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7 Serious Risks You Face Using AI for Marketing

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1 year ago

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7 Serious Risks You Face Using AI for Marketing

A couple of years ago, most of us marketers hadn’t the foggiest idea of how to use AI for marketing. Suddenly, we’re writing LinkedIn posts about the best prompts to whisper into ChatGPT’s ear.

There’s plenty to celebrate about the sudden infusion of artificial intelligence into our work. McKenzie reckons AI will unlock $2.6 trillion in value for marketers.

But our rapid adoption of AI may be getting ahead of important ethical, legal, and operational questions—which will leave marketers exposed to risks we never had to think about before (i.e. can we be sued for telling AI to “write like Stephen King?”).

There’s a metric ton of AI dust in the marketing atmosphere that won’t settle for years. No matter how hard you squint you won’t make out every potential pitfall of using large language models and machine learning to create content and manage ads.

So our goal in this article is to view seven of the most prominent risks of using AI for marketing from a very high ground. We’ve rounded up advice from experts to help mitigate these risks. And we’ve added plenty of resources so you can dig deeper into the questions that concern you most.

Risk #1: Machine learning bias

Sometimes machine learning algorithms give results that are unfairly in favor or against someone or something. It’s called machine learning bias, or AI bias, and it’s a pervasive problem with even the most advanced deep neural networks.

It’s a data problem

It’s not that AI networks are inherently bigoted. It’s a problem with the data that’s fed into them.

Machine learning algorithms work by identifying patterns to calculate the probability of an outcome, like whether or not a particular group of shoppers will like your product.

But what if the data the AI trains on is skewed towards a particular race, gender, or age group? The AI will come to the conclusion that those people are a better match and skew ad creative or placement accordingly.

Here’s an example. Researchers recently tested for gender bias in Facebook’s ad targeting systems. The investigators placed an ad to recruit delivery drivers for Pizza Hut, and a similar ad with the same qualifications for Instacart.

The existing pool of Pizza Hut drivers skews male, so Facebook showed those ads disproportionately to men. Instacart has more women drivers, so ads for their job were placed in front of more women. But there’s no inherent reason that women wouldn’t want to know about the Pizza Hut jobs, so that’s a big misstep in ad targeting.

AI bias is common

The problem extends way beyond Facebook. Researchers from USC looked at two large AI databases and found that over 38% of the data in them was biased. ChatGPT’s documentation even warns that their algorithm may associate “negative stereotypes with black women.”

AI for marketing - graphic showing that 38% of data was biased

Machine learning bias presents several implications for marketers; the least of which is poor ad performance. If you’re hoping to reach the most potential customers possible, an ad targeting platform that excludes large chunks of the population is less than ideal.

Of course there are bigger ramifications if our ads unfairly target, or exclude, certain groups. If your real estate ad discriminates against protected minorities, you could land on the wrong end of the Fair Housing Act and the Federal Trade Commision. Not to mention completely missing the inclusive marketing boat.

How to avoid AI bias

So what do we do when our AI tools run amok? There are a couple of steps you can take to make sure your ads treat everyone equitably.

First and foremost, make sure a someone reviews your content, writes Alaura Weaver, the senior manager of content and community at Writer. “While AI technology has advanced significantly, it lacks the critical thinking and decision-making abilities that humans have,” she explains. “By having human editors review and fact-check AI-written content, they can ensure that it’s free from bias and follows ethical standards.”

Human oversight will reduce the risk of negative outcomes in paid ad campaigns, too.

“Currently, and perhaps indefinitely, it is not advisable to let AI completely take over campaigns or any form of marketing,” says Brett McHale, the Founder of Empiric Marketing.  “AI performs optimally when it receives accurate inputs from organic intelligence that has already accumulated vast amounts of data and experiences.”

Risk #2: Factual fallacies

Google recently cost its parent company $100b in valuation when its new AI chatbot, Bard, gave an incorrect answer in a promotional tweet.

Google’s goof highlights one of the biggest limitations of AI, and one of the biggest risks for marketers using it: AI doesn’t always tell the truth.

AI hallucinates

Ethan Mollic, a professor at the Wharton School of Business, recently described AI-powered systems like ChatGPT as an “omniscient, eager-to-please intern who sometimes lies.”

Of course, AI isn’t sentient, despite what some may claim. It doesn’t intend to deceive us. It can, however, suffer from “hallucinations” that lead it to just make stuff up.

AI for marketers - meme with two choices for Chat GPT, lie or hallucinate

AI is a prediction machine. It looks to fill in the next word or phrase that’ll answer your query. But it’s not self-aware; AI doesn’t have gut-check logic to know if what it’s stringing together makes sense.

Unlike bias, this doesn’t seem to be a data problem. Even when the network has all the right info, it can still tell us the wrong thing.

Consider this example where a user asked ChatGPT “how many times did Argentina win the FIFA world cup?” It said once and referenced the team’s 1978 victory. The tweeter then asked which team won in 1986.

The chatbot admitted it was Argentina with no explanation for its former gaffe.

The troubling part is that AI’s erroneous answers are often written so confidently, they blend into the text around them, making them seem completely plausible. They can also be comprehensive, as detailed in a lawsuit filed against Open.ai, where ChatGPT allegedly concocted an entire story of embezzlement that was then shared by a journalist.

How to avoid AI’s hallucinations

While AI can lead you astray with even single-word answers, it’s more likely to go off the rails when writing longer texts.

“From a single prompt, AI can generate a blog or an eBook. Yes, that’s amazing – but there’s a catch,” Weaver warns. “The more it generates, the more editing and fact-checking you’ll have to do.”

To reduce the chances that your AI tool starts spinning hallucinatory narratives, Weaver says it’s best to create an outline and have the bot tackle it one section at a time. And then, of course, have a person review the facts and stats it adds.

Risk #3: Misapplication of AI tools

Every morning we wake up to a new crop of AI tools that seemingly sprouted overnight like mushrooms after a rainstorm.

But not every platform is built for all marketing functions, and some marketing challenges can’t (yet) be solved by AI.

AI tools have limitations

ChatGPT is a great example. The belle of the AI ball is fun to play with (like writing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible). And it can churn out some surprisingly well-written short form answers that bust up writer’s block. But don’t ask it to help you do keyword research.

ChatGPT fails because of its relatively old data set which only includes information pre-2022. Ask it to offer keywords for “AI marketing” and its answers won’t jive with what you find in other tools like Thinword or Contextminds.

