? Take an inventory of your skills, strengths, and accomplishments. Reflect on any distinctive experiences, passions, or qualities that have helped define your professional identity. Craft a personal UVP reflecting your purpose, goals, and vision from this.

Remember: A good UVP is concise yet powerful, helping guide your decisions and serving as a reference point for your brand.

Here’s an example:

• Enhancing team success by providing clarity, efficiency, and strategic insight. Through a hands-on approach and proven methods, I help businesses minimize waste, increase productivity, and cultivate continuous improvement—delivering significant results.

3 Craft your brand story

Your brand story should be cohesive, highlighting your experiences, skills, and values. Begin by outlining the steps that brought you to your current role, the challenges you’ve faced, and the goals you’ve achieved.

Make your story relatable by sharing the moments that inspired or challenged you and what you learned along the way. Remember, a compelling brand story is honest and relatable—it’s a journey of growth and learning, not merely a list of accomplishments.

4 Build your online presence

Your online presence is often the first impression others will have of your brand. Start with LinkedIn, crafting a headline and summary that capture your unique strengths and expertise. On social media, select platforms where your target audience is most active.

Consider creating a personal website or blog that serves as a central hub for your brand. A strong website might include sections for your résumé, portfolio, bio, mission, testimonials, and links to social media profiles, allowing visitors to gain a comprehensive understanding of who you are all in one place.

5 Network and build relationships

Networking is essential to building a strong brand. Attend industry events, join relevant online communities, and engage with your peers regularly. But simply showing your face isn’t the solution—be intentional about who you connect with, focusing on people who align with your goals and values.

Networking requires time and energy from you—respect that. This is how you will get the most value out of networking events. Networking allows you to establish yourself as a thought leader, while connections made through networking often lead to collaborative opportunities and referrals that can enhance your credibility.

6 Share thought leadership content

Sharing thought leadership content positions you as an expert and a valuable resource in your field. Write articles, create videos, or share insights about your industry’s latest trends, challenges, or innovations. You could also consider hosting webinars or contributing guest posts to reputable blogs.

Regularly sharing valuable insights strengthens your brand and attracts followers who value your expertise, expanding your professional reach.

7 Monitor and evolve your brand

A personal brand isn’t static—it should evolve as you grow. Regularly evaluate your brand to ensure it aligns with your goals and stays relevant. This may involve adjusting your brand story, updating your visual identity, or refreshing your values as your career progresses.

Being adaptable shows that you’re in tune with professional growth and responsive to industry changes, helping you maintain an authentic and dynamic brand.

Building your personal brand online

Optimizing your LinkedIn profile

LinkedIn is a powerful platform for building and showcasing your brand. Start with a clear and concise headline, capturing your role and strengths. Use the summary to tell your brand story, focusing on your accomplishments and values.

Feature relevant work samples to showcase tangible deliverables and achievements and seek endorsements from colleagues to add credibility. LinkedIn’s features, like publishing articles and participating in groups, can further expand your influence and establish you as an industry expert.

Leveraging social media

Beyond LinkedIn, social media offers additional platforms to extend your brand’s reach. Each platform has its strengths: X (as well as its competitor Threads) is ideal for sharing quick insights and industry news, while Instagram allows for more visual storytelling.

Use social media strategically by posting content that reinforces your brand message and engaging with followers through comments and discussions. Be sure to familiarize yourself with the platform(s) where your audience is most present. Social media can connect you with new audiences, help you stay current on trends, and showcase your personality.

Creating a personal website or blog

A personal website allows you to create a lasting impression by consolidating your brand in one central place. Include a portfolio, blog, testimonials, and an “About Me” or bio section to provide a complete picture of your expertise and personality.

Regularly update your blog to share industry insights or case studies, reinforcing your expertise. Your personal website is a professional resource that strengthens your brand and offers easy access to all relevant information for clients, employers, or collaborators.

Growing and maintaining your personal brand

Building relationships

Make it a priority to engage in industry communities, attend conferences, and follow up with connections. Building strong relationships can lead to mentorship opportunities, strong partnerships, and new career paths. Familiarity with the interconnections of your network will enhance your brand’s influence and expand your professional influence.

Consistent content creation

Creating consistent content reinforces your brand as a credible source, positioning you as a unique thought leader within your industry. Regularly share insights on industry trends, challenges, or new strategies, whether through articles, infographics, videos, or podcasts.

Consistency is critical to staying relevant and growing as your industry evolves.

Public speaking and events

Public speaking is a powerful way to amplify your brand. Seek opportunities to speak at conferences, workshops, or webinars to share your knowledge and interact with live audiences. It highlights your expertise and communication skills, allowing you to establish yourself as an industry authority and connect with potential collaborators, clients, and more.

The more you hone your personal brand and express your unique positioning and experience within and beyond your field, the more you will be sought out to speak at such events.

Continuous learning

Continually improving your skills is essential to maintaining a competitive brand. Invest in learning through workshops, certifications, or industry-specific courses. Growth-oriented professionals are more credible and demonstrate a commitment to excellence, which strengthens your personal brand over time.

Common mistakes to avoid in personal branding

Inconsistency across platforms

Consistency in messaging and tone is critical for a successful brand. Though different platforms are home to slightly different audiences and demographics, inconsistent language or imagery between platforms can confuse your audience and weaken your brand’s impact.

Whether online or offline, each aspect of your brand should reflect the same core message, creating a cohesive and memorable impression.

Ignoring online reputation management

Regularly monitor reviews, comments, and mentions related to your brand, and respond thoughtfully to feedback. Addressing negative feedback professionally shows accountability and commitment to improvement. Keeping track of your online reputation protects your brand’s integrity and ensures a positive perception. When you take the high road in response to all feedback, that shines through to your audience.

Personal brand FAQs

What is the difference between a personal brand and a professional reputation?

A personal brand is a proactive representation of who you are and what you stand for, while a professional reputation is what others perceive based on your actions.

How do I find my personal brand’s unique value proposition?

Reflect on your skills, experiences, and values to determine what sets you apart from others in your field.

Do I need a personal website to build my personal brand?

While not mandatory, a personal website offers a dedicated space to showcase your work, achievements, and professional journey.

How can I ensure consistency in my personal brand across platforms?

Use similar language, tone, and visuals (such as headshots and logos) on all professional and social media platforms.

How often should I update my personal brand?

Regularly evaluate your brand, especially after major career changes, to ensure it aligns with your current goals and industry trends.

The post Building a Strong Personal Brand: The Complete Guide appeared first on Grammarly Blog.

In a world where AI-driven tools are revolutionizing communication, the basics of effective business writing remain vital for lasting impact. Building more effective business communication skills can improve team relationships, boost productivity, and increase your workforce’s confidence and job satisfaction.

In this post, we introduce nine essential characteristics of good business writing and explore how you can apply them to your writing process. We’ll also show how AI writing assistants, like Grammarly, enhance these skills, making proofreading faster and more effective so that everyone on your team can communicate with clarity and confidence.

The basics of effective business writing

Mastering business communication is more than just getting familiar with active voice and passive voice. Effective business writing starts with a strong foundation, guided by nine core characteristics that ensure messages are clear, professional, and impactful. These principles are essential for anyone looking to communicate effectively in a business environment:

1. Clarity: Ensuring that your message is easily understood without confusion or ambiguity
2. Conciseness: Delivering information in a brief, focused manner without unnecessary details
3. Correctness: Using accurate information, proper grammar, and correct terminology in communication
4. Completeness: Providing all necessary information so your recipient fully understands the message 
5. Coherence: Structuring your message logically so that all parts connect and support your overall point
6. Consideration: Communicating with empathy by taking your audience’s needs, feelings, and perspectives into account 
7. Courtesy: Maintaining politeness and respect throughout your communication, regardless of the situation 
8. Concreteness: Using specific, tangible facts and examples to support your message, avoiding vagueness 
9. Consistency: Ensuring that your message aligns with past communications and maintains a uniform tone and content

Now that you know the basics of the core characteristics of good business writing, let’s get into how you can apply the principles in practice. 

How to apply effective business writing principles

Understanding the fundamentals is just the beginning—helping your team put them into practice is where effective communication really takes shape. No matter if your team is communicating in an internal business document like a memo or communicating externally via sales emails, social media, or a press release, it’s essential to coach them on how to improve their business writing skills. Here are actionable strategies you can use to guide your team in incorporating each of the nine principles into their writing:

• Clarity: Are your team’s messages clear and easy to understand?
  • Encourage team members to start with a main point in each paragraph and ensure every sentence supports it.
  • Proofread content, looking for areas to avoid business jargon and spell out acronyms.
  • Advise them to use caution with idioms, slang, and local references that could confuse a broader audience.
  • Suggest re-reading messages from the audience’s or recipient’s perspective to spot potential ambiguities.
• Conciseness: Are your team’s messages crisp and focused?
  • Guide the team to be direct, eliminating filler phrases and words like “in order to” (use “to” instead) and “very.”
  • Encourage combining short sentences and cutting wordiness and unnecessary details without losing meaning.
  • Recommend using readability tools or scores to assess and improve brevity and clarity.
• Correctness: Is the information accurate, and have team members proofread for mistakes?
  • Set the standard for fact-checking all figures, names, and technical terms to ensure accuracy.
  • Emphasize the importance of setting aside time to proofread before sending.
  • Leverage AI tools like Grammarly to help team members catch grammatical errors and maintain brand-specific terminology.
• Completeness: Do messages include all the information the reader needs?
  • Remind team members to provide all relevant information up front, especially for complex topics.
  • Encourage including background context when communicating with audiences that may be less familiar with the subject.
  • Suggest adding next steps, a strong call to action, or additional resources to avoid follow-up questions.
• Coherence: Are messages structured in a logical, easy-to-follow way?
  • Instruct the team to use logical sentence structure, presenting key points in sequence.
  • Recommend using subheadings or bullet points to make information easier to scan.
  • Reinforce the use of transition phrases to ensure a smooth flow between sections.
• Consideration: Is the message crafted with the audience in mind?
  • Encourage your team to tailor their messages to the audience’s level of expertise and needs.
  • Coach team members to acknowledge the reader’s challenges or goals to build rapport.
  • Remind them to use language that aligns with the audience’s values, as well as inclusive and respectful terminology in line with company guidelines.
• Courtesy: Is the tone polite, respectful, and business appropriate?
  • Set a positive example by avoiding inside jokes, sarcasm, or aggressive language.
  • Recognize the reader’s time and contributions by maintaining a respectful tone.
  • Encourage team members to rephrase criticism constructively and to wait before responding if feeling emotional.
• Concreteness: Do team members provide specific examples and data?
  • Set the standard to use quantifiable data, numbers, or concrete examples instead of vague terms.
  • Suggest visuals or charts for more complex points where possible.
  • Advise avoiding nonspecific terms like “many” or “a lot” by encouraging precision in their communications.
• Consistency: Is messaging and tone aligned across communications?
  • Promote consistency by setting up templates or checklists that standardize formatting and style.
  • Share a style guide to align on writing preferences, terminology, and brand tone across the team.
  • Use AI writing assistants, like Grammarly, to provide contextually aware suggestions and help the team write consistently in line with your brand voice.

