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8 Machine Learning Examples From Brands To Inspire Digital Marketers

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8 Machine Learning Examples From Brands To Inspire Digital Marketers

Machine learning is all the rage but what does it actually look like in practice, as part of a digital marketing strategy?

You’ve encountered a machine learning strategy if you’ve used a website that recommends products based on previous purchases.

Machine learning is a facet of artificial intelligence (AI) that uses algorithms to complete specific tasks, such as product recommendations.

It can achieve a multitude of functions for digital marketers, including:

Machine learning has been in digital marketing for years.

In fact, you are using machine learning whenever you use search engines.

While still a new strategy for most, many businesses have begun implementing this technology into their marketing campaigns.

Below are eight examples of machine learning in digital marketing.

1. Chase

In 2019, the banking giant, Chase Bank, partnered with Persado to help create marketing copy for its campaigns.

They challenged the AI company to generate copy that yields more clicks — which they did.

Examples of the machine learning generated copy are:

Human copy: “Go paperless and earn $5 Cash Back.”

Machine-generated copy: “Limited Time Offer: We’ll reward you with $5 Cash Back when you go paperless.”

Results: AI copy generated nearly double the clicks.

Human copy: “Access cash from the equity in your home” with a “Take a look” button.

Machine-generated copy: “It’s true – You can unlock cash from the equity in your home” with a quick “Click To Apply.”

Results: AI copy attracted 47 applicants a week, while human copy attracted 25 applicants a week.

Human copy: “Hurry, It Ends December 31 Earn 5% Cash Back At Department Stores, Wholesale Clubs.”

Machine-generated copy: “Regarding Your Card: 5% Cash Back Is Waiting For You”

Results: AI copy generated nearly five times the unique clicks.

While the machine-generated copy may have performed better with customers, it’s important to remember that it worked with human copywriters feeding it ideas.

Together, human copywriters and machine learning can create and optimize copy that resonates.

2. Starbucks

With stores worldwide, Starbucks obtains a lot of data.

Starbucks can access purchase insights and turn this information into marketing collateral with the Starbucks loyalty card and mobile app. This strategy is called predictive analysis.

For example, machine learning collects the drinks each customer buys, where they buy them, and when they buy them, and matches this with outside data such as weather and promotions to serve ultra-personalized ads to customers.

One instance includes identifying the customer through Starbucks’ point-of-sale system and providing the barista with their preferred order.

The app can also suggest new products based on previous purchases (which can change according to weather conditions or holidays).

Machine learning can take the guesswork out of product recommendations.

Retail giants like Starbucks have millions of customers, yet they can make each feel like they get personalized recommendations because they can sift through data quickly and efficiently.

3. eBay

eBay has millions of email subscribers. Each email needed engaging subject lines that would cause the customer to click.

However, delivering over 100 million eye-catching subject lines proved overwhelming to human writers.

Enter machine learning.

eBay partnered with Phrasee to help generate engaging subject lines that didn’t trigger spam filters. Additionally, the machine-generated copy aligned with eBay’s brand voice.

Their results show success:

  • 15.8% increase in open rates.
  • 31.2% increase in average clicks.
  • Over 700,000 incremental opens per campaign.
  • Over 56,000 incremental clicks per campaign.

Machine learning can take the most daunting tasks and complete them within minutes at scale.

As a result, businesses can focus more on big-picture campaigns than microtasks.

4. Doordash

Doordash operates thousands of marketing campaigns across its marketing channels.

Their team manually updates bids based on the ads’ performance.

However, the team found that this task was time-consuming and overwhelming.

So Doordash turned to machine learning to optimize its marketing spend.

It built a marketing automation platform based on attribution data.

This data tells the company which channel the customer converted on and with what campaign.

However, it can be hard to promptly collect that type of data with thousands of campaigns going on at once.

Machine learning helps tackle this task by collecting that data and creating spending recommendations so they can optimize their budget quickly and efficiently.

5. Autodesk

Autodesk saw the need for more sophisticated chatbots.

Consumers are often frustrated by the limitations of chatbots and therefore prefer to speak with a human.

However, chatbots can help efficiently guide customers to the content, salesperson, or service page they need.

