If it feels like every vendor suddenly has a “State of Something” report, you’re not imagining it. Benchmark studies and branded data have become the new calling cards of content marketing.

While blog posts and brand manifestos still have their place in the ecosystem, they rarely break through on their own anymore. Content fatigue has reached critical mass, and audiences simply scroll past “5 tips for better marketing” articles. At the same time, AI search has changed the SEO game and raised the bar for credibility; in order to rank and get cited by large language models, marketers need original insights no one else can offer.

One solution emerging across industries has been to double down on proprietary data. From HubSpot’s State of Marketing to LinkedIn’s Workplace Learning Report, companies are mining their unique data assets to create content that commands attention, drives citations, and builds lasting authority.

Here’s why the trend is on the rise — and why it works.

Why Now? The AI Search Effect

When ChatGPT answers a question about average email open rates or Perplexity summarizes industry trends, these AI systems pull from sources with transparent, compelling data and authoritative positioning. Branded benchmarking reports check these boxes by providing structured, factual content with clear methodologies and context.

Even in a “zero click” scenario, your brand still benefits from being cited as the source of record. There can be a compounding effect to such attributions: When your report becomes the default data point for “average B2B sales cycle length” or “content marketing budget allocation,” you gain visibility across thousands of AI-generated responses, journalist articles, analyst reports, and competitor presentations. Each mention amplifies your brand’s authority, and the qualified traffic that does flow back to your domain is more likely to convert than traffic from traditional SEO.

How Benchmarking Reports Drive Value

Smart marketing leaders recognize benchmarking reports deliver measurable value across three critical dimensions:

Public Relations Impact

Proprietary data transforms your brand into a media magnet. Journalists constantly hunt for fresh statistics to anchor their stories. When you publish exclusive insights about industry trends, you hand them ready-made hooks — and the result is earned media coverage that would cost six figures through traditional PR campaigns.

Pipeline Generation

Whether gated or ungated, benchmarking reports tend to attract high-intent prospects. Gated reports identify serious buyers willing to exchange contact information for valuable insights, and ungated versions can maximize reach by getting your data in front of analysts and influencers.

Trust and Authority

Publishing rigorous, methodology-driven research signals deep expertise. You shift from vendor to trusted advisor. Transparent methodology matters here — you’ll want to ensure you’re clearly explaining data sources, sample sizes, and analysis methods to give readers confidence in the validity of your findings.

What It Takes to Create a Report That Sticks

Building a benchmarking report that achieves these outcomes requires strategic planning across a few key areas:

Data Sourcing Strategy

Start with data only you possess, like first-party usage data from your platform that provides unmatched insights competitors cannot replicate. Combine this with customer surveys or supplement with subject matter expert quotes for qualitative depth. Aggregate and anonymize to protect individual customer data while revealing category-wide patterns.

Design and Format Excellence

The most successful reports balance comprehensive analysis with scannable highlights. Transform raw data into compelling visual stories by partnering with designers who understand data visualization. Create charts that reveal insights at a glance. Write copy that explains why the data matters, not just what it shows. Package statistics as “snackable” social media content, and include downloadable one-pagers for easy sharing.

Multi-Channel Distribution

Great data dies without strong distribution. To maximize impact, launch your report with coordinated campaigns across PR, social media, email, webinars, and sales enablement. Create tiered assets: executive summary for time-pressed leaders, full report for practitioners, slide decks for internal sharing, etc. And don’t forget to train sales teams to reference key statistics during their conversations with prospective clients.

Optimizing for Citations

You’ll also want to structure your content for maximum quotability and citability by AI engines. To boost discoverability, use descriptive subheadings that work as standalone facts, and be sure to create FAQ sections addressing common questions. Build infographics for visual learners, and implement schema markup to help search engines understand your data. Include methodology sections that establish credibility and, finally, make statistics easy to cite with clear sourcing guidelines.