How AI writing assistants take the effort out of effective writing

While mastering the principles of effective business writing is essential, AI writing assistants make it easier than ever to apply them consistently. With real-time suggestions for clarity, conciseness, and correctness, AI tools like Grammarly act as your team’s writing coach, ensuring that every message meets your brand’s professional standards. 

By automating proofreading and providing insights tailored to your audience, AI frees your team to focus on crafting impactful, well-considered content. Embracing these tools allows your workforce to communicate day to day with confidence and save valuable time—elevating writing across your enterprise without the added effort.

Ready to put these principles into practice in the business world and improve writing skills across your team? Talk to our team to learn how Grammarly can coach your entire organization to become more effective writers. 

The post 9 Principles of Effective Business Writing appeared first on Grammarly Blog.

Think about characters. Not specific characters like Katniss Everdeen or Atticus Finch. Think about the kinds of characters they are and the roles they play. You might describe Katniss Everdeen as a hero or a chosen one. Because she’s one of many characters who can be described this way, you could say she fits an archetype.

What is an archetype in literature?

An archetype in literature is a symbol, pattern, plot, or character template that appears in multiple stories from across cultures. They’re recognizable because they’re familiar and often represent our collective experiences and perceptions.

An archetype, pronounced ARK-uh-type, is a story element that gets reused repeatedly in various forms of storytelling, including folklore, books, and movies. This concept also applies to nonfiction narratives, such as historical accounts and news stories.

Archetypes provide a shorthand for characters, settings, and plot elements. For example, a plot archetype might be the story of a long-lost love. A character archetype may be the wise, solitary sage who advises a story’s protagonist.

Psychiatrist Carl Jung coined the term archetype in his 1919 essay “Instinct and the Unconscious.” He used the term to refer to concepts that all people seem familiar with, dubbing them the “collective unconscious.”

Later, literary critic Northrop Frye played an integral role in applying the term and concept to literature. But his application is descriptive, not prescriptive—the use of archetypes in literature dates back thousands of years, with many ancient character and plotline types still appearing in works today.

Types of archetypes

There are lots of different kinds of archetypes in writing. See if you recognize any of the following.

Character archetypes

Hero

The hero archetype is a character who heeds a call to take action and protect others. Heroes generally have virtuous qualities, like kindness, compassion, and a sense of duty.

Antihero

The antihero archetype is similar to the hero in that they protect others, but unlike the hero, the antihero often doesn’t possess virtuous qualities. Instead, an antihero may be a reluctant protector or an antagonistic character who ultimately defeats evil.

Trickster

The trickster archetype is a character that subverts expectations and breaks social rules. They may be a funny character, but this isn’t a requirement. Trickster characters often voice profound insights in the guise of jokes.

Innocent

The innocent is a character, typically a young one, who is naive and has only pure motivations. Often, the hero is tasked with rescuing or protecting the innocent.

To learn more about two key parts of writing well-rounded characters, read our articles on indirect characterization and direct characterization.

Situational archetypes

Lost love

A lost love plot is a story about a protagonist searching for “the one who got away,” a love interest or other relationship from their past. They might or might not find the lost love. In stories with this plot, the archetype is primarily about how this experience changes and shapes the protagonist.

Rags to riches

Stories that fit this archetype are about protagonists who come from humble backgrounds and somehow find themselves wealthy and successful. In some stories, they remain wealthy, and in others, they return to their humble lives by the end.

The voyage

The voyage is a plot archetype that involves the protagonist going on a trip that takes them far from home, exposing them to new people and experiences. The protagonist overcomes the story’s conflict through this journey and the trip back.

Symbolic archetypes

Light

Among archetypes in literature, light is often symbolic of goodness. When light is used as the archetype for “good,” darkness is often the archetype that communicates “bad.”

Darkness

In contrast to light, darkness is a symbolic archetype of evil. It may take the form of a shadow or appear in references to night and darkness.

Trees

Trees are often symbolic of life. One famous tree archetype is the tree of life. It may communicate ideas of immortality, life after death, or nature.

Water

Similar to trees, water can symbolize life. In contrast to trees, which tend to symbolize life and longevity, water can often symbolize support, life force, and emotion.

Archetypes vs. related terms

How is an archetype different from a trope, cliché, or stock character?

Archetype vs. stock character

Think of an archetype as a blueprint for crafting nuanced characters, plots, and symbols. It’s the starting point, rather than the endpoint, for story elements. A stock character, in contrast, is a flat character that doesn’t go beyond this basic blueprint.

A stock character can be an archetype, but an archetype isn’t, by definition, a stock character.

Archetype vs. trope

In fiction, tropes are familiar motifs, like a villain explaining their evil plan to the hero or a simple misunderstanding creating enough drama to fuel an entire story. Tropes can exist alongside archetypes, but while an archetype is a template for a story element, a trope is a plot point found across stories. The word trope can also refer to the figurative language used in a work of fiction, like describing a character’s eyes as deep pools.

Archetype vs cliché

A cliché is a turn of phrase that’s been overused to the point of losing its impact. A few examples of clichés include:

• It’s not over until it’s over.
• Rise and shine.
• A broken clock is right twice a day.

Archetype examples

Antihero

• Heathcliff from Wuthering Heights
• Batman

The voyage

• Alice in Wonderland

Rags to riches

• Cinderella
• Oliver Twist

Innocent

• Lenny from Of Mice and Men
• Phoebe Caulfield from Catcher in the Rye

Archetype FAQs

What are archetypes in literature?

Archetypes in literature are templates for characters, plots, and other story elements that can be found in stories from across the world.

What are some types of archetypes?

A few types of archetypes in writing include character archetypes, plot archetypes, and symbolic archetypes.

How do archetypes differ from tropes?

Archetypes are different from tropes in that while archetypes are templates for story elements, tropes are predictable plot devices and instances of figurative language.

What are some well-known examples of archetypes?

A few well-known examples of archetypes in writing are:

• The voyage plot structure
• The hero archetype
• Light as symbolic of goodness
• Rags-to-riches plot structure

The post What Is an Archetype? Definition and Examples appeared first on Grammarly Blog.

You get only one chance to make a first impression, and the opening of your cover letter is your opportunity to make it count. Hiring managers often decide within the first few sentences if they’re interested in learning more, so a strong opening can make all the difference. Mastering how to start a cover letter could be the key to standing out and landing your next role.

Below, we explain the best practices for how to start a cover letter, from writing the greeting to crafting a compelling first paragraph, and give expert tips to help you capture attention right from the start.

Table of contents:

• Why your cover letter’s opening matters
• How to write a greeting (salutation)
• Crafting the opening paragraph of your cover letter
• Tips for how to start a cover letter
• Examples of strong cover letter openings
• How to start a cover letter FAQs

Why your cover letter’s opening matters

The opening of your cover letter is the first thing a hiring manager sees, so it plays a big role in shaping their opinion of you. A strong opening should accomplish several goals:

• Introduce yourself and your job title
• Hint at your work experience
• Demonstrate your knowledge of the position and company
• Prove you’re a good fit for the role
• Set yourself apart from all the other candidates
• Show your communication and writing skills
• Establish the tone for the rest of the letter

Knowing how to write a cover letter can be the deciding factor in whether you get hired and allows you to go beyond your résumé to showcase your personality and unique qualities.

If you’re unsure how to start, try using an AI tool to generate a first draft and get ideas. AI-generated content can provide structure and inspiration, but remember to personalize it in your own words. Check out our free AI cover letter generator to see for yourself.

How to write a cover letter greeting or salutation

Every cover letter begins with a brief greeting, also known as a salutation, such as “Dear Mr. Smith” or “To Whom It May Concern.” Salutations should end with a comma, followed by a blank line before the body of the letter begins.

If you know the recipient’s name, use it in the greeting along with any relevant titles or honorifics, like “Ms.” or “Dr.” Using a first name is optional but preferred for formal cover letters, as shown in the examples below:

Dear Dr. Deere,

Dear Mrs. Karen Lynch,

If you don’t know the recipient’s name, use a generic salutation. Here are some common options:

• To Whom It May Concern,
• Dear Sir or Madam,
• Dear Hiring Manager,

Whenever possible, try to personalize the greeting with the recipient’s name, as it shows attention to detail and interest in the specific role.

How to start a cover letter: opening paragraph

The opening paragraph is arguably the most important part of a cover letter because it sets the tone for the rest. On top of that, the first paragraph also provides expected information such as your name, current job title, and experience. Moreover, the opening paragraph has to impress the hiring manager, prove you’re a good fit for the role, and set yourself apart from the other candidates. And it must do all that in just a single paragraph.

Think of this paragraph as introducing yourself to a new contact. You don’t need to dive into your full work history here—that’s for later paragraphs. Instead, focus on making a memorable first impression by sharing who you are and why you’re a great choice for the role.

In practical terms, this means stating your name and job title, plus some highlights of your career. Just like a hook in the opening of a story, the beginning of your cover letter should make the reader want to learn more about you.

The final sentence in your opening paragraph should set up or transition into the next paragraph, where you discuss your work history and professional experience. Remember, this is an introduction, so keep it concise and focus on your most impressive qualities.

Tips for how to open a cover letter

1 Show enthusiasm.

Landing a job is about more than just meeting qualifications—hiring managers value candidates with genuine enthusiasm for the role and the company. Expressing your passion early in your cover letter helps you make a memorable impression. For example, you might say, “I’ve followed [Company Name]’s work since high school” or “This position aligns perfectly with my passion for [industry/field].”

2 Tell your story, not just facts.

One of the advantages of cover letters over résumés is that you can show your human side. After all, you’re more than just a list of old jobs and dates.

Use the cover letter as an opportunity to tell your story: Who are you, why do you want this job, and what makes you a good fit? Hiring managers want to know you before making a decision, so sharing some personal details about yourself could give you an edge over other candidates. Just make sure what you’re sharing is relevant to the job.

3 Position yourself as the solution to their problem.

Position yourself as the solution to a problem the company faces. If you have a unique skill or experience, explain how it could benefit them. For instance, you might say, “I specialize in improving social media engagement, which I noticed could help increase [Company Name]’s reach.” This approach not only highlights your skills but also shows your initiative and attention to detail.

4 Demonstrate knowledge of the company.

As we mentioned in our guide on how to write a cover letter, it’s best to research the company you’re applying to. This helps you understand its company culture and what kinds of employees it’s looking for.

