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Turn Dry Data Into Rich, Relatable Stories With These Tips

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Turn Dry Data Into Rich, Relatable Stories With These Tips

One of the best things about being a content writer is that no matter the topic, we have a lot of insights at our fingertips. You can use it to provide perspective, validate ideas, give more context, etc.

Of course, all that data also is one of the worst things for a content writer. How do you dig out the story behind the numbers without getting buried under the mountains of facts and stats?

At Stacker, we shape our newswire stories around data and use it to drive all our storytelling. We’ve found the best-performing articles – regardless of topic – share similar strategic data-centered approaches. Here’s some of what we learned by creating data-driven content that engages audiences and earns links from other sites.

Go local and meet readers where they are

A story tailored to a region, state, or city feels instantly relatable and captures the attention of readers’ living in that geographic area. In fact, 71% of our publishing partners say their most-prioritized stories have local news angles.

Narrowing data-driven stories to a state or metro level may seem limiting. Content writers think the more hyper-focused a story, the smaller the reach. But presenting localized data doesn’t have to be an either-or choice.

#Content writers can use data to give stories both a hyper-local and national appeal, says @Stacker’s Elisa Huang via @CMIContent. Click To Tweet

CNBC didn’t make a choice in their story about how much the top 1% of U.S. households earn each year. It mentioned the broadest geographic figure – the national number ($597,815 a year on average). Then it detailed the average for each state, from West Virginia’s $350,000 to Connecticut’s $896,490.

1656498824 782 Turn Dry Data Into Rich Relatable Stories With These Tips

CNBC gives its data-driven story national and hyper-local appeal.

One of our top-performing stories for a brand partner looked at the rural hospitals most at risk of closing. It broke down the status of rural hospitals over 43 states, then distilled local versions that would feel most meaningful for targeted audiences from California to New York.

Takeaway: Data at a state- or city-level can have local appeal while still connecting to a newsy national trend. It also opens up your content’s promotion potential to national and local news sites.

HANDPICKED RELATED CONTENT:

Host a hometown showdown by comparing data

People love comparing their corner of the world with others. A recent Redfin report found an unprecedented 8% of U.S. homes are now worth at least $1 million. The story didn’t just reveal the top five or 10 cities but ranked 99 so readers can see how million-dollar neighborhoods compare to other million-dollar neighborhoods.

In this snippet of the comparison content, six of the cities are in California – half of which have a 50% or greater share of homes worth at least $1 million in 2022. Other cities at the top of the list include Honolulu, Seattle, and New York City.

1656498824 187 Turn Dry Data Into Rich Relatable Stories With These Tips

A snippet from Redfin’s story that ranks home price data by state.

When people can see their cities’ results juxtaposed with others, it puts the information into a more powerful context. Ranking stories, such as states with the lowest income taxes or the cities with the highest rent, often perform well.

Ranking stories – where readers can see how their locale compares to others – perform well, says @Stacker’s Elisa Huang via @CMIContent. Click To Tweet

Writing headlines with phrases like “highest-to-lowest,” “biggest increase,” and “lowest-priced” also signals to readers the underlying numbers-driven methodology used in the content. They not only reinforce the data-first approach, but they build confidence in the prospective reader that the content is powered by data, not opinion.

Takeaway: Use data-driven rankings to tap into readers’ curiosity by showing how their region compares with others in timely trends.

Let time tell the story by thinking past the latest data

Many content creators understandably focus on building a story around the latest numbers or study results to be relevant and trendy.

But pulling in a bit of history through older data sets can add a richer dimension to the storytelling. Not only does historic information add more context to the latest data or breaking news, but it helps the piece become more evergreen. Long after a news headline fades, readers may be still interested in the richly layered content.

Historical data can lead to a more relevant story today, says @Stacker’s Elisa Huang via @CMIContent. #ContentMarketing Click To Tweet

We did this with a story about how commuting in America changed over the past 50 years.

1656498824 217 Turn Dry Data Into Rich Relatable Stories With These Tips

Stacker used historical data to highlight how the American commute has changed over time.

Without adding historical data, it would have been impossible to highlight that the average length of work commutes has increased 10% since 2006. This contextualization offers a perspective that wouldn’t be possible by only detailing the current average commute time.

Self, a credit-building app, mapped poverty levels state by state using data from the U.S. Census Bureau. Instead of just mapping the country with the latest poverty rates for each state, the story also charted the rates over time. With this valuable context, readers could see how states’ poverty rates rose and sank after natural disasters, financial booms and busts, and ultimately COVID-19, giving a more thoughtful story that identified contributors to those poverty rate changes.

Georgetown University’s Center on Education and the Workforce tackled the value of higher education with another data-centered approach: It looked at the salaries of college graduates in 10-year increments since their enrollment. The findings, picked up by Yahoo! Finance and others, assessed how many decades it took for a student to earn a return on investment on the cost of their college.

Takeaway: Using data over time can add richer context to what numbers mean today – and make the content feel more evergreen.

Liven up humdrum stories with different data filters

Data-driven stories emphasize relatability – they can connect better with your audience and often present a new angle that stands out from your same old story approaches. You can find local angles, make a comparison, and use historical data to provide unique context.

Unsure what data to start with? Poke around government sites like the U.S. Bureau of Labor Statistics and the Department of Education. They can be great places to dig into and see how national-level data looks when filtered across industries, career fields, household incomes, metropolitan areas, and more. By adding focused data to your content, you can tell stories that feel more personalized – and meaningful – to your readers.

Want more content marketing tips, insights, and examples? Subscribe to workday or weekly emails from CMI.

Cover image by Joseph Kalinowski/Content Marketing Institute



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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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