The custom part of the proposal is a key feature. Marketing must graduate from “embedded, out-of-the-box” solutions, Frank says. He quotes Gartner research that shows that, while 55% of business leaders consider AI for every use case (rising to 71% if AI has been in use for more than four years), marketing comes seventh in the top twelve list of business functions seen by the leaders as benefitting from AI.

Why start with brand. Presenting at the Gartner Marketing Symposium, Frank made the case that brand is actually a “fuzzy, abstract” concept, and pointed correctly to the immense progress made by AI, and notably by generative AI, in handling the fuzzy. Generative AI like Chat GPT, for example, tends to sacrifice precision for broadly relevant and more-or-less accurate output. “It’s easier for them to tell you whether a story is happy or sad than whether it’s true.”

Ideal for brand, Frank says, which is not one precise concept, but a panoply of imagery, color, tone, mood and values.

“You have a brand, you care about that brand and you have been developing assets for that brand,” Frank told us. “That is actually a perfect situation to begin custom modeling.”

Of course, just starting is going to be daunting, but Frank is not calling on brands to start from scratch; “That’s out of the scope of most organizations,” he said. ChatGPT is just one of a number of foundational AI models out there, including offerings from Google and Amazon. The strategy should be to deploy one of these models and then customize it by training it on the brand’s own data. “It becomes a copy of the original model,” Frank explained, “with your own custom additions.”

As well as training data there should be human oversight and feedback, especially to represent brand values.

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This doesn’t mean that humans are themselves going to have to feed the model with what it needs to know. “The beauty of these models is, you don’t even have to understand the concepts that it’s extracting. It will do that for you. All you have to do is feed it with a corpus of examples and all of the subtle semantic connections that we consider it really hard to think about, it does that for you.”

Who’s on the team? This project will need input from both marketers and from IT and data scientists and AI experts. At the heart of the team, however, is a role Frank refers to as the Model Owner. The Model Owner will not be a hands-on data or AI expert, but she will be able to interact with the experts and translate between their operational challenges and the needs of the marketers. “It’s not a technical role at all,” Frank said. “It’s more of a supervisory role that articulates and owns the training process. They don’t have to know how the training process works.”

The operational framework for the model envisages generative AI creating paid media, content and social ads, sites, apps, videos and chatbots, but all within the parameters of the brand it has come to understand.

Why we care. Among all the use cases currently being described for AI, this is an ambitious one. It’s easy to see how branding could go off the rails without close human attention. Frank admits that. Also, once one starts introducing an IT team (with time on its hands) and data scientists, one begins to think this is primarily an enterprise project.

Nevertheless, Frank is bold enough to posit that custom training of AI by brands will be mainstream by 2026.

The post It’s time to teach AI about your brand appeared first on MarTech.

To stay competitive, you’ll need to understand the technology and how it will impact our marketing efforts, said Christopher Penn, chief data scientist at TrustInsights.ai, speaking at The MarTech Conference.  

Learn ways to scale the use of large language models (LLMs), the value of prompt engineering and how marketers can prepare for what’s ahead. 

The premise behind large language models

Since its launch, ChatGPT has been a trending topic in most industries. You can’t go online without seeing everybody’s take on it. Yet, not many people understand the technology behind it, said Penn.

ChatGPT is an AI chatbot based on OpenAI’s GPT-3.5 and GPT-4 LLMs.

LLMs are built on a premise from 1957 by English linguist John Rupert Firth: “You shall know a word by the company it keeps.”

This means that the meaning of a word can be understood based on the words that typically appear alongside it. Simply put, words are defined not just by their dictionary definition but also by the context in which they are used. 

This premise is key to understanding natural language processing. 

For instance, look at the following sentences:

• “I’m brewing the tea.” 
• “I’m spilling the tea.” 

The former refers to a hot beverage, while the latter is slang for gossiping. “Tea” in these instances has very different meanings. 

Word order matters, too. 

• “I’m brewing the tea.” 
• “The tea I’m brewing.”

The sentences above have different subjects of focus, even though they use the same verb, “brewing.”

How large language models work

Below is a system diagram of transformers, the architecture model in which large language models are built. 

Simply put, a transformer takes an input and turns (i.e., “transforms”) it into something else.

LLMs can be used to create but are better at turning one thing into something else. 

OpenAI and other software companies begin by ingesting an enormous corpus of data, including millions of documents, academic papers, news articles, product reviews, forum comments, and many more.

Consider how frequently the phrase “I’m brewing the tea” may appear in all these ingested texts.

The Amazon product reviews and Reddit comments above are some examples.

Notice the “the company”  that this phrase keeps — that is, all the words appearing near “I’m brewing the tea.” 

“Taste,” “smell,” “coffee,” “aroma,” and more all lend context to these LLMs.

Machines can’t read. So to process all this text, they use embeddings, the first step in the transformer architecture.

Embedding enables models to assign each word a numeric value, and that numeric value occurs repeatedly in the text corpus. 

Word position also matters to these models.