When it comes time to write your cover letter, include what you’ve learned. Mentioning a recent award, product launch, or industry trend shows that you have done your homework and take the role seriously.

5 Use statistics and quantifiable data.

Anyone can simply say they’re the best, but no one can argue with cold, hard facts. Statistics and quantifiable data are extra impressive in cover letters because they give evidence that what you’re saying is true. Instead of claiming to be a good choice, use statistics and data to prove it.

Feel free to use some calculations from your previous jobs. Instead of simply saying you improved performance, provide specifics: “I increased team efficiency by 15%,” or “I reduced error rates by 20%.” Hard data helps hiring managers see the real impact you can bring to their company.

6 Avoid generic openings.

The easiest way to stand out is to avoid doing what everyone else is doing, like using the same, tired clichés as other applicants. Hiring managers read tons of cover letters and see these same phrases repeated over and over again, not just at the start but at the end of a cover letter too.

Try to think of something original to say at the start of your cover letter. You can dive straight into a fun and unique fact about yourself or open with a story that shows off what kind of worker you are. Cover letters don’t have to be creative, but if they are, it’s an added bonus.

7 Use the correct format.

Using the correct cover letter format ensures your application looks polished and professional. Most cover letters follow a traditional format with a header, greeting, and signature. If you’re applying to a more traditional company, stick with this formal layout. For modern or creative companies, a simpler header and conversational tone can work well. Either way, a clean and well-organized format always makes a positive impression.

Examples of strong cover letter openings

Here are some examples of impactful cover letter openings that capture attention and demonstrate genuine interest. Each one showcases a unique approach to making a strong first impression:

“Growing up in a family of small business owners, I understand the importance of customer loyalty and personalized service—values I see reflected in [Company Name]’s commitment to excellence.”

“As a long-time admirer of [Company Name]’s dedication to sustainability, I am thrilled at the opportunity to contribute to a company that shares my passion for environmental responsibility.”

“After researching [Company Name]’s recent growth into digital markets, I’m eager to bring my expertise in data-driven marketing strategies to help your team reach its expansion goals.”

Cover letter example

Dear AcmeCo,

As a skilled sales representative in the tech and electronics industry, I am excited to apply for the sales associate role listed on LinkedIn. AcmeCo’s commitment to groundbreaking AI innovation has always impressed me, and I am eager to bring my expertise in strategic sales and customer-centric service to a company I’ve followed and admired for years. With a proven track record of exceeding targets and building strong client relationships, I am confident I can contribute to your team’s continued success.

In my previous role at Boltvern, I built a loyal customer base and increased client retention by 25% by prioritizing tailored solutions and consistent follow-up. I was their youngest employee to be promoted to Senior Sales Representative in just under two years. Not only do I understand the client-side aspect of sales, but also I have a Bachelor’s in Computer Science from Cornell, which allowed me to understand the intricacies of the product and communicate its benefits effectively. While it’s unfortunate that my time in Weyland was cut short due to company-wide layoffs, I am eager to bring my skills to AcmeCo’s sales team.

My background in sales, combined with my passion for technology and commitment to client success, makes me an ideal candidate for this role. I look forward to the opportunity to discuss how my skills align with AcmeCo’s goals, and I am available for an interview at your convenience. Thank you for considering my application.

Best, Jonathan Conner

Cover letter template

Here’s a cover letter template you can use to write your own cover letter. Simply plug in your information to the corresponding part. For more details, check out our guide on cover letter format.

[Your name] [Address] [Phone number] [Email]

[Today’s date]

[Recipient’s name] [Recipient’s professional title] [Company name] [Address]

[Salutation/greeting],

[Introduce yourself. Explain your profession, the position title you’re applying for, and how you heard about it. Briefly mention why this role and company interest you and why you’d be a good match. Show enthusiasm. End with a sentence that transitions or leads into the next paragraph.]

[Summarize your job history, focusing on relevant experience. Add extra context, such as what you learned from these jobs or why certain experiences prepared you for this role. Feel free to address problems with your résumé, like gaps or short tenures. Mention related skills and achievements and any quantifiable results or metrics.]

[Reiterate the main benefits of hiring you, including any soft skills or attributes that align with the company culture. Restate your enthusiasm, thank them for considering your application, and add a call to action to suggest a follow-up, such as scheduling an interview.]

[Simple sign-off], [Signature]

How to start a cover letter FAQs

How should I address my cover letter if I don’t know the hiring manager’s name?

If you don’t know the hiring manager’s name, use a general greeting. Formal options include “To Whom It May Concern,” “Dear Hiring Manager,” or “Dear [Company Name] Team.” If the company has a more casual culture, a simple “Hello” may also be appropriate. When in doubt, it’s best to use a professional greeting.

What’s the most effective way to grab attention in the opening of a cover letter?

The most effective way to grab attention in the opening of a cover letter is to make a memorable first impression that shows why you’re an ideal fit. Try connecting personally with the company by mentioning a value or an achievement of theirs that resonates with you, such as “I admire [Company’s] commitment to innovation in sustainable practices.” Alternatively, highlight a unique skill or start with a recent accomplishment relevant to the role, like “In my last position, I led a project that increased team productivity by 20%.” Each approach sets you apart and draws the reader into the rest of your cover letter.

How do you personalize the start of a cover letter for each job?

The opening of a cover letter should state what role you’re applying to and why you’re interested in this specific job. It’s best to customize this part for each application, rather than copying and pasting a generic introduction. Be honest about what appeals to you about this particular position and company as opposed to others you’ve applied to.

If you’re short on time, our free cover letter generator can help you create a personalized draft quickly. It uses AI to streamline the process, allowing you to focus on adding your unique touch.

The post How to Open a Cover Letter—With Examples appeared first on Grammarly Blog.

Cover letters are meant to impress, but poor formatting won’t impress anyone. Cover letter formatting is fairly standard across industries, and following the proper format shows that you understand professional conventions. Knowing how to format your cover letter correctly can make a positive impact and help you stand out for the right reasons.

In this guide, we explain everything you need to know about cover letter formatting. We’ll provide step-by-step instructions on formatting the header, greeting, introduction, body, closing, and signature, along with templates and examples to help you create a polished and professional cover letter.

Why does cover letter formatting matter?

While we’ve discussed what a cover letter is, here we’ll explain why formatting it is so important. Formatting plays a crucial role in making your cover letter effective and professional. Cover letters generally follow an industry-standard format, making it easier for hiring managers to quickly find key information. For example, if they need your full name or contact details, they’ll know exactly where to look.

But there’s more to cover letter formatting than just searchability. Cover letters are formal business documents, and using the correct format demonstrates your professionalism and attention to detail, making you appear more capable and qualified.

If formatting your cover letter feels overwhelming, try our free AI cover letter generator. It saves time by creating a well-formatted draft in seconds that you can then easily personalize to showcase your unique experience and style.

Cover letter formatting for applicant tracking systems (ATS)

Many companies now use applicant tracking systems (ATS) to automatically screen cover letters and résumés for specific information. Using the correct format ensures that your cover letter is easily processed by ATS software, helping your application reach hiring managers.

The good news is that structuring your cover letter by following the established guidelines here will already make it compatible with ATS. Mentioning criteria like your work history, reasons for applying, and professional skills are exactly what ATS scans for, and following the template below will ensure that ATS can find what it needs.

These additional guidelines can further help ATS:

• Add keywords from the job post throughout your cover letter.
• Avoid images or page decorations.
• Stick with simple layouts—complicated layouts can confuse ATS and render your data unsearchable.
• Use standard fonts like Times New Roman or Arial.

Cover letter formatting basics

Usually, the length of a cover letter is less than a page, typically around three paragraphs. Our guide on how to write a cover letter explains what to say in those paragraphs, but the short version is to dedicate a paragraph each for the introduction, body, and conclusion.

When sending your cover letter as the body of an email (rather than as an attachment), formatting is simpler. Use the default font and size of your email app and skip the header, but be sure to include a clear greeting, sign-off, and your contact information at the end.

Formatting cover letters for print and file attachments

When printing your cover letter or submitting it as an email attachment, use a more structured format. Here are some guidelines to keep in mind:

• Use single-spaced lines within paragraphs, and leave a space between each paragraph.
• Choose a professional font, such as Times New Roman or Arial, with a font size of 12 for readability.
• Set one-inch margins on all sides of the page.
• Include a header with your contact information, the recipient’s contact information, and the date.

If you’re attaching your cover letter as a digital file, there are some additional formatting rules as well. For starters, always use the file type requested by the company. Different employers have different preferences for file types, such as a PDF (.pdf) or Word document (.doc). In the event no preference is given, we recommend PDF as it preserves the document’s appearance on all devices.

It’s also recommended to title the document in a way that’s convenient for the hiring manager. You can add your first and last name, plus the words “cover letter” in the file name so the employers can search for it easily. This also makes your cover letter come across as more professional. You can follow this template when naming your file:

Firstname_Lastname_CoverLetter.pdf

Matt_Ellis_CoverLetter.pdf

How to format a cover letter

How to format a cover letter header

In a formal cover letter, use a traditional header that includes both the sender’s and recipient’s contact information, along with the date. This format is optional for emailed cover letters but is generally used for attachments to job applications. Including this header adds a professional touch and makes your contact information easily accessible.

The header is aligned to the top-left corner of the page. Begin with your name, address, phone number, and email. After an empty line, add the date, followed by another empty line, then the recipient’s details. Although it’s acceptable to omit the recipient’s full address, be sure to include their name, title, and the company’s name.

Here’s a template you can follow:

[Your name] [Address] [Phone number] [Email]

[Today’s date]

[Recipient’s name] [Recipient’s professional title] [Company name] [Address]

How to format a cover letter greeting

After the header, open your cover letter with a greeting, or salutation, that is professional and appropriate. When you know the recipient’s name, personalize the greeting by including their full name and honorific, such as “Dear Ms. Abby Nightingale.”

If the recipient’s name is unknown, use a general greeting. Options include “To Whom It May Concern” or “Dear Hiring Manager.” Always end the greeting with a comma and leave an empty line below before starting the body of your letter.

How to format a cover letter body paragraph

The paragraphs in a cover letter use block formatting, which means they start flush left with no indentation for the first line of each paragraph. For readability, consider using bullet points to break up longer sections or highlight specific accomplishments. Bullet points are optional, so use them only when they improve the flow.

How to format a cover letter signature

At the end of your cover letter, include your signature with specific formatting. The signature has three parts:

• A sign-off (such as “Sincerely,” or “Best Regards,”)
• Your signature (handwritten for print letters or typed for digital submissions)
• Your contact information, if not already included in the header

The sign-off should be brief and end with a comma. Common choices include:

• Sincerely,
• Regards,
• Best,

Sign your name below the sign-off. For print letters, sign in ink; for digital letters, simply type your name. If you didn’t include your contact information at the top, add it beneath your signature.

