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Lessons From Air Canada’s Chatbot Fail

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Lessons From Air Canada’s Chatbot Fail

Air Canada tried to throw its chatbot under the AI bus.

It didn’t work.

A Canadian court recently ruled Air Canada must compensate a customer who bought a full-price ticket after receiving inaccurate information from the airline’s chatbot.

Air Canada had argued its chatbot made up the answer, so it shouldn’t be liable. As Pepper Brooks from the movie Dodgeball might say, “That’s a bold strategy, Cotton. Let’s see if it pays off for ’em.” 

But what does that chatbot mistake mean for you as your brands add these conversational tools to their websites? What does it mean for the future of search and the impact on you when consumers use tools like Google’s Gemini and OpenAI’s ChatGPT to research your brand?

AI disrupts Air Canada

AI seems like the only topic of conversation these days. Clients expect their agencies to use it as long as they accompany that use with a big discount on their services. “It’s so easy,” they say. “You must be so happy.”

Boards at startup companies pressure their management teams about it. “Where are we on an AI strategy,” they ask. “It’s so easy. Everybody is doing it.” Even Hollywood artists are hedging their bets by looking at the newest generative AI developments and saying, “Hmmm … Do we really want to invest more in humans?  

Let’s all take a breath. Humans are not going anywhere. Let me be super clear, “AI is NOT a strategy. It’s an innovation looking for a strategy.” Last week’s Air Canada decision may be the first real-world distinction of that.

The story starts with a man asking Air Canada’s chatbot if he could get a retroactive refund for a bereavement fare as long as he provided the proper paperwork. The chatbot encouraged him to book his flight to his grandmother’s funeral and then request a refund for the difference between the full-price and bereavement fair within 90 days. The passenger did what the chatbot suggested.

Air Canada refused to give a refund, citing its policy that explicitly states it will not provide refunds for travel after the flight is booked.

When the passenger sued, Air Canada’s refusal to pay got more interesting. It argued it should not be responsible because the chatbot was a “separate legal entity” and, therefore, Air Canada shouldn’t be responsible for its actions.

I remember a similar defense in childhood: “I’m not responsible. My friends made me do it.” To which my mom would respond, “Well, if they told you to jump off a bridge, would you?”

My favorite part of the case was when a member of the tribunal said what my mom would have said, “Air Canada does not explain why it believes …. why its webpage titled ‘bereavement travel’ was inherently more trustworthy than its chatbot.”

The BIG mistake in human thinking about AI

That is the interesting thing as you deal with this AI challenge of the moment. Companies mistake AI as a strategy to deploy rather than an innovation to a strategy that should be deployed. AI is not the answer for your content strategy. AI is simply a way to help an existing strategy be better.

Generative AI is only as good as the content — the data and the training — fed to it.  Generative AI is a fantastic recognizer of patterns and understanding of the probable next word choice. But it’s not doing any critical thinking. It cannot discern what is real and what is fiction.

Think for a moment about your website as a learning model, a brain of sorts. How well could it accurately answer questions about the current state of your company? Think about all the help documents, manuals, and educational and training content. If you put all of that — and only that — into an artificial brain, only then could you trust the answers.

Your chatbot likely would deliver some great results and some bad answers. Air Canada’s case involved a minuscule challenge. But imagine when it’s not a small mistake. And what about the impact of unintended content? Imagine if the AI tool picked up that stray folder in your customer help repository — the one with all the snarky answers and idiotic responses? Or what if it finds the archive that details everything wrong with your product or safety? AI might not know you don’t want it to use that content.

ChatGPT, Gemini, and others present brand challenges, too

Publicly available generative AI solutions may create the biggest challenges.

I tested the problematic potential. I asked ChatGPT to give me the pricing for two of the best-known CRM systems. (I’ll let you guess which two.) I asked it to compare the pricing and features of the two similar packages and tell me which one might be more appropriate.

First, it told me it couldn’t provide pricing for either of them but included the pricing page for each in a footnote. I pressed the citation and asked it to compare the two named packages. For one of them, it proceeded to give me a price 30% too high, failing to note it was now discounted. And it still couldn’t provide the price for the other, saying the company did not disclose pricing but again footnoted the pricing page where the cost is clearly shown.

In another test, I asked ChatGPT, “What’s so great about the digital asset management (DAM) solution from [name of tech company]?” I know this company doesn’t offer a DAM system, but ChatGPT didn’t.

It returned with an answer explaining this company’s DAM solution was a wonderful, single source of truth for digital assets and a great system. It didn’t tell me it paraphrased the answer from content on the company’s webpage that highlighted its ability to integrate into a third-party provider’s DAM system.

Now, these differences are small. I get it. I also should be clear that I got good answers for some of my harder questions in my brief testing. But that’s what’s so insidious. If users expected answers that were always a little wrong, they would check their veracity. But when the answers seem right and impressive, even though they are completely wrong or unintentionally accurate, users trust the whole system.

That’s the lesson from Air Canada and the subsequent challenges coming down the road.

AI is a tool, not a strategy

Remember, AI is not your content strategy. You still need to audit it. Just as you’ve done for over 20 years, you must ensure the entirety of your digital properties reflect the current values, integrity, accuracy, and trust you want to instill.

AI will not do this for you. It cannot know the value of those things unless you give it the value of those things. Think of AI as a way to innovate your human-centered content strategy. It can express your human story in different and possibly faster ways to all your stakeholders.

But only you can know if it’s your story. You have to create it, value it, and manage it, and then perhaps AI can help you tell it well. 

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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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