In the example above, the numerical values remain the same but are in a different sequence. This is positional encoding. 

In simple terms, large language models work like this: 

• The machines take text data.
• Assign numerical values to all the words.
• Look at the statistical frequencies and the distributions between the different words.
• Try to figure out what the next word in the sequence will be. 

All this takes significant computing power, time and resources.

Prompt engineering: A must-learn skill 

The more context and instructions we provide LLMs, the more likely they will return better results. This is the value of prompt engineering.

Penn thinks of prompts as guardrails for what the machines will produce. Machines will pick up the words in our input and latch onto them for context as they develop the output. 

For instance, when writing ChatGPT prompts, you’ll notice that detailed instructions tend to return more satisfactory responses. 

In some ways, prompts are like creative briefs for writers. If you want your project done correctly, you won’t give your writer a one-line instruction. 

Instead, you’ll send a decently sized brief covering everything you want them to write about and how you want them written.

Scaling the use of LLMs

When you think of AI chatbots, you might immediately think of a web interface where users can enter prompts and then wait for the tool’s response. This is what everyone’s used to seeing.

“This is not the end game for these tools by any means. This is the playground. This is where the humans get to tinker with the tool,” said Penn. “This is not how enterprises are going to bring this to market.” 

Think of prompt writing as programming. You are a developer writing instructions to a computer to get it to do something. 

Once you’ve fine-tuned your prompts for specific use cases, you can leverage APIs and get real developers to wrap those prompts in additional code so that you can programmatically send and receive data at scale.

This is how LLMs will scale and change businesses for the better. 

Because these tools are being rolled out everywhere, it’s critical to remember that everyone is a developer. 

This technology will be in Microsoft Office — Word, Excel and PowerPoint — and many other tools and services we use daily.

“Because you are programming in natural language, it’s not necessarily the traditional programmers that will have the best ideas,” added Penn.

Since LLMs are powered by writing, marketing or PR professionals — not programmers — may develop innovative ways to use the tools. 

An extra tip for search marketers

We’re starting to see the impact of large language models on marketing, specifically search.

In February, Microsoft unveiled the new Bing, powered by ChatGPT. Users can converse with the search engine and get direct answers to their queries without clicking on any links.

“You should expect these tools to take a bite out of your unbranded search because they are answering questions in ways that don’t need clicks,” said Penn.  

“We’ve already faced this as SEO professionals, with featured snippets and zero-click search results… but it’s going to get worse for us.”

He recommends going to Bing Webmaster Tools or Google Search Console and looking at the percentage of traffic your site gets from unbranded, informational searches, as it’s the biggest risk area for SEO. 

The post How to scale the use of large language models in marketing appeared first on MarTech.

That was the focus of a panel at The MarTech Conference, here are some of the takeaways from that discussion.

AI requires human supervision

As AI evolves, capabilities will expand. Can AI take over a specific business function and run it unaided? Not yet, according to Ricky Ray Butler, CEO of BENlabs, which uses AI to place brands’ products in entertainment and influencer content.

Artificial general intelligence or AGI is the kind of technology that is completely automated, and that’s simply not available yet.

“There is still human supervision [required] when it comes to data inputs or [telling the AI] what the purpose is to have successful outcomes,” said Butler.

“What AI really brings to the table is when it comes to the feedback loop,” he said. “It can structure data and a massive amount of data in a way that the human mind can’t even comprehend or compute. And it can do that at a scale where it can look at millions and millions of videos and monitor, prioritize and then also…make predictions with successful outcomes or or potentially unsuccessful outcomes. We are literally building a brain when we’re leveraging this type of technology to do what the human mind does, but to be able to do it even better and even more accurately.”

Dig deeper: A beginner’s guide to artificial intelligence

Generative AI writing tools need writers

Generative AI writing tools position themselves as writing assistants, not writers, said Anita Brearton, CEO of marketing technology management platform CabinetM.

“[These tools] describe their value prop as productivity,” she said. “They can help you write faster, they can improve SEO in fact.”

They can also help writers get started when all they’re staring at is a blank page. “They’re good for refining texts and creating some A/B versions of texts,” Brearton said.

Generative AI continues to improve in order to help creatives make text-based and visual content.

“I think we’re entering a very disruptive phase for creativity for designers, illustrators, video producers and writers,” said Paul Roetzer, CEO of the Marketing AI Institute 

A marketer’s point of view is more important than ever

As AI gets adopted for more marketing functions, marketers using these tools are needed to guide the technology and point it toward specific marketing objectives.

“The issue right now is the AI doesn’t have your knowledge of your product, it doesn’t have a knowledge of your customers, it doesn’t have knowledge about the internal politics of your company,” said Pam Didner, VP of marketing for consultancy Relentless Pursuit. “[AI doesn’t] have knowledge about even the road map that you are going to produce for your company. So AI can write very well, but you still need to add your own point of view. That’s where a human comes into play.”

Leaders need to know about AI when hiring

When AI is adopted by organizations, leadership needs to know how work has changed so they make the right hires.

