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GenAI and the Future of Branding: The Crucial Role of the Knowledge Graph



6 Ways ChatGPT Can Improve Your SEO

The author’s views are entirely their own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

The one thing that brand managers, company owners, SEOs, and marketers have in common is the desire to have a very strong brand because it’s a win-win for everyone. Nowadays, from an SEO perspective, having a strong brand allows you to do more than just dominate the SERP — it also means you can be part of chatbot answers.

Generative AI (GenAI) is the technology shaping chatbots, like Bard, Bingchat, ChatGPT, and search engines, like Bing and Google. GenAI is a conversational artificial intelligence (AI) that can create content at the click of a button (text, audio, and video). Both Bing and Google use GenAI in their search engines to improve their search engine answers, and both have a related chatbot (Bard and Bingchat). As a result of search engines using GenAI, brands need to start adapting their content to this technology, or else risk decreased online visibility and, ultimately, lower conversions.

As the saying goes, all that glitters is not gold. GenAI technology comes with a pitfall – hallucinations. Hallucinations are a phenomenon in which generative AI models provide responses that look authentic but are, in fact, fabricated. Hallucinations are a big problem that affects anybody using this technology.

One solution to this problem comes from another technology called a ‘Knowledge Graph.’ A Knowledge Graph is a type of database that stores information in graph format and is used to represent knowledge in a way that is easy for machines to understand and process.

Before delving further into this issue, it’s imperative to understand from a user perspective whether investing time and energy as a brand in adapting to GenAI makes sense.

Should my brand adapt to Generative AI?

To understand how GenAI can influence brands, the first step is to understand in which circumstances people use search engines and when they use chatbots.

As mentioned, both options use GenAI, but search engines still leave a bit of space for traditional results, while chatbots are entirely GenAI. Fabrice Canel brought information on how people use chatbots and search engines to marketers’ attention during Pubcon.

The image below demonstrates that when people know exactly what they want, they will use a search engine, whereas when people sort of know what they want, they will use chatbots. Now, let’s go a step further and apply this knowledge to search intent. We can assume that when a user has a navigational query, they would use search engines (Google/Bing), and when they have a commercial investigation query, they would typically ask a chatbot.

Image source: Type of intent/Pubcon Fabrice Canel

The information above comes with some significant consequences:

1. When users write a brand or product name into a search engine, you want your business to dominate the SERP. You want the complete package: GenAI experience (that pushes the user to the buying step of a funnel), your website ranking, a knowledge panel, a Twitter Card, maybe Wikipedia, top stories, videos, and everything else that can be on the SERP.

Aleyda Solis on Twitter showed what the GenAI experience looks like for the term “nike sneakers”:

SERP results for the keyword 'nike sneakers'

2. When users ask chatbots questions, they typically want their brand to be listed in the answers. For example, if you are Nike and a user goes to Bard and writes “best sneakers”, you will want your brand/product to be there.

Chatbot answer for the query 'Best Sneakers'

3. When you ask a chatbot a question, related answers are given at the end of the original answer. Those questions are important to note, as they often help push users down your sales funnel or provide clarification to questions regarding your product or brand. As a consequence, you want to be able to control the related questions that the chatbot proposes.

Now that we know why brands should make an effort to adapt, it’s time to look at the issues that this technology brings before diving into solutions and what brands should do to ensure success.

What are the pitfalls of Generative AI?

The academic paper Unifying Large Language Models and Knowledge Graphs: A Roadmap extensively explains the problems of GenAI. However, before starting, let’s clarify the difference between Generative AI, Large Language Models (LLMs), Bard (Google chatbot), and Language Models for Dialogue Applications (LaMDA).

LLMs are a type of GenAI model that predicts the “next word,” Bard is a specific LLM chatbot developed by Google AI, and LaMDA is an LLM that is specifically designed for dialogue applications.

To make it clear, Bard was based initially on LaMDA (now on PaLM), but that doesn’t mean that all Bard’s answers were coming just from LamDA. If you want to learn more about GenAI, you can take Google’s introductory course on Generative AI.

As explained in the previous paragraph, LLM predicts the next word. This is based on probability. Let’s look at the image below, which shows an example from the Google video What are Large Language Models (LLMs)?