If you included your contact details in a header at the top, you can stop after the signature. If not, it’s customary to include your contact details under your signature.

While sometimes you may see the signature flush right at the bottom of the page, considering the formality of cover letters, we recommend aligning it flush left, in line with the text.

How to write a cover letter: example

Dear AcmeCo,

As a skilled sales representative in the tech and electronics industry, I am excited to apply for the Sales Associate role listed on LinkedIn. AcmeCo’s commitment to groundbreaking AI innovation has always impressed me, and I am eager to bring my expertise in strategic sales and customer-centric service to a company I’ve followed and admired for years. With a proven track record of exceeding targets and building strong client relationships, I am confident I can contribute to your team’s continued success.

In my previous role at Boltvern, I built a loyal customer base and increased client retention by 25% by prioritizing tailored solutions and consistent follow-up. I was their youngest employee to be promoted to Senior Sales Representative in just under two years. Not only do I understand the client-side aspect of sales, but also I have a Bachelor’s in Computer Science from Cornell, which allowed me to understand the intricacies of the product and communicate its benefits effectively. While it’s unfortunate that my time in Weyland was cut short due to company-wide layoffs, I am eager to bring my skills to AcmeCo’s sales team.

My background in sales, combined with my passion for technology and commitment to client success, makes me an ideal candidate for this role. I look forward to the opportunity to discuss how my skills align with AcmeCo’s goals, and I am available for an interview at your convenience. Thank you for considering my application.

Best, Jonathan Conner

How to write a cover letter: template

Here’s a cover letter template you can use to write your own cover letter. Simply plug in your information to the corresponding part. For more details, check out our guide on cover letter format.

[Your name] [Address] [Phone number] [Email]

[Today’s date]

[Recipient’s name] [Recipient’s professional title] [Company name] [Address]

[Salutation/greeting],

[Introduce yourself. Explain your profession, the position title you’re applying for, and how you heard about it. Briefly mention why this role and company interest you and why you’d be a good match. Show enthusiasm. End with a sentence that transitions or leads into the next paragraph.]

[Summarize your job history, focusing on relevant experience. Add extra context, such as what you learned from these jobs or why certain experiences prepared you for this role. Feel free to address problems with your résumé, like gaps or short tenures. Mention related skills and achievements and any quantifiable results or metrics.]

[Reiterate the main benefits of hiring you, including any soft skills or attributes that align with the company culture. Restate your enthusiasm, thank them for considering your application, and add a call-to-action to suggest a follow-up, such as scheduling an interview.]

[Simple sign-off], [Signature]

Cover letter formatting FAQs

How long should my cover letter be?

Usually, the length of a cover letter is less than a page, typically around three paragraphs. Cover letters work best when they’re short and straight to the point, so aim to only highlight your most relevant qualifications and enthusiasm for the role.

What font should I use for my cover letter?

For an email cover letter, the default font of your email app is typically acceptable. For print letters or email attachments, use a professional font like Times New Roman or Arial in 12-point size. Choose a font that’s easy to read and fits the company’s tone—Times New Roman is best for more formal settings, while Arial works well for casual environments.

Are cover letters single- or double-spaced?

Cover letters should be single-spaced, except when extra spaces are needed for formatting, such as after the greeting or around the date.

The post How to Format a Cover Letter: A Step-by-Step Guide for Job Seekers appeared first on Grammarly Blog.

While machine learning and deep learning are often used interchangeably, they refer to two sub-domains of artificial intelligence. They represent different (but related) approaches to data analysis; deep learning is a subset of machine learning. The distinctions between them are important and become especially relevant when communicating on technical topics—such as when evaluating data analysis tools and services, deciding which approaches to take to solving a data problem, or having deep conversations with engineers and domain experts.

Table of Contents:

• What is machine learning?
• What is deep learning?
• Machine learning vs deep learning
• Applications of ML and deep learning
• Benefits of ML and deep learning
• Challenges of ML and deep learning
• Conclusion

What is machine learning?

Machine learning (ML) is a subset of artificial intelligence (AI). The name refers to all systems where a machine (usually a computer or combination of computers) applies automatic mathematical and statistical techniques to discover or learn patterns from data.

ML aims to build systems that are intelligent and independent. That means it will learn patterns and improve on them from data, aiming to have few hard-coded components and reduced human interaction. The field of machine learning has existed since the 1960s, and there is a large body of algorithms and techniques that have been developed and studied since then.

Types of Machine Learning

Many different types of systems qualify as ML. These are the most common:

• Unsupervised learning models use unstructured data with little or no human guidance.
• Supervised learning models require guidance and depend on human input—including, for example, input on data format and structure.
• Semi-supervised models receive guidance from a small amount of structured data, then use insights from that data to refine their accuracy on a larger pool of unstructured data.
• Reinforcement models learn to make decisions by interacting with their environment. These models take actions, receive positive or negative feedback, then adjust behavior to achieve the desired goal.
• Self-supervised models create their own labels using raw, unstructured data.

What is deep learning?

Recent advances in ML have come primarily from a specialized subset of ML known as deep learning. Deep learning refers to the subset of ML systems that are implemented on top of a subset of neural networks that are called deep neural networks. Deep neural networks are neural networks that are large, heavily interconnected, and have many layers of neurons available for processing.

Types of deep learning networks

Like other advanced ML techniques, deep learning systems can learn in supervised or unsupervised ways. They can be built with just one or a combination of two or more advanced neural network architectures. Some networks, such as feed-forward neural networks (FNNs), only move data in one direction between neuron layers. In contrast, others, such as recurrent neural networks (RNNs), might form internal loops and, as a side effect, behave as if they have memory.

Advanced architectures such as convolutional neural networks (CNNs) structure how and when parts of the data are repeated in an NN’s input. This gives hints about where in the data the network should look for relationships.

Multiple neural networks can be developed in tandem, with each sub-network specializing in a subset of the problem space. For example, generative adversarial networks (GANs) tend to train models that try to compete with each other (one faking new data that should belong in a data set, and the other training to detect frauds), and two-tower architectures collaborate to learn about two deeply interconnected, but distinct, parts of a dataset.

More complex combinations of architectures are also commonly used together when building deep learning systems. These architectures can be extended using specialized neuron structures, such as transformers and rectifier units, or employed to emulate, implement, and enhance other machine learning systems, such as decision trees.

Machine learning vs. deep learning: key differences

Deep learning is a subset of machine learning and has some specific constraints and advantages built into it. Machine learning is a general term and covers a broader range of options and trade-offs for analyzing data and detecting patterns within it. This table highlights the differences. You’ll find more details below.

Deep learning depends on large data sets

Deep learning techniques often depend on having access to extremely large data sets, while ML systems can be useful when little to no data is available. Also, if skilled and specialized human input is available, ML can take advantage of it more explicitly than any deep learning system.

In general, ML systems cover a broader range of techniques and feature a more flexible range of implementation. Deep learning focuses exclusively on techniques suitable for working with large data sets, such as deep neural networks and their supporting algorithms.

ML can be easier to optimize and understand

With its much broader scope, ML covers many traditional and well-studied approaches to data processing, such as decision trees, clustering, and many flavors of regression. With decades devoted to their study, many of these approaches have well-known built-in performance and other trade-offs.

They offer more flexible implementations than neural-network-dependent deep learning systems and can be more resource- and cost-efficient. Deep learning is usually resource-heavy and sits at the high end of the cost scale.

Deep learning is more powerful and not as general

Deep learning systems are best for applications with a narrower scope and focus, for example, problems with large data troves of available related data, enough time for the lengthy training of a neural network, and when accuracy of execution is prioritized over the ability to trace exactly what the system is doing and why.

ML systems can be applied to the full range of problems where machines can automatically find and apply patterns in data, including ones where less data is available, where the systems are easy for humans to understand, and where high accuracy is less relevant.

Applications of ML and deep learning

ML-based and deep learning systems and applications are continuously embedded into more and more aspects of our lives. Here are some well-known examples below.

Spam detection

One of the earliest large-scale machine learning applications was to detect and filter spam email messages. The problem is an ideal one for applied machine learning.

There are large volumes of emails, and they have a well-defined structure. It’s easy to mark unwanted emails as spam, so it’s not difficult to create large data sets of emails marked as “spam” or “ham” (ham is the opposite of spam). Classification systems can be easily built on top of this data and then used to filter out spam emails at internet-scale quickly.

Spam detection is one example in which deep learning systems are not (yet) as well suited to solve as are more traditional ML techniques. Despite significant improvements, the time and cost required to keep deep learning systems up to date with the latest advancements in spam is not yet worth their higher accuracy. Deep learning systems can be used to optimize ML pipelines in general, and large-scale spam detection ML training systems may integrate them for that purpose.

Recommendation systems

E-commerce stores, media streaming services, and online marketplaces are just some of the examples of services that depend on being able to make recommendations about where users should spend their money. Recommendation systems are another typical example of a problem well suited to machine learning.

As users consume media and buy online, the underlying systems can build up large data sets with clear signals (the user consumed vs. the user didn’t consume). Both deep learning and more traditional ML techniques can be applied to this problem; large-scale recommender systems use clever combinations of both algorithm types.

Path-finding and self-driving cars

Traditional unsupervised ML algorithms built on well-known route-finding techniques, such as Dijkstra’s algorithm and the A* algorithm, are best suited for finding the best path between two points on a road map. These algorithms can study maps, traffic, and other data in advance, discover patterns, adjust in real-time based on real-world conditions, and work quite well.

When it comes to actually navigating a car between two points, though, the amount and complexity of information to be processed is much too high for any traditional ML technique to work with. Self-driving systems are almost exclusively built with deep-learning techniques.

Benefits of ML and deep learning

When used well, machine learning and specialized deep learning systems are transformative. They can augment human experts, making their output faster, more impactful, less expensive, higher quality, or a mix of all of the above.

Improved speed, scale, and cost with ML

ML systems can replace some or all of an expert’s work and processes, reducing the time and attention needed to complete a task. As a result, their work can be applied at a much higher scale than before.

For example, a team of technicians who evaluate MRI scans for abnormalities might each be able to evaluate six scans an hour, or around 200 a week. If the same team were to instead focus on training a set of machine learning algorithms to do the most routine parts of their analysis, the algorithms could evaluate thousands of MRI scans a week, at a fraction of the cost.

Higher impact and quality with deep learning

When applied to problems deep learning systems are well suited toward, they can augment systems that incorporate ML and increase their overall quality and impact.

Continuing the example above, deep learning systems might be applied to conditions with a large enough volume of MRI scans. If the volume of scans is sufficient, and after enough time and resources are devoted to building up the deep learning systems, they will likely do a better job than the experts can at identifying the narrow set of abnormalities they have been trained to identify.