“ChatGPT woke everyone up to AI, so we’re all testing the tools,” said Roetzer. “There’s pressure on CMOs and CEOs from boards and investors to figure out AI. Everybody needs to have a plan, and you have a whole bunch of leaders who don’t understand the underlying technology that now have to make decisions around staffing.”

He added, “We need to rapidly accelerate the comprehension of what AI is and what it’s capable of doing, what its limitations are. But, also [we need] to come to grips with where it’s going.”

Register and watch The MarTech Conference here.

The post What marketers should keep in mind when adopting AI appeared first on MarTech.

Is it more “visual, snackable, personal, and human?” Yes.

But, for now, you can only gain access to Google’s new search generative experience (SGE) through a Google Labs waitlist — which means you may be waiting weeks before you can play with it directly.

Don’t worry. We’ve got your first look and a deep dive into the new Google Search generative AI experience.

What the new Google Search Generative Experience looks like

The interface. SGE may display an AI-generated answer above the search results listings. Google clearly labels the answer as Generative AI is experimental, which is then followed by an answer to your query.

The answer is boxed in. Google cites the websites it used to generate the answer. Those sites can be clicked on to dig deeper. Or you can follow up with an additional question or even click the toggle button at the top right to dive deeper.

• “You’ll see an AI-powered snapshot of key information to consider, with links to dig deeper,” Google said.

When you click the expand button to toggle to show a deeper response, you are given additional responses from the generative AI.

Here is a GIF of it in action:

Throughout the answers generated by AI, Google gives you websites in these clickable boxes with images, so you can click over to the website to learn more.

The color of the generative AI answer box will change to “reflect specific journey types and the query intent itself,” Google said.

Vertical search with AI. This also works for vertical search experiences, such as Google Shopping results. Google’s SGE can pull in 35 billion product listings from the Google Shopping Graph, which has 1.8 billion updates every hour, Google told us. The generative AI needs to update fast, almost in real-time, to provide some answers.

Google can give you a good answer for which products to consider when searching for specific types of products, such as [bluetooth speaker for a pool party]:

Conversations. You can also follow up with your query by adding more details or additional prompts to the Ask a follow up box. Google will then generate a follow-up answer.

• “Context will be carried over from question to question, to help you more naturally continue your exploration. You’ll also find helpful jumping-off points to web content and a range of perspectives that you can dig into,” Google explained.

Conversational mode is especially useful for follow-up questions, as well as more complex or evolving information journeys, Google explained.

• “It uses AI to understand when a person is searching for something that is related to a previous question. It carries over context from previous questions to reformulate the query to better reflect the intent,” Google added.

Why we care. Most of us use Google search an awful lot of the time. It’s been clear that generative AI would change the search experience, but it wasn’t easy to guess how. We now have a better idea.

For marketers there will be some relief that Google isn’t using generated text to simply replace search results (that wouldn’t make much sense for Google’s business model, of course). Publishers will be relieved that there are plenty of links to websites. All of us, as users, should be pleased that we get a generous glimpse of what the information in the answers is based on.

How it works

Technology. Google said SGE uses a “variety of LLMs,” including but not limited to MUM and PaLM2.

This search experience was “purposefully trained to carry out tasks specific to Search, including identifying high-quality web results that corroborate the information presented in the output,” Google said.

Where Google won’t give answers. Google won’t give you answers for everything you might ask it, Liz Reid, VP of Search at Google, told us. Google is trying to be careful with this new version of Google Search, which will show answers for safer queries.

For example, Google won’t show an answer to a question about giving a child Tylenol because it is in the medical space. Google may also not show answers to questions in the financial space.

Sound familiar? Yes, Google is playing it safe in YMYL (Your Money, Your Life) categories. Google is expanding YMYL to include civic information.

• “Just as our ranking systems are designed not to unexpectedly shock or offend people with potentially harmful, hateful, or explicit content, SGE is designed not to show such content in its responses,” Google explained.

Google added that they hold this new search experience “to an even higher standard when it comes to generating responses about certain queries where information quality is critically important.”

This new search experience “places even more emphasis on producing informative responses that are corroborated by reliable sources,” Google told us.

When it comes to “data voids” or “information gaps” – where Google’s systems have lower confidence in its responses, Google “aims to not generate an AI-powered snapshot,” they said.

Plus, for explicit or dangerous topics, Google will stay away from generating a response.

Google’s approach. Google has a five-point approach to generative AI in search:

1. Information needs: How can Google reduce the number of steps it takes for the searcher to accomplish a task or complete a goal and how can Google make the experience more fluid and seamless?
2. Information quality: The information Google responds with needs to be quality, and the way the AI responds needs to be high level. So should Google answer health or financial-related queries?
3. Safety constraints: Should Google provide first-person responses? Should Google provide fluid answers that users would trust to be 100% accurate, when Google might not be able to verify the accuracy of all the answers?
4. Ecosystem: Google wants to provide traffic and credits to the source of the content. Google wants to design an experience that encourages the users and searcher to dig deeper into those sources.
5. Ads: Can ads be relevant and provide additional information to the user and how is it best to show the ads to the user in this experience.