Considering the sentence that was written, it predicts the highest chance of the next word. Another option could have been the garden was full of beautiful “butterflies.” However, the model estimated that “flowers” had the highest probability. So it selected “flowers.”

An image showing how Large Language Models work.
Image source: YouTube: What Are Large Language Models (LLMs)?

Let’s come back to the main point here, the pitfall.

The pitfalls can be summarized in three points according to the paper Unifying Large Language Models and Knowledge Graphs: A Roadmap:

  1. “Despite their success in many applications, LLMs have been criticized for their lack of factual knowledge.” What this means is that the machine can’t recall facts. As a result, it will invent an answer. This is a hallucination.

  2. “As black-box models, LLMs are also criticized for lacking interpretability. LLMs represent knowledge implicitly in their parameters. It is difficult to interpret or validate the knowledge obtained by LLMs.” This means that, as a human, we don’t know how the machine arrived at a conclusion/decision because it used probability.

  3. “LLMs trained on general corpus might not be able to generalize well to specific domains or new knowledge due to the lack of domain-specific knowledge or new training data.” If a machine is trained in the luxury domain, for example, it will not be adapted to the medical domain.

The repercussions of these problems for brands is that chatbots could invent information about your brand that is not real. They could potentially say that a brand was rebranded, invent information about a product that a brand does not sell, and much more. As a result, it’s good practice to test chatbots with everything brand-related.

This is not just a problem for brands but also for Google and Bing, so they have to find a solution. The solution comes from the Knowledge Graph.

What is a Knowledge Graph?

One of the most famous Knowledge Graphs in SEO is the Google Knowledge Graph, and Google defines it: “Our database of billions of facts about people, places, and things. The Knowledge Graph allows us to answer factual questions such as ‘How tall is the Eiffel Tower?’ or ‘Where were the 2016 Summer Olympics held?’ Our goal with the Knowledge Graph is for our systems to discover and surface publicly known, factual information when it’s determined to be useful.”

The two key pieces of information to keep in mind in this definition are:

1. It’s a database

2. That stores factual information

This is precisely the opposite of GenAI. Consequently, the solution to solving any of the previously mentioned problems, and especially hallucinations, is to use the Knowledge Graph to verify the information coming from GenAI.

Obviously, this looks very easy in theory, but it’s not in practice. This is because the two technologies are very different. However, in the paper ‘LaMDA: Language Models for Dialog Applications,’ it looks like Google is already doing this. Naturally, if Google is doing this, we could also expect Bing to be doing the same.

The Knowledge Graph has gained even more value for brands because now the information is verified using the Knowledge Graph, meaning that you want your brand to be in the Knowledge Graph.

What a brand in the Knowledge Graph would look like

To be in the Knowledge Graph, a brand needs to be an entity. A machine is a machine; it can’t understand a brand as a human would. This is where the concept of entity comes in.

We could simplify the concept by saying an entity is a name that has a number assigned to it and which can be read by the machine. For instance, I like luxury watches; I could spend hours just looking at them.

So let’s take a famous luxury watch brand that most of you probably know — Rolex. Rolex’s machine-readable ID for the Google knowledge graph is /m/023_fz. That means that when we go to a search engine, and write the brand name “Rolex”, the machine transforms this into /m/023_fz.

Now that you understand what an entity is, let’s use a more technical definition given by Krisztian Balog in the book Entity-Oriented Search: “An entity is a uniquely identifiable object or thing, characterized by its name(s), type(s), attributes, and relationships to other entities.”

Let’s break down this definition using the Rolex example:

  • Unique identifier = This is the entity; ID: /m/023_fz

  • Name = Rolex

  • Type = This makes reference to the semantic classification, in this case ‘Thing, Organization, Corporation.’

  • Attributes = These are the characteristics of the entity, such as when the company was founded, its headquarters, and more. In the case of Rolex, the company was founded in 1905 and is headquartered in Geneva.

All this information (and much more) related to Rolex will be stored in the Knowledge Graph. However, the magic part of the Knowledge Graph is the connections between entities.

