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

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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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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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Why The Sales Team Hates Your Leads (And How To Fix It)

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Why The Sales Team Hates Your Leads (And How To Fix It)

Why The Sales Team Hates Your Leads And How To

You ask the head of marketing how the team is doing and get a giant thumbs up. 👍

“Our MQLs are up!”

“Website conversion rates are at an all-time high!”

“Email click rates have never been this good!”

But when you ask the head of sales the same question, you get the response that echoes across sales desks worldwide — the leads from marketing suck. 

If you’re in this boat, you’re not alone. The issue of “leads from marketing suck” is a common situation in most organizations. In a HubSpot survey, only 9.1% of salespeople said leads they received from marketing were of very high quality.

Why do sales teams hate marketing-generated leads? And how can marketers help their sales peers fall in love with their leads? 

Let’s dive into the answers to these questions. Then, I’ll give you my secret lead gen kung-fu to ensure your sales team loves their marketing leads. 

Marketers Must Take Ownership

“I’ve hit the lead goal. If sales can’t close them, it’s their problem.”

How many times have you heard one of your marketers say something like this? When your teams are heavily siloed, it’s not hard to see how they get to this mindset — after all, if your marketing metrics look strong, they’ve done their part, right?

Not necessarily. 

The job of a marketer is not to drive traffic or even leads. The job of the marketer is to create messaging and offers that lead to revenue. Marketing is not a 100-meter sprint — it’s a relay race. The marketing team runs the first leg and hands the baton to sales to sprint to the finish.

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via GIPHY

To make leads valuable beyond the vanity metric of watching your MQLs tick up, you need to segment and nurture them. Screen the leads to see if they meet the parameters of your ideal customer profile. If yes, nurture them to find out how close their intent is to a sale. Only then should you pass the leads to sales. 

Lead Quality Control is a Bitter Pill that Works

Tighter quality control might reduce your overall MQLs. Still, it will ensure only the relevant leads go to sales, which is a win for your team and your organization.

This shift will require a mindset shift for your marketing team: instead of living and dying by the sheer number of MQLs, you need to create a collaborative culture between sales and marketing. Reinforce that “strong” marketing metrics that result in poor leads going to sales aren’t really strong at all.  

When you foster this culture of collaboration and accountability, it will be easier for the marketing team to receive feedback from sales about lead quality without getting defensive. 

Remember, the sales team is only holding marketing accountable so the entire organization can achieve the right results. It’s not sales vs marketing — it’s sales and marketing working together to get a great result. Nothing more, nothing less. 

We’ve identified the problem and where we need to go. So, how you do you get there?

Fix #1: Focus On High ROI Marketing Activities First

What is more valuable to you:

  • One more blog post for a few more views? 
  • One great review that prospective buyers strongly relate to?

Hopefully, you’ll choose the latter. After all, talking to customers and getting a solid testimonial can help your sales team close leads today.  Current customers talking about their previous issues, the other solutions they tried, why they chose you, and the results you helped them achieve is marketing gold.

On the other hand, even the best blog content will take months to gain enough traction to impact your revenue.

Still, many marketers who say they want to prioritize customer reviews focus all their efforts on blog content and other “top of the funnel” (Awareness, Acquisition, and Activation) efforts. 

The bottom half of the growth marketing funnel (Retention, Reputation, and Revenue) often gets ignored, even though it’s where you’ll find some of the highest ROI activities.

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Most marketers know retaining a customer is easier than acquiring a new one. But knowing this and working with sales on retention and account expansion are two different things. 

When you start focusing on retention, upselling, and expansion, your entire organization will feel it, from sales to customer success. These happier customers will increase your average account value and drive awareness through strong word of mouth, giving you one heck of a win/win.

Winning the Retention, Reputation, and Referral game also helps feed your Awareness, Acquisition, and Activation activities:

  • Increasing customer retention means more dollars stay within your organization to help achieve revenue goals and fund lead gen initiatives.
  • A fully functioning referral system lowers your customer acquisition cost (CAC) because these leads are already warm coming in the door.
  • Case studies and reviews are powerful marketing assets for lead gen and nurture activities as they demonstrate how you’ve solved identical issues for other companies.

Remember that the bottom half of your marketing and sales funnel is just as important as the top half. After all, there’s no point pouring leads into a leaky funnel. Instead, you want to build a frictionless, powerful growth engine that brings in the right leads, nurtures them into customers, and then delights those customers to the point that they can’t help but rave about you.

So, build a strong foundation and start from the bottom up. You’ll find a better return on your investment. 

Fix #2: Join Sales Calls to Better Understand Your Target Audience

You can’t market well what you don’t know how to sell.

Your sales team speaks directly to customers, understands their pain points, and knows the language they use to talk about those pains. Your marketing team needs this information to craft the perfect marketing messaging your target audience will identify with.