Citations and links

When Google launched Bard, we were all taken aback by the lack of citations and links to publishers. It was rare to see links from Google Bard to publisher websites.

However, in SGE, we see a healthier way of linking to publishers and supporting the ecosystem.

Not only are the explicit answers generated in this search experience made up of specific websites, but those websites that make up those answers are also prominently displayed in the answer with a thumbnail image, title, and URL, all that is clickable to the publisher’s website.

Google, however, will not directly cite or attribute a particular page. Google’s AI model synthesizes information from a variety of sources.

In fact, Google looks for factual corroboration across sources to build the answers and then show the citations. These are generally from high-quality online sources. Google is using many of the signals Google has had in place for decades to understand information quality.

Links to publisher sites. Here is a screenshot showing those websites in the answer:

Toggle deeper. You can then click at the top right, on that toggle button to do a deeper dive into more sources, where the generative AI shows more answers with more sources that you can click on. The arrow in this image is pointing to the toggle, directly above the website links:

With search results below. Plus, you can continue to scroll down and access classic search results, in a more “snackable” format. You can see some of the links to search results, in a more boxed-in format here:

Dig deeper: Why we care about search marketing

Taking AI seriously

Google also spoke about its AI principles and emphasized they take all these AI technologies seriously.

“We’re taking a responsible and deliberate approach to bringing new generative AI capabilities to Search,” Google said.

This is not Bard, Bard was designed to showcase what the LLM models can do. This experience is specifically designed for search and works differently, as showcased above.

Google has deployed its search quality raters to do some early testing over the next few weeks before launching it to the first set of public users.

You will be able to signup for the waitlist today with the first wave of approvals to try out this new search experience in the coming weeks.

A version of this story first appeared on Search Engine Land. Additional reporting by Kim Davis.

The post Here’s what Google’s new AI Search Generative Experience will look like appeared first on MarTech.

Of course it’s exciting.

But stop for a moment and ask yourself a few questions:

• Do I really know what AI is?
• Do I know how long it has been around?
• Do I know the difference, if any, between AI and machine learning?
• And do I know what the heck is deep learning?

If you answered all those questions affirmatively, this article may not be for you. If you hesitated over some of them, read on.

The AI revolution starts…now?

Let’s start by filling in some background.

Is AI something new?

No. Conceptually, at least, AI dates as far back as 1950 (more on that later). As a practical pursuit it began to flourish in the 1960s and 1970s as computers became faster, cheaper and more widely available.

Is AI in marketing something new?

No. It’s worth bearing in mind that AI has long had many, many applications in marketing other than creating content. Content recommendations and product recommendations have been powered by AI for years. Predictive analytics — used to predict user behavior based on large datasets of past behavior, as well as to predict the next-best-action (show her a relevant white paper, show him a red baseball cap, send an email) — has been AI-powered for a long time.

Well-known vendors have been baking AI into their solutions for almost a decade. Adobe Sensei and Salesforce Einstein date from 2016. Oracle’s involvement with AI goes back at least as far and likely further; it just never gave it a cute name. Another veteran deployer of AI is Pega, using it first to predict next-best-actions in its business process management offering, and later in its CRM platform.

Well…is generative AI something new?

Generative AI. Conversational AI. AI writing tools. All phrases of the moment, all overlapping in meaning. Generative AI generates texts (or images, or even videos). Conversational AI generates texts in interaction with a human interlocutor (think AI-powered chatbots). AI writing tools aim to create customized texts on demand. All of these solutions use, in one sense or another, “prompts” — that is, they wait to be asked a question or set a task.

Is all this new? No. What’s new is its wide availability. Natural language processing (NLP) and natural language generation (NLG) have been around for years now. The former denotes AI-powered interpretation of texts; the latter, AI-powered creation of texts. As long ago as 2015, based on my own reporting, AI-powered NLG was creating written reports for physicians and for industrial operations — and even generating weather forecasts for the Met Office, the U.K.’s national weather service.

Data in, text out. Just not as widely available as something like ChatGPT.

Video too. At least by 2017, AI was being used to create, not just personalized but individualized video content — generated when the user clicks on play, so fast that it appears to be streaming from an existing video library. Again, not widely available, but rather, a costly enterprise offering.

Dig deeper: ChatGPT: A marketer’s guide

What AI is: the simple version

Let’s explain it from the ground up.

Start with algorithms

An algorithm can be defined as a set of rules to be followed in calculations or other problem-solving or task-completing operations, especially by a computer. Is “algorithm” from the Greek? No, it’s actually from part of the name (al-Khwārizmī) of a 9^th century Arab mathematician. But that doesn’t matter.