For example, the owner of Rolex, Hans Wilsdorf, is also an entity, and he was born in Kulmbach, which is also an entity. So, now we can see some connections in the Knowledge Graph. And these connections go on and on. However, for our example, we will take just three entities, i.e., Rolex, Hans Wilsdorf, Kulmbach.

Knowledge Graph connections between the Rolex entity

From these connections, we can see how important it is for a brand to become an entity and to provide the machine with all relevant information, which will be expanded on in the section “How can a brand maximize its chances of being on a chatbot or being part of the GenAI experience?”

However, first let’s analyze LaMDA , the old Google Large Language Model used on BARD, to understand how GenAI and the Knowledge Graph work together.

LaMDA and the Knowledge Graph

I recently spoke to Professor Shirui Pan from Griffith University, who was the leading professor for the paper “Unifying Large Language Models and Knowledge Graphs: A Roadmap,” and confirmed that he also believes that Google is using the Knowledge Graph to verify information.

For instance, he pointed me to this sentence in the document LaMDA: Language Models for Dialog Applications:

“We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.”

I won’t go into detail about safety and grounding, but in short, safety implies that the model respects human values and grounding (which is the most important thing for brands), meaning that the model should consult external knowledge sources (an information retrieval system, a language translator, and a calculator).

Below is an example of how the process works. It’s possible to see from the image below that the Green box is the output from the information retrieval system tool. TS stands for toolset. Google created a toolset that expects a string (a sequence of characters) as inputs and outputs a number, a translation, or some kind of factual information. In the paper LaMDA: Language Models for Dialog Applications, there are some clarifying examples: the calculator takes “135+7721” and outputs a list containing [“7856”].

Similarly, the translator can take “Hello in French” and output [“Bonjour”]. Finally, the information retrieval system can take “How old is Rafael Nadal?” and output [“Rafael Nadal / Age / 35”]. The response “Rafael Nadal / Age / 35” is a typical response we can get from a Knowledge Graph. As a result, it’s possible to deduce that Google uses its Knowledge Graph to verify the information.

Image showing the input and output of Language Models of Dialog Applications
Image source: LaMDA: Large Language Models for Dialog Applications

This brings me to the conclusion that I had already anticipated: being in the Knowledge Graph is becoming increasingly important for brands. Not only to have a rich SERP experience with a Knowledge Panel but also for new and emerging technologies. This gives Google and Bing yet another reason to present your brand instead of a competitor.

How can a brand maximize its chances of being part of a chatbot’s answers or being part of the GenAI experience?

In my opinion, one of the best approaches is to use the Kalicube process created by Jason Barnard, which is based on three steps: Understanding, Credibility, and Deliverability. I recently co-authored a white paper with Jason on content creation for GenAI; below is a summary of the three steps.

1. Understand your solution. This makes reference to becoming an entity and explaining to the machine who you are and what you do. As a brand, you need to make sure that Google or Bing have an understanding of your brand, including its identity, offerings, and target audience.
In practice, this means having a machine-readable ID and feeding the machine with the right information about your brand and ecosystem. Remember the Rolex example where we concluded that the Rolex readable ID is /m/023_fz. This step is fundamental.

2. In the Kalicube process, credibility is another word for the more complex concept of E-E-A-T. This means that if you create content, you need to demonstrate Experience, Expertise, Authoritativeness, and Trustworthiness in the subject of the content piece.

A simple way of being perceived as more credible by a machine is by including data or information that can be verified on your website. For instance, if a brand has existed for 50 years, it could write on its website “We’ve been in business for 50 years.” This information is precious but needs to be verified by Google or Bing. Here is where external sources come in handy. In the Kalicube process, this is called corroborating the sources. For example, if you have a Wikipedia page with the date of founding of the company, this information can be verified. This can be applied to all contexts.

If we take an e-commerce business with client reviews on its website, and the client reviews are excellent, but there is nothing confirming this externally, then it’s a bit suspicious. But, if the internal reviews are the same as the ones on Trustpilot, for example, the brand gains credibility!

So, the key to credibility is to provide information on your website first, and that information to be corroborated externally.