These systems can then be deployed at scale for maximum impact, processing individual MRI scans at negligible costs. MRI technicians’ and other experts’ analyses can augment the deep learning systems’ output for unusual or exceptional cases, achieving even higher combined quality.

Challenges of ML and deep learning

While many kinds of work can benefit tremendously from applied ML or deep learning, incorporating AI like these into a system can be hard. Here are some of the most common challenges and obstacles that come up.

The trade-off between cost and accuracy

Larger and more expensive computer systems can run more advanced ML and deep learning algorithms faster and at a larger scale. As a result, there is a trade-off between how much money is spent on the system and how effective it is in terms of hardware and hiring more talented experts to assemble it. Effectively utilizing limited resources in ML and deep learning systems requires considerable care.

A dependency on large data sets

ML, in general, and deep learning specifically, depend on having access to continuously updated large data sets during their training phase. The algorithms are only as good as the quality and volume of data they are trained on. Managing large data sets effectively is difficult, and it takes time and ingenuity to apply ML most effectively to a given data set.

The trade-off between accuracy and clarity

Deep learning systems can be trained to be extremely accurate, much more so than other ML systems built with equivalent parameters. The accuracy comes at a cost; the systems manipulate data at a scale and use advanced algorithms that are impossible for humans to understand in a practical timeframe.

More traditional ML algorithms have been studied for much longer, have better-defined characteristics, and can be induced to work in ways that are easy for humans to understand. Any ML and deep learning implementation must find the ideal trade-off between accuracy and clarity.

The trade-off between technical bias and variance

As ML systems increase in the complexity of algorithms, the resources dedicated to training, and the amount of data used for training, they can learn more and more about the properties of their training data. This phenomenon is called (technical) bias; extremely biased systems will be very accurate when they see data similar to what they were trained on.

High bias often comes at the expense of too low a variance—the system won’t react much to new data that is very different from what it saw in training. Ideal systems, which are both low bias and low variance, are difficult to build. Finding the correct balance between bias and variance for a specific application is easier for better studied and more established traditional ML algorithms. It can be difficult to achieve with the more complex deep learning algorithms.

Conclusion

Deep learning systems are a specialized subset of ML that leverage deep, multilayered neural networks to tackle complex problems with large data sets. While they offer superior accuracy and processing capabilities, they come with trade-offs, such as reduced interpretability, reliance on extensive data, and limited optimization flexibility.

In contrast, traditional ML methods are often more cost-effective, easier to deploy, and provide more transparent and predictable outcomes. They are also simpler to fine-tune for specific tasks. Both approaches have distinct strengths and weaknesses, and understanding their applications and limitations is crucial for effective implementation in real-world scenarios.

The post Machine Learning vs. Deep Learning: Key Differences Explained appeared first on Grammarly Blog.

Classification is a core concept in data analysis and machine learning (ML). This guide explores what classification is and how it works, explains the difference between classification and regression, and covers types of tasks, algorithms, applications, advantages, and challenges.

Table of contents

• What is classification?
• Classification vs. regression
• Types of classification tasks in ML
• Algorithms used for classification analysis
• Applications of classification
• Advantages of classification
• Disadvantages of classification

What is classification in machine learning?

Classification is a supervised learning technique in machine learning that predicts the category (also called the class) of new data points based on input features. Classification algorithms use labeled data, where the correct category is known, to learn how to map features to specific categories. This process is also referred to as categorization or categorical classification.

To perform classification, algorithms operate in two key phases. During the training phase, the algorithm learns the relationship between input data and their corresponding labels or categories. Once trained, the model enters the inference phase, where it uses the learned patterns to classify new, unseen data in real-world applications. The effectiveness of classification largely depends on how these phases are handled and the quality of the preprocessed data available during training.

Understanding how classification algorithms manage these phases is essential. One key difference is how they approach learning. This leads us to two distinct strategies that classification algorithms may follow: lazy learning and eager learning.

Lazy learners vs. eager learners

Classification algorithms typically adopt one of two learning strategies: lazy learning or eager learning. These approaches differ fundamentally in how and when the model is built, affecting the algorithm’s flexibility, efficiency, and use cases. While both aim to classify data, they do so with contrasting methods that are suited to different types of tasks and environments.

Let’s examine the operations of lazy and eager learners to better understand each approach’s strengths and weaknesses.

Lazy learners

Also known as instance-based or memory-based learners, lazy learning algorithms store the training data and delay actual learning until a query needs to be classified. When one of these algorithms is put into operation, it compares new data points to the stored instances using a similarity measure. The quality and quantity of available data significantly influence the algorithm’s accuracy, with access to larger datasets typically improving their performance. Lazy learners often prioritize recent data, which is known as a recency bias. Because they learn in real time, they can be slower and more computationally expensive when responding to queries.

Lazy learners excel in dynamic environments where real-time decision-making is crucial, and the data is constantly evolving. These algorithms are well suited for tasks where new information continuously streams in, and there is no time for extensive training cycles between classification tasks.

Eager learners

Eager learning algorithms, in contrast, process all training data in advance, constructing a model before any classification tasks are performed. This upfront learning phase is typically more resource-intensive and complex, allowing the algorithm to uncover deeper relationships in the data. Once trained, eager learners do not need access to the original training data, making them highly efficient during the prediction phase. They can classify data quickly and handle large volumes of queries with minimal computational cost.

However, eager learners are less flexible in adapting to new, real-time data. Their resource-heavy training process limits the amount of data they can handle, making it difficult to integrate fresh information without retraining the entire model.

Later in this post, we will see how lazy and eager algorithms can be used in tandem for facial recognition.

Classification vs. regression: What’s the difference?

Now that we’ve explored how classification works, it’s important to distinguish it from another key supervised learning technique: regression.

Both classification and regression are used to make predictions based on labeled data from the training phase, but they differ in the type of predictions they generate.

Classification algorithms predict discrete, categorical outcomes. For example, in an email classification system, an email may be labeled as “spam” or “ham” (where “ham” refers to non-spam emails). Similarly, a weather classification model might predict “yes,” “no,” or “maybe” in response to the question “Will it rain tomorrow?”

Regression algorithms, on the other hand, predict continuous values. Rather than assigning data to categories, regression models estimate numerical outputs. For instance, in an email system, a regression model might predict the probability (e.g., 70%) that an email is spam. For a weather prediction model, it could predict the expected volume of rainfall, such as 2 inches of rain.

While classification and regression serve different purposes, they are sometimes used together. For instance, regression might estimate probabilities that feed into a classification system, enhancing the accuracy and granularity of predictions.

Types of classification tasks in ML

Classification tasks vary, each tailored for specific data types and challenges. Depending on the complexity of your task and the nature of the categories, you can employ different methods: binary, multiclass, multilabel, or imbalanced classification. Let’s delve deeper into each approach below.

Binary classification

Binary classification is a fundamental task that sorts data into two categories, such as true/false or yes/no. It is widely researched and applied in fields like fraud detection, sentiment analysis, medical diagnosis, and spam filtering. While binary classification deals with two classes, more complex categorization can be handled by breaking the problem down into multiple binary tasks. For example, to classify data into “apples,” “oranges,” “bananas,” and “other,” separate binary classifiers could be used to answer “Is it an apple?,” “Is it an orange?,” and “Is it a banana?”

Multiclass classification

Multiclass classification, also known as multinomial classification, is designed for tasks where data is classified into three or more categories. Unlike models that decompose the problem into multiple binary classification tasks, multiclass algorithms are built to handle such scenarios more efficiently. These algorithms are typically more complex, require larger datasets, and are more resource-intensive to set up than binary systems, but they often provide better performance once implemented.

Multilabel classification

Multilabel classification, also known as multi-output classification, assigns more than one label to a given piece of data. It is often confused with multiclass classification, where each instance is assigned only one label from multiple categories.

To clarify the difference: A binary classification algorithm could sort images into two categories—images with fruit and images without fruit. A multiclass system could then classify the fruit images into specific categories like bananas, apples, or oranges. Multilabel classification, on the other hand, would allow for assigning multiple labels to a single image. For example, a single image could be classified as both “fruit” and “banana,” and the fruit could also be labeled “ripe” or “not ripe.” This enables the system to account for multiple independent characteristics simultaneously, such as (“no fruit,” “no banana,” “nothing is ripe”), (“fruit,” “banana,” “ripe”, or (“fruit,” “banana,” “nothing is ripe”).

Imbalanced classification

Frequently, the data that’s available for training doesn’t represent the distribution of data seen in reality. For example, an algorithm might only have access to 100 users’ data during training, where 50% of them make a purchase (when in reality, only 10% of users make a purchase). Imbalanced classification algorithms address this problem during learning by using oversampling (reusing some portions of training data) and undersampling (underusing some portions of training data) techniques. Doing so causes the learning algorithm to learn that a subset of the data occurs a lot more or less frequently in reality than it does in the training data. These techniques are usually a kind of training optimization since they allow the system to learn from significantly less data than it would take to learn otherwise.

Sometimes accumulating enough data to reflect reality is difficult or time-consuming, and this type of optimization can allow models to be trained sooner. Other times, the amount of data is so large that classification algorithms take too long to train on it all, and imbalanced algorithms allow them to be trained anyway.

Algorithms used for classification analysis

Classification algorithms are well studied, and no single form of classification has been found to be universally appropriate for all situations. As a result, there are large toolkits of well-known classification algorithms. Below, we describe some of the most common ones.

Linear predictors

Linear predictors refer to algorithms that predict outcomes based on linear combinations of input features. These methods are widely used in classification tasks because they are straightforward and effective.

Logistic regression

Logistic regression is one of the most commonly used linear predictors, particularly in binary classification. It calculates the probability of an outcome based on observed variables using a logistic (or sigmoid) function. The class with the highest probability is selected as the predicted outcome, provided it exceeds a confidence threshold. If no outcome meets this threshold, the result may be marked as “unsure” or “undecided.”

Linear regression

Linear regression usually is used for regression use cases, and it outputs continuous values. However, values can be repurposed for classification by adding filters or maps to convert their outputs to classes. If, for example, you’ve already trained a linear regression model that outputs rain volume predictions, the same model can become a “rainy day”/”not rainy day” binary classifier by arbitrarily setting a threshold. By default, it’s only the sign of the regression result that’s used when converting models to binary classifiers (0 and positive numbers are mapped to the “yes” answer or “+1”, and negative numbers to the “no” answer or “-1”). Maps can be more complex and tuned to the use case, though. For instance, you might decide that any prediction above five ml of rain will be considered a “rainy day,” and anything below that will predict the opposite.