When marketers join sales calls or speak to existing customers, they get firsthand introductions to these pain points. Often, marketers realize that customers’ pain points and reservations are very different from those they address in their messaging. 

Once you understand your ideal customers’ objections, anxieties, and pressing questions, you can create content and messaging to remove some of these reservations before the sales call. This effort removes a barrier for your sales team, resulting in more SQLs.

Fix #3: Create Collateral That Closes Deals

One-pagers, landing pages, PDFs, decks — sales collateral could be anything that helps increase the chance of closing a deal. Let me share an example from Lean Labs. 

Our webinar page has a CTA form that allows visitors to talk to our team. Instead of a simple “get in touch” form, we created a drop-down segmentation based on the user’s challenge and need. This step helps the reader feel seen, gives them hope that they’ll receive real value from the interaction, and provides unique content to users based on their selection.

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So, if they select I need help with crushing it on HubSpot, they’ll get a landing page with HubSpot-specific content (including a video) and a meeting scheduler. 

Speaking directly to your audience’s needs and pain points through these steps dramatically increases the chances of them booking a call. Why? Because instead of trusting that a generic “expert” will be able to help them with their highly specific problem, they can see through our content and our form design that Lean Labs can solve their most pressing pain point. 

Fix #4: Focus On Reviews and Create an Impact Loop

A lot of people think good marketing is expensive. You know what’s even more expensive? Bad marketing

To get the best ROI on your marketing efforts, you need to create a marketing machine that pays for itself. When you create this machine, you need to think about two loops: the growth loop and the impact loop.

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  • Growth loop — Awareness ➡ Acquisition ➡ Activation ➡ Revenue ➡ Awareness: This is where most marketers start. 
  • Impact loop — Results ➡ Reviews ➡ Retention ➡ Referrals ➡ Results: This is where great marketers start. 

Most marketers start with their growth loop and then hope that traction feeds into their impact loop. However, the reality is that starting with your impact loop is going to be far more likely to set your marketing engine up for success

Let me share a client story to show you what this looks like in real life.

Client Story: 4X Website Leads In A Single Quarter

We partnered with a health tech startup looking to grow their website leads. One way to grow website leads is to boost organic traffic, of course, but any organic play is going to take time. If you’re playing the SEO game alone, quadrupling conversions can take up to a year or longer.

But we did it in a single quarter. Here’s how.

We realized that the startup’s demos were converting lower than industry standards. A little more digging showed us why: our client was new enough to the market that the average person didn’t trust them enough yet to want to invest in checking out a demo. So, what did we do?

We prioritized the last part of the funnel: reputation.

We ran a 5-star reputation campaign to collect reviews. Once we had the reviews we needed, we showcased them at critical parts of the website and then made sure those same reviews were posted and shown on other third-party review platforms. 

Remember that reputation plays are vital, and they’re one of the plays startups often neglect at best and ignore at worst. What others say about your business is ten times more important than what you say about yourself

By providing customer validation at critical points in the buyer journey, we were able to 4X the website leads in a single quarter!

1716755164 910 Why The Sales Team Hates Your Leads And How To1716755164 910 Why The Sales Team Hates Your Leads And How To

So, when you talk to customers, always look for opportunities to drive review/referral conversations and use them in marketing collateral throughout the buyer journey. 

Fix #5: Launch Phantom Offers for Higher Quality Leads 

You may be reading this post thinking, okay, my lead magnets and offers might be way off the mark, but how will I get the budget to create a new one that might not even work?

It’s an age-old issue: marketing teams invest way too much time and resources into creating lead magnets that fail to generate quality leads

One way to improve your chances of success, remain nimble, and stay aligned with your audience without breaking the bank is to create phantom offers, i.e., gauge the audience interest in your lead magnet before you create them.

For example, if you want to create a “World Security Report” for Chief Security Officers, don’t do all the research and complete the report as Step One. Instead, tease the offer to your audience before you spend time making it. Put an offer on your site asking visitors to join the waitlist for this report. Then wait and see how that phantom offer converts. 

This is precisely what we did for a report by Allied Universal that ended up generating 80 conversions before its release.

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The best thing about a phantom offer is that it’s a win/win scenario: 

  • Best case: You get conversions even before you create your lead magnet.
  • Worst case: You save resources by not creating a lead magnet no one wants.  

Remember, You’re On The Same Team 

We’ve talked a lot about the reasons your marketing leads might suck. However, remember that it’s not all on marketers, either. At the end of the day, marketing and sales professionals are on the same team. They are not in competition with each other. They are allies working together toward a common goal. 

Smaller companies — or anyone under $10M in net new revenue — shouldn’t even separate sales and marketing into different departments. These teams need to be so in sync with one another that your best bet is to align them into a single growth team, one cohesive front with a single goal: profitable customer acquisition.

Interested in learning more about the growth marketing mindset? Check out the Lean Labs Growth Playbook that’s helped 25+ B2B SaaS marketing teams plan, budget, and accelerate growth.


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