Against this background, Gartner principal analyst Tia Smart described how to build the kind of efficient, focused martech function that can get much more out of the technology.

1. Getting prepped

The three first steps to getting your martech function on track are:

• Identify the product owners (with overall responsibility for a particular solution) and the solution’s daily users.
• Audit the stack (find out what’s there and what is and isn’t being used).
• Assess the results of the above and proceed to the next steps.

Smart believes it’s crucial to define staff roles clearly. “Is this person a daily user, so they can give you candid feedback; or is this the product owner, based on a specific product like Salesforce or Adobe; or is this person the leader of a category of products like advertising solutions or direct marketing channels?”

It’s also important to align the audit with business use cases. “Is this tool meeting specific use cases it was intended for, or is it not? That will start informing the next steps you’re able to take,” she told us.

2. Developing a robust roadmap

Times have changed. Just in the last year, the pendulum has swung from a preference for best-of-breed stacks to a preference for integrated suites (60% to 25%).

There remain challenges with both approaches. Paradoxically, marketers find integration and configuration challenges within the integrated suites themselves, especially those built from a series of independent acquisitions. On the other hand, it’s difficult to recruit and retain the talent to handle a wide range of point solutions.

DIg deeper: Marketers need a unified platform, not more standalone tools

“The biggest driver of the shift to integrated suites,” said Smart, “is that marketers don’t have the right talent in place, they have difficulty integrating their current martech ecosystems; I think there’s a perception that an integrated suite is going to solve for some of those challenges and complexities.”

A decision has to be made whether to be integrated suite-first or best-of-breed-first. Then take steps to build the roadmap:

• Identify business needs and the marketing tech needs that align with them.
• Develop a roadmap aimed at filling gaps (and eliminating duplication and waste).
• Communicate the roadmap to stakeholders.
• Continue to evolve it (this isn’t “one and done”).

The truth is, Smart explained, that at the end of the day the martech function is not going to commit fully to an integrated suite approach or a best-of-breed approach. “It’s understanding that it’s going to be a balance,” she said. “You’re going to need a blend, it’s just what is going to be your primary focus?”

3. Making sure you fill the talent gaps

There are four possibilities to evaluate here:

• Develop and utilize existing talent that understands the business and its needs.
• Get support from IT.
• Outsource elements to consultancies or agencies.
• Hire new talent.

Of course, challenges exist with these approaches. IT may have limited capacity, outsourcing can be expensive and there’s a widely recognized talent shortage.

Finally, pay attention to the actual users. “Sometimes marketers focus so much on the technology that they forget about the people who are planning to utilize the tools. If we think about the reason for the talent shortage, burnout is a large push for wanting to leave a company. Being sure that your team is not frustrated with the tools will help to overcome some of these challenges.”

Are you getting the most from your stack? Take the 2023 MarTech Replacement Survey

The post 3 steps to building a three-star marketing technology function appeared first on MarTech.

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.

Are you getting the most from your stack? Take the 2023 MarTech Replacement Survey

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.

Gartner cites recessionary fears, inflation and a talent gap as stoking concerns in the enterprise that have knock-on effects on marketing and marketing technology investments. Perhaps unexpectedly, although media allocation is flat, spending on digital channels actually showed a slight decline.

The state of martech spend. The bad news for the martech space is that no less than 75% of CMOs feel under pressure from other parts of the enterprise to cut their technology spending. The consolation is that 63% plan to resist the pressure, to some degree at least, and grow their martech spending. But almost one quarter, 23%, do expect to make cuts.

CMOs do propose to increase social advertising spend, but among the categories likely to take a hit are search advertising, SEO and digital OOH.

It’s necessary to “make a clear value case for martech investment,” said Ewan McIntyre, chief of research for the Gartner for Marketers Practice, presenting the survey’s findings. He also said, using the analogy of a voyage, that what was needed was “not a bigger boat, but a more efficient boat.”

Dig deeper: Digital ad spend growth drops to 7.8% this year

Catalytic marketing. His comments reflected the prominent theme of the Gartner keynote delivered by Lizzy Foo Kune, VP analyst and Carlos Guerrero, VP advisory in the Gartner Marketing Practice. They insisted that, despite pressures to realize growth in an uncertain environment, CMOs should not take the familiar route of increasing activity and taking on more projects.

They also questioned the value of “customer obsession.” “Customer obsession goes too far,” said Guerrero, “to unprofitable extremes that customers find intrusive.” Rather than trying to meet customers in every conceivable channel, leveraging customer data to deliver countless relevant messages, the keynote speakers introduced the concept of “catalytic marketing.” Gartner data shows, they said, that more important than quantity of engagement are experiences that bring about some change in the customer.

Are you getting the most from your stack? Take the 2023 MarTech Replacement Survey

In essence, catalytic marketing is not about “more.” “Progressive CMOs are breaking free from the cycle of more by embracing catalytic marketing and, in the process, shifting from growing marketing’s scope to growing marketing’s success,” said Guerrero.