Discriminant analysis

Linear discriminant analysis (LDA) is another important linear predictor used for classification. LDA works by finding linear combinations of features that best separate different classes. It assumes that the observations are independent and normally distributed. While LDA is often employed for dimensionality reduction, it is also a powerful classification tool that assigns observations to classes using discriminant functions—functions that measure the differences between classes.

Bayesian classification

Bayesian classification algorithms use Bayes’ theorem to calculate the posterior probability of each class given the observed data. These algorithms assume certain statistical properties of the data, and their performance depends on how well these assumptions hold. Naive Bayes, for example, assumes that features are conditionally independent given the class.

k-NN classification

The k-nearest neighbor (k-NN) algorithm is another widely used classification method. Although it can be applied to both regression and classification tasks, it is most commonly used for classification. The algorithm assigns a class to a new data point based on the classes of its k nearest neighbors (where k is a variable), using a distance calculation to determine proximity. The k-NN algorithm is simple, efficient, and effective when there is local structure in the data. Its performance depends on selecting an appropriate distance metric and ensuring the data has local patterns that can aid in classification

Decision trees and random forests

Decision trees are a popular algorithm used for classification tasks. They work by recursively splitting the data based on feature values to make a decision about which class a given observation belongs to. However, decision trees tend to overfit the training data, capturing noise and leading to high variance. This overfitting results in poor generalization to new data.

To mitigate overfitting, random forests are used as an ensemble method. A random forest trains multiple decision trees in parallel on random subsets of the data, and each tree makes its own prediction. The final prediction is made by aggregating the predictions of all the trees, typically through majority voting. This process, known as “bagging” (a shortened word for bootstrap aggregation), reduces variance and improves the model’s ability to generalize to unseen data. Random forests are effective in balancing bias and variance, making them a robust off-the-shelf algorithm for classification tasks.

Applications of classification

Classification algorithms are widely used in various fields to solve real-world problems by categorizing data into predefined groups. Below are some common applications of classification, including facial recognition, document classification, and customer behavior prediction.

Facial recognition

Facial recognition systems match a face in a video or photo in real time against a database of known faces. They are commonly used for authentication.

A phone unlock system, for example, would start by using a facial detection system, which takes low-resolution images from the face-directed camera every few seconds, and then infers whether a face is in the image. The facial detection system could be a well-trained, eager binary classifier that answers the question “Is there a face present or not?”

A lazy classifier would follow the eager “Is there a face?” classifier. It would use all the photos and selfies of the phone owner to implement a separate binary classification task and answer the question “Does this face belong to a person who is allowed to unlock the phone?” If the answer is yes, the phone will unlock; if the answer is no, it won’t.

Document classification

Document classification is a crucial part of modern data management strategies. ML-based classifiers catalog and classify large numbers of stored documents, supporting indexing and search efforts that make the documents and their contents more useful.

The document classification work begins with the preprocessing of the documents. Their contents are analyzed and transformed into numerical representations (since numbers are easier to process). Important document features, such as mathematical equations, embedded images, and the language of the document, are extracted from the documents and highlighted for the ML algorithms to learn. This is followed by other similar processing tasks in the same vein.

A subset of the documents is then classified by hand, by humans, to create a training dataset for classification systems. Once trained, a classifier will catalog and classify all incoming documents rapidly and at scale. If any classification errors are detected, manual corrections can be added into the training materials for the ML system. Every once in a while, the classifier model can be retrained with the corrections added in, and its performance will be improved.

Customer behavior prediction

Online retail and e-commerce shops collect fine-grained and detailed information about their customers’ behavior. This information can be used to categorize new customers and answer such questions as “Is this new customer likely to make a purchase?” and “Will offering a 25% discount influence this customer’s buying behavior?”

The classifier is trained using data from previous customers and their eventual behavior, such as whether they made a purchase. As new customers interact with the platform, the model can predict whether and when they will make a purchase. It can also perform what-if analysis to answer questions like “If I offer this user a 25% discount, will they make a purchase?”

Advantages of classification

Classification offers several benefits in the machine learning domain, making it a widely used approach for solving data categorization problems. Below, we explore some of the key advantages of classification, including its maturity, flexibility, and ability to provide human-readable output.

Well-studied and understood

Classification is one of the most well-studied and understood problems in the machine learning domain. As a result, there are many mature toolkits available for classification tasks, allowing users to balance trade-offs between speed, efficiency, resource usage, and data quality requirements.

Standard techniques, such as accuracy, precision, recall, and confusion matrices, are available to evaluate a classifier’s performance. With these tools, it can be relatively straightforward to choose the most appropriate classification system for a given problem, assess its performance, and improve it over time.

Provide human-readable output

Classifiers often allow a trade-off between predictive power and human readability. Simpler, more interpretable models, such as decision trees or logistic regression, can be tuned to make their behavior easier to understand. These interpretable models can be used to explore data properties, enabling human users to gain insights into the data. Such insights can then guide the development of more complex and accurate machine learning models.

Disadvantages of classification

While classification is a powerful tool in machine learning, it does come with certain challenges and limitations. Below, we discuss some of the key disadvantages of classification, including overfitting, underfitting, and the need for extensive preprocessing of training data.

Overfitting

When training classification models, it’s important to tune the training process to reduce the chances that the model will overfit its data. Overfitting is a problem where a model memorizes some or all of its source data, instead of developing an abstract understanding of the relationships in the data. A model that has overfit the training data will work well when it sees new data that closely resembles the data it was trained on, but it may not work as well in general.

Underfitting

Classification systems’ performance depends on having sufficient amounts of training data available, and on being applied to problems that work well for the chosen classification algorithms. If not enough training data is available, or if a specific classification algorithm doesn’t have the right tools to interpret the data correctly, the trained model might never learn to make good predictions. This phenomenon is known as “underfitting.” There are many techniques available to try to mitigate underfitting, and applying them correctly is not always easy.

Preprocessing of training data

Many classification systems have relatively rigid requirements for data structure and formatting. Their performance is often closely correlated with how well the data has been processed before they are exposed to it or trained on it. As a result, classification systems can be rigid and inflexible, having strict boundaries around which problems and data contexts they are best suited to.

The post Classification in Machine Learning: What It Is and How It Works appeared first on Grammarly Blog.

What is a cover letter and why is it essential in today’s job market? A well-written cover letter can be the key to landing your dream job, giving you the opportunity to introduce yourself, highlight your qualifications, and make a memorable first impression. But knowing what to talk about and how to phrase it isn’t always obvious. In this guide, we’ll explain everything you need to know about cover letters: what they are, the different types, and what employers look for.

Table of contents

• What is a cover letter?
• Ideal cover letter length
• Types of cover letters
• Tips for writing strong cover letters
• Cover letters FAQ

What is a cover letter?

A cover letter is a brief document accompanying your résumé or CV that provides additional context about your interest in the role and the company, and highlights relevant qualifications and experiences. Unlike résumés, cover letters allow you to explain gaps in employment, highlight specific achievements, and showcase your personality.

Cover letters also allow hiring managers to get a sense of your communication style. Details such as typos, word choice, and tone can influence an employer’s decision. Hiring managers also use cover letters to determine whether a new hire would likely be a good cultural fit.

Because it can be challenging to write about yourself, using AI can be a helpful way to get started. Our free AI cover letter generator can help you come up with a first draft, which you can then personalize and refine to reflect your unique style.

Cover letter vs. résumé

You might wonder, “Why write a cover letter when my résumé already has everything they need to know?”

Unlike résumés, cover letters are personal, allowing you to show your communication style and personality. While résumés are essential for listing your qualifications, they can feel a bit impersonal. A cover letter offers a chance to connect on a human level, giving hiring managers a sense of who you are beyond the data.

Cover letters also let you add valuable context to your résumé. For instance, you can briefly explain employment gaps or short job tenures, which can help ease any concerns and show transparency. There’s no need to go into detail; a simple, honest explanation can make a big difference. Cover letters are also a great place to share relevant passions or personal connections to the role—things that don’t always fit in a résumé but could set you apart.

How long should a cover letter be?

A typical cover letter is around three or four paragraphs, ideally under 500 words. Like a résumé, it should convey essential information, not a detailed story. Cover letters should be concise and succinct, highlighting the key points while leaving out unnecessary details.

Types of cover letters

Application cover letter

The most common type, an application cover letter, is a brief letter or email that accompanies your résumé. This type of cover letter is typically used in response to a specific job posting or advertisement. When people talk about “cover letters,” they’re usually talking about this type.

Application cover letters are best suited to job postings and advertised roles. They are sometimes listed as a requirement in a job posting, but sometimes employers just assume you’ll include one. However, if you’re sending a letter to a company that was not a response to a job post, you’ll need a different type of cover letter.

Letter of interest

A letter of interest, also known as a cold-call letter or prospecting cover letter, is a cover letter that was not solicited. You’re writing out of the blue and not in response to a job posting. Unlike application cover letters, which follow more standardized guidelines, letters of interest should be highly customized to the recipient and the organization.

In a letter of interest, include a brief overview of your job history, as you would in a standard cover letter, but also focus on why you’re specifically drawn to this company and the types of roles you’re seeking. Approach the letter with respect and professionalism, as you’re introducing yourself without a formal invitation.

Letters of interest are often passion projects, written for companies you want to work for even if they’re not explicitly hiring. While letters of interest have a somewhat low success rate for job placement, you’d be surprised how much a little initiative and enthusiasm impresses some employers.

Networking cover letter

A networking cover letter is like a blend of an application cover letter and a letter of interest. Networking cover letters are addressed to someone specific within a company or industry, usually a contact shared by a colleague (hence the name, networking cover letter). They’re not necessarily solicited like an application cover letter, but they’re not out of the blue like a letter of interest.

When writing a networking cover letter, aim to make a strong impression. While a mutual connection can provide an initial advantage, you’ll still need to highlight your relevant skills and explain why you’d be a valuable addition to the team. Networking cover letters provide a unique opportunity to leverage personal connections while showcasing your professional strengths.

Key elements to a successful cover letter

What should a good cover letter include? A successful cover letter has several key elements: clear contact information, a professional greeting, a strong opening, a compelling body, and a confident closing. Additionally, successful cover letters make a connection with the hiring manager. Below, we’ll walk you through each section to help you create a polished, impactful cover letter. For more details, check out our full guide on how to write a cover letter.

Contact information

If you’re writing a print cover letter, include your contact information at the top left corner. Start with your name, address, phone number, email, and the date, followed by the recipient’s name and contact details. For an email cover letter, you can skip the header, but be sure to include your phone number and email address below your signature.

Greeting

Cover letter openers should be professional and respectful. Address the recipient by their full name and title, such as “Dear Ms. Williams.” Avoid casual greetings like “Hey” or using only their first name. Ending the greeting with a comma is typical.