What does matter is that using algorithms for a calculation or a task is not — repeat, not — the same as using AI. An algorithm is easily created; let’s take a simple example. Let’s say I run an online bookstore and want to offer product recommendations. I can write a hundred rules (algorithms) and train my website to execute them. “If she searches for Jane Austen, also show her Emily Bronte.” “If he searches for WW1 books, also show him WW2 books.” “If he searches for Agatha Christie, show him other detective fiction.”

I’ll need to have my volumes of detective fiction appropriately tagged of course, but so far so easy. On the one hand, these are good rules. On the other hand, they are not “intelligent” rules. That’s because they’re set in stone unless I come back and change them. If people searching for WW1 books consistently ignore WW2 books, the rules don’t learn and adapt. They carry on dumbly doing what they were told to do.

Now, if I had Amazon’s resources, I’d make my rules intelligent — which is to say, able to change and improve in response to user behavior. And if I had Amazon’s market share, I’d have a deluge of user behavior that the rules could learn from.

If algorithms can teach themselves — with or without some human supervision — we have AI.

But wait. Isn’t that just machine learning?

AI versus machine learning

To the purist, AI and machine learning are not originally the same thing. But — and it’s a big but — the terms are used so interchangeably that there’s no going back. Instead, the term “general AI” is now used when people want to talk about pure AI, AI in its original sense.

Let’s go back to 1950 (I warned you we would). Alan Turing was a brilliant computer scientist. He helped the Allies beat the Nazis through his code-cracking intelligence work. His reward was to be abominably treated by British society for his (then illegal) homosexuality, treatment that resulted in an official apology from Prime Minister Gordon Brown, more than 50 years after his death: “On behalf of the British government, and all those who live freely thanks to Alan’s work, I am very proud to say: We’re sorry. You deserved so much better.”

So what about AI? In 1950, Turing published a landmark paper, “Computing machinery and intelligence.” He published it, not in a scientific journal, but in the philosophy journal “Mind.” At the heart of the paper is a kind of thought experiment that he called “the imitation game.” It’s now widely known as “the Turing test.” In the simplest terms, it proposes a criterion for machine (or artificial) intelligence. If a human interlocuter cannot tell the difference between responses to her questions from a machine and responses from another human being, we can ascribe intelligence to the machine.

Of course, there are many, many objections to Turing’s proposal (and his test is not even smartly designed). But this did launch the quest to replicate — or at least create the equivalent of — human intelligence. You can think of IBM Watson as an ongoing pursuit of that objective (although it has many less ambitious and more profitable use cases).

Nobody really thinks that an Amazon-like product recommendation machine or a ChatGPT-like content creation engine is intelligent in the way humans are. For one thing, they are incapable of knowing or caring if what they are doing is right or wrong — they do what they do based on data and predictive stats.

In fact, all the AI discussed here is really machine learning. But we’re not going to stop anyone calling it AI. As for the pursuit of human-level or “general AI,” there are good reasons to think it’s not just around the corner. See, for example, Erik J. Larson’s “The myth of artificial intelligence: Why computers can’t think the way we do.”

What about ‘deep learning’?

“Deep learning” is another AI-related term you might come across. Is it different from machine learning? Yes it is; it’s a big step beyond machine learning and its importance is that it greatly improved the ability of AI to detect patterns and thus to handle images (and video) as competently as it handles numbers and words. This gets complicated; here’s the short version.

Deep learning is based on a neural network, a layer of artificial neurons (bits of math) which are activated by an input, communicate with each other about it, then produce an output. This is called “forward propagation.” As in traditional machine learning, the nodes get to find out how accurate the output was, and adjust their operations accordingly. This is called “back propagation” and results in the neurons being trained.

However, there’s also a multiplication of what are known as the “hidden layers” between the input layer and the output layer. Think of these layers literally being stacked up: That’s simply why this kind of machine learning is called “deep.”

A stack of network layers just turns out to be that much better at recognizing patterns in the input data. Deep learning helps with pattern recognition, because each layer of neurons breaks down complex patterns into ever more simple patterns (and there’s that backpropagating training process going on too).

Are there AI vendors in the martech space?

It depends what you mean.

Vendors using AI

There are an estimated 11,000-plus vendors in the martech space. Many of them, perhaps most of them, use AI (or can make a good argument that that’s what they’re doing). But they’re not using AI for its own sake. They are using it to do something.

• To create commerce recommendations.
• To write email subject lines.
• To recommend next-best-actions to marketers or sales reps.
• To power chatbots.
• To write advertising copy.
• To generate content for large-scale multivariate testing.

The list is endless.

The point I want to make is that AI is a bit like salt. Salt is added to food to make it taste better. Most of us, at least, like the appropriate use of salt in our food. But who ever says, “I’ll have salt for dinner,” or “I feel like a snack; I’ll have some salt”?

We put salt in food. We put AI in marketing technology. Aside, perhaps, for research purposes, salt and AI aren’t much used on their own.

So yes, there are countless martech vendors using AI. But are there martech vendors selling AI as an independent product?