So Autodesk turned to machine learning and AI.

Autodesk’s chatbot uses machine learning to create dialogue based on search engine keywords.

Then, the chatbot can connect to the customer on the other end, allowing for faster conversion rates.

Since implementing their chatbot, Autodesk had three times the chat engagement and a 109% increase in time spent on the page.

6. Baidu

In 2017, Baidu, the Chinese search engine, built a system called Deep Voice that uses machine learning to convert text to speech. This system can learn 2,500 voices with a half-hour of data each.

Baidu explains that Deep Voice can lead to more immersive experiences in video games and audiobooks.

Baidu’s goal with Deep Voice is to teach machines to speak more human-like by imitating thousands of human voices.

Soon, the search engine hopes the system can master 10,000 or more voices with different accents.

When perfected, Deep Voice could improve things we use every day, like:

  • Siri.
  • Alexa.
  • Google Assistant.
  • Real-time translation.
  • Biometric security.

It can even help people who have lost their voice communicate again.

While there haven’t been any recent updates, Baidu remains hopeful that Deep Voice will revolutionize our tech.

7. Tailor Brands

Tailor Brands uses machine learning to help its users create logos.

The machine, “This or That,” helps Tailor Brands understand a user’s taste using decision-making algorithms.

By choosing examples of what they like, users tell the logo generator their preferences for styles, fonts, and other design aspects.

Tailor Brands uses linear algebra.

Each user’s decision is fed into an equation that helps the machine learn the user’s preferences.

The next time someone generates a logo, Tailor Brands can show styles similar to what they’ve used before.

8. Yelp

Yelp receives millions of photos every day worldwide.

The company realized it needed a sophisticated way to match photos to specific businesses.

So they developed a photo understanding system to create semantic data about individual photographs.

This system allows Yelp to sort photos into categories relevant to the user’s search.

First, Yelp created labels for the photos they received from users, such as “drinks” or “menu.”

Next, the company collected data from photo captions, photo attributes, and crowdsourcing.

Then, it implemented machine learning to recognize the photo labels, from which the system could put the photos into categories.

This photo classification system helps create a better user experience on Yelp.

For instance, it can help diversify cover photos and create tabs that let users jump to the exact information they are looking for.

Digital marketers are only scratching the surface of what machine learning can do for them.

Humans and machines can work together to create more meaningful customer experiences and more optimized campaigns in less time. It’s a win-win-win.

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Twitter Will Share Ad Revenue With Twitter Blue Verified Creators

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Twitter Will Share Ad Revenue With Twitter Blue Verified Creators

Elon Musk, owner and CEO of Twitter, announced that starting today, Twitter will share ad revenue with creators. The new policy applies only to ads that appear in a creator’s reply threads.

The move comes on the heels of YouTube launching ad revenue sharing for creators through the YouTube Partner Program in a bid to become the most rewarding social platform for creators.

Social networks like Instagram, TikTok, and Snapchat have similar monetization options for creators who publish reels and video content. For example, Instagram’s Reels Play Bonus Program offers eligible creators up to $1,200 for Reel views.

The catch? Unlike other social platforms, creators on Twitter must have an active subscription to Twitter Blue and meet the eligibility requirements for the Blue Verified checkmark.

The following is an example of a Twitter ad in a reply thread (Promoted by @ASUBootcamps). It should generate revenue for the Twitter Blue Verified creator (@rowancheung), who created the thread.

Screenshot from Twitter, January 2023

To receive the ad revenue share, creators would have to pay $8 per month (or more) to maintain an active Twitter Blue subscription. Twitter Blue pricing varies based on location and is available in the United States, Canada, Australia, New Zealand, Japan, the United Kingdom, Saudi Arabia, France, Germany, Italy, Portugal, and Spain.

Eligibility for the Twitter Blue Verified checkmark includes having an active Twitter Blue subscription and meeting the following criteria.

  • Your account must have a display name, profile photo, and confirmed phone number.
  • Your account has to be older than 90 days and active within the last 30 days.
  • Recent changes to your account’s username, display name, or profile photo can affect eligibility. Modifications to those after verification can also result in a temporary loss of the blue checkmark until Twitter reviews your updated information.
  • Your account cannot appear to mislead or deceive.
  • Your account cannot spam or otherwise try to manipulate the platform for engagement or follows.