Consistent Refresh Cadence

A report is only as valuable as it is current. To keep your data fresh, commit to regular updates, e.g. annually for comprehensive reports, quarterly for trend data. Mark your calendar now: If you launch in January, start data collection in October. Teasing an ongoing data initiative creates anticipation and provides reasons for re-engagement.

Pitfalls to Avoid

Even with the best intentions and compelling data, a few critical mistakes can undermine even well-executed benchmarking reports. Here are three to avoid:

Weak Methodology

Small sample sizes and cherry-picked data can destroy your credibility. Invest in rigorous methodology, even if it means less favorable results. Consider partnering with research professionals if your own team doesn’t have the resources necessary to produce a truly top-notch report. And always disclose limitations or margins of error.

Sales-First Content

Readers — both humans and machines — detect and reject reports that exist primarily to promote products. Focus on category-wide insights, include competitor data where relevant, and save product mentions for subtle footer CTAs.

Underinvesting in Distribution

It doesn’t matter how brilliant your report is if nobody reads it. Budget 40% of project resources for distribution and amplification. That number may sound daunting, but without aggressive distribution, even the most groundbreaking data won’t move the needle.

The Future of Authority Marketing

The window of opportunity is open now, but it won’t be forever. Categories without established benchmark reports offer first-mover advantages. So, it’s a good idea to start now: Begin by auditing your data assets, surveying your customers, or analyzing your platform metrics. Then, transform these insights into the authoritative report your industry needs but doesn’t yet have.

As AI search changes how information is surfaced and cited, the brands supplying reliable benchmarks will own the reference points that everyone else leans on. Those who wait risk competing in categories already defined by others’ data.

Need help turning raw data into a report that drives citations and pipeline? Talk to Contently about building your next benchmark study.

Frequently Asked Questions (FAQs):

What makes a benchmarking report different from a white paper?

A white paper typically presents a company’s perspective or solution, while a benchmarking report is rooted in original data and industry-wide trends. The latter is designed to be cited, compared against, and referenced as an objective standard.

How much data do I need to publish a credible benchmarking report?

There’s no magic number, but larger sample sizes improve credibility. What matters most is transparency: Clearly explain your methodology, sample size, and any limitations so readers trust your findings.

What resources are required to create a strong benchmarking report?

Successful reports usually require collaboration across data, design, and distribution. This might mean partnering with research specialists, investing in design for clarity and impact, and budgeting a sizable share for promotion.

The post Why Branded Benchmarking Reports Are Everywhere Right Now appeared first on Contently.

Few content teams have someone with such a keen eye for facts. And honestly, the kinds of content businesses publish rarely require the rigor expected by publishers of long-form investigative journalism.

All content teams do, however, need everyone to buy into and use the following practices.

Inventory the facts and fact-ish details in your content.

Just as you copyedit every piece of content before publication, each piece should go through a fact review.

Someone (not the writer) reads the piece and highlights all the facts and fact-like sentences. These include the sentences or phrases that depend on interpretation or source selection. Group the inventory of facts into three categories:

• Category One includes the core facts central to the piece’s argument.
• Category Two includes important core facts that are part of the central argument but without which the piece could still stand.
• Category Three includes facts that add color but are peripheral to the asset’s central thesis.

Use a multi-level process for fact-checking the piece.

Content teams should fact-check all category one facts of every piece of content that runs through their process—this is a level one fact check. If all the facts check out in that first set, the foundations of that content asset are sound. For everyday content like blog posts, social messages, and so on, organizations that want to do the bare minimum can stop there.

If you find inconsistencies right out of the gate, however, that piece of content should move to the second level, which involves more intensive checking of all category one and category two facts. If more problems arise, it should either move to level three (see next paragraph) or the content quality police should send it to the content lockup, depending on your internal decision criteria.

