SEO

8 Machine Learning Examples From Brands To Inspire Digital Marketers

Published

2 years ago

June 2, 2022

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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Best Practices For Keyword Localization

Published

17 hours ago

October 7, 2024

Max

As brands expand into new international markets, the challenge of running successful PPC campaigns becomes increasingly complex.

Navigating the differences in culture, language, consumer behavior, and market dynamics requires a more nuanced approach than simply translating ads.

For PPC marketers using platforms like Google or Microsoft Ads, it’s critical to adapt campaign strategies for these global audiences.

This article will cover best practices for optimizing international PPC campaigns, with a specific focus on keyword localization.

We’ll explore four key themes that can drive more successful international PPC results:

Keyword localization.
Geo-specific bid adjustments.
Market-specific creative adaptation.
Leveraging automation tools for international scaling.

1. Keyword Localization: Translating Intent, Not Just Language

Keyword localization is a cornerstone of international PPC success, but it’s often misunderstood as a simple translation exercise.

When translating keywords from one language to another, it’s not a “2+2=4” equation most of the time.

In reality, it’s much more complex.

Keyword localization involves understanding the intent behind searches and adapting keywords to match the local language, cultural context, and user behavior.

Steps To Effective Keyword Localization

Market Research: Before diving into translation, research how consumers in the target country search for products or services. This involves understanding search intent, popular terms, slang, and regional dialects.
Translation with a twist: Work with native speakers or linguists familiar with the market. Tools like Google Translate can give you a starting point, but they won’t capture cultural subtleties. Manual keyword research in local search engines is vital.
Use local search engines: Google may dominate globally, but other regions may favor different search engines. Baidu in China, Yandex in Russia, and Naver in South Korea have distinct algorithms and keyword trends. Tailor your keywords to the dominant platform in each market.
Test and optimize: International markets are fluid. What works in one month might need refinement in the next. Regularly review performance and optimize based on search trends, conversion data, and shifting customer behaviors.

For example, in Spain, the keyword “coches baratos” (cheap cars) may seem like a direct translation of its English counterpart.

However, further research might reveal that “ofertas coches” (car deals) or “vehículos económicos” (affordable vehicles) performs better depending on user intent.

2. Geo-Specific Bid Adjustments: Tailor Bids For Performance By Region

International campaigns are prone to fluctuations in performance, driven by differences in local competition, purchasing power, and user behavior.

Geo-specific bid adjustments allow you to tailor your bidding strategy to the realities of each market, maximizing return on ad spend (ROAS).

Below are some best practices for geo-specific bidding:

Analyze Regional Performance: Use data to assess performance on a country or even city level. Look for patterns like higher conversion rates in certain regions and adjust bids accordingly. This is especially important in diverse markets where sub-regions may perform differently, like the UK or Canada.
Adjust Bids Based on Currency Value and Buying Power: Regions with lower purchasing power or fluctuating currency values may require different bid strategies. In some markets, a lower cost-per-click (CPC) approach could help maintain profitability.
Consider Time Zone Differences: Adjust bids based on peak performance hours in each time zone. A broad international campaign can benefit from time-based adjustments that ensure ads show during peak periods in each country.

For instance, if your campaign targets both New York and Berlin, you may find that your peak performance hours vary drastically, necessitating different bid adjustments to maximize efficiency.

In this instance, it’s likely worth segmenting your campaigns by region to account for maximum return on investment or ROI in each region.

In larger enterprise accounts, most regions have different audience sizes, which require different budgets.

If your brand falls into that category, it may be worth creating a separate Google Ads account per region, which can roll up into one MCC account for easier management.

3. Market-Specific Creative Adaptation: Speak The Local Language Through Ad Copy

One of the most common mistakes in international PPC campaigns is failing to adapt ad creatives to local contexts.

Just as keyword localization requires cultural adaptation, ad creatives must be tuned to resonate with local audiences.

A few approaches to localized creative to think about include:

Ad Copy and Messaging: Localize ad copy to reflect cultural preferences, holidays, humor, and common phrases. Avoid literal translations that may miss the mark. Collaborate with local copywriters who understand the nuances of language and sentiment.
Visual Adaptations: Imagery that works in one region may not resonate in another. If your ad visuals feature people, clothing, or settings, make sure they align with local norms and expectations.
Calls to Action (CTAs): CTAs should be adapted based on local shopping behaviors. In some regions, urgency works well (“Buy Now”), while in others, a softer approach may perform better (“Learn More” or “Discover”).

For example, a successful ad campaign in the US using a humorous tone may need to be entirely rethought for a market like Japan, where subtlety and respect play a bigger role in advertising.

4. Leveraging Automation Tools For International Scaling

Managing international PPC campaigns across multiple markets can quickly become overwhelming.