Did you receive a Blue Verified checkmark before the Twitter Blue subscription? That will not help creators who want a share of the ad revenue. The legacy Blue Verified checkmark does not make a creator account eligible for ad revenue sharing.

When asked about accounts with a legacy and Twitter Blue Verified checkmark, Musk tweeted that the legacy Blue Verified is “deeply corrupted” and will sunset in just a few months.

Regardless of how you gained your checkmark, it’s important to note that Twitter can remove a checkmark without notice.

In addition to ad revenue sharing for Twitter Blue Verified creators, Twitter Dev announced that the Twitter API would no longer be free in an ongoing effort to reduce the number of bots on the platform.

While speculation looms about a loss in Twitter ad revenue, the Wall Street Journal reported a “fire-sale” Super Bowl offer from Musk to win back advertisers.

The latest data from DataReportal shows a positive trend for Twitter advertisers. Ad reach has increased from 436.4 million users in January 2022 to 556 million in January 2023.

Twitter is also the third most popular social network based on monthly unique visitors and page views globally, according to SimilarWeb data through December 2022.


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AI Content Detection Software: Can They Detect ChatGPT?

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AI Content Detection Software: Can They Detect ChatGPT?

We live in an age when AI technologies are booming, and the world has been taken by storm with the introduction of ChatGPT.

ChatGPT is capable of accomplishing a wide range of tasks, but one that it does particularly well is writing articles. And while there are many obvious benefits to this, it also presents a number of challenges.

In my opinion, the biggest hurdle that AI-generated written content poses for the publishing industry is the spread of misinformation.

ChatGPT, or any other AI tool, may generate articles that may contain factual errors or are just flat-out incorrect.

Imagine someone who has no expertise in medicine starting a medical blog and using ChatGPT to write content for their articles.

Their content may contain errors that can only be identified by professional doctors. And if that blog content starts spreading over social media, or maybe even ranks in Search, it could cause harm to people who read it and take erroneous medical advice.

Another potential challenge ChatGPT poses is how students might leverage it within their written work.

If one can write an essay just by running a prompt (and without having to do any actual work), that greatly diminishes the quality of education – as learning about a subject and expressing your own ideas is key to essay writing.

Even before the introduction of ChatGPT, many publishers were already generating content using AI. And while some honestly disclose it, others may not.

Also, Google recently changed its wording regarding AI-generated content, so that it is not necessarily against the company’s guidelines.

Image from Twitter, November 2022

This is why I decided to try out existing tools to understand where the tech industry is when it comes to detecting content generated by ChatGPT, or AI generally.

I ran the following prompts in ChatGPT to generate written content and then ran those answers through different detection tools.

  • “What is local SEO? Why it is important? Best practices of Local SEO.”
  • “Write an essay about Napoleon Bonaparte invasion of Egypt.”
  • “What are the main differences between iPhone and Samsung galaxy?”

Here is how each tool performed.

1. Writer.com

For the first prompt’s answer, Writer.com fails, identifying ChatGPT’s content as 94% human-generated.

Writer.com resultsScreenshot from writer.com, January 2023

For the second prompt, it worked and detected it as AI-written content.

Writer.com test resultScreenshot from writer.com, January 2023

For the third prompt, it failed again.

Sample ResultScreenshot from writer.com, January 2023

However, when I tested real human-written text, Writer.com did identify it as 100% human-generated very accurately.

2. Copyleaks

Copyleaks did a great job in detecting all three prompts as AI-written.

Sample ResultScreenshot from Copyleaks, January 2023

3. Contentatscale.ai

Contentatscale.ai did a great job in detecting all three prompts as AI-written, even though the first prompt, it gave a 21% human score.

Contentscale.aiScreenshot from Contentscale.ai, January 2023

4. Originality.ai

Originality.ai did a great job on all three prompts, accurately detecting them as AI-written.

Also, when I checked with real human-written text, it did identify it as 100% human-generated, which is essential.