Level three involves checking everything—all facts in all three categories. I recommend doing a level three fact-check on every pillar piece of content your team produces and making it optional for others. How intensive your process is depends on your industry and internal practices. Highly regulated industries or those with high levels of market risk, for example, should probably level-three fact-check everything.

Each organization will set standards for how much to check and what happens to articles with core or incidental fact issues.

Define what you view as “reliable sources” and inventory them.

Fact-checkers rely on sources to validate information, facts, statements, etc. Content teams define which sources are trustworthy, reliable, reputable in your industry, or for the subjects they validate.

Common sense rules apply here. If you define a common business term, for instance, a published dictionary is a better source than your cousin, the English teacher. If you need data on consumer spending, the U.S. Federal Reserve is a better source than Amazon.

Content teams should create and maintain a list of fact sources they deem reputable for their market.

Your list of reliable sources might include media publications, databases, industry associations, journals, published books, etc. It should not include sources that will have AI-generated content in the immediate future. Wikipedia, for example, should never be a source of validation for a fact.

Find the original source.

Interesting anecdotes, research findings, truisms, quotes… they have a way of making the rounds of the content world. We’ve all done it—that is, heard a story that piqued our interest and repeated it, only to find out the person we heard it from got the details wrong. Exhibit A: Malcolm Gladwell and that “10,000 hours” theory.

The best and most reliable way to avoid recycling inaccuracies is to go to the original source. For example, say you are looking for a statistic and find one that fits the bill in an article published in The Economist—but it’s from a research study conducted by a third party. Don’t just assume The Economist got it right. Go find the study itself and read it.

In a similar vein, if you want to use a quote from an actual person, go find it. This might be in original footage when they said it (again, from a reliable source), the page from their book, a post on their blog, an article under their byline, etc.

When in doubt, double-check

If a fact sounds off, too perfect for what you are trying to say, or from a borderline source, double-check it—especially if it is a core fact for an important piece of content. As humans, we can be fooled, but some of us also have good instincts. Trust yours. If a fact sounds wrong, it might be, and you don’t want it eroding your brand trust.

Generative AI is here to stay, but that doesn’t give us the green light to shut our brains off and let it take the lead on the content we create with it. AI is helpful, but it still needs humans to guide and direct what’s produced.

Stay informed! Subscribe to The Content Strategist for more insight on the latest news in digital transformation, content marketing strategy, and rising tech trends.

The post Best Practices for Fact-Checking AI-Generated Content appeared first on Contently.

The Ringer: Measuring the Impact of Uber’s Many Controversies

Selected by Brian Maehl, talent development manager

If Uber CEO Travis Kalanick made a deal with the devil to create an invincible tech company, 2017 is the year the devil reminded Kalanick just who he’s dealing with. In this story, The Ringer’s Victor Luckerson dives into Uber’s scandal-plagued first quarter—from its broken HR department, to sexual harassment allegations. In the words of Luckerson, “Uber is not doomed, but it’s certainly more vulnerable than it was a couple of months ago.”

Uber may be a mess, but I struggle with the word “vulnerability” when it refers to troubled tech companies. Facebook’s failure to get ahead of fake news raised ethical concerns last year. Amazon’s culture was torn apart (or, you know, revealed) by some fantastic New York Times reporting in 2015. Snapchat was scrutinized after letters from CEO Evan Spiegel’s fraternity surfaced online in 2014. Yet they’re all doing just fine. While these missteps may not be as big of a deal as Uber’s chaos, they still have a serious impact on customer perception.

The monopolistic tendencies of Silicon Valley companies mean customers have few alternatives if they want to opt for a competitor. There’s such a long leash that ethical allegations become footnotes in their histories, rather than causes for their descent.

