MARKETING
What It Is and How To Increase It
Is your website ready to attract and convert mobile website visitors into leads?
According to Adobe, companies with mobile-optimized sites triple their chances of increasing mobile conversation rate to 5% or above.
If that’s not enough to sell you on the importance of delivering a mobile-optimized experience, Google recently announced that more Google searches take place on mobile devices than on computers in 10 different countries including the United States and Japan.
All this talk of mobile got me thinking about how website visitors were accessing our offers. And after a closer look, I discovered that conversion rates on our landing pages were 20-30% lower from visitors coming from mobile. (As a lead generation geek, you can imagine how psyched I was to uncover such a huge opportunity for gathering more leads.)
With this information in tow, I set out to solve this problem — and I think you’ll be intrigued by what I found.
The Methodology
The hypothesis of this experiment was that by making content more easily digestible on mobile devices, it would increase conversion rate. However, getting inside the heads of our mobile visitors took a bit of reflection. I had to ask myself, “What would cause someone to bounce?”
Some answers I came up with were:
- The form is too long.
- There is too much text on the landing page to read.
- The design isn’t formatted for a mobile phone.
When presented with information that is not super mobile-friendly, a visitor won’t hesitate to bounce from your landing page.
Why?
Not only are poorly formatted pages time-consuming, but they also don’t appear very reputable, which often causes visitors to lose trust. With that decided, we knew we needed a way to condense all the information on the landing page to fit the size of a mobile screen.
The Experiment
To give you a better idea of what we were working with, check out what our landing pages looked like initially:
As you can see, it was quite long with a lot of content. So in order to improve the user experience on these landing pages, we leveraged smart content to shorten the display for mobile users. (To learn more about how smart content works, check out this resource.)
The first step we took was shortening the content and formatting the images for mobile:
Once that was completed, we tackled the form:
Voilà! With the help of smart content, mobile visitors are now shown a shorter, more digestible form.
The Analysis
With the changes in place, we decided that measuring the page’s bounce rate would help us determine if the mobile smart forms helped improve our conversion rates. Essentially, bounce rate refers to the percentage of people who only viewed a single page — it’s the number of people who visit our landing page and then “bounce” without converting on a form.
For this experiment specifically, we needed to figure out how many people filled out the form that came from a mobile device. Here’s a step-by-step explanation of how we approached this:
- We used Google Analytics to find the number of “new users” to hubspot.com. I measured new people to hubspot.com on mobile (and not repeat visitors) because existing people in our database would not be net new prospects (which is what I’m solving for).
- I used HubSpot to determine the number of new prospects from the mobile smart form.
- I calculated the conversion rate using the following formula: Conversion Rate = New Prospects / New User PVs
- I calculated the bounce rate using the following formula: Bounce Rate = 100% – Conversion Rate
The Results
Results from Mobile Smart Form Test
By switching to mobile smart forms, we managed to decrease bounce rate (and therefore increase conversion rate) on each landing page tested by an average of 27%. Bounce rates that were previously between 50-90% are now between 20-50%.
Visitors now have a smoother experience and are less likely to leave the page before viewing and completing the form.
Results from Mobile Optimized Content Test
After optimizing the mobile smart forms, we tested shortening the content and optimizing the images for mobile. This produced a 10.7% decrease in bounce rate. (We expect this number will keep decreasing with continued optimization.)
The Takeaways
Through this experiment, I learned to solve for the user. I also learned the importance of placing myself into the shoes of the user to better determine why and how conversions happen (or don’t happen) in the first place.
While marketers don’t always think of UX, this experiment proved that there is no denying its importance. If your website is slow to load, visitors might leave. If the user has to scroll through six screens worth of content to reach a form, they might leave. If the form they arrive at has 10 tiny fields, they might leave.
See my point here? To improve the odds of a conversion actually taking place, always solve for the user.
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.”