The interesting part is that all this generates a cycle because by working on convincing search engines of your credibility both onsite and offsite, you will also convince your audience from the top to the bottom of your acquisition funnel.

3. The content you create needs to be deliverable. Deliverability aims to provide an excellent customer experience for each touchpoint of the buyer decision journey. This is primarily about producing targeted content in the correct format and secondly about the technical side of the website.

An excellent starting point is using the Pedowitz Group’s Customer Journey model and to produce content for each step. Let’s look at an example of a funnel on BingChat that, as a brand, you want to control.

A user could write: “Can I dive with luxury watches?” As we can see from the image below, a recommended follow-up question suggested by the chatbot is “Which are some good diving watches?”

Chatbot answer for the query 'can I dive with luxury watches?”

If a user clicks on that question, they get a list of luxury diving watches. As you can imagine, if you sell diving watches, you want to be included on the list.

In a few clicks, the chatbot has brought a user from a general question to a potential list of watches that they could buy.

Bing chatbot suggesting luxury diving watches.

As a brand, you need to produce content for all the touchpoints of the buyer decision journey and figure out the most effective way to produce this content, whether it’s in the form of FAQs, how-tos, white papers, blogs, or anything else.

GenAI is a powerful technology that comes with its strengths and weaknesses. One of the main challenges brands face is hallucinations when it comes to using this technology. As demonstrated by the paper LaMDA: Language Models for Dialog Applications, a possible solution to this problem is using Knowledge Graphs to verify GenAI outputs. Being in the Google Knowledge Graph for a brand is much more than having the opportunity to have a much richer SERP. It also provides an opportunity to maximize their chances of being on Google’s new GenAI experience and chatbots — ensuring that the answers regarding their brand are accurate.

This is why, from a brand perspective, being an entity and being understood by Google and Bing is a must and no more a should!

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AI driving an exponential increase in marketing technology solutions



AI driving an exponential increase in marketing technology solutions

The martech landscape is expanding and AI is the prime driving force. That’s the topline news from the “Martech 2024” report released today. And, while that will get the headline, the report contains much more.

Since the release of the most recent Martech Landscape in May 2023, 2,042 new marketing technology tools have surfaced, bringing the total to 13,080 — an 18.5% increase. Of those, 1,498 (73%) were AI-based. 

Screenshot 2023 12 05 110428 800x553

“But where did it land?” said Frans Riemersma of Martech Tribe during a joint video conference call with Scott Brinker of ChiefMartec and HubSpot. “And the usual suspect, of course, is content. But the truth is you can build an empire with all the genAI that has been surfacing — and by an empire, I mean, of course, a business.”

Content tools accounted for 34% of all the new AI tools, far ahead of video, the second-place category, which had only 4.85%. U.S. companies were responsible for 61% of these tools — not surprising given that most of the generative AI dynamos, like OpenAI, are based here. Next up was the U.K. at 5.7%, but third place was a big surprise: Iceland — with a population of 373,000 — launched 4.6% of all AI martech tools. That’s significantly ahead of fourth place India (3.5%), whose population is 1.4 billion and which has a significant tech industry. 

Dig deeper: 3 ways email marketers should actually use AI

The global development of these tools shows the desire for solutions that natively understand the place they are being used. 

“These regional products in their particular country…they’re fantastic,” said Brinker. “They’re loved, and part of it is because they understand the culture, they’ve got the right thing in the language, the support is in that language.”

Now that we’ve looked at the headline stuff, let’s take a deep dive into the fascinating body of the report.

The report: A deeper dive

Marketing technology “is a study in contradictions,” according to Brinker and Riemersma. 

In the new report they embrace these contradictions, telling readers that, while they support “discipline and fiscal responsibility” in martech management, failure to innovate might mean “missing out on opportunities for competitive advantage.” By all means, edit your stack meticulously to ensure it meets business value use cases — but sure, spend 5-10% of your time playing with “cool” new tools that don’t yet have a use case. That seems like a lot of time.

Similarly, while you mustn’t be “carried away” by new technology hype cycles, you mustn’t ignore them either. You need to make “deliberate choices” in the realm of technological change, but be agile about implementing them. Be excited by martech innovation, in other words, but be sensible about it.