Likewise, both Google and Facebook have new AI-powered tools to help marketers create ads, optimize ad spend, and personalize the ad experience. A chatbot can’t solve those challenges.

 

AI for marketing - large screen from Google's recent Live event

Google announced a slew of AI upgrades to its search and ads management products at the 2023 Google Marketing Live event.

You can overuse AI

If you give an AI tool a singular task, it can over index on just one goal. Nick Abbene, a marketing automation expert, sees this often with companies focused on improving their SEO.

“The biggest problem I see is using SEO tools blindly, over-optimizing for search engines, and disregarding customer search intent,” Abbene says. “SEO tools are great for signaling to search engines quality content. But ultimately, Google wants to match the searcher’s ask.”

How to avoid misapplication of AI tools

A wrench isn’t the best option for pounding nails. Likewise, an AI writing assistant may not be good for creating web pages. Before you go all in on any one AI option, Abbene says to get feedback from the tool’s builder and other users.

“In order to avoid mis selection of AI tools, understand if other marketers are using the tool for your use case,” he says. “Feel free to request a product demo, or trial it alongside some other tools that offer the same functionality.”

Websites like Capterra let you quickly compare multiple AI platforms.

AI for marketers - screenshot from Capterra

 

And once you find the right AI tool stack, use it to aid the process, not take it over. “Don’t be afraid to use AI tools to augment your workflow, but use them just for that,” Abbene says. “Begin each piece of content from first principles, with quality keyword research and understanding search intent.”

Risk #4: Homogeneous content

AI can write an entire essay in about 10 seconds. But as impressive as generative AI has become, it lacks the nuance to be truly creative, leaving its output often feeling, well, robotic.

“While AI is great at producing content that’s informative, it often lacks the creative flair and engagement that humans bring to the table,” Weaver says.

AI is made to imitate

Ask a generative AI writing bot to pen your book report, and it’ll easily spin up 500 words that competently explain the main theme of Catcher in the Rye (assuming it doesn’t hallucinate Holden Caulfield as a bank robber).

It can do that because it’s absorbed thousands of texts about J. D. Salinger’s masterpiece.

Now ask your AI pal to write a blog post that explains a concept core to your business in a way that encapsulates your brand, audience, and value proposition. You might be disappointed. “AI-generated content doesn’t always account for the nuances of a brand’s personality and values and may produce content that misses the mark,” Weaver says.

In other words, AI is great at digesting, combining, and reconfiguring what’s already been created. It’s not great at creating something that stands out against existing content.

Generative AI tools are also not good at making content engaging. They’ll happily churn out huge blocks of words with nary an image, graph, or bullet point to give weary eyes a rest. They won’t pull in customer stories or hypothetical examples to make a point more relatable. And they’d struggle to connect a news story from your industry to a benefit your product provides.

How to avoid homogenous content

Some AI tools, like Writer, have built-in features to help writers maintain a consistent brand personality. But you’ll still need an editor to “review, and edit the content for brand voice and tone to ensure that it resonates with the audience and reinforces the organization’s messaging and objectives,” Weaver advises.

Editors and writers can also see an article like other humans will. If there’s an impenetrable block of words, they’ll be the ones to break it up and add a little visual zhuzh.

Use AI content as a starting point—as a way to help kickstart your creativity and research. But always add your own personal touch.

Risk #5: Loss of SEO

Google’s stance on AI content has been a little murky. At first, it seemed the search engine would penalize posts written with AI.

[Image: tweet from John Mueller on AI]

More recently, Google’s developer blog said that AI is OK in their book. But there is a significant wink with that confirmation. Only “content that demonstrates qualities of what we call E-E-A-T: expertise, experience, authoritativeness, and trustworthiness” will impress the human search raters that continually evaluate Google’s ranking systems.

Trust is clutch for SEO

Among Google’s E-E-A-T, the one factor that rules them all is trust.

AI for marketing - diagram showing Google's EEAT method for judging content

[source]

We’ve already discussed that AI content is prone to fallacies, making it inherently untrustworthy without human supervision. It also fails to meet the supporting requirements because, by nature, it isn’t written by someone with expertise, authority, or experience on the topic.

Take a blog post about baking banana bread. An AI bot will give you a recipe in about two seconds. But it can’t wax poetic on the chilly winter days spent baking for its family. Or talk about the years it spent experimenting with various types of flour as a commercial baker. Those perspectives are what Google’s search raters look for.

It also seems to be what people crave, too. That’s why so many of them are turning to real people on TikTok videos to learn things they used to find on Google.

How to avoid losing SEO

The great thing about AI is it doesn’t mind sharing bylines. So when you do use a chatbot to speed up content production, make sure you reference a human author with credentials.

This is especially true for sensitive subjects like healthcare and personal finance, which Google calls Your Money, Your Life topics. “If you’re in a YMYL vertical, prioritize authority, trust and accuracy above all else in your content,” advises Elisa Gabbert, Director of Content and SEO for WordStream and LocaliQ.

AI for marketers - screenshot of a WebMD article

When writing about healthcare, for example, have your posts reviewed by a medical professional and reference them in the post. That’s a strong signal to Google that your content is trustworthy, even if it was started in a chatbot.

Risk #6: Legal challenges

Generative AI learns from work created by humans, then creates something new(ish). The question of copyright is unclear for both the input and output of the AI content model.

Existing work is likely fair game for AI

To illustrate (pun intended) the copyright question for works that feed large learning models, we turn to a case reported by technologist Andy Baio. As Baio explains, an LA-based artist named Hollie Mengert learned that 32 of her illustrations had been absorbed into an AI model, then offered via open license to anyone that wanted to recreate her style.

AI for marketers - comparison of original artist work to AI generated versions

Caption: a collection of artist Hollie Mengert’s illustrations (left) compared with AI generated illustrations based on her style, as curated by Andy Baio.

The story gets more complicated when you learn that she created many of her images for clients like Disney, who actually own the rights to them.

Can illustrators (or writers or coders) who find themselves in the same spot as Mengert successfully sue for copyright infringement?

There’s not yet a clear answer to the question. “I see people on both sides of this extremely confident in their positions, but the reality is nobody knows,” Baio told The Verge. “And anyone who says they know confidently how this will play out in court is wrong.”

If the AI you use to create an image or article was trained on thousands of works from many creators, you’re not likely to lose a court case. But if you feed the machine ten Stephen King books and tell the bot to write a new one in that style, you could be in trouble.

Disclaimer: We aren’t lawyers so please get legal advice if you’re unsure.