Opening paragraph

The opening paragraph of a cover letter introduces you to the reader and should include a compelling “hook” that sets you apart. This is also an ideal place to say why you’re interested in a particular position or company before you get into your job history. If you have a certain passion for this type of work, mention that too—enthusiasm and genuine interest make a strong first impression.

Body paragraph

In the body paragraph, outline your work history and relevant experience in a way that brings your résumé to life. Share a few background details, such as why you chose certain roles, what you enjoyed, and key skills you developed. This is also a good place to address any résumé gaps or short tenures by offering brief, positive explanations. Feel free to include additional skills or achievements that are relevant to the job but may not fit in your résumé.

Closing paragraph

Like all good conclusions, ending a cover letter should summarize your key points and leave a lasting impression. This is a good opportunity to remind the hiring manager of the most important aspects that set you apart as a candidate.

Cover letters usually include some kind of call-to-action to incite hiring managers to choose you. For example, you can say something confident like, “I look forward to the opportunity to discuss my application further.”

Personal connection with the hiring manager

Don’t underestimate the appeal of personality in job applications. Employers are often looking for people who fit in with the current team or company culture, even if there are more technically suitable candidates.

Let your personality shine through in your cover letter. You should still maintain a proper degree of formality and professionalism, but it’s OK to mention details about yourself that help people understand what you’re about. In fact, these seemingly minor details are sometimes what influences employers to ultimately hire you.

How to write a cover letter: example

Dear AcmeCo,

As a skilled sales representative in the tech and electronics industry, I am excited to apply for the Sales Associate role listed on LinkedIn. AcmeCo’s commitment to groundbreaking AI innovation has always impressed me, and I am eager to bring my expertise in strategic sales and customer-centric service to a company I’ve followed and admired for years. With a proven track record of exceeding targets and building strong client relationships, I am confident I can contribute to your team’s continued success.

In my previous role at Boltvern, I built a loyal customer base and increased client retention by 25% by prioritizing tailored solutions and consistent follow-up. I was their youngest employee to be promoted to Senior Sales Representative in just under two years. Not only do I understand the client-side aspect of sales, but also I have a Bachelor’s in Computer Science from Cornell, which allowed me to understand the intricacies of the product and communicate its benefits effectively. While it’s unfortunate that my time in Weyland was cut short due to company-wide layoffs, I am eager to bring my skills to AcmeCo’s sales team.

My background in sales, combined with my passion for technology and commitment to client success, makes me an ideal candidate for this role. I look forward to the opportunity to discuss how my skills align with AcmeCo’s goals, and I am available for an interview at your convenience. Thank you for considering my application.

Best, Jonathan Conner

How to write a cover letter: template

Here’s a cover letter template you can use to write your own cover letter. Simply plug in your information to the corresponding part. For more details, check out our guide on cover letter formatting.

[Your name] [Address] [Phone number] [Email]

[Today’s date]

[Recipient’s name] [Recipient’s professional title] [Company name] [Address]

[Salutation/greeting],

[Introduce yourself. Explain your profession, the position title you’re applying for, and how you heard about it. Briefly mention why this role and company interest you and why you’d be a good match. Show enthusiasm. End with a sentence that transitions or leads into the next paragraph.]

[Summarize your job history, focusing on relevant experience. Add extra context, such as what you learned from these jobs or why certain experiences prepared you for this role. Feel free to address problems with your résumé, like gaps or short tenures. Mention related skills and achievements and any quantifiable results or metrics.]

[Reiterate the main benefits of hiring you, including any soft skills or attributes that align with the company culture. Restate your enthusiasm, thank them for considering your application, and add a call-to-action to suggest a follow-up, such as scheduling an interview.]

[Simple sign-off], [Signature]

Cover letter FAQ

What is a cover letter?

A cover letter is a brief document sent with your résumé or CV to provide additional information about your background, motivations, and interest in the role. Cover letters allow you to share context that doesn’t fit in a résumé, such as your reasons for applying or unique qualifications. Hiring managers also use cover letters to assess communication skills and get a sense of your personality.

What’s the best way to address employment gaps in a cover letter?

You don’t need to focus too much on employment gaps, but cover letters offer a good place to briefly address them. A simple, honest explanation can prevent assumptions and reassure employers. For example, mentioning a career break for family or education can provide helpful context without going into detail.

What do employers look for in a cover letter?

Cover letters give you an opportunity to showcase your personality and communication style in a way that résumés can’t. Employers use cover letters to determine someone’s personality and whether they’re a good fit for that team and company culture—so don’t be afraid to let your unique qualities and enthusiasm shine through.

The post What Is a Cover Letter? Everything You Need to Know appeared first on Grammarly Blog.

Taking a well-earned vacation from work is meant to allow employees to decompress from the stress of their jobs and return recharged. However, some workers experience a common phenomenon called post-holiday blues, or post-vacation depression, after taking paid time off (PTO).

Although post-vacation depression isn’t a clinical disorder, feeling stressed and overwhelmed when going back to work after vacation is a sentiment that professionals widely experience. A Tripadvisor survey found that 34 percent of 1,400 U.S. respondents said that returning from a leisurely trip led to feelings of melancholy.

Addressing tasks and emails that you might have accumulated while you were away can feel exhausting. You might have lowered productivity as you reacclimate to your work duties and everyday routine.

Avoiding overwhelm can be possible with a few strategies, like delegating tasks while you’re away and setting realistic expectations for yourself leading up to, during, and after your vacation.

Why returning from vacation can be overwhelming

There are a handful of reasons you might be struggling with anxiety, fatigue, listlessness, or irritability when returning from your time off.

• The accumulation of work: Although you might have hit pause on your work duties, your company’s day-to-day operations must continue. If your oversight or approval is an integral part of the process, you might have tasks with tight deadlines that piled up.
• Feeling disconnected from projects: Not being apprised of work updates, like pending deliverables or issues while you’re out of the office, can result in feeling out of sync or disjointed from projects and your team when you come back.
• The pressure to catch up quickly: A desire to maintain strong work performance—despite the days missed for deserved time off—can put pressure on professionals to “make up” for perceived loss of productivity.

9 strategies for avoiding overwhelm when returning from vacation

1 Ease back into work gradually.

Gradually get back into your work rhythm after a vacation. If you have enough PTO, you might even take an extra day off before returning to work to fully wind down from the whirlwind of traveling. Some professionals call this “taking a vacation from your vacation.”

When you return to work, preemptively keep your first day back clear of meetings or strenuous projects. You’ll likely need the day to reorient yourself to your typical routine and determine which tasks require your attention.

2 Prioritize and organize tasks.

Use a prioritization method, like the Eisenhower Matrix, to help you organize which tasks you’ll tackle first when coming back to work after vacation. The Eisenhower Matrix divides tasks into four quadrants:

• Quadrant 1: Do – Tasks that are urgent and important, like if a key client is experiencing a payment processing bug that’s preventing them from making online sales.
• Quadrant 2: Decide – Tasks that are not urgent but are important, like writing annual performance reviews for the employees who report to you.
• Quadrant 3: Delegate – Tasks or requests that are urgent but less important. Often, these tasks can or should be performed by someone else. A colleague might request details for a project one of your team members leads.
• Quadrant 4: Delete – This quadrant identifies tasks and asks that are neither urgent nor important so you can avoid time-suck pitfalls. This might include meticulously reading unimportant emails and attending meetings that don’t require your attention.

3 Set realistic expectations for yourself.

It’s impractical to expect you’ll get seven days’ worth of work and emails on your first day back from a weeklong vacation. Set yourself up for success by establishing realistic expectations when you return to work.

Consider sharing these expectations with your manager. Ensuring that you’re both aligned about what a realistic return-to-office workload looks like can ease some of the pressure you put on yourself.

4 Use technology to help manage your workflow.

Depending on your line of work, you might have access to project management tools and software designed to streamline your tasks. Programs like Trello, Asana, and even simple digital calendar to-do lists can help you stay on top of overlapping project timelines and action items.

Many of these tools also have reminder and automation features, so key tasks don’t fall through the cracks.

Managing email overload after vacation

5 Tackle emails strategically.

Determine which emails need to be prioritized. This might include emails about high-impact projects that urgently require your approval or deliverables, or emails from VIP clients that are integral to the business.

Filter out messages that don’t require a response or your immediate attention (for example, newsletters or emails you were copied on as an FYI).

6 Use templates for quick responses

You might receive recurring messages in your inbox that typically require the same information in your response. Create a custom email template specifically for this type of message.

7 Flag and schedule emails to address later

Use built-in email organization features, like flags, folders, and reply reminders. They let you declutter your inbox and offer distinct buckets about the types of actions each message requires.

For example, you might identify a red flag as an email that requires an immediate response, while a starred tag is used for emails that require a detailed reply that you’ll address later in the week.

How to avoid future overwhelm before your next vacation

8 Create a coverage plan.

Coming up with a coverage plan while you’re on vacation goes a long way in avoiding a task backlog to contend with when you return. Identify responsibilities that others on your team can take off your plate and determine the best candidate for each task.

Hand off project details they should be aware of, including deadlines, assets, and key stakeholders they might need to collaborate with.

If you need guidance with your coverage plan, speak to your manager. They may offer coverage suggestions to keep projects moving forward while you’re gone and to to help you avoid burnout after your trip.

9 Communicate boundaries with clients and teams.

Set expectations with your team, vendors, and clients in advance. Let them know your PTO dates, when you’re expected to return, and the next-best person to reach out to with questions, along with their contact information.

Setting up an automated out-of-office email message can help clearly communicate details about your availability. For example, be transparent if you expect to completely disconnect from work communications while on vacation. Mention that you might be a bit slow to respond to messages upon your return but will reply as soon as you’re able.

Quick takeaways

• Identify colleagues to whom you can delegate essential tasks while you’re on PTO.
• Prioritize your tasks, focusing on the most urgent items first.
• Don’t overextend yourself on your first day back; expect to spend that day getting up to speed on updates and projects.
• Take advantage of email organization features, like setting up filters, flagging important messages, and creating reminders.
• Establish realistic expectations about your post-vacation productivity—you likely won’t get to everything that’s piled up on your first day back!

Conclusion

You might still feel lingering pangs of post-vacation blues, but preemptive planning and leveraging time-saving tools can make the transition back to work smoother.

If you have an upcoming vacation planned, consider getting a head start with some of these strategies as soon as you request PTO from human resources or your manager. Taking these meaningful steps early can help you avoid burnout after your return and will go a long way toward maintaining a healthier work-life balance.

Vacation Overwhelm FAQs

How can I manage email overload after PTO?

Prioritize the urgent and important emails that are the unexpected “fires” that need to be addressed immediately to avoid larger consequences later. Afterward, add a time-block to your schedule to devote to less urgent emails.

Should I work extra hours to catch up after PTO?

Working extra hours after taking PTO counteracts the purpose of going on vacation. Instead of working extra hours, consider creating a realistic roadmap for scaling back to a pre-vacation workload.