Automation tools, both native to ad platforms and third-party solutions, can help streamline campaign management while still allowing for localized control.

Automation Tactics To Help Scale International PPC Campaigns

Smart Bidding: Utilize Google or Microsoft’s automated bidding strategies tailored to individual market performance. Smart bidding leverages machine learning to optimize bids for conversions or ROAS, adjusting bids based on real-time data.
Dynamic Search Ads (DSAs): Dynamic Search Ads can help expand your reach by automatically generating ad headlines based on your website’s content. For international campaigns, ensure that your website is properly localized to ensure the DSAs serve relevant, accurate ads.
Automated Rules and Scripts: Set up automated rules or scripts to adjust bids, pause underperforming keywords, or raise budgets during peak times. For example, you might set rules to increase bids during holidays specific to individual regions, like Singles’ Day in China or Diwali in India.

Automation tools should be used to complement your manual efforts, not replace them. While they can help manage large campaigns more efficiently, regular oversight and optimization are still essential.

A Holistic Approach To International PPC Success

Expanding into international PPC campaigns presents both challenges and opportunities.

Success depends on taking a holistic approach that incorporates keyword localization, tailored bidding strategies, localized creatives, and effective use of automation.

By adapting your strategies to each specific market, you’ll be able to tap into the unique search behaviors, cultural nuances, and competitive dynamics of global consumers.

Remember that the global PPC landscape is constantly evolving, and regular monitoring, testing, and optimization will be key to staying ahead of the competition.

Whether you’re managing campaigns in-house or as part of an agency, these best practices will help you optimize your international PPC efforts and drive better performance across borders.

More resources:

Featured Image: Mer_Studio/Shutterstock

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SEO

Google’s AI Overviews Avoid Political Content, New Data Shows

Published

2 days ago

October 6, 2024

Max

Google's AI Overviews Avoid Political Content, New Data Shows

Study reveals Google’s cautious approach to AI-generated content in sensitive search results, varying across health, finance, legal, and political topics.

Google shows AI Overviews for 50% of YMYL topics, with legal queries triggering them most often.
Health and finance AI Overviews frequently include disclaimers urging users to consult professionals.
Google avoids generating AI Overviews for sensitive topics like mental health, elections, and specific medications.

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SEO

Executive Director Of WordPress Resigns

Published

2 days ago

October 5, 2024

Max

WordPress Executive Director Josepha Haden Chomphosy resigns,

Josepha Haden Chomphosy, Executive Director of the WordPress Project, officially announced her resignation, ending a nine-year tenure. This comes just two weeks after Matt Mullenweg launched a controversial campaign against a managed WordPress host, which responded by filing a federal lawsuit against him and Automattic.

She posted an upbeat notice on her personal blog, reaffirming her belief in the open source community as positive economic force as well as the importance of strong opinions that are “loosely held.”

She wrote:

“This week marks my last as the Executive Director of the WordPress project. My time with WordPress has transformed me, both as a leader and an advocate. There’s still more to do in our shared quest to secure a self-sustaining future of the open source project that we all love, and my belief in our global community of contributors remains unchanged.

…I still believe that open source is an idea that can transform generations. I believe in the power of a good-hearted group of people. I believe in the importance of strong opinions, loosely held. And I believe the world will always need the more equitable opportunities that well-maintained open source can provide: access to knowledge and learning, easy-to-join peer and business networks, the amplification of unheard voices, and a chance to tap into economic opportunity for those who weren’t born into it.”

Turmoil At WordPress

The resignation comes amidst the backdrop of a conflict between WordPress co-founder Matt Mullenweg and the managed WordPress web host WP Engine, which has brought unprecedented turmoil within the WordPress community, including a federal lawsuit filed by WP Engine accusing Mullenweg of attempted extortion.

Resignation News Was Leaked

The news about the resignation was leaked on October 2nd by the founder of the WordPress news site WP Tavern (now owned by Matt Mullenweg), who tweeted that he had spoken with Josepha that evening, who announced her resignation.

He posted:

“I spoke with Josepha tonight. I can confirm that she’s no longer at Automattic.

She’s working on a statement for the community. She’s in good spirits despite the turmoil.”

Screenshot Of Deleted Tweet

Josepha tweeted the following response the next day:

“Ok, this is not how I expected that news to come to y’all. I apologize that this is the first many of you heard of it. Please don’t speculate about anything.”

Rocky Period For WordPress

While her resignation was somewhat of an open secret it’s still a significant event because of recent events at WordPress, including the resignations of 8.4% of Automattic employees as a result of an offer of a generous severance package to all employees who no longer wished to work there.

Read the official announcement:

Thank you, WordPress

Featured Image by Shutterstock/Wirestock Creators

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