Your AI content may not be protected either

What about content you create using a chatbot, is it covered by copyright laws? For the most part, it’s not unless you’ve done considerable work to edit it. Which means you’d have little recourse if someone repurposes (read: steals) your posts for their own blog.

For content that is protected it may be the AI’s programmer, not you, that holds the rights. Many countries consider the maker of the tool that produced a work to be its creator, not the person that typed in the prompt.

How to avoid legal challenges

Start by using a reputable AI content creation tool. Find one with plenty of positive reviews and that clearly addresses their stance on copyright laws.

Also, use your good judgment to decide if you’re intentionally copying a creator’s work or simply using AI to augment your own.

And if you want a fighting chance in court to protect what you produce, make lots of substantial changes. Or use AI to help create an outline, but write most of the words yourself.

Risk #7: Security and privacy breaches

AI tools present marketers with a broad range of potential threats to their system’s security and data privacy. Some are direct attacks from malicious actors. Others are simply users unwittingly giving sensitive information to a system designed to share it.

Security risks from AI tools

“There are plenty of products out there that look, feel, and behave like legitimate tools, but are in fact malware,” Elaine Atwell, the Senior Editor of Content Marketing at endpoint security provider Kolide, told us. “They’re extremely difficult to differentiate from legitimate tools and you can find them in the Chrome store right now.”

Type any version of “AI tools” into the Google Chrome store and you’ll find no shortage of options.

AI for marketing - screenshot from the Chrome store

Atwell wrote about these risks on the Kolide blog. In her article, she referenced an incident where a Chrome extension called “Quick access to Chat GPT” was actually a ruse. Once downloaded, the software hijacked users’ Facebook accounts and swiped all of the victim’s cookies—even those for security. Over 2,000 people downloaded the extension every day, Atwell reported.

Privacy unprotected

Atwell says even a legitimate AI tool can present a security risk. “…right now, most companies don’t even have policies in place to assess the types and levels of risk posed by different extensions. And in the absence of clear guidance, people all over the world are installing these little helpers and feeding them sensitive data.”

Let’s say you’re writing an internal financial report to be shared with investors. Remember that AI networks learn from what they’re given to produce outputs for other users. All the data you place in the AI chatbot could be fair game for people outside of your company. And may pop up if a competitor asks about your bottom line.

How to avoid privacy and security risks

The first line of defense is to make sure a piece of software is what it claims to be. Beyond that, be cautious about how you use the tools you choose. “If you’re going to use AI tools (and they do have uses!) don’t feed them any data that could be considered sensitive,” Atwell says.

Also, while you’re reviewing AI tools for usefulness and bias, ask about their privacy and security policies.

Mitigate the risks using AI for marketing

AI is advancing at an incredible rate. In less than a year Chat GTP has already seen significant boosts in its capabilities. It’s impossible to know what we’ll be able to do with AI in even the next six to twelve months. Nor can we anticipate the potential problems.

Here are several ways you can improve your AI marketing outcomes while avoiding some of the most common risks:

  • Have human editors review content for quality, readability, and brand voice
  • Scrutinize each tool you use for security and capability
  • Regularly review AI-directed ad targeting for bias
  • Assess copy and images for potential copyright infringement

We’d like to thank Elain Attwell, Brett McHale, Nick Abenne, and Alaura Weaver for contributing to this post.

To recap, let’s review our list of risks that come with using AI for marketing:

  1. Machine learning bias
  2. Factual fallacies
  3. Misapplication of AI tools
  4. Homogeneous content
  5. Loss of SEO
  6. Legal challenges
  7. Security and privacy breaches



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The Important Takeaways from Google I/O 2024

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2 months ago

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May 28, 2024

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Google I/O 2024

Google I/O 2024, the Silicon Valley giant’s annual developer conference, brought a bunch of exciting updates and advancements across various Google platforms and services. signalling a new era of intelligent, creative, and responsible technology.

Here’s an in-depth look at the key announcements and features unveiled during the event.

Google Search Gets Smarter

No surprises that one of the biggest stories to come out of Google I/O 2024 is about the enhancement of Google Search. AI Overviews are now being rolled out to all users in the U.S., providing deeper insights with just one search query. This feature leverages Google’s latest AI model, Gemini, allowing users to ask complex questions and receive comprehensive answers.

For example, users can now search for “best yoga or pilates studios in Boston” and not only receive a list of studios, but also specific details such as introductory offers and walking times from their location. This level of detail and integration aims to make search results more actionable and relevant, and improve user experience.

Enhanced Brainstorming Capabilities

Google Search is also becoming a tool for creativity and inspiration. The new brainstorming feature helps users find tailored suggestions for various needs. For example, if someone searches for “anniversary celebration dinner places Dallas,” they will elicit personalized recommendations, complete with categories to explore, such as types of cuisine, ambiance, and special offers.

This enhancement transforms Google Search into more than just an information retrieval tool—it becomes a creative assistant, helping users plan and make decisions with ease and confidence.

Interactive Video Search

Another ground-breaking update is the introduction of Interactive Video Search. This feature allows users to search within video content to find specific insights. Imagine watching a cooking video and being able to search for a particular step or ingredient explanation within the video. This capability deciphers complex video content, making it easier to locate and understand the information presented.

Interactive Video Search is expected to be a game-changer for educational content, tutorials, and entertainment, providing a more dynamic and user-friendly way to engage with video media.

Gemini Tools for Developers

Google is also empowering developers with new tools. The Gemini 1.5 Pro and Flash models are now available in over 200 countries, offering advanced capabilities and integrated collaboration features within Workspace apps like Gmail and Docs. These tools is to enhance productivity and innovation in the development community.

The integration within Workspace means developers can collaborate more effectively, leveraging AI to streamline coding, debugging, and deployment processes. The global rollout ensures developers everywhere have access to the latest technologies to build and improve their applications.

Generative Media Models

Content creation is set to become more intuitive with the introduction of generative media models. Google unveiled Imagen 3 and Veo, tools that allow users to create images and videos from text prompts. This technology is especially useful for marketing campaigns, social media content, and other visual storytelling demands.

With Imagen 3, users can generate high-quality images simply by describing them, while Veo enables the creation of compelling video content from text-based descriptions. These tools lower the barrier to professional-grade content creation, making it accessible to individuals and businesses alike.

Responsible AI Initiatives

Amid all these advancements, Google says it remains committed to the responsible deployment of AI. The introduction of SynthID is a significant step towards easier identification of AI-generated content. SynthID embeds a subtle but detectable watermark in AI-generated images, ensuring transparency and authenticity in digital media.

