Connect with us
Cloak And Track Your Affiliate Links With Our User-Friendly Link Cloaking Tool, Try It Free

MARKETING

How Estimated Reading Times Increase User Content Engagement

Published

on

The advent of digital platforms has increased the amount of content people read on computers, smartphones, and tablets. The average person spends almost seven hours a day viewing internet-connected content on a screen, according to data from Comparitech. And it’s even higher in the U.S., with the average person spending over seven hours viewing screen content each day.

This shows that there is a huge potential to engage customers digitally. It’s worth asking how much of their time is spent on your content.

Estimated Online Reading Time

Marketers can use advanced marketing analytics tools to determine how much time users spend engaging with your content. Customer traffic to your article can be thought of as a consumption funnel – starting with the total number of people who load the page and narrowing it down to those who start reading, reach the bottom of your article, and eventually hit the bottom of your page. These tools also show how much time the customers take to reach a particular point in the article. 

One example of these tools is Page Analytics from Google. This Chrome extension lets you analyze how customers interact with each page on your website. 

If these tools tell you a large number of people view your article but few reach the end, this might indicate a need to make your content more engaging or a nudge to ensure the readers go further. 

An effective way to encourage customers to read your article is to mention the estimated reading time. Showing site visitors how many minutes it takes to read your article can help convince them that the time commitment will be less than what they originally thought. This can lead to better engagement with your content.


Get the daily newsletter digital marketers rely on.


Why it’s worth mentioning reading time

Mentioning the estimated reading time of articles seems to have positive impacts – it can reduce bounce rates and increase time spent onsite. A study from Simpleview Europe even found that engagement rates have increased by up to 40% after reading times were added to the post.

There is also psychological evidence supporting estimated reading time mentions. The “paradox of choice” is a phenomenon in which having a large number of choices can negatively impact your decision-making experience. 

Intuitively, you might think the more choices you have, the more capable you are of choosing something that suits your needs. However, having too many choices can overwhelm customers.

Having fewer options can put less burden on customers. And, fewer choices ensure greater confidence in their decisions and lower chances of regret. 

If you can tell readers how long it will take to finish reading an article, your content will can become more enticing. This reduces the burden on readers to figure out how much time they need to invest. 

Knowing precisely how much time they need to invest helps customers set aside time to read your article. For example, if someone has 10 minutes to spare on their morning commute, and they know that the article is less than 10 minutes long, they will be more likely to read your article.

Calculating estimated reading time

There are multiple methods you can use to get an accurate reading time for your article. Depending on what suits you the best, you can either choose to do this manually or with an online tool.

Estimate manually

Research varies, but generally, the average adult reads 200-250 words in one minute. You can use this information to calculate the estimated time to read.

Here’s how:

  1. Find your total word count. Let’s say it’s 938 words.
  2. Divide your total word count by 200. You’ll get a decimal number, in this case, 4.69.
  3. The first part of your decimal number is your minute. In this case, it’s 4.
  4. Take the second part — the decimal points — and multiply that by 0.60. Those are your seconds. Round up or down as necessary to get a whole second. In this case, 0.69 x 0.60 = 0.414. We’ll round that to 41 seconds.

The result? A four-minute, 41-second read.

You can also round up that time to make things simpler for your reader. That would make your 938-word article a 5-minute read.

The most important parameter to keep in mind while using this method is the average speed of reading you are assuming. Depending on the complexity of your material or the audience type, this number is subject to change. For example, if you are talking about a straightforward subject to a knowledgeable audience, you can assume a higher number of words per minute. This allows you to customize the estimated reading type according to the context of a particular article.

Use online tools

There are many online tools that you can use to calculate the estimated reading time of your content. Read-o-meter is an easy-to-use online tool that lets you cut and paste your content into their dashboard. It will then give you an output of the estimated time to read your article. The tool assumes a 200 words per minute reading average. 

However, keep in mind that while 200 words per minute is the average, this number may have to be adjusted depending on your article and audience. If you think the average reading time for your audience is different, using the manual method might be a better option.

Other websites that help with these calculations are The Read Time and Words to Time. The Read Time calculates this speed based on an average reading time of 238 words per minute, whereas Words to Time uses an average of 130 words per minute for calculation.

Lastly, if you want to move a step further, you can also incorporate a reading bar in your article. This bar will show your users how much of the article is left to read as your readers keep scrolling down.

If your readers know what percentage of the article they’ve read in real-time, it will encourage them to finish reading your article.


About The Author

Why we care about social media marketing A guide for
Akshat Biyani is a Contributing Editor to MarTech, a former analyst who has a strong interest in writing about technology and its effect on marketing.

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address

MARKETING

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

Published

on

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!

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

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

Published

on

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

Amazon pillows.

(more…)

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

A deeper dive into data, personalization and Copilots

Published

on

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.”

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

Trending