MARKETING
6 Marketing AI Predictions to Watch in 2023
AI has gone mainstream, and it’s slowly creeping into the day-to-day lives of marketers.
Although this technology is still in its early stages, it’s already changing how we work. With the help of AI-powered tools, marketers can automate a variety of tasks, from drafting email subject lines to scaling entire marketing campaigns.
If you’re curious about where AI is going — and how you can leverage it — we’ve listed six AI predictions to watch in 2023.
6 Marketing AI Predictions for 2023
- AI will give content marketers a significant lift.
- Consumers will demand more personalization, and AI can make it happen.
- We’ll see an influx of mediocre content generated by AI.
- Companies will embrace Responsible AI.
- AI will become a secret weapon for SEO strategists.
- AI will fit more naturally into the daily lives of marketers.
1. AI will give content marketers a significant lift.
Sure, artificial intelligence isn’t close to writing the next New York Times best seller. But it can streamline many content marketing tasks. Specifically, it can give marketers a lift in the writing process.
For example, picture a content marketer stuck in the content ideation stage. Instead of waiting for inspiration to strike, she inputs a prompt into an AI chatbot — such as, “Provide a few blog ideas about TikTok marketing” — and receives a list of ideas to get the ball rolling.
AI tools — like ChatGPT, Jasper, and HubSpot’s content assistant — can also do much of the legwork when it comes to research. These tools use complex algorithms to gather, analyze, and interpret information from across the web, identifying patterns and trends in a matter of seconds.
For example, suppose you need to write a video script to promote a product. To get started, you paste hundreds of customer reviews into an AI chatbot and ask it to summarize the key takeaways. Then, you use its output as a rough draft. In the end, you have a new ad highlighting everything your customers love about the product — and you did it in a fraction of the time.
Ultimately, we predict that AI will assist content marketers by executing tedious, repetitive tasks — like coming up with ideas, writing rough drafts, and summarizing large amounts of data.
2. Consumers will demand more personalization, and AI can make it happen.
78% of marketers say personalization has a “strong” or “extremely strong” impact on customer relationships.
While it was once extremely hard to create these experiences with older marketing techniques and technology, AI has opened the door for more pinpointed personalization opportunities.
Right now, AI is primarily used to help us “get things done,” but it has the power to help marketers scale faster, personalize more, and find target audiences easily.
We’re already seeing heavy AI personalization in the marketing industry. For example, many tools allow brands to send marketing emails with names and personalized information based on contact list information. In retail, consumers regularly get emails or e-commerce recommendations for certain products based on what they’ve recently viewed or purchased.
With evolving technology and our ability to capture data on prospects and customers, it’s not shocking to think that AI-based personalization trend will grow stronger in the near future. In 2023 specifically, expect AI to be used more and more to create solid one-to-one personalization.
3. We’ll see an influx of mediocre content generated by AI.
Because AI can churn out content at lightning speeds, some marketers may ramp up their content demands too quickly. As a result, we’ll see an influx of AI-generated content that’s far from perfect.
This leads to an important point: AI is better left for the first draft, not the last. For example, AI-written content may look flawless on the surface, but it lacks critical human elements, like humor, empathy, and cultural context.
Copying and pasting it’s output into your marketing isn’t enough. You need to add your own brand voice and perspective.
On top of that, generative AI works with limited data, so the information it uses may be outdated, incorrect, or even biased. To get around this, marketers must put guardrails in place to maintain quality.
4. Companies will embrace “Responsible AI.”
To state the obvious, AI systems rely on data to make decisions. This data comes from various places, including social media posts, online databases, public records, and general online activity (e.g., posting a review on Yelp).
While this process seems harmless enough, it can reveal a lot about a person’s life. What’s more, consumers may not be aware that their information is being used to make decisions that can affect them.
Currently, companies are expected to self-regulate when it comes to using AI. But as privacy concerns continue to mount, we predict more companies will implement their own AI guidelines.
For example, Microsoft has developed its own Responsible AI Standard which relies on six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Ultimately, data is the fuel for most AI systems. The problem? If consumers don’t trust AI, they’ll keep a safe distance. Therefore, accountability and transparency from companies will go a long way in 2023.
5. AI will become a secret weapon for SEO strategists.
We predict SEO strategists will reap huge benefits from AI in the coming year, thanks to its ability to automate time-consuming tasks.
This type of self-driving technology can identify relevant keywords, conduct competitive analysis, and optimize webpages, such as fixing broken links, duplicate content, and incomplete meta descriptions.
For example, Campbell’s Soup uses AI-powered SEO automation to compress 75,000 images in a single day. This allows the brand to rank on page one of SERPs for 4,000 keywords within a few weeks.
The best part? AI is helping human SEO marketers rather than making their jobs obsolete. AI technology allows SEO experts to get results that aren’t possible without machines. Not worrying about a mountain of SEO-related tasks frees them up to work on more intensive projects.
Because it benefits web traffic and results, expect the investment into AI-powered SEO tools to grow.
6. AI will fit more naturally into the daily lives of marketers.
AI is already having a huge impact on the marketing industry. That said, many marketers are still exploring AI and its potential.
Those who embrace this technology — and integrate it into their workflow— can maintain a competitive edge while saving time in the process.
As AI tools get more user-friendly, eventually they’ll becoming so natural to your work that you don’t notice it. As John McCarthy, one of the fathers of AI, once said, “As soon as it works, no one calls it AI anymore.”
Back to You
It’s 2023, and AI has gone mainstream. There’s no denying its potential to transform a variety of industries, and marketing is no exception. It can help companies create more, scale faster, and build more personalized experiences. But to pull it off, marketers must stay agile as they embrace and innovate with AI.
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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