Additionally, LearnLM is another innovative tool aimed at promoting responsible AI use. It provides educational resources and best practices for developing and deploying AI models, helping developers understand the ethical implications and technical standards required for safe AI usage.

In Summary

Google I/O 2024 showcased a range of innovations that not only enhance user experience but also push the boundaries of what’s possible with technology. From smarter search capabilities and creative brainstorming tools to advanced developer resources and responsible AI practices, Google continues to lead the way in making technology more accessible, intuitive, and ethical.

These updates reflect Google’s ongoing commitment to leveraging AI for the betterment of society, ensuring that their technological advancements are both innovative and responsible. Users and developers alike can look forward to a more connected, efficient, and creative future with these new tools and features.



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Ultimate Guide to Product Data Feed Management

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2 months ago

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May 24, 2024

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Ultimate Guide to Product Data Feed Management

From the early days of simple online catalogs to today’s dynamic, data-driven shopping experiences, the e-commerce landscape has seen a monumental shift, driven by advances in technology and changes in consumer behavior. This transformation has not only expanded the reach of retailers but also heightened the competition and complexity of selling online.

Overview of the E-commerce Landscape

The current e-commerce landscape is a vast, interconnected ecosystem. It is one where businesses of all sizes compete to capture the attention and loyalty of digital consumers. 

Ecommerce spans various channels, including online marketplaces like Amazon and eBay. It involves social commerce platforms such as Instagram and Facebook. It is conducted by countless individual online stores powered by platforms like Shopify, BigCommerce, and WooCommerce. 

Each of these channels offers unique opportunities and challenges. Each demands a particular approach to engaging with potential customers.

As the digital marketplace continues to grow, so does the importance of maintaining a strong online presence. 

For businesses, this means more than just listing products online. It involves creating comprehensive, engaging, and personalized shopping experiences that resonate with consumers across multiple touchpoints.

The Evolution of Online Shopping and the Role of Data

The evolution of online shopping is a story of technological innovation and changing consumer expectations. 

Initially, online shopping offered a convenient alternative to physical stores, allowing consumers to purchase products from the comfort of their homes. 

Over time, the advent of mobile technology, social media, and advanced data analytics has transformed online shopping into an immersive and interactive experience. 

Today, consumers can receive personalized product recommendations, see targeted ads, use augmented reality to “try on” products, and enjoy seamless omnichannel shopping experiences that blur the lines between online and in-store.

At the heart of this evolution is data. 

Data fuels the algorithms that predict shopping behavior, tailor marketing messages, and optimize the online shopping experience. 

Effective product data feed management plays a crucial role in this ecosystem. It involves not just listing products online but strategically managing and optimizing product information, ensuring it reaches the right audience, at the right time, in the right way. 

This process is vital for improving product visibility, enhancing customer experiences, and ultimately driving sales in a crowded and competitive digital marketplace.

As we delve into the intricacies of product data feed management, it’s important to recognize its significance as the backbone of successful e-commerce strategies. 

By understanding and leveraging the power of data, businesses can navigate the complexities of the digital marketplace and create meaningful connections with their customers.

What is Product Data Feed Management?

The ability to efficiently distribute, update, and optimize product information across multiple online channels is paramount. Product data feed management facilitates this critical function. It is a process that stands at the core of successful online retailing.

Definition and Explanation of Product Feeds

A product feed, fundamentally, is a structured file—often in formats like XML, CSV, or JSON—that contains detailed information about the products in an online store’s catalog. 

This file serves as a digital product list, designed to be ingested by various e-commerce platforms, search engines, social media channels, and comparison shopping websites. 

Product feeds include essential details such as product titles, descriptions, images, prices, stock levels, and more—each attribute meticulously organized to meet the specific requirements of different digital channels.

Product data feed management encompasses the creation, maintenance, and optimization of these product feeds. 

It involves regular updates to ensure accuracy of product information, strategic modifications to enhance product visibility and appeal, and careful adherence to the data standards and specifications of each target platform. 

The goal is to streamline the process of listing and advertising products across the web, ensuring that potential customers encounter consistent, accurate, and engaging product presentations, no matter where they find them.

Importance of Product Data in E-commerce

We cannot overstate the significance of product data in e-commerce. In an online marketplace where consumers rely heavily on product information to make purchasing decisions, the quality and presentation of this data directly impact sales performance. 

High-quality product feeds enable:

  • Improved Visibility: Optimized product data feeds help products to surface in search results and feature prominently in comparison shopping engines, directly influencing discoverability.
  • Enhanced Customer Experience: Detailed, accurate product descriptions and high-quality images help build consumer trust and reduce the likelihood of returns. They provide the necessary information to aid consumers in making informed purchasing decisions, enhancing the overall shopping experience.
  • Increased Conversion Rates: By ensuring product listings are optimized for relevance and appeal (including SEO-friendly product titles and descriptions, compelling images, and competitive pricing), merchants can significantly improve their chances of converting browsers into buyers.
  • Streamlined Operations: Effective data feed management simplifies the process of listing products on multiple channels, reducing manual effort and minimizing the risk of errors. This efficiency is crucial for businesses scaling their online presence across various platforms.

In the context of today’s online shopping environment, where customer engagement and satisfaction are key drivers of success, the role of data feed management extends beyond mere product listings. 

It is about crafting a narrative for each product that resonates with potential buyers, leveraging data to tell compelling stories that captivate and convert. 

As such, product data feed management is a critical component of any e-commerce strategy, ensuring that products are not just seen but also chosen, liked, and purchased.

Why Product Data Feed Management is Important

In the digital marketplace, where competition is fierce and consumer attention is fleeting, the strategic management of product data feeds emerges as a crucial lever for e-commerce success. 

Its importance is multifaceted, impacting everything from how products are discovered to how they’re evaluated by potential customers.

Impact on Visibility and Sales Across Channels

A well-managed product data feed is instrumental in amplifying a product’s visibility across various online channels. 

Each e-commerce platform, marketplace, and comparison shopping engine has its own unique set of requirements for listing products. By meticulously optimizing product feeds to meet these specifications, businesses ensure that their products are not only listed but also positioned favorably within these channels. 

This optimization can include keyword-rich product titles and descriptions, high-quality images, and competitive pricing information, all tailored to align with the search behaviors and preferences of the target audience.

The direct result of increased visibility is, quite naturally, an uplift in sales. 