Vendors selling AI

The answer is, in the martech space, very few. AI as a product really means AI software designed by engineers that can then be incorporated and used in the context of some other solution. It’s easy to find engineering vendors that are selling AI software, but for the most part they are selling to IT organizations rather than marketing organizations, and selling it to be used for a very wide range of back-office purposes rather than to enable marketing or sales.

There are one or two exceptions out there, clearly targeting their products at marketers. Not enough, however, to create a populous category in a marketing technology landscape.

We scratched the surface

That’s all this article is intended to do: scratch the surface of an enormously complex topic with a rich history behind it and an unpredictable future ahead. There are ethical questions to address, of course, such as the almost inevitable cases where machine learning models will be trained on biased data sets, as well as the equally inevitable plagiarising of human content by generative AI.

But hopefully this is enough to chew on for now.

The post Artificial Intelligence: A beginner’s guide appeared first on MarTech.

Take Williamsburg Farmers Market in Virginia, for example. It’s open on Saturday from 8 a.m. to noon, almost but not quite year round. On the management side, it has one full-time employee, three part-time, plus some help for student volunteers.

The full-time employee is market manager Tracy Frey. Let’s see how busy she is.

Operations, vendor support, community outreach and sponsorships

“We’re a not-for-profit farmers market, so that makes us a little different,” Frey told us. “I’m responsible for the majority of the operations of the farmers market, so recruiting vendors and assisting them with their business — if they haven’t been inspected and need to be, I assist them in connecting them. They have to have liability insurance and those kinds of things, so I do a lot of hand-holding with very small businesses.”

Frey considers herself an incubator for the vendor’s businesses. “We actually visit every single farm and food processing operation prior to them joining our market. We ask them what their hopes, dreams and goals are and we try to get them to wherever that is.” That might mean, for example, supporting a small baker who hopes to graduate from selling products at the market to opening a bakery or coffee shop.

In addition to working with vendors, Frey is responsible for outreach to stakeholders and the community. “A lot of time is spent doing networking, whether it’s Chamber of Commerce or leadership classes.” She also does all the programming coordination: a children’s program, a music program and a chefs tent where people can learn how to turn the produce into meals.

In addition to working with the vendors and the community, Frey is responsible for creating partnerships with sponsors. “We don’t actively fundraise, but we do have sponsors and partners like the City of Williamsburg, Merchants Square, Colonial Williamsburg, and the Historic Virginia Land Conservancy.” We have to know who … stakeholders, justify to them. Insights touches, we collect that data just in case they do.

And then there’s marketing

Frey finds it amusing that she’s not only the market manager, but also the marketing manager for the market. “That’s my job too. I also do our web design. It’s critical for us to keep really good data. I analyze and study data. I can tell you the weather from our very first market up to last Saturday.”

The main marketing channel for the farmers market has been a weekly newsletter. “We know our demographics in Williamsburg — there’s still a generation that reads newspapers and magazines and likes newsletters.”

The email lists are grown organically rather than purchased and currently run to thousands of subscribers. This led to the market outgrowing its former email platform around 2006 or 2007. Because of the limit on the number of emails that could be sent simultaneously, Frey found she had to execute multiple, separate sends. She turned instead to the digital and email marketing platform Constant Contact.

“In the non-profit farmers market world, a lot of people were going for free options, but they didn’t quite meet our needs or have the support that we needed — and since we are quite a structured non-profit, with a strategic plan and a budget, we did have money for marketing. It seemed a really good use of that money to invest in newsletter software.“

Constant Contact offered more than just email distribution. “I love bells and whistles, especially if they make my life easier or I can reach more people,” said Frey. One feature Frey treasured was help in building out templates. “That’s not something that’s part of my skill-set or that I want to spend a whole lot of time on, though I realize it’s super-important. If I can streamline it and feel like I’m still doing a good job, that’s a perfect world.”

The weekly newsletter averages a very impressive 50% open rate. “It’s worth my time for the thousands of people who get it and the thousands who open it.” She also uses Constant Contact to schedule the publication of the newsletter on social media. Also: “We’re newly dipping our toes into Reels, which has been a lot of fun.” Student volunteers create multiple videos to post throughout the week.

Dig deeper: Two afforable AI writing assistants in action

Where the AI comes in

Constant Contact recently unveiled an AI Content Generator for emails and other marketing content. Frey was not slow to adopt it.

“At the beginning of my newsletter every week, I write a little paragraph or two about why you should come to the market this Saturday,” she explained. “I do that 52 weeks a year and it’s really hard to come up with a new thing every time. What I like about the AI is I can say something like ‘There are strawberries and onions this week, come visit us at the market,’ and it can make that into something very intelligent and fun-sounding.”

The AI will add content about things to do in the area, such as visiting Colonial Williamsburg. “It just takes my very short prompt and turns it into something that always makes me smile and I hope makes other people feel the same way. It takes the brainwork out of me trying to figure out how to say what I want to say.”

The AI is also good at turning prompts into calls-to-action, something Frey felt she always struggled with.