Originality.aiScreenshot from Originality.ai, January 2023

You will notice that Originality.ai doesn’t detect any plagiarism issues. This may change in the future.

Over time, people will use the same prompts to generate AI-written content, likely resulting in a number of very similar answers. When these articles are published, they will then be detected by plagiarism tools.

5. GPTZero

This non-commercial tool was built by Edward Tian, and specifically designed to detect ChatGPT-generated articles. And it did just that for all three prompts, recognizing them as AI-generated.

GPTZeroScreenshot from GPTZero, January 2023

Unlike other tools, it gives a more detailed analysis of detected issues, such as sentence-by-sentence analyses.

sentence by sentence text perplexityScreenshot from GPTZero, January 2023

OpenAI’s AI Text Classifier

And finally, let’s see how OpenAi detects its own generated answers.

For the 1st and 3rd prompts, it detected that there is an AI involved by classifying it as “possibly-AI generated”.

AI Text Classifier. Likely AI-generatedAI Text Classifier. Likely AI-generated

But surprisingly, it failed for the 2nd prompt and classified that as “unlikely AI-generated.” I did play with different prompts and found that, as of the moment, when checking it, few of the above tools detect AI content with higher accuracy than OpenAi’s own tool.

AI Text Classifier. Unlikely AI-generatedAI Text Classifier. Unlikely AI-generated

As of the time of this check, they had released it a day before. I think in the future, they will fine tune it, and it will work much better.

Conclusion

Current AI content generation tools are in good shape and are able to detect ChatGPT-generated content (with varying degrees of success).

It is still possible for someone to generate copy via ChatGPT and then paraphrase that to make it undetectable, but that might require almost as much work as writing from scratch – so the benefits aren’t as immediate.

If you think about ranking an article in Google written by ChatGPT, consider for a moment: If the tools we looked at above were able to recognize them as AI-generated, then for Google, detecting them should be a piece of cake.

On top of that, Google has quality raters who will train their system to recognize AI-written articles even better by manually marking them as they find them.

So, my advice would be not to build your content strategy on ChatGPT-generated content, but use it merely as an assistant tool.

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Five things you need to know about content optimization in 2023

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5 Things You Need To Know About Optimizing Content in 2023

30-second summary:

  • As the content battleground goes through tremendous upheaval, SEO insights will continue to grow in importance
  • ChatGPT can help content marketers get an edge over their competition by efficiently creating and editing high-quality content
  • Making sure your content rank high enough to engage the target audience requires strategic planning and implementation

Google is constantly testing and updating its algorithms in pursuit of the best possible searcher experience. As the search giant explains in its ‘How Search Works’ documentation, that means understanding the intent behind the query and bringing back results that are relevant, high-quality, and accessible for consumers.

As if the constantly shifting search landscape weren’t difficult enough to navigate, content marketers are also contending with an increasingly technology-charged environment. Competitors are upping the stakes with tools and platforms that generate smarter, real-time insights and even make content optimization and personalization on the fly based on audience behavior, location, and data points.

Set-it-and-forget-it content optimization is a thing of the past. Here’s what you need to know to help your content get found, engage your target audience, and convert searchers to customers in 2023.

AI automation going to be integral for content optimization

Technologies-B2B-organizations-use-to-optimize-content

As the content battleground heats up, SEO insights will continue to grow in importance as a key source of intelligence. We’re optimizing content for humans, not search engines, after all – we had better have a solid understanding of what those people need and want.

While I do not advocate automation for full content creation, I believe next year – as resources become stretched automation will have a bigger impact on helping with content optimization of existing content.

CHATGPT

ChatGPT, developed by OpenAI, is a powerful language generation model that leverages the Generative Pre-trained Transformer (GPT) architecture to produce realistic human-like text. With Chat GPT’s wide range of capabilities – from completing sentences and answering questions to generating content ideas or powering research initiatives – it can be an invaluable asset for any Natural Language Processing project.

ChatGPT-for-content

The introduction on ChatGPT has caused considerable debate and explosive amounts of content on the web. With ChatGPT, content marketers can achieve an extra edge over their competition by efficiently creating and editing high-quality content. It offers assistance with generating titles for blog posts, summaries of topics or articles, as well as comprehensive campaigns when targeting a specific audience.