Products that are easy to find and presented compellingly are more likely to attract clicks and, subsequently, purchases. 

Furthermore, optimized product feeds contribute to more effective and efficient advertising campaigns. By targeting the right consumers with the most relevant and appealing product information, businesses can significantly improve their return on investment (ROI) in marketing, driving both sales and profitability.

Role in Improving Customer Decision-Making and Satisfaction

Beyond the immediate benefits of visibility and sales, product data feed management plays a vital role in enriching the customer’s shopping experience. 

In an online environment devoid of physical touchpoints, product information is the primary means through which consumers interact with and evaluate offerings. 

Detailed and accurate product feeds help bridge the gap between online browsing and the tangible experience of shopping in a store. They provide customers with the information needed to make informed purchasing decisions, reducing uncertainty and the likelihood of dissatisfaction.

High-quality product data feeds also allow for the personalization of the shopping experience. By leveraging data insights, businesses can tailor product recommendations, ads, and promotions to match the specific interests and preferences of their audience. 

This level of personalization enhances customer engagement and loyalty, as shoppers feel understood and valued by the brand. It also streamlines the shopping process, making it easier and more satisfying for customers to find products that meet their needs and desires.

Moreover, effective management of product data feeds ensures consistency across channels, further improving customer trust and confidence. 

When product information, pricing, and availability are synchronized across all platforms, it creates a cohesive and reliable brand experience. This consistency is crucial for maintaining customer satisfaction and fostering long-term loyalty.

Optimized product feeds are a powerhouse for e-commerce marketing, offering substantial benefits for ad campaigns and search relevancy. These advantages are pivotal in navigating the competitive landscape of online retail, where the ability to capture consumer attention at the right moment can make the difference between a sale and a missed opportunity.

Benefits for Ad Campaigns

For advertising campaigns, particularly those running on platforms like Google Shopping, Facebook, and Instagram, the quality and optimization of the product feed directly influence the campaign’s effectiveness. 

A well-optimized product feed ensures that ads are not only displayed but also resonate with the target audience. This optimization includes accurate and enticing product descriptions, high-quality images, and the right use of keywords and categories that align with what potential customers are searching for.

An optimized feed allows for more targeted and personalized ad campaigns. 

By segmenting feeds based on product categories, price ranges, or even customer behaviors, businesses can create tailored ad experiences that speak directly to the interests of different audience segments. 

This targeted approach increases the relevance of ads, improving click-through rates (CTR) and conversion rates, thereby maximizing the ROI of advertising budgets. 

Furthermore, dynamic remarketing campaigns, which display products that a visitor has previously viewed or shown interest in, rely heavily on the precision and detail of product feeds to re-engage potential customers effectively.

Data feeds play a pivotal role in the integration and success of paid search campaigns on platforms like Google Ads and Microsoft Advertising. 

Understanding how these feeds interact with paid search platforms can significantly enhance the effectiveness of your advertising efforts, leading to better targeting, higher conversion rates, and improved ROI. 

Here’s an in-depth look at how data feeds work within the context of paid search platforms.

Fundamentals of Data Feeds in Paid Search

At the core of paid search advertising, especially for e-commerce, are product data feeds. 

These feeds serve as the foundation for creating dynamic and highly targeted ads based on the product information stored in your e-commerce platform. 

For platforms like Google Shopping and Microsoft Shopping Campaigns, your product feed is uploaded to their Merchant Center, where it’s used to generate Shopping ads that are displayed across search results and other Google or Microsoft properties.

Structure and Optimization

A product data feed for paid search is typically structured in a CSV, XML, or a Google Sheets format, containing detailed attributes of each product such as title, price, image URL, product ID, and stock status. 

Optimizing these attributes is crucial for the success of your campaigns. 

Effective titles and descriptions that incorporate relevant keywords can improve the visibility of your ads, while high-quality images enhance click-through rates. 

Additionally, accurate pricing and availability information helps to reduce the bounce rate and increase consumer trust.

Dynamic Ad Creation

Paid search platforms utilize the information in your product feed to automatically create ads that are tailored to the search queries of potential customers.

This process involves matching the keywords and product categories in your feed with the terms users are searching for. 

As a result, when someone searches for a product that matches an item in your feed, the platform can dynamically generate an ad that showcases the product, complete with its image, title, and price.

Targeting and Personalization

Data feeds enable sophisticated targeting and personalization options in paid search campaigns. 

By analyzing the data in your feed, these platforms can serve ads to users based on their previous interactions with your website, search history, and purchasing behavior. 

For instance, remarketing campaigns can target users who have viewed specific products on your site but did not make a purchase, showing them ads for those very products as they browse the web or use social media.

Performance Tracking and Optimization

Integrating your product feed with paid search platforms allows for detailed performance tracking at the product level. 

You can see which products are generating clicks, impressions, and conversions, and adjust your feed and campaign settings accordingly. 

This might involve pausing ads for underperforming products, increasing bids for high-value items, or optimizing product titles and descriptions for better performance.

Continuous Updates

To maintain the relevance and effectiveness of your paid search campaigns, it’s vital to keep your product feed updated. 

Changes in product availability, pricing, or promotional offers need to be reflected in your feed in real-time or as close to it as possible. 

Many platforms offer the option to schedule regular feed uploads or enable direct API connections for continuous updates, ensuring that your ads always display the most current information.

Enhancing Search Relevancy

For search engines and online marketplaces, the relevancy of product listings plays a crucial role in visibility. 

Optimized product feeds contribute to higher search relevancy by ensuring that product information is comprehensive, accurate, and keyword-optimized. 

This means that when consumers search for products, the chances of your listings appearing in their search results are significantly increased.

Moreover, detailed and well-structured product feeds help algorithms better understand and categorize your products, making it more likely for them to show up in relevant searches and for related products. 

This alignment with consumer search intent not only boosts visibility but also drives more qualified traffic to your listings—consumers who are actively seeking what you’re offering.

Optimizing product feeds for search relevancy also involves updating feeds regularly to reflect changes in inventory, pricing, and product details. This consistency ensures that search engines and marketplaces have the most current information, further improving the accuracy of search results. 

It reduces the likelihood of customer frustration caused by outdated information, such as discontinued products or incorrect prices, enhancing the overall shopping experience and fostering trust in your brand.

Who Needs to Conduct Product Data Feed Management?

While product data feed management is a universal necessity in e-commerce, the scale and approach can vary significantly based on several factors.