What should I do if I feel overwhelmed on my first day back?

Expect to use your first day back to regain your bearings at work. Focus on one high-priority task at a time, and take your required breaks to get your work rhythm back gradually.

How can I ensure that my team covers my work during PTO?

Communicate with your colleagues about project statuses, tasks, due dates, and other key details that you’re handing off. To avoid workplace miscommunication, send each person a follow-up email about what you discussed and your delegated tasks.

What’s the best way to prevent overwhelm in the future?

The best way to avoid feeling overwhelmed in the future is by planning and collaborating with teammates about delegated tasks well in advance and setting expectations early on about when you can realistically respond to questions after your PTO.

The post 9 Tips to Avoid Feeling Overwhelmed After Vacation appeared first on Grammarly Blog.

Getting your dream job isn’t easy, but a well-crafted cover letter can be exactly what gets you hired. If you know how to write a cover letter, you can demonstrate your aptitude and personality and forge a connection with the hiring manager—things a résumé or CV can’t do on their own.

Below we explain in detail how to write a cover letter. We share the key parts of a well-written cover letter and discuss how to write each section to “wow” hiring managers and leave a lasting impression.

Table of contents

• What is a cover letter?
• Preparing to write a cover letter
• How to write a cover letter
• Cover letter example
• Cover letter template
• How to write a cover letter FAQ

What is a cover letter?

A cover letter is a document sent to hiring managers that describes your work history, professional skills, education, and other data pertinent to your career while highlighting why you’re a strong fit for the specific role and company. It is usually sent together with a résumé or CV as a way of demonstrating your personality and explaining the reasons why you are the right candidate for the position. You can read more information in our guide What Is a Cover Letter?

Why do employers ask for cover letters?

While cover letters repeat some of the information on your résumé, they also add context and additional details that give hiring managers a fuller idea of who you are. A well-written cover letter reveals both your personality and your communication skills, important factors for getting a new job.

Hiring managers like cover letters because they can see more than just your fact sheet. Cover letters also give job applicants the opportunity to mention details that don’t fit in a résumé, such as motivations for applying, a connection to the company, or whether your values and work style align with the company culture.

Preparing to write a cover letter

Before you begin to write your cover letter, prepare what you’re going to say. That way you can be sure to include everything you want without having to worry about it while you write.

For starters, make sure your résumé/CV is up-to-date. You’ll be repeating some of this information, so it’s good to have it handy as a reference. You also don’t want any contradiction between your cover letter and résumé.

From there, consider what aspects of yourself and your professional experience make you a valuable candidate for the job. Is it your education? Your work experience? Do you have a personal connection with this line of work? You can use cover letters to stand out from the other applicants, especially if your résumé alone isn’t enough.

You should find a way to mention your best qualities in a cover letter if they’re relevant to the job. While you can figure out exactly when to mention them later, having a list of these qualities can save you time during the writing process.

Moreover, we recommend researching the company and its culture before writing a cover letter. Take a look at its website, social media, and any mentions in the news to understand what it’s looking for. This will help you choose the appropriate tone and give you ideas about what to mention.

Once you know what you want to say, it’s time to get started on the actual writing. If the thought of writing from scratch is overwhelming, consider using an AI cover letter generator for the first draft and then editing it to fit your writing style. While AI cover letters may sound … well, like AI … using an AI tool for a first draft can give you something to build from, which is very useful when you’re just getting started. It doesn’t have to be complicated; you can use our free AI cover letter generator to generate something in just a few clicks.

How to write a cover letter

Ideally, cover letters should be about three full-sized paragraphs, although it’s acceptable to add smaller paragraphs as an introduction or conclusion. Still, three paragraphs is all you need, and cover letters benefit from being brief.

However, cover letters also have special conventions for headers, greetings, and sign-offs, some of which are not always self-evident. Below, we explain the best practices for writing each, along with what to put in those three paragraphs.

Header and contact information

If you’re writing a formal cover letter, the header is a big deal. Traditionally, the header of a cover letter has your contact information, the date, and the employer’s contact information in the upper-left corner of the page.

To be specific, write your name, address, phone number, and email first. Leave an empty line, and then write the date, spelling out the full month. Leave another empty line, and then write your employer’s name, title, company, and/or address. Add one more empty line, and then begin your letter with the greeting. We explain more details in our guide on cover letter format.

This formal practice is only for printed cover letters or file attachments, however. If you’re writing a cover letter as an email, you can forget the header and begin directly with the greeting.

Greeting or salutation

Use the name of the hiring manager if you know it, along with any honorifics like “Mrs.” or “Dr.” A simple “Dear … ” at the beginning is fine. Use a comma at the end, and then leave an empty line before continuing your cover letter.

Dear Dr. Alfonso Pepper,

Dear Mr. Deere,

If, however, you don’t know the recipient’s name, you have a few options. The formal option is to open with To Whom It May Concern, but more casual alternatives include Dear Hiring Manager, Dear Sir or Madam, and Dear [Company] Recruiter.

Opening paragraph

The opening paragraph of your cover letter has to hook the reader. Most likely, the hiring manager is reading dozens of cover letters, sometimes one after another, so your cover letter opener only has a limited window to stand out from the crowd.

You can demonstrate both your enthusiasm and your capabilities by simply mentioning the reason you want this position. Employers prefer candidates who want to work with them, so take some time to explain why you’re passionate about this particular role.

Include your best highlights to make a good first impression: mention an accomplishment, personal statistic, or how hiring you could solve an existing problem for the company. If you can’t think of anything, talk about your vision for a future working there, or why you’d be the perfect fit for this particular company.

Also, state the position you’re applying for and where you heard about the job. The opening paragraph should end with a lead-in or transition to the next paragraph, where you discuss your work history and job expertise.

Body paragraph

Your second paragraph is the substance of a cover letter, the filling of a sandwich. Here is where you present your work history, achievements, skills, and any other benefits of hiring you that didn’t fit in the first paragraph. For this, data and statistics work well; state how you benefited other companies with quantifiable results, such as “increased sales by 15%” or “maintained an error rate of 0.1%.”

For the most part, body paragraphs repeat the information of your résumé/CV, but they also add context. You can explain, in your own words, what you learned at a previous job or why you don’t work there anymore.

You should tailor your writing to the company and the position. Try to match the style and voice of the initial job posting, or if that’s unavailable the website text. Also, consider using keywords and the language from the company’s job post; if it mentions being “punctual” and “diligent” in the post, use those words in the cover letter to describe yourself. Some companies use automatic keyword scans to search through applicants, so this strategy can help you get noticed as well.

Closing paragraph

The rules for how to end cover letters follow traditional story structure. The closing should reiterate the main points and end in a memorable way, like other conclusions.

Most closing paragraphs include a call to action, a statement that urges the reader to do something, like “click here” or “buy now.” The call to action for cover letters usually revolves around some kind of follow-up, such as requesting an interview.

Thanking the company for the opportunity to apply is an old-fashioned convention, but it works with formal or traditional applications. If you can, restate your enthusiasm in the closing paragraph, just to remind them.

Sign-off

Cover letters use professional sign-offs, often a single word followed by the sender’s signature. Standard sign-offs include:

• Regards,
• Best,
• Sincerely,

How to write a cover letter: example

Dear AcmeCo,

As a skilled sales representative in the tech and electronics industry, I am excited to apply for the sales associate role listed on LinkedIn. AcmeCo’s commitment to groundbreaking AI innovation has always impressed me, and I am eager to bring my expertise in strategic sales and customer-centric service to a company I’ve followed and admired for years. With a proven track record of exceeding targets and building strong client relationships, I am confident I can contribute to your team’s continued success.

In my previous role at Boltvern, I built a loyal customer base and increased client retention by 25% by prioritizing tailored solutions and consistent follow-up. I was their youngest employee to be promoted to Senior Sales Representative in just under two years. Not only do I understand the client-side aspect of sales, but also I have a Bachelor’s in Computer Science from Cornell, which allowed me to understand the intricacies of the product and communicate its benefits effectively. While it’s unfortunate that my time in Weyland was cut short due to company-wide layoffs, I am eager to bring my skills to AcmeCo’s sales team.

My background in sales, combined with my passion for technology and commitment to client success, makes me an ideal candidate for this role. I look forward to the opportunity to discuss how my skills align with AcmeCo’s goals, and I am available for an interview at your convenience. Thank you for considering my application.

Best, Jonathan Conner

How to write a cover letter: template

Here’s a cover letter template you can use to write your own cover letter. Simply plug in your information to the corresponding part. For more details, check out our guide on cover letter format.

[Your name] [Address] [Phone number] [Email]

[Today’s date]

[Recipient’s name] [Recipient’s professional title] [Company name] [Address]

[Salutation/greeting],

[Introduce yourself. Explain your profession, the position title you’re applying for, and how you heard about it. Briefly mention why this role and company interest you and why you’d be a good match. Show enthusiasm. End with a sentence that transitions or leads into the next paragraph.]

[Summarize your job history, focusing on relevant experience. Add extra context, such as what you learned from these jobs or why certain experiences prepared you for this role. Feel free to address problems with your résumé, like gaps or short tenures. Mention related skills and achievements and any quantifiable results or metrics.]

[Reiterate the main benefits of hiring you, including any soft skills or attributes that align with the company culture. Restate your enthusiasm, thank them for considering your application, and add a call to action to suggest a follow-up, such as scheduling an interview.]

[Simple sign-off], [Signature]

Key takeaways for how to write a cover letter

• A cover letter complements your résumé by showcasing your personality and enthusiasm.
• Keep the letter concise—ideally three paragraphs on a single page.
• Tailor the content to the company and show a connection to its work culture.
• Repeat keywords and specific qualities mentioned in the job description.
• End with a polite call to action, such as requesting an interview.

How to write a cover letter FAQ

How do I write a cover letter?

Cover letters fit well into a three-paragraph structure: an opening paragraph that introduces yourself in a way that stands out, a middle paragraph that presents your job history and professional skill, and a closing paragraph that recaps the information and requests a follow-up. Pay attention to specific details on formatting, too, such as what to include in the header and which salutation or sign-off to use.

What do employers look for in a cover letter?

Employers and hiring managers use cover letters to gauge an applicant’s personality, communication style, and fit for the role and company. The way you write your cover letter, as well as what you choose to discuss, helps employers decide how well you’d fit in the position. Repeating keywords and phrases from the job description also signals that you understand the role and are a good match.

What should I include in a cover letter?

Your cover letter should include any work history, professional skills, and qualities that make you a good fit for the role. Use this opportunity to highlight your personality, address résumé gaps, or explain short tenures.

The post How to Write a Cover Letter: Step-by-Step Guide for Job Seekers appeared first on Grammarly Blog.