The growing landscape

Consolidation for the martech space is not in sight, Brinker and Riemersma say. Despite many mergers and acquisitions, and a steadily increasing number of bankruptcies and dissolutions, the exponentially increasing launch of new start-ups powers continuing growth.

It should be observed, of course, that this is almost entirely a cloud-based, subscription-based commercial space. To launch a martech start-up doesn’t require manufacturing, storage and distribution capabilities, or necessarily a workforce; it just requires uploading an app to the cloud. That is surely one reason new start-ups appear at such a startling rate. 

Dig deeper: AI ad spending has skyrocketed this year

As the authors admit, “(i)f we measure by revenue and/or install base, the graph of all martech companies is a ‘long tail’ distribution.” What’s more, focus on the 200 or so leading companies in the space and consolidation can certainly be seen.

Long-tail tools are certainly not under-utilized, however. Based on a survey of over 1,000 real-world stacks, the report finds long-tail tools constitute about half of the solutions portfolios — a proportion that has remained fairly consistent since 2017. The authors see long-tail adoption where users perceive feature gaps — or subpar feature performance — in their core solutions.

Composability and aggregation

The other two trends covered in detail in the report are composability and aggregation. In brief, a composable view of a martech stack means seeing it as a collection of features and functions rather than a collection of software products. A composable “architecture” is one where apps, workflows, customer experiences, etc., are developed using features of multiple products to serve a specific use case.

Indeed, some martech vendors are now describing their own offerings as composable, meaning that their proprietary features are designed to be used in tandem with third-party solutions that integrate with them. This is an evolution of the core-suite-plus-app-marketplace framework.

That framework is what Brinker and Riemersma refer to as “vertical aggregation.” “Horizontal aggregation,” they write, is “a newer model” where aggregation of software is seen not around certain business functions (marketing, sales, etc.) but around a layer of the tech stack. An obvious example is the data layer, fed from numerous sources and consumed by a range of applications. They correctly observe that this has been an important trend over the past year.

Build it yourself

Finally, and consistent with Brinker’s long-time advocacy for the citizen developer, the report detects a nascent trend towards teams creating their own software — a trend that will doubtless be accelerated by support from AI.

So far, the apps that are being created internally may be no more than “simple workflows and automations.” But come the day that app development is so democratized that it will be available to a wide range of users, the software will be a “reflection of the way they want their company to operate and the experiences they want to deliver to customers. This will be a powerful dimension for competitive advantage.”

Constantine von Hoffman contributed to this report.

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Mastering The Laws of Marketing in Madness



Mastering The Laws of Marketing in Madness

Mastering The Laws of Marketing in Madness

Navigating through the world of business can be chaotic. At the time of this publication in November 2023, global economic growth is expected to remain weak for an undefined amount of time.

However, certain rules of marketing remain steadfast to guide businesses towards success in any environment. These universal laws are the anchors that keep a business steady, helping it thrive amidst uncertainty and change.

In this guide, we’ll explore three laws that have proven to be the cornerstones of successful marketing. These are practical, tried-and-tested approaches that have empowered businesses to overcome challenges and flourish, regardless of external conditions. By mastering these principles, businesses can turn adversities into opportunities, ensuring growth and resilience in any market landscape. Let’s uncover these essential laws that pave the way to success in the unpredictable world of business marketing. Oh yeah, and don’t forget to integrate these insights into your career. Follow the implementation steps!

Law 1: Success in Marketing is a Marathon, Not a Sprint

Navigating the tumultuous seas of digital marketing necessitates a steadfast ship, fortified by a strategic long-term vision. It’s a marathon, not a sprint.

Take Apple, for instance. The late ’90s saw them on the brink of bankruptcy. Instead of grasping at quick, temporary fixes, Apple anchored themselves in a long-term vision. A vision that didn’t just stop at survival, but aimed for revolutionary contributions, resulting in groundbreaking products like the iPod, iPhone, and iPad.

In a landscape where immediate gains often allure businesses, it’s essential to remember that these are transient. A focus merely on the immediate returns leaves businesses scurrying on a hamster wheel, chasing after fleeting successes, but never really moving forward.

