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Balancing Creativity With Caution When Using AI to Create Content

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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.

I’m the kind of writer who hates to write but loves having written. Leading a marketing consultancy, where 99% of my work involves writing, only amplifies this conundrum.

If this statement resonates with you, you’ll understand the allure of generative artificial intelligence (AI) tools like ChatGPT for marketers, whether they are client-side or agency-side. These technologies have the potential to simplify an arduous writing process, helping writers skip the torture of the blank page and fast-forward to the gratification of a published article. It’s a junk food promise, satisfaction without effort.

I first dipped my toes into the world of generative AI in November 2022 and was initially captivated by the quick wins ChatGPT seemed to offer. Here was a tool that could churn out paragraph after paragraph of seemingly well-crafted copy at lightning speed. It was easy to envision how this might revolutionize my work and allow me to become a prose powerhouse. But the more I played with a number of large language models (LLM)/generative AI tools, the more I became aware of the risks. Especially as someone who works with clients and has a duty to provide them with well-researched, well-articulated, and credible advice.

This article is my attempt to provide guardrails and advice for marketers who are rightfully skeptical of the AI revolution.

Some basic rules everyone should be following

Whether you’re using generative AI tools to create content for yourself, your employer, or a client, there are some basic tenants to follow.

Safeguard proprietary information

Never, ever input proprietary or sensitive data into the AI model, including company data and IP that is not freely available in the public domain. This also includes client-specific information like private datasets, business strategies, internal reports, customer information, and other confidential materials. Several companies, including Amazon, have restricted employees from using tools like GitHub Co-Pilot and ChatGPT due to fears AI could lead to a potential leak of confidential data due to the potential for inputs to be stored and used as training data.

I’d go one step further and always replace the subject’s name with a pseudonym. If you need to use real data for context, replace all personally identifiable information (PII) and sensitive business information with anonymized or fictional substitutes.

Consent is critical

Before using any client data, ensure you have the necessary permissions. Sharing data with an AI model can be considered data sharing and can violate confidentiality agreements and data protection laws, so you do need to tread carefully and should not assume you have consent to share information. Get legal advice if you need it.

Most clients and businesses will be aware people use generative AI now as part of their work. If you can be transparent about how you use AI tools and how you approach consent and data sharing, you can go a long way toward demonstrating that you understand and have mitigated any risks.

Rigorously review outputs

Always review the generated content for accidental inclusion of sensitive data. AI models might infer from the data provided and unintentionally generate 3rd party sensitive content based on their extant dataset.

You should also thoroughly review outputs to ensure they don’t unintentionally reference proprietary or sensitive client/business information.

Avoid intellectual property infringements

When using Midjourney to create imagery or ChatGPT to create copy, avoid using “in the style of X” prompts which direct the model to imitate an individual’s work. This could violate copyright laws and is frankly extremely lazy, even when you’re referencing historical artists whose work is no longer protected by copyright. A recent example of the backlash of using generative AI has been discussed by artists on imitating their style. However, you can absolutely leverage client brand tone of voice directions to guide copy outputs.

In addition to not replicating the style of specific authors or artists, respect all intellectual property rights. This includes text, images, designs, or any other content that may be subject to copyright.

Don’t mindlessly trust outputs

Full Fact CEO Will Moy recently told the UK Online Harms and Disinformation inquiry on misinformation and trusted voices that “the ability to flood public debate with automatically generated text, images, video and datasets that provide apparently credible evidence for almost any proposition is a game changer in terms of what trustworthy public debate looks like, and the ease of kicking up so much dust that no one can see what is going on. That is a very well-established disinformation tactic.”

As members of a democratic society striving for transparent public discourse, we must recognize our role in counteracting the ease with which AI can be harnessed to disseminate disinformation that could materially damage our way of life. The responsibility of fostering an informed society lies not only with fact-checkers and official authorities but also with us as content creators, curators, and consumers of information.

There are two significant issues with large language models such as ChatGPT. The first is hallucination – which refers to the generation of outputs that are not based on input data or that significantly deviate from factual information present in the input. Secondly, the models are only as good as the data they are trained on – if the training data contains misinformation, the model can learn and replicate it.

Sadly there isn’t a technological solution to verifying if outputs are factually correct. Automated fact-checking has been around for some time now, and while it is making significant strides in verifying a select range of basic factual assertions with available authoritative data, it still has its limitations. As of yet, there is no tool that can fully automate the checking of outputs from another tool with 100% accuracy.