Groove: How We Built an Online Course That Generated $120,679 in 5 Days

Selected by Joe Lazauskas, editor-in-chief

Some key takeaways from this piece:

• Groove, a SaaS company, built a content marketing course that generated $120K in revenue.
• By all accounts, this took months, not five days. Not sure where that came from, but it makes for a great headline. Game respect game.
• Still, I feel like an idiot for not doing this first.
• The blueprint they lay out here is really strong and easily replicable, especially if you’re, say, the editor-in-chief of a blog that’s published thousands of free content marketing resources over the past year.
• I’m an even bigger idiot if I don’t do this now.
• I’m really excited for their next installment when they reveal how they sold and marketed this course.
• After reading this post, I feel like Groove and this bird have a lot in common:

Mashable: The tricky art of marketing women’s empowerment in the era of Trump

Selected by Erin Nelson, marketing editor

The revelation that Thinx CEO Miki Agrawal was allegedly harassing her employees led to headlines like this one from the Huffington Post: “Thinx Controversy Proves You Can’t Sell Feminism.” But in this Mashable article, Rebecca Ruiz asks whether feminist messaging can actually boost business. (Full disclosure: A “Wild Feminist” bomber jacket is hanging on the back of my chair.)

Companies like Dove, which has a history of campaigns that emphasize women embracing non-patriarchal beauty standards, have pulled it off. Others, like Audi, which released a commercial about equal pay during the 2017 Super Bowl, have received flack for lacking diversity on their own boards. Ruiz finds that feminist ads from soap bars to automobiles run the risk of backlash if they patronize women with stereotypical messaging—or perpetuate an openly sexist environment. But sometimes, she argues, campaigns that show women overcoming diversity, like Motrin’s #WomenInProgress, can operate as “a vivid reminder that we can and should insist on gender equality.”

The New York Times Magazine: CNN Had a Problem. Donald Trump Solved It

Selected by Adrienne Todd, communications specialist

To say that Donald Trump has a contentious relationship with TV networks is an understatement. His first few months as president have provided plenty of fodder for late-night talk show hosts like Stephen Colbert and Seth Meyers, and in turn, their critical yet funny takes on the Trump White House have helped shape their personas.

News programs, on the other hand, have struggled to find their footing. Jonathan Mahler’s article for The New York Times Magazine asserts that while Trump excels at using the media to his advantage, CNN—with Jeff Zucker leading the charge—is flipping the proverbial script, leveraging the Trump team’s penchant for chaos to hone its message and solidify its brand.

The Wall Street Journal: The Morning Download: Exxon Mobil’s Supercomputing Feat Speeds Up Reservoir Simulation Times

Selected by Dillon Baker, tech editor

Thanks to the constant hyperbole of tech marketing, I can’t help but reflexively roll my eyes when I hear words like “AI” and “big data.” But then I read articles like this one on Exxon’s use of a record-breaking super computer to run reservoir simulations, and I’m reminded that this technology is a big, big, big deal (and that may not even be enough “bigs”). Operational optimization has already revolutionized the economy (See: Amazon). Add machine learning, the IoT, and real-time optimization to that list, and things are going to get crazy.

The New Yorker: “Paging Dr. Fraud”: The Fake Publishers That Are Ruining Science

Selected by Jordan Teicher, managing editor

A year ago, nobody used the term “fake news.” That’s weird to think about now since we hear it all the time. But propaganda, spin, misinformation, and so on have existed for a long time; the internet just helped them scale. Without any barriers to entry, people can publish whatever they want. The worst part is the garbage gets to compete against information that’s considerate, creative, and trustworthy.

Perhaps we could’ve seen this coming: the same thing has been happening to science. Over the past few decades, the number of “predatory journals” that solicit academic papers without legitimate peer-review standards has “jumped into the thousands.” Sometimes, these pubs even spam writers for pitches. Not surprisingly, the motive here is money. Shady journals accept all papers sent their way, as long as you send them a check too. The cumulative effect of this nonsense has tarnished the authority of all publishers. Instead of thinking about the content, readers and contributors have to waste time trying to verify if the sources are legitimate. Sound familiar?