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A long-term vision, however, acts as the north star, guiding businesses through immediate challenges while ensuring sustainable success and consistent growth over time.

Consider This Analogy: 

Building a business is like growing a tree. Initially, it requires nurturing, patience, and consistent care. But with time, the tree grows, becoming strong and robust, offering shade and fruits—transforming the landscape. The same goes for business. A vision, perseverance, and a long-term strategy are the nutrients that allow it to flourish, creating a sustainable presence in the market.

Implementation Steps: 

  • Begin by planning a content calendar focused on delivering consistent value over the next six months. 
  • Ensure regular reviews and necessary adjustments to your long-term goals, keeping pace with evolving market trends and demands. 
  • And don’t forget the foundation—invest in robust systems and ongoing training, laying down strong roots for sustainable success in the ever-changing digital marketing landscape.

Law 2: Survey, Listen, and Serve

Effective marketing hinges on understanding and responding to the customer’s needs and preferences. A robust, customer-centric approach helps in shaping products and services that resonate with the audience, enhancing overall satisfaction and loyalty.

Take Netflix, for instance. Netflix’s evolution from a DVD rental company to a streaming giant is a compelling illustration of a customer-centric approach.

Their transition wasn’t just a technological upgrade; it was a strategic shift informed by attentively listening to customer preferences and viewing habits. Netflix succeeded, while competitors such a Blockbuster haid their blinders on.

Here are some keystone insights when considering how to Survey, Listen, and Serve…

Customer Satisfaction & Loyalty:

Surveying customers is essential for gauging their satisfaction. When customers feel heard and valued, it fosters loyalty, turning one-time buyers into repeat customers. Through customer surveys, businesses can receive direct feedback, helping to identify areas of improvement, enhancing overall customer satisfaction.


Engaging customers through surveys not only garners essential feedback but also makes customers feel valued and involved. It cultivates a relationship where customers feel that their opinions are appreciated and considered, enhancing their connection and engagement with the brand.

Product & Service Enhancement:

Surveys can unveil insightful customer feedback regarding products and services. This information is crucial for making necessary adjustments and innovations, ensuring that offerings remain aligned with customer needs and expectations.

Data Collection:

Surveys are instrumental in collecting demographic information. Understanding the demographic composition of a customer base is crucial for tailoring marketing strategies, ensuring they resonate well with the target audience.

Operational Efficiency:

Customer feedback can also shed light on a company’s operational aspects, such as customer service and website usability. Such insights are invaluable for making necessary enhancements, improving the overall customer experience.


Consistent surveying allows for effective benchmarking, enabling businesses to track performance over time, assess the impact of implemented changes, and make data-driven strategic decisions.

Implementation Steps:

  • Regularly incorporate customer feedback mechanisms like surveys and direct interactions to remain attuned to customer needs and preferences.
  • Continuously refine and adjust offerings based on customer feedback, ensuring products and services evolve in alignment with customer expectations.
  • In conclusion, adopting a customer-centric approach, symbolized by surveying, listening, and serving, is indispensable for nurturing customer relationships, driving loyalty, and ensuring sustained business success.

Law 3: Build Trust in Every Interaction

In a world cluttered with countless competitors vying for your prospects attention, standing out is about more than just having a great product or service. It’s about connecting authentically, building relationships rooted in trust and understanding. It’s this foundational trust that transforms casual customers into loyal advocates, ensuring that your business isn’t just seen, but it truly resonates and remains memorable.

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For instance, let’s talk about Oprah! Through vulnerability and honest connections, Oprah Winfrey didn’t just build an audience; she cultivated a community. Sharing, listening, and interacting genuinely, she created a media landscape where trust and respect flourished. Oprah was known to make her audience and even guests cry for the first time live. She had a natural ability to build instant trust.

Here are some keystone insights when considering how to develop and maintain trust…

The Unseen Fast-Track

Trust is an unseen accelerator. It simplifies decisions, clears doubts, and fast-forwards the customer journey, turning curiosity into conviction and interest into investment.

The Emotional Guardrail

Trust is like a safety net or a warm embrace, making customers feel valued, understood, and cared for. It nurtures a positive environment, encouraging customers to return, not out of necessity, but a genuine affinity towards the brand.