The challenge lies in context – the complexity and contextual sensitivity required for comprehensive fact-checking is still beyond the scope of fully automated systems. Subtle changes in a claim’s wording, timing, or context can make it more or less reasonable. Even a perfectly accurate statistic can misinform when the correlation is mistaken for causation (for example, by year, the number of people drowned in pools correlates with the number of films featuring Nicholas Cage).

So how can we use our human powers of reasoning and decision-making to ensure that facts and figures are verified and used in the correct context?

Verify sources, figures, and facts with multiple third-party trusted sources

Refrain from taking the information presented at face value. Make a habit of cross-checking any facts, figures, or sources presented in AI-generated content with multiple trusted sources. This could include reputable news outlets, government databases, or academic journals.

Don’t trust links generated by Generative AI tools; find your own

While AI models like ChatGPT may suggest links related to the topic, verifying these before using them is crucial. Ensure that the links are active, the domains are reputable, and the specific pages are relevant and reliable. In many cases, it’s best to find your own sources from established, trustworthy sites that you’re familiar with.

Use fact-checking websites

Websites like Full Fact, Snopes, or FactCheck.org can be invaluable when verifying facts. They provide detailed analyses of claims, often referencing their sources, and can help you separate fact from fiction.

Get up-to-date data

The accuracy of data is often time-sensitive. What was true a year ago may not hold today. When using data in your content, always check the date it was published or collected. Try to use the most recent and relevant data available, and remember ChatGPT’s training data has the cut off-date of September 2021. So, if you ask where the Queen of England currently resides, it will tell you Buckingham Palace.

Even using the most up-to-date model, such as ChatGPT 4, does not guarantee data or accuracy will be improved. While ChatGPT 4 is better at synthesizing information from multiple sources, OpenAI still admits its hallucination rate is similar to previous models.

Still unsure? How to deal with uncertain information

When encountering uncertain or unverified information, it’s essential to exercise caution and transparency.

If you come across dubious or unsupported facts, consider excluding them to maintain credibility. However, if the information is key to your topic but its validity is unclear, it’s important to express this uncertainty to your audience, presenting any alternate perspectives if available. If possible, consult subject matter experts in the relevant field to gain further insight and possibly resolve the ambiguity. (Also, remember the expertise element of E-E-A-T – it’s in your interest to cite expert opinions.)

Speaking of expert opinions, it’s important to verify that the expert you’re quoting is credible. Think like Google here – is the individual mentioned on other high-quality websites? Do they have relevant qualifications, are they cited in professional journals or publications? You are responsible for fact-checking the status of the fact checker.

How should we be using AI then?

So far, I’ve explained how you can reduce risk when using AI tools and how to prevent the dissemination of misinformation. After all this, you might feel like tools like ChatGPT sound more trouble than they’re worth. After all, considering the due diligence required, you might question if it’s easier to simply create the content unaided. There is an element of truth to this perspective.

However, as a marketing advisor and consultant, instead of treating AI as a tool to create the raw material, I’m using it to improve my creativity and efficiency in three ways. You’ll note that none of these involve asking the technology to come up with something from scratch.

Acceleration

During the initial stages of the creative process, my first batch of ideas often lacks originality or spark. This is something I hate about writing; it can take me a long time to get into the flow of it.

A wise creative writing tutor once told me that the first 30 minutes of writing is about getting the crap ideas out of your head to make way for the good ones. That’s why it feels so painful. Since AI-generated content like ChatGPT is based on existing material, using a deterministic engine to return the most probable result, I use it to quickly generate these “bad” ideas, effectively taking the reductive concepts off the table. If ChatGPT can come up with it, it’s probably not a novel or interesting idea.

Reflection

Another way I use AI to enhance my creativity is by reflecting on my own creative output. For example, after writing an article or developing a piece of work, I often use AI to summarise the key points or arguments I’ve made, which I can then review for completeness. This helps me ensure that I haven’t missed anything important and that my messaging is consistent and coherent. Additionally, AI can help me identify gaps in my arguments or inconsistencies in my messaging. This process is akin to “rubber-ducking” my copy at scale. Interestingly I still prefer to pass things by a human editor for a full review once I’m happy.

Variation

I also use AI to generate variations of my original content, giving me different perspectives on presenting my ideas. By exploring alternative phrasings, sentence structures, or even entire paragraph arrangements, I can identify more engaging and impactful ways to convey my message. I don’t typically copy and paste the variants word for word, but cherry-pick the best bits from the outputs. Sometimes that’s just a word.

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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

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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!

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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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