If science academia wasn’t safe, then the rest of us in media never stood a chance.

The Atlantic: Who Owns Your Face?

Selected by Craig Davis, editorial intern

Privacy doesn’t seem to exist in 2017. Last week, we received another reminder, in the form of a House resolution that will allow internet service providers to sell your personal browsing history. (Side note: Go clear those cookies.) And now, data collectors are going after something even more personal: your face.

Facial-recognition systems are nothing new; the tech has been a part of Facebook’s tagging feature for years. But the F.B.I. has started using its latest algorithm to collect an image database of millions of Americans, 80 percent of whom are law-abiding citizens. As Adrienne LaFrance writes in The Atlantic, the increased surveillance blurs the lines of what is and isn’t acceptable to monitor.

“Your face is yours,” she writes. “It is a defining feature of your identity… It’s entirely reasonable to wonder how companies are collecting and using images of you.” Given the ubiquity of security cameras, those wishing to stay off of the facial grid may not have the option.

The post The Tricky Art of Marketing Women’s Empowerment, and 6 Other Stories We Loved in March appeared first on Contently.

My customer account for Pizza Hut linked to Messenger in seconds and didn’t require any new payment information. One large pepperoni, please. Same as last time? Yes, please. But instead of dealing with a stranger or another checkout screen, I was typing to a chatbot instead. No small talk. No dropped calls. The bot offered me a few promo items, but otherwise, the whole exchange was quick, easy, and largely indistinguishable from a human interaction. Is this still my address? Yep. The receipt came via email.

The company credited with selling the first item on the internet is now making the e-commerce even easier. Pizza Hut recently announced its new chatbot ordering feature as part of a massive social media rollout that debuted a few months ago. Conversational commerce is hotter than a Samsung battery right now, and the largest pizza chain in the world knows it, following competitors and contemporaries alike into a space still very much evolving.

But what if businesses are approaching chatbots all wrong?

What is the secret sauce?

Magnus Jern, president of mobile solutions company DMI, recently told the BBC that when chatbots try too hard to be natural, it diverts from the purpose of conversational commerce. Jern helped launch IKEA’s Anna chatbot in 2005, which was recently retired after 10 years. “In the beginning, we tried to impersonate a person, and we found that there was no reason to do that,” he said.

But the move is a curious one, especially when chatbots are on the rise. Earlier this year, KPCB’s Mary Meeker referred to them as the “secret sauce” of messaging in her keynote on digital trends.

However, academic research has suggested that consumers don’t want robots that can talk like humans. Some would argue, instead, that all we want is a smoother ordering process. A Harvard Business Review report from 2010 found that “loyalty has a lot more to do with how well companies deliver on their basic, even plain-vanilla promises than on how dazzling the service experience might be.”

In his book, Influence, Dr. Robert Cialdini, a professor who teaches psychology and marketing at Arizona State University, concluded that we’re more motivated (to act, purchase, click, etc.) when choice is limited.

Also, there’s a difference between talking to a human and a bot that technology may never be able to reconcile. According to a study published by JAMA, conversational agents like Siri or Google Now simply don’t understand the difference between “I’m dying” and “I’m dying of hunger” in a crisis.

In other words, ordering a pizza via chat isn’t so unique anymore, but predicting how that conversation might look in the future is a bit more challenging.

The commerce of chatbots

The term “chatterbox” was coined in 1994, the same year Pizza Hut filled its first online order. Today, the company is the largest pizza chain in the world—with roughly half of its orders coming through digital channels and more than 60 percent of those via mobile devices, per internal data.

“We all have to become students of human behavior,” Baron Concors, Pizza Hut’s global chief digital officer, told a MobileBeat audience in June.

Conversable, the Austin-based company behind Pizza Hut’s chatbot technology, is turning the study of human behavior into a thriving business. The software company is partnering rapidly with major brands like TGI Fridays and Whole Foods, using conversational messaging for self-service and on-demand content.