Differentiation by Business Size, Catalog Complexity, and Sales Channels

Small Businesses and Startups: Small businesses, especially those with a limited number of products, may initially manage their product feeds manually or with minimal automation. 

However, even small operations can benefit from basic product data feed management practices to ensure their products are accurately listed across preferred sales channels. 

As they grow, the complexity and time investment required to manage feeds manually can quickly become impractical.

Mid-sized Businesses: For mid-sized businesses with larger catalogs and sales across multiple channels, the complexity of managing product feeds escalates. 

These businesses often deal with dynamic inventories, frequent promotions, and the need to optimize product listings for different platforms. 

At this stage, the efficiency, accuracy, and scalability provided by a dedicated product feed management solution become increasingly critical.

Large Enterprises: Large enterprises with extensive product catalogs, global markets, and sales across numerous channels face significant challenges in maintaining consistency, accuracy, and optimization of product data feeds. 

Advanced product feed management solutions, often customized and integrated with other enterprise systems, are essential to manage the scale and complexity of their operations effectively.

Indications Your Business Needs a Data Feed Management Solution

Expanding Product Catalog: As your product range grows, so does the complexity of managing each product’s data. A solution that can automate updates and optimize listings becomes invaluable.

Increasing Sales Channels: Selling across multiple platforms (e.g., your website, Amazon, eBay, Google Shopping) introduces specific requirements and complexities for each channel. Managing feeds for each platform manually can become overwhelming.

Time and Resource Constraints: If updating product listings is consuming a disproportionate amount of time or if errors are becoming more frequent due to manual updates, it’s time to consider a more streamlined approach.

Marketing and Sales Challenges: If you’re finding it difficult to effectively target or retarget potential customers through ad campaigns due to poor data quality or if you’re unable to leverage dynamic pricing and promotions effectively, a product data feed management solution can offer significant advantages.

International Expansion: Selling in multiple countries requires tailoring product information to different languages, currencies, and cultural nuances. Managing these variations without a robust feed management system can limit your ability to scale globally.

Inventory Management Issues: Difficulty in synchronizing inventory levels across different channels, leading to overselling or stock discrepancies, indicates a need for better feed management.

How to Do Product Data Feed Management

Effective product data feed management is a multifaceted process, requiring attention to detail, strategic planning, and the right tools. Here’s a comprehensive approach to managing your product data feeds efficiently and effectively.

1. Assess Your Current Data Feed Status

  • Audit Your Product Data: Begin by evaluating the quality and completeness of your current product data. Identify gaps, inaccuracies, or areas lacking optimization, such as missing product descriptions, poor-quality images, or inadequate use of keywords.
  • Understand Channel Requirements: Each sales channel has its own set of requirements for product feeds. Familiarize yourself with these specifications to ensure your product data aligns with each channel’s format, data fields, and quality standards.

2. Optimize Your Product Data

  • Enhance Product Titles and Descriptions: Make them descriptive and keyword-rich to improve search visibility and relevancy. Tailor content to match the search behavior of your target audience.
  • Improve Image Quality: Use high-resolution images and ensure they accurately represent the product. Consider multiple angles and use cases to provide a comprehensive visual overview.
  • Standardize and Enrich Data: Ensure consistent use of categories, types, and attributes across your product range. Add any missing information that could enhance the listing, such as dimensions, materials, or special features
  • Map Your Product Attributes to Channel Specifications: Create a mapping document that aligns your product attributes with the requirements of each sales channel. This ensures that critical product information is translated correctly and efficiently into each channel’s specific format, minimizing the risk of errors and omissions.
  • Utilize High-Quality Data Sources: Ensure your product information is being pulled from high-quality, reliable sources within your organization. This might involve integrating with your ERP or inventory management system to access the most up-to-date and accurate product data.
  • Implement Rich Media: Beyond standard images, consider incorporating videos, 360-degree views, and other rich media into your product feeds. This can significantly improve engagement and conversion rates by providing a more immersive product experience.
  • Optimize for Mobile: Given the increasing prevalence of mobile shopping, ensure your product feeds are optimized for mobile platforms. This includes mobile-friendly images, concise and impactful product titles, and descriptions that are easy to read on smaller screens.
  • Adopt Schema Markup: Utilize schema markup for your online store’s pages to help search engines better understand and display your product information in search results, potentially increasing visibility and click-through rates.
  • Ensure Cross-Channel Consistency: Regularly review your product feeds across all channels to ensure information is consistent and up-to-date. Discrepancies in pricing, availability, or product details can erode customer trust and hurt your brand’s reputation.
  • Regularly Refresh Promotional Content: Update your product feeds to reflect current promotions, seasonal offers, or limited-time discounts. This keeps your listings fresh and encourages repeat visits and purchases.
  • Implement Dynamic Pricing: Where possible, use dynamic pricing strategies within your product feeds to remain competitive. Adjust prices based on market demand, competitor pricing, and inventory levels to optimize sales and margins.

3. Select the Right Product Feed Management Tool

  • Evaluate Features and Compatibility: Choose a tool that not only offers feed creation and optimization features but also integrates seamlessly with your e-commerce platform and preferred sales channels.
  • Consider Scalability: The tool should be able to grow with your business, handling an increasing number of products and complexity without performance issues.
  • Look for Automation Capabilities: To save time and reduce errors, opt for a solution that automates routine tasks like feed updates and inventory management.

4. Implement Feed Management Best Practices

  • Regularly Update Your Feeds: Ensure your product feeds are refreshed frequently to reflect inventory changes, price updates, and any modifications to product details.
  • Monitor Feed Performance: Use analytics to track how your products are performing across different channels. Identify trends, such as top-performing products or channels, and adjust your strategy accordingly.
  • Test and Optimize: Continuously experiment with different aspects of your product data (e.g., titles, descriptions, images) to see what resonates best with your audience and leads to higher conversion rates.
  • Conduct Competitive Analysis: Regularly review your competitors’ product listings on key channels to identify trends and strategies that may be effective. This could include promotional tactics, use of specific keywords, or presentation styles. Understanding what works for competitors can offer valuable insights to refine your own product feed strategy.
  • Engage in Continuous Learning: Stay informed about the latest trends and best practices in e-commerce and product data management. Participating in webinars, following industry blogs, and joining professional groups can provide ongoing education and insights into how to manage your product feeds more effectively.

5. Stay Compliant and Up-to-Date

  • Keep Abreast of Channel Updates: Sales channels often update their feed requirements and algorithms. Stay informed about these changes to ensure your feeds remain compliant and optimized.
  • Adapt to Market Trends: Be responsive to shifts in consumer behavior and market trends. Update your product data to highlight relevant features or benefits that meet evolving customer needs.