Implementation Steps:

  • Real Stories: Share testimonials and experiences, both shiny and shaded, to build credibility and show authenticity.
  • Open Conversation: Encourage and welcome customer feedback and discussions, facilitating a two-way conversation that fosters understanding and improvement.
  • Community Engagement: Actively participate and engage in community or industry events, align your brand with genuine causes and values, promoting real connections and trust.

Navigating through this law involves cultivating a space where authenticity leads, trust blossoms, and genuine relationships flourish, engraving a memorable brand story in the hearts and minds of the customers.

Guarantee Your Success With These Foundational Laws

Navigating through the world of business is a demanding odyssey that calls for more than just adaptability and innovation—it requires a solid foundation built on timeless principles. In our exploration, we have just unraveled three indispensable laws that stand as pillars supporting the edifice of sustained marketing success, enabling businesses to sail confidently through the ever-shifting seas of the marketplace.

Law 1: “Success in Marketing is a Marathon, Not a Sprint,” advocates for the cultivation of a long-term vision. It is about nurturing a resilient mindset focused on enduring success rather than transient achievements. Like a marathon runner who paces themselves for the long haul, businesses must strategize, persevere, and adapt, ensuring sustained growth and innovation. The embodiment of this law is seen in enterprises like Apple, whose evolutionary journey is a testament to the power of persistent vision and continual reinvention.

Law 2: “Survey, Listen, and Serve,” delineates the roadmap to a business model deeply intertwined with customer insights and responsiveness. This law emphasizes the essence of customer-centricity, urging businesses to align their strategies and offerings with the preferences and expectations of their audiences. It’s a call to attentively listen, actively engage, and meticulously tailor offerings to resonate with customer needs, forging paths to enhanced satisfaction and loyalty.

Law 3: “Build Trust in Every Interaction,” underscores the significance of building genuine, trust-laden relationships with customers. It champions the cultivation of a brand personality that resonates with authenticity, fostering connections marked by trust and mutual respect. This law navigates businesses towards establishing themselves as reliable entities that customers can resonate with, rely on, and return to, enriching the customer journey with consistency and sincerity.

These pivotal laws form the cornerstone upon which businesses can build strategies that withstand the tests of market volatility, competition, and evolution. They stand as unwavering beacons guiding enterprises towards avenues marked by not just profitability, but also a legacy of value, integrity, and impactful contributions to the marketplace. Armed with these foundational laws, businesses are empowered to navigate the multifaceted realms of the business landscape with confidence, clarity, and a strategic vision poised for lasting success and remarkable achievements.

Oh yeah! And do you know Newton’s Law?The law of inertia, also known as Newton’s first law of motion, states that an object at rest will stay at rest, and an object in motion will stay in motion… The choice is yours. Take action and integrate these laws. Get in motion!

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Intro to Amazon Non-endemic Advertising: Benefits & Examples



Intro to Amazon Non-endemic Advertising: Benefits & Examples

Amazon has rewritten the rules of advertising with its move into non-endemic retail media advertising. Advertising on Amazon has traditionally focused on brands and products directly sold on the platform. However, a new trend is emerging – the rise of non-endemic advertising on this booming marketplace. In this article, we’ll dive into the concept of non-endemic ads, their significance, and the benefits they offer to advertisers. This strategic shift is opening the floodgates for advertisers in previously overlooked industries.

While endemic brands are those with direct competitors on the platform, non-endemic advertisers bring a diverse range of services to Amazon’s vast audience. The move toward non-endemic advertising signifies Amazon’s intention to leverage its extensive data and audience segments to benefit a broader spectrum of advertisers.

Endemic vs. Non-Endemic Advertising


Let’s start by breaking down the major differences between endemic advertising and non-endemic advertising… 

Endemic Advertising

Endemic advertising revolves around promoting products available on the Amazon platform. With this type of promotion, advertisers use retail media data to promote products that are sold at the retailer.

