MARKETING
What Are the Best Tools for Storytelling With Data Visualization?
Storytelling with data is a crucial part of any content marketer’s toolbox. Whether you are using data visualization to illustrate a point you’re trying to make, or you want to showcase data from an analysis your team has done, proper data design is key to creating effective visuals that everyone is happy with. Charts and infographics can be pretty, but if they aren’t also properly breaking down data in a way that makes an impact on the audience, they are likely not worth the time and effort.
Below, we discuss how storytelling ties into data visualization, and what tools can help you bring more data into your content. We also recently updated our Learn Center article about storytelling with data, to highlight how data transforms our content and legitimizes the points we’re trying to make, no matter the topic. Be sure to check it out for even more insights!
How does storytelling tie into data visualization?
Visualization is the act of taking data and breaking it down in a visual way that helps the audience understand at a glance what the data is telling us. This could be something like taking population data from a town and creating a pie chart that shows the age ranges of all residents or looking at a bar chart to see that the number of apps an average user downloads on their smartphone has slowly increased over time. Then, after this data is introduced, we use storytelling through content to further explain what the data is telling us.
For instance, if we know that the average user downloads two more apps to their phone then they did five years prior, we can deduce that users are likely using their phones more. This can help us introduce our main point or solution, such as an app cleaning utility to help users remove apps they no longer use, or behavioral modifications for users that want to be on their phone less.
The best tools for data visualization
If you’re looking to create your unique data visuals, which is recommended so you don’t use someone else’s data without their permission, there are several tools you can use to gather data that will influence the main points in your content narrative. These free and paid tools range from the following:
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providing the data for you in a chart format
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giving you raw data to build into graphics
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allowing you to import your raw data so you can build the visuals you need to properly summarize the data points
Creating your own data visualizations can help you create imagery that illustrates your point, influences users to take action, or helps you explain your points in a visual way. Whether you need data trends over time or an analysis of your data to determine next steps, these tools can help.
Google Trends
Most SEOs are aware of Google Trends, but almost any industry can use it to get a quick pulse on what is trending in their specific field of products or services. For instance, if you are an e-commerce, you can check out the Google shopping trends to see what products are being searched for most recently. The page also points out large spikes for specific product terms for e-commerce, such as “y2k aesthetic.”
Additionally, Google Trends also shows daily overall trending search topics in specific countries. This is really useful if you’re looking for data that applies to a specific country or the pulse of a certain area overall, such as music or current events.
The main section of Google Trends allows you to compare multiple topics as once to see how user interest has ebbed and flowed over time.
This data can be an effective way to showcase how specific audiences have gained or lost interest in a topic over a set period of time.
Google Charts
If you already have data that you need to plot into charts, Google Charts under Google’s development tools is a great way to do that. It allows you to import data which you can create visualizations from and then place on your website.
It’s free and completely customizable. It also has a gallery you can browse for examples of available charts, which can help you decide which is best for your data.
This tool may require more developer knowledge since you’ll have to HTML5 and other code to pull in the data.
Additionally, Google Data Studio is similar to Google Charts, where you can import several different data sources to create graphics and live charts based on API-connected data. However, it is focused more on providing an internal data dashboard rather than public-facing charts for content pages.
Moz
If you’re looking to share keyword research or search data over time, consider using Moz. Moz Pro allows you to track your campaign data over time (as well as research competitors), and the suite of free tools lets you view data on specific keywords or links.
This data analysis can be used in marketing pieces to describe trends in search over time, or you can use this type of data in your internal stakeholder content, such as when you want to illustrate the success of your organic content campaigns or how the number of links to specific pages has increased over time since you started updating old posts.
Tableau
Tableau is arguably the most well-known data visualization tool available. It has paid and free versions. The free version, Tableau Public, requires a software download, but then lets you create data visualizations for free (with some limitations that are lifted in the paid version).
To see some of the data visualizations that were created using Tableau, they have their free 3D VizGallery that lets you walk through a 3D “art gallery” of real projects. Here’s an example covering “Work Like an Artist: Daily Routines of Famous Creatives” from a user who adapted information from books on creatives’ work schedules by Mason Currey, Wikipedia, and blog posts.
External data from company user data
If you were looking for data from large companies, many make some of their data public, which can be pulled to create a data analysis or trend report over time. Two good examples of this are:
Spotify Charts
If you want to see how specific music or other media hosted on Spotify is performing over time, check out Spotify Charts, which shows you trends in specific genres of music or by country.
Amazon Sales Data
You can also view trends in Amazon products, such as its best-selling books list or lists of top-selling products in specific categories. External tools, like Amzscout, pull this data to help you see how specific products are selling over time.
Pivot tables
If you want the most simple way to chart your raw data, don’t discount the power of pivot tables and charts in Excel or Google Sheets. These can automatically provide you with charts and other data graphics fast, right within your saved data spreadsheet. There are lots of resources to create effective charts and graphics. It’s important to note Google Sheets may have slightly different formula functions than Excel in some cases.
In conclusion
To learn more about storytelling with data, don’t forget to review our recently updated Learn Center page. Whether you are using a simpler tool like Google Sheets or want to build a beautifully-designed infographic in Tableau, data visualization is a great way to further your storytelling narrative by illustrating your point and growing users’ understanding of the topic at hand.
To see more examples of great data visualizations, check out Juice Analytics’ thoughtful roundup of examples across several different topics and industries.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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