Product Data Feed Management Tools and Services.

There are many companies who offer some form software that aids the potentially laborious process of product data management. Some only work for certain marketplaces, others are limited to certain ecommerce platforms like shopify or woocommerce.

Amongst them you should be able to find a suitable partner to manage your product data feed though.

Feedonomics (https://feedonomics.com/) offers a leading full-service data feed management platform that optimizes and syndicates product data across a wide range of digital marketing channels and marketplaces. 

Their service emphasizes improving feed quality for better ad performance and e-commerce success.

Adsmurai (https://www.adsmurai.com/) provides advanced marketing technology solutions with a focus on optimizing social media advertising campaigns. 

They offer tools for creative management, campaign automation, and performance analysis across platforms like Facebook, Instagram, and Pinterest.

FeedSpark (https://www.feedspark.com/) specializes in data feed optimization and management, helping businesses improve their online presence and sales through better product visibility across shopping channels and search engines.

ShoppingIQ (https://www.shoppingiq.com/) offers a technology platform geared towards optimizing e-commerce operations, particularly in managing and optimizing product feeds for shopping comparison engines and marketplaces to enhance ROI.

DataFeedWatch (https://www.datafeedwatch.com/) is a comprehensive data feed management tool designed to help merchants and agencies optimize and customize their product feeds for over 1000 shopping channels and marketplaces to improve campaign performance.

WakeupData (https://www.wakeupdata.com/) provides a powerful feed management platform that allows e-commerce businesses to transform, optimize, and automate their product data feeds to increase sales and performance across multiple marketing channels.

Channable (https://www.channable.com/) offers an e-commerce tool for feed management, PPC automation, and order synchronization, helping online retailers and marketers streamline their sales and advertising operations across various platforms.

Feedoptimise (https://www.feedoptimise.com/) provides services for managing and optimizing product feeds for e-commerce businesses, focusing on maximizing product visibility and performance across shopping channels and marketplaces.

SellerApp (https://www.sellerapp.com/) specializes in e-commerce analytics and intelligence, offering tools and services that help sellers optimize their presence and sales on platforms like Amazon with data-driven insights and strategies.

Scale Insights (https://scaleinsights.com/) is platform focused around Amazon PPC which helps their customers scale and automate their advertising campaigns on the mega successful ecomerce marketplace.

Arthy (https://www.getarthy.com/) is another Amazon focused tool. It’s broader than just feeds offering functionality around managing reviews, inventory etc.

Adverso (https://adverso.io/) is a platform to manage, optimize and track your Amazon campaigns smoothly with a solution designed for Amazon teams & agencies

ExportFeed (https://www.exportfeed.com/) specializes in creating and managing product feeds for e-commerce businesses, ensuring their products are listed across multiple shopping channels and marketplaces efficiently.

Lengow (https://www.lengow.com/) provides an e-commerce automation platform that helps merchants optimize their product listings and manage their sales across various online channels, including marketplaces, comparison shopping engines, and affiliate platforms.

Rithum (https://www.rithum.com/) came about from the combination of CommerceHub and ChannelAdvisor and claim to be a company providing end-to-end platform and network capabilities that create more durable, sustainable, e-commerce businesses to the leading brands, retailers, and suppliers of the world.

Baselinker (https://baselinker.com/) offers an integrated e-commerce platform that connects online stores with marketplaces, couriers, and sales support tools, automating sales processes and order management to increase efficiency.

Versafeed (https://www.versafeed.com/) provides a managed service for optimizing and managing product data feeds, focusing on enhancing product visibility and performance on search engines and shopping channels.

GoDataFeed (https://www.godatafeed.com/) offers a cloud-based feed management platform designed to simplify and automate the process of syndicating product data across a multitude of shopping channels, improving reach and efficiency.

AdNabu (https://www.adnabu.com/) specializes in Google Ads automation, offering software solutions that help e-commerce businesses optimize their Google Shopping campaigns for better performance and higher returns.

Relayter (https://www.relayter.com/) Simplify your marketing production for promotions and products. Automate creative work and streamline content workflows.

Adcore (https://www.adcore.com/technologies/feeditor/) provides a suite of marketing automation tools designed to help advertisers streamline their digital advertising efforts, with a focus on simplifying campaign management and optimization. Feeditor is there feed management tool.

Productsup (https://www.productsup.com/) offers a leading cloud-based platform for product content integration, syndication, and feed management, empowering businesses to manage and optimize their product data across various e-commerce channels.



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PPC

Advanced Google Ads Techniques To Master In 2024

Published

2 months ago

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May 23, 2024

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Advanced Google Ads Techniques To Master In 2024

We’re nearly halfway through 2024, and already we PPC heroes have experienced a plethora of changes to get our heads around. How can we cut through the noise and focus on the specific tactics that will make an impact for the better?

Today we’ll take a look at a few advanced Google Ads techniques tips and tricks to master in 2024 – everything from making account management easier to tailoring your messaging at scale, and making your campaigns as effective and efficient as possible.

1. Auto-apply (some) recommendations

Fighting those pesky ‘optimization score’ reminders can be time-consuming – especially when they’re not always applicable. With targets to hit and maintain for Google’s partnership and support, it’s important to keep our optimization scores high at 80% or above.

Google’s optimization recommendations are split into the following categories:

  • Ads and assets
  • Automated campaigns
  • Bidding and budgets
  • Keywords and targeting
  • Repairs
  • Measurement

Each of these will have a unique score that will affect your overall optimization total for each of your accounts. Repairs are usually critical fixes, while minor keyword tweaks may come further down the priority list. (You can dismiss recommendations if they’re irrelevant, but I recommend reading the details behind each of them before rejecting them.)

To save time on manual campaign management, you can ask Google to auto-apply some of these tweaks for you – with a thorough ‘auto-applied recommendations’  history as well as optional email alerts. 

I recommend adding these four as must-have auto-optimizations:

  1. Removing redundant keywords (keywords that have a close match within the same ad group and bidding strategy that performs better)
  2. Removing non-serving keywords (keywords with no impressions over a set period)*
  3. Updating keywords bids to meet ‘top of page’ bids etc. (You can still set an upper limit on this)
  4. Use optimized ad rotation (to show the best-performing ads more often instead of all ads within the same ad group equally, despite performance)

*As of June 2024, Google will automatically pause low-activity keywords: “Positive keywords in search ads campaigns are considered low-activity if they were created over 13 months ago and have zero impressions over the past 13 months.”