Non-Endemic Advertising

In contrast, non-endemic advertising ventures beyond the confines of products sold on Amazon. It encompasses industries such as insurance, finance, and services like lawn care. If a brand is offering a product or service that doesn’t fit under one of the categories that Amazon sells, it’s considered non-endemic. Advertisers selling products and services outside of Amazon and linking directly to their own site are utilizing Amazon’s DSP and their data/audience segments to target new and relevant customers.

7 Benefits of Running Non-Endemic Ad Campaigns


Running non-endemic ad campaigns on Amazon provides a wide variety of benefits like:

Access to Amazon’s Proprietary Data: Harnessing Amazon’s robust first-party data provides advertisers with valuable insights into consumer behavior and purchasing patterns. This data-driven approach enables more targeted and effective campaigns.

Increased Brand Awareness and Revenue Streams: Non-endemic advertising allows brands to extend their reach beyond their typical audience. By leveraging Amazon’s platform and data, advertisers can build brand awareness among users who may not have been exposed to their products or services otherwise. For non-endemic brands that meet specific criteria, there’s an opportunity to serve ads directly on the Amazon platform. This can lead to exposure to the millions of users shopping on Amazon daily, potentially opening up new revenue streams for these brands.

No Minimum Spend for Non-DSP Campaigns: Non-endemic advertisers can kickstart their advertising journey on Amazon without the burden of a minimum spend requirement, ensuring accessibility for a diverse range of brands.

Amazon DSP Capabilities: Leveraging the Amazon DSP (Demand-Side Platform) enhances campaign capabilities. It enables programmatic media buys, advanced audience targeting, and access to a variety of ad formats.

Connect with Primed-to-Purchase Customers: Amazon’s extensive customer base offers a unique opportunity for non-endemic advertisers to connect with customers actively seeking relevant products or services.

Enhanced Targeting and Audience Segmentation: Utilizing Amazon’s vast dataset, advertisers can create highly specific audience segments. This enhanced targeting helps advertisers reach relevant customers, resulting in increased website traffic, lead generation, and improved conversion rates.

Brand Defense – By utilizing these data segments and inventory, some brands are able to bid for placements where their possible competitors would otherwise be. This also gives brands a chance to be present when competitor brands may be on the same page helping conquest for competitors’ customers.

How to Start Running Non-Endemic Ads on Amazon


Ready to start running non-endemic ads on Amazon? Start with these essential steps:

Familiarize Yourself with Amazon Ads and DSP: Understand the capabilities of Amazon Ads and DSP, exploring their benefits and limitations to make informed decisions.

Look Into Amazon Performance Plus: Amazon Performance Plus is the ability to model your audiences based on user behavior from the Amazon Ad Tag. The process will then find lookalike amazon shoppers with a higher propensity for conversion.

“Amazon Performance Plus has the ability to be Amazon’s top performing ad product. With the machine learning behind the audience cohorts we are seeing incremental audiences converting on D2C websites and beating CPA goals by as much as 50%.” 

– Robert Avellino, VP of Retail Media Partnerships at Tinuiti


Understand Targeting Capabilities: Gain insights into the various targeting options available for Amazon ads, including behavioral, contextual, and demographic targeting.

Command Amazon’s Data: Utilize granular data to test and learn from campaign outcomes, optimizing strategies based on real-time insights for maximum effectiveness.

Work with an Agency: For those new to non-endemic advertising on Amazon, it’s essential to define clear goals and identify target audiences. Working with an agency can provide valuable guidance in navigating the nuances of non-endemic advertising. Understanding both the audience to be reached and the core audience for the brand sets the stage for a successful non-endemic advertising campaign.



Amazon’s venture into non-endemic advertising reshapes the advertising landscape, providing new opportunities for brands beyond the traditional ecommerce sphere. The  blend of non-endemic campaigns with Amazon’s extensive audience and data creates a cohesive option for advertisers seeking to diversify strategies and explore new revenue streams. As this trend evolves, staying informed about the latest features and possibilities within Amazon’s non-endemic advertising ecosystem is crucial for brands looking to stay ahead in the dynamic world of digital advertising.

We’ll continue to keep you updated on all things Amazon, but if you’re looking to learn more about advertising on the platform, check out our Amazon Services page or contact us today for more information.

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