However, it is important to remember that this technology should be used to enhance human creativity rather than completely replacing it.

For many years now AI-powered technology has been helping content marketers and SEOs automate repetitive tasks such as data analysis, scanning for technical issues, and reporting, but that’s just the tip of the iceberg. AI also enables real-time analysis of a greater volume of consumer touchpoints and behavioral data points for smarter, more precise predictive analysis, opportunity forecasting, real-time content recommendations, and more.

With so much data in play and recession concerns already impacting 2023 budgets in many organizations, content marketers will have to do more with less this coming year. You’ll need to carefully balance human creative resources with AI assists where they make sense to stay flexible, agile, and ready to respond to the market.

It’s time to look at your body of content as a whole

Google’s Helpful Content update, which rolled out in August, is a sitewide signal targeting a high proportion of thin, unhelpful, low-quality content. That means the exceptional content on your site won’t rank to their greatest potential if they’re lost in a sea of mediocre, outdated assets.

It might be time for a content reboot – but don’t get carried away. Before you start unpublishing and redirecting blog posts, lean on technology for automated site auditing and see what you can fix up first. AI-assisted technology can help sniff out on-page elements, including page titles and H1 tags, and off-page factors like page speed, redirects, and 404 errors that can support your content refreshing strategy.

Focus on your highest trafficked and most visible pages first, i.e.: those linked from the homepage or main menu. Google’s John Mueller confirmed recently that if the important pages on your website are low quality, it’s bad news for the entire site. There’s no percentage by which this is measured, he said, urging content marketers and SEOs to instead think of what the average user would think when they visit your website.

Take advantage of location-based content optimization opportunities

Consumers crave personalized experiences, and location is your low-hanging fruit. Seasonal weather trends, local events, and holidays all impact your search traffic in various ways and present opportunities for location-based optimization.

AI-assisted technology can help you discover these opportunities and evaluate topical keywords at scale so you can plan content campaigns and promotions that tap into this increased demand when it’s happening.

Make the best possible use of content created for locally relevant campaigns by repurposing and promoting it across your website, local landing pages, social media profiles, and Google Business Profiles for each location. Google Posts, for example, are a fantastic and underutilized tool for enhancing your content’s visibility and interactivity right on the search results page.

Optimize content with conversational & high-volume keywords

Look for conversational and trending terms in your keyword research, too. Top-of-funnel keywords that help generate awareness of the topic and spur conversations in social channels offer great opportunities for promotion. Use hashtags organically and target them in paid content promotion campaigns to dramatically expand your audience.

Conversational keywords are a good opportunity for enhancing that content’s visibility in search, too. Check out the ‘People Also Ask’ results and other featured snippets available on the search results page (SERP) for your keyword terms. Incorporate questions and answers in your content to naturally optimize for these and voice search queries.

SEO-and-creating-content-in-2023

It’s important that you utilize SEO insights and real-time data correctly; you don’t want to be targeting what was trending last month and is already over. AI is a great assist here, as well, as an intelligent tool can be scanning and analyzing constantly, sending recommendations for new content opportunities as they arise.

Consider how you optimize content based on intent and experience

The best content comes from a deep, meaningful understanding of the searcher’s intent. What problem were they experiencing or what need did they have that caused them to seek out your content in the first place? And how does your blog post, ebook, or landing page copy enhance their experience?

Look at the search results page as a doorway to your “home”. How’s your curb appeal? What do potential customers see when they encounter one of your pages in search results? What kind of experience do you offer when they step over the threshold and click through to your website?

The best content meets visitors where they are at with relevant, high-quality information presented in a way that is accessible, fast loading, and easy to digest. This is the case for both short and long form SEO content. Ensure your content contains calls to action designed to give people options and help them discover the next step in their journey versus attempting to sell them on something they may not be ready for yet.

2023, the year of SEO: why brands are leaning in and how to prepare

Conclusion

The audience is king, queen, and the entire court as we head into 2023. SEO and content marketing give you countless opportunities to connect with these people but remember they are a means to an end. Keep searcher intent and audience needs at the heart of every piece of content you create and campaign you plan for the coming year.

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