For some companies, customer service is one long conversation. For others, the conversation ends with a pizza delivery.

The chatbot ecosystem is exploding. Facebook now supports over 11,000 chatbots, plus a dedicated store. Apple recently debuted its own iMessage app store with iOS 10. And messaging apps, well suited for brand chatbots, have never been more popular. WhatsApp, for example, now has over 1 billion users. WeChat and Viber have hundreds of millions.

In June, Tommy Hilfiger announced its own chatbot designed with the help of Facebook’s Creative Shop and bot creator Msg.ai. According to TechCrunch, the social giant caught flack for hosting too many clumsy bots from outside developers. The partnership with Tommy Hilfiger lets the company reclaim some control over its new chatbot platform, while heeding the call for online concierges among high-end fashion brands.

As Tommy Hilfiger himself told TechCrunch, “We are obviously distributed in our own stores and in department stores, but going directly to the consumer is really part of the motive and the future of the omni-channel process.” Gigi, named after supermodel Gigi Hadid, will answer customers in a more natural style since, as CMO Avery Baker argued, no one wants to feel like they’re talking to the corporate animal anyway.

Calls for conversation

For some companies, customer service is one long conversation. For others, the conversation ends with a pizza delivery.

“Even the most digitally tuned-in customer will want to know that they are connected with someone who can put themselves in their shoes,” said Simon Hunt, director of customer experience at Firstsource Solutions, a business-process outsourcing firm based out of India.

In August, the travel app Skyscanner estimated a layover of 413,768 hours to a shocked consumer looking for a cheap flight. When the man posted about the error on Facebook, a Skyscanner rep responded with a clever and lighthearted comment that eventually went viral and generated press coverage. To clarify, the technology screwed up, and then a human came in to clear things up.

“Bots are easy. Conversations are hard.”

Conversocial’s CMO Paul Johns told Digiday that such unscripted rapport is a growing trend. Proving a resolution is great, but opening up a meaningful dialogue may be even better. Per CeBit, 71 percent of people who receive a quick response from a brand on social media are likely to recommend that brand to others. It’s no surprise that chatbots are being considered to automate the job.

But despite these developments, there’s still a weird tension surrounding the conversational commerce movement. People want quick, straightforward service, but they also seem to value human empathy. Is it possible for a chatbot to provide both, even if consumers know they’re talking to an algorithm?

Conversations are hard

Ben Lamm, the CEO and co-founder of Conversable, probably said it best: “Bots are easy. Conversations are hard.”

In the race for creating tech with personality, businesses seem stuck on naming their bots after anything other than a tool. Anna. Gigi. Facebook even has a bartender bot named Shaky.

But chatbots don’t care what we call them, and, let’s face it, expressing our trust in AI impacts my comfort, not their effectiveness. That’s why we’re still anthropomorphizing machines.

“Giving something a human name is a way of exerting control over it,” writes Adrienne LaFrance in The Atlantic, “a reminder that it works for you, that it exists within a human construct, even when the machine itself is wholly indifferent.”

It’s hard to pinpoint how much conversation and functionality is necessary, but it’s safe to say that functionality is ultimately what will drive revenue.

Chris Messina, the developer who coined the term “conversational commerce” about two years ago (and also came up with the word “hashtag”), gave a talk at the MobileBeat conference where Pizza Hut made its bot announcement this summer. Messina showed the audience his own bot, an integrated messaging platform, and went over the evolution of the bot movement.

Toward the end of his presentation, Messina went over a few rules for both ethics. “A bot should be able to describe itself,” he said. “What it does, how it handles information, if there’s a human on the other end monitoring stuff. Bots should have a similar type of disclosure statement.”

That’s all well and good, but for the most part, the only rule I care about is if the bot can get my order right the next time I want a pizza.

The post Why We May Be Thinking About Chatbots All Wrong appeared first on Contently.