To opt-in to certain auto-applied recommendations:

  1. In your Google Ads account, click the Campaigns icon 
  2. Click Recommendations.

At the upper right-hand corner, click Auto-apply, and select which recommendations to auto-apply.

2. Drive personalization through audiences

One way to drive personalization via search ads is by leveraging Google’s audiences. While marketers of yesteryear used to rely on keywords and geotargeting, today Google has a multitude of interested audiences to exploit across search, performance max, display, video, and demand gen campaigns. Don’t forget, audiences can be applied with both the observation setting and the targeting setting. Consider adding audiences to the observation setting first, adjusting to targeting once you have sufficient data.

By applying the following audience types to your campaigns and ad groups, you can double down on efforts to reach your target audiences through search.

Custom audiences

Create your own custom audience based on signals such as interests, behaviors, website viewing history (by URL), and app history. Think competitor brands or products, industry-related websites and apps, and recent relevant Google searches.

You could use custom audiences to personalize your ad copy on campaigns where you’re targeting customers of your competitors. For example, by encouraging them to ‘switch’ to your brand, product, or service, rather than treating them like a first-time purchaser. You could focus on the benefits of your product or service over the one they currently have, rather than focusing your ad copy on educating the audience from scratch.

In-market audiences

In-market audiences are a must-have in 2024. Curated by Google, these audiences actively research a specific product or service and are actively considering their options ahead of purchasing. 

While there isn’t a master list of in-market audiences (because many of these are hidden!), head to the Audiences tab on your current Google Ads campaigns. Click “Edit Audience Segments”, then the Browse tab, and navigate to In-Market Audiences. You can look at all available groupings by industry, and add the most relevant ones to your campaigns. You can also use this function to type in keywords under the Search tab, and type in relevant keywords to find relevant in-market audience suggestions to apply.

Knowing these audiences are already convinced of the benefits of the general product or service you’re advertising, you can use your ad copy to highlight the USPs of your brand.

RLSAs

While the use of RLSAs (remarketing lists for search ads) has dropped since their arrival in 2013, they still have a place in an effective PPC strategy in 2024. By creating an RLSA, you can personalize your ad copy at scale.

The use of RLSAs is particularly applicable for brands with lengthier sales cycles, or longer customer consideration and comparison stages. Your brand could be 1 of 5 that a consumer is considering buying a hot tub from – it’s uncommon that a hot tub is an impulse purchase decision. A user may use Google to search multiple times for generic hot tub terms, and may whittle this down to certain brands based on their needs. Once a user who is actively looking for a hot tub has visited your website without converting, upon their next Google search, your ad may contain a coupon code, a complimentary gift item, or other differentiating ad copy to encourage them to purchase through your website.

It’s important with RLSAs to ensure that you have separate ad groups or campaigns. Also to separate RLSA audiences from other custom, in-market or demographic-based audiences.

Remember to test all new audiences by adding them as ‘observation’ audiences, before switching to the ‘targeting’ setting.

3. Harness your data

One of the more critical elements of a top-performing PPC campaign is data. You can have the best keywords, ad copy, and landing page in the world, but you need the right data to meet your goals.

A big data piece for 2024 is the perfection of conversion tracking, conversion events, and key events. With enhanced conversions also forcing their way to the fore, Google is no longer letting a lack of data confuse the attribution story.

At one time it was best practice to aim for a single conversion goal across all campaigns. In 2024, it’s important to measure a mixture of lighter conversion events too. For example, measuring PDF downloads and highly engaged video views on the path to a lead form submission. Or tracking customers who have abandoned their carts. Not only do these signals give you a clearer picture of the path to conversion, but these lighter goals can better guide Google’s machine learning and automated bidding strategy efforts.

Not only is conversion tracking crucial to success, but your conversion settings are key. Review the conversions list on your Google Ads account and check each goal for whether it’s a primary or secondary, or account default conversion setting. Having multiple account-default primary conversion goals will make it harder for Google to auto-optimize conversion-based bidding strategies. Choose one or two must-haves to keep as your primary conversion goal, and set the rest to secondary conversion goals.

4. Stop working on your Google Ads in isolation

One of the most valuable traits of a top-performing PPC manager is their knowledge of where PPC fits within the marketing funnel and wider marketing mix. Traditionally, PPC tactics have been assigned a bottom-of-funnel or lower-funnel position in the marketing mix. 

In 2024, we need to adapt our thinking. Google Ads is no longer a BOF-only strategy. In fact, Google Ads can generate upper-funnel, mid-funnel, and lower-funnel results with the right strategy, campaign type, and goal tracking in place. 

Not only that but Google Ads can support a multitude of cross-channel activities. You can use Google Ads to:

  • Drive brand awareness and consideration on YouTube and other video partner platforms
  • Capture brand demand generated from activity on social platforms such as Meta, TikTok, or Snapchat
  • Similarly, capture brand demand generated from offline or traditional channels such as TV advertising, billboards, or print media
  • Remarket to website traffic (from all sources) to generate conversions
  • Boost brand loyalty, cross-sell, and up-sell opportunities using current customer data

This is another reason why data-driven attribution is a must-have in 2024. Today, Google Ads can influence multiple customer touchpoints. Last-click attribution is no longer an effective, representative, or scientific way of measuring the success of Google Ads activity.

5. Perfect your exclusions

For peak efficiency, exclusions are a must-have throughout your account. Particularly with the increased push for automated campaigns and campaign management that we’re experiencing. 

It doesn’t matter if you’re only running search or performance max activity. Exclusions are almost always a part of an efficient campaign structure. The exclusions on your account might include negative keywords, specific audience exclusions (such as remarketing and already-converted audiences), brand exclusions, geotargeting exclusions, or placement exclusions.

Common negative keywords to consider may include:

  • Free
  • Jobs
  • Download
  • Cheap
  • How to
  • YouTube
  • Amazon
  • Facebook
  • Sample
  • Guide
  • Logo
  • Resource
  • DIY

Without exclusions, you may find your ads are appearing to the wrong audiences, next to questionable or harmful content, or even that your ads are being triggered by irrelevant search terms entirely. 

Summary 

In 2024, there is a lot of noise in PPC advertising. By getting to grips with the above fundamentals of a healthy Google Ads account – targeting, personalization, data, simpler campaign management techniques, and adding relevant exclusions – you’ll be able to successfully navigate the complexities of managing your accounts at an advanced level.



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