Constant Contact’s Content Generator does incorporate ChatGPT technology, but enhances that model by applying proprietary data and algorithms that are tuned to the needs of the specific Constant Contact customer. Given that ChatGPT has been made widely available by OpenAI, why use the Constant Contact version?

The answer is simple. “It’s really nice that it’s all in the same place that I’m creating my newsletter. If I can hit send five minutes sooner, that is amazing.”

Can Frey believe that a non-profit farmers market is leveraging AI in its marketing? “I am mesmerized by that every day,” she said.

The post How a non-profit farmers market is leveraging AI appeared first on MarTech.

Welcome to the BETA version of MarTechBot, the first generative AI chatbot for marketing technology professionals. MarTechBot has been trained on the MarTech.org content, allowing you to explore, experiment and learn more about marketing technology. It’s MarTech + ChatGPT!

MarTechBot is BETA software powered by AI which will make mistakes, errors, and sometimes even invent things.

To help get you started, here are some best practices and sample prompts.

Share your feedback!

Release notes:

May 23, 2023

• Disabled PDF upload, working on a better experience
• Added “Whenever possible, format replies in markdown” to the context provided for all prompts for additional formatting

May 17, 2023

• Enabled PDF upload (max 50 pages). Upload a PDF and start asking questions.

May 2, 2023

• Enabled speech to text – you can talk to MarTechBot!
• Enabled download option.
• Added Elevenlabs API key to test voice synthesis.

The post Welcome to MarTechBot appeared first on MarTech.

There have been indications that other European regulators may swiftly follow suit. Reports suggest that France is conducting its own inquiry; Ireland has asked Italy for more details about the basis for the ban; and the German data commissioner has said that the same action could “in principle” be taken in Germany.

Why we care. Given the immense excitement created by the availability of ChatGPT and similar tools, it was perhaps too easy to overlook warnings emerging from the legal profession over the last few months that it could run afoul of European data regulations — regulations which, in many ways, have become a de facto global standard.

If the questions that arise need to work their way through the European legal system for adjudication, that could take some time, of course. But it’s clear that regulators in European nations can take swift action in the meantime.

Dig deeper: ChatGPT: A marketer’s guide

Lawful bases for processing data. One fundamental challenge for large language models like ChatGPT is that under European law, specifically the GDPR, there are only six lawful bases for processing personal data at all (data that can be used directly to identify an individual or indirectly to identify an individual in combination with other information). The bases are:

• Consent.
• Performance of a contract.
• A legitimate interest.
• A vital interest (a matter of life and death).
• A legal requirement.
• A public interest.

To the extent a large language model is being trained on data obtained without explicit consent, it’s by no means clear that any of these bases are applicable — unless, perhaps, one makes the bold assumption that the availability of AI solutions is in the public interest.

Data erasure. Another challenge is whether a solution by ChatGPT is competent to support the “right to be forgotten.” Under GDPR, in certain circumstances, an individual can request the erasure of their data. To be clear, ChatGPT is not scraping the web and heedlessly collecting large quantities of personal data. But it is being trained on very large sets of texts, and the question OpenAI might have to address is whether it knows what’s in those sets in terms of personally identifying information or data it might be asked to erase.

The post ChatGPT under threat from European regulators appeared first on MarTech.

1: Be specific. 

Detail precisely what you are looking for. 

• General: What are best practices for CRM?
• Precise: What are best practices for using CRM with account based marketing?

Explain the context of the question: Do you need talking points? Blog ideas? If so, include that in the prompt.

Dig deeper: AI is used in marketing by two thirds of B2B orgs, Forrester finds

2: Be brief. 

• Using fewer words forces you to pinpoint what it is you want. Before you enter your prompt, read it and ask yourself, “Is this word really necessary?” Eliminating unnecessary words makes it more likely ChatGPT will give you precisely what you are looking for.
• One of the great things about ChatGPT is that it understands normal language. One of the things it understands is what parts to ignore. When I entered “What are best practices for using CRM with account based marketing” into Bing, it ignored the words “what are.” While you can use everyday language, knowing what it uses gets you thinking about what ChatGPT responds to.
• For a complex request, use several simple sentences instead of one complex sentence with several clauses or subpoints. 
• Do not say please or thank you. While this may seem odd, several experts I spoke with say it happens quite often and can confuse the program. 

3: Be clear.

• Use words that are easy to understand. Keep a thesaurus handy. Not only can it help you find a simpler word, it can also suggest subtly different words you may want to try in a prompt. 
• Avoid jargon and slang. Smart content. Top-of-the-funnel. Lead flows. While you and I know what those are, ChatGPT may not. Or each one may have several different meanings depending on the context.
• Watch out for acronyms. Top-of-the-funnel is frequently written as TOFU. I’m sure you can see where that could be confusing. 

In my example I used the acronym CRM and spelled out account-based marketing. That’s because there are very few other uses of CRM besides customer relationship management. However, ABM has many very common uses (Anti-Ballistic Missile, Agent-Based Modeling, Activity-Based Management, etc.). I suspect ChatGPT would get the right one given the context of CRM but I wanted to be sure.