Having pitches ignored by busy editors is nothing new to freelancers. Editors receive so many emails every day, and the increasing demands on everyone’s time at publications fighting to stay alive means getting someone’s attention is a real challenge.

So on August 23, when the makers of Boomerang (a Gmail tool that lets you schedule messages to be sent later) launched a new tool called Respondable (which is supposed to help you improve email response rates), I started using it immediately.

Based on data from millions of emails, Respondable uses artificial intelligence to rate the likelihood of an email getting a response. It scores messages based on factors like reading level, subject length, and word count. (Premium features rate messages on politeness, positivity, and other factors, but I used the free version.)

As you revise your email by shortening your subject line or choosing simpler words, you can see the likelihood of a response rate increase.

A tool like this has an obvious appeal to freelancers. However, it doesn’t understand important context like the sender’s relationship to the recipient or the recipient’s individual preferences. Regardless of what I write in an email to my mother, she’ll probably respond in a few days with “Sounds great! Love, Mom”—even if Respondable warns me that I’m writing at a 12+ grade reading level.

On the other hand, even if I draft an email that Respondable deems “very likely to receive a response” and send it to an editor at The New Yorker I’ve never worked with before, I wouldn’t hold my breath.

Suffice to say, Respondable not taking the social context of an email into account left me skeptical, so I decided to put it to the test. I spent one month sending emails without Respondable and then sent every email with the tool the following month.

Obviously, this is an unscientific study. It’s a small sample size and doesn’t control for variables like the quality of different pitches, competition level of the market, or timing of the emails. But my little experiment still led to some interesting results.

Here’s a look at the numbers.

Pre-Respondable: July 23–August 23

I sent 25 emails to editors (19 initial pitches, two letters of introduction, and four follow-up emails). I received seven replies (two responses to follow-ups and five to initial pitches). That’s a 28 percent response rate. About a third of the respondents were editors I’d worked with before. Of those who didn’t respond, all but two were editors I hadn’t worked with before, so I had less expectation of a speedy response anyway.

Post-Respondable: August 24–September 24

I sent 23 emails to editors (14 initial pitches and nine follow-up emails) and I revised every email until Respondable scored it as likely or very likely to get a response. This time, I got 12 responses (three responses to follow-ups and nine to initial pitches. Three replies came from editors I’d worked with before.

That’s a 52 percent response rate, a significant jump from the previous month. However, I wouldn’t give Respondable all the credit. My hunch is that editors are more likely to respond in September when they’re back from vacation and looking to use freelance budgets before the end of the year, which may have also upped my odds.

One thing worth noting is Respondable’s general guidelines. Here are the tool’s four recommendations to users who download the free version:

• Keep the subject length to 3–7 words: I use the format “Freelance Query: [Article Topic]” so this is a tough one, since a third of my word count is already eaten up by “Freelance Query.”
• Limit the body of the email to 50–250 words: I want my pitches to provide enough detail to pique editors’ interest in the topic and also sell them on my credentials, so I tend to push the upper end of this range.
• Ask 1–3 questions in your email: Questions serve as a call to action for the recipient. I typically ask a single question like “May I cover this trend for _____ magazine?”
• Write at a grade 1–7 reading level: Most of my initial email drafts were rated a 12+ grade level, so this is a good reminder to use shorter sentences and simpler words. Even after simplifying my pitches, they tend to fall at the upper end of this range, and I’m okay with that.

These are all good rules to live by, but they’re not enough on their own. Just for fun, I drafted a nonsensical pitch that fit all the above criteria and netted me a “Very Likely to receive a response” rating. I doubt I’d have much luck if I actually sent it.

In the end, I think it’s more important for writers to focus on writing well-developed, interesting pitches than to agonize over the number of questions or the reading level—but Respondable can be a nice reminder to clean up your email before you hit send.

The post Can AI Really Help You Write Better Emails? appeared first on Contently.