If at first you don’t succeed

Also, one final tip: If you’re not happy with the results you got, hit the “retry” button. This tells ChatGPT to generate something different than the first results it gave you.

The post Three essentials for writing a good ChatGPT prompt appeared first on MarTech.

4. WriteSonic

WriteSonic is one of the most affordable AI writing tools designed to generate long-form pieces of up to 3,000 words. You can find helpful comments about it on the software review site G2. The kicker is that you can find pretty much the same reviews for each tool on the list, so let’s test WriteSonic on a real task.

How to generate a B2B blog post with WriteSonic

What sets WriteSonic (WS) apart from other tools is that it prompts you to use SEO-ready templates (workflows). It’s suitable for straightforward content like “How to grow avocados at home” but is more limited for B2B pieces, where you must constantly tweak your content or ask AI to rewrite paragraphs on the fly.

That said, I’ll use a template to craft a 3,000-word article.

Step 1: Find your keywords

WriteSonic puts content optimization front and center. First, the tool asks you to specify a topic for the article and automatically puts together a list of relevant keywords. Select those you want to optimize a piece for.

Step 2: Get ideas for a title and generate an outline

The set-up process is similar to other tools, but WriteSonic limits you to choose only one tone of voice for title ideas and content generation. It cannot combine and write in different styles like Jasper or ChatGPT. 

Regarding outlines, WriteSonic offers six options revolving around B2B appointment-setting services. However, I’ll go with my initial outline and see whether it can best human-written content. 

Step 3: Generate a whole piece

WriteSonic did strictly follow my outline and expand on all my given points. Unfortunately, it was too repetitive and created a piece that would be easier to trash rather than edit. Read the text in the red boxes.

That’s not a usable result. But let’s give WriteSonic one more try and see what content quality we’ll get for a simple B2C piece like “How to Pick a Ripe Pineapple.“

Sadly, the tool continued to generate repetitive, slightly paraphrased paragraphs, though they certainly don’t lack sense. If I wrote a piece about pineapples, I could use this text as a rough draft and quickly extract usable ideas.

But let’s admit it, long-form content is not WriteSonic’s strong side. Jasper can do way better.

Although these comments sound like a solid “no-go” for WriteSonic, I recommend you try it for other copywriting tasks like creating social media copy or landing pages.

Pricing

• Try all features for free until you reach 2,500 words.
• Paid plans start at $19/month with 19,000 words and one user.

5. Copy.ai

Copy.ai is a one-stop shop for long-form and sales copywriting. It generates full product descriptions, landing pages and emails. The system remembers your writing style and preferences.

Copy.ai has a set of features for blog post creation. It can:

• Generate titles and meta descriptions in seconds.
• Check for duplicate content.
• Generate FAQs and listicles in one click.
• Pick up the tone to match your brand voice.
• Help with search engine optimization.
• Create “cliffhangers” for your copy.

How to generate a blog post with Copy.ai

Copy.ai offers two ways to write a blog post: Freestyle and Blog Post Wizard. The Freestyle mode suggests related ideas for paragraphs or sentences based on the purpose of a piece and its title. After testing this feature, I didn’t see much practical value for long-form content.

Blog Post Wizard mode follows a step-by-step process for generating blog posts. It starts by giving you an idea, follows by writing the outline and creating talking points and ends with an industry-standard article. 

Craft an article with Blog Post Wizard

First, I haven’t seen a tool that would give talking points to an outline before composing a whole piece. Copy.ai has generated surprisingly solid talking points for B2B appointment setting. Check it out.

What’s more, you can generate more talking points for a heading and pick the best ones. Likewise, you can rewrite the output or add/edit a talking point.

Here’s the final draft. 

This is actually good. An uncanned intro, short and on-point paragraphs without awkward or repetitive blocks. Highlighted in green is auto-completed text chosen from four Copy.ai suggestions.

I would use this copy for the rough draft without second-guessing. What’s left for a writer is to piggyback on ideas and bridge the gap with insights, real-life examples and visuals.

What else can you do with Copy.ai for blog post writing?

• Develop a relevant and engaging story about a topic in two sentences.
• Find stronger alternatives to verbs and adjectives, like using a thesaurus.
• Make up relatable analogies for any topic.

Pricing

• Freemium up to 2,000 words/month.
• A 7-day free trial of the Pro plan.
• The Pro plan costs $49/month and comes with unlimited words.

The verdict

All tools have their target audiences, but Jasper and ChatGPT (see part one) beat any competition for blog post writing. They give you full control over composing paragraphs and rewriting, which the other tools do not.

If you are looking for a cheaper alternative, try Copy.ai. As you’ve seen in the samples, it can deliver high-quality results for one-shot articles and comes with engaging features for storytelling. And let’s not forget that Copy.ai offers unlimited words on the Pro plan.

The post Two afforable AI writing assistants in action appeared first on MarTech.