Why we care. The pressures on marketing and martech investment are clearly real. It’s an environment that demands efficiency and demonstrable ROI. The catalytic marketing concept needs to be fleshed out (an example they cited was L’Oreal’s Skin Genius experience); that’s the positive part of the Gartner message.

The part that might be perceived as negative is the sense that attempting to develop a 360 degree view of the customer and apply it to engagement on countless channels, might be counter-productive, despite everything we’ve heard over the past few years.

About the survey. 410 CMOs and marketing leaders were surveyed in March and April 2023. Respondents were based in North America and Europe, representing various industries and company sizes, with most reporting annual revenue exceeding $1 billion.

The post Marketers under pressure to cut martech spend appeared first on MarTech.

We’re all familiar with the concept of providing connected, engaging CX across marketing, sales and customer service channels. Over the last couple of years, the importance of supply chain transparency has become key to CX too. But what about the need to verifiably identify yourself as part of the registration process for services — financial services, for example — that really need to know that you are who you say you are?

Why we care. We care because there are widely used identity services out there (used by branches of government) that are far from friction-free. Anyone who has spent time trying to take photographs of physical documents and upload acceptable versions of them to a website understands this.

From the marketing perspective, anything that encourages users to abandon registration and look elsewhere for more easily accessible services is a bad thing. Think of this as reflecting the changes we’re seeing in the buyer’s journey (both B2B and B2C). Buyers are looking for ease, speed and as much self-service as possible.

Onfido acquires Airside. The international automated identity verification platform Onfido seeks to smooth the sign-up process by using scanners to gather and match documentary and biometric information. Today it announced the acquisition of private, digital identity sharing technology platform Airside Mobile.

Airside, through its Airside Digital Identity App, allows users to carry their digital identities on their smartphones. Once verified, the identity can be used to access many services without the need to re-verify. For example, the app is used by major airlines and the TSA to fast-track passengers through airports.

“We plan to take Airside’s proven approach to the airline industry and apply it to other sectors requiring high customer assurance, such as financial services — providing a single, trusted view of each customer’s identity,” said Onfido CEO Mike Tuchen.

The post ‘Sign-up’ is part of the customer experience appeared first on MarTech.

Under budget constraints but also under pressure to generate growth, marketing and marketing ops leaders have been taking a close look at the ROI on martech solutions. We want to know what conclusions you have been reaching. Have you been consolidating your existing stack? Have you been gambling on promising new tools? Are you perhaps reducing your tech holdings?

The need for better features. The 2022 survey showed solutions being replaced in a quest for better features, in particular:

• Better integrations/open API.
• Improved data capabilities.
• Ability to measure ROI.
• Better customer experience.

Those were all higher on the list than cost. What, if anything, has changed? We’re in a very different place than we were just a year ago. The world opened for business again. Many people returned to the workplace. It was no longer necessary to do almost everything — from shopping to hanging out with friends — digitally.

That doesn’t mean we’ve abandoned our multi-faceted digital environments. We discovered that many virtual experiences worked just fine. There was, however, something of a deceleration in digital transformation (after the insane acceleration of 2020), arguably leading to the retrenchment we saw in a number of marketing tech companies that had perhaps grown too fast.

Taking the temperature. Against a backdrop of economic uncertainty, our hunch is that there’s still a thirst out there for innovation, for tech-enhanced efficiency, and for better-supported data-based decision making. After all, marketers have hardly hesitated to get their hands on generative AI.

But we need data too so see whether our hunch is right. So please take about three minutes or so to complete our Replacement Survey and let us know how your martech world is evolving.

The 2023 MarTech Replacement Survey is here.

The post Are you getting the most from your stack? Take the 2023 MarTech Replacement Survey 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.

The contestants were UC Berkeley students from various disciplines (plus one or two alumna) and the awards were made by a panel of judges. According to Kuan-Ju Wu, a professor at the Jacobs Institute, the team were encouraged to look at current technology — “at signals from the space” — and then imagine what the future might be like.

Why we care. No, none of this needs to be in your marketing budget for 2024. One thing to understand, though, is that this contest got underway before everyone was talking about generative AI and ChatGPT. Which goes to show that there are many other concepts out there that could significantly change the internet and our relationship with it. Imagine how some of these concepts could play into marketing strategies…one day. “In this society, it’s really hard to determine how fast those ideas could become (reality),” he told us.

And the prize goes to…

Here are the winners in each of five categories.

Community choice and best technical concept: Web decentralization

According to the winning team, the future of the internet is about “creating systems where end-users get closer to the design process. Websites are no longer a form of presenting but also a tool for us to personalize our digital world and experience.”

As Wu described it: “Everyone has their building blocks to create their digital version of their own realm. In this future, it is all connected; they can share, they can transform data. It’s a combination of the new web and Minecraft. They use Minecraft a lot as an analogy.”

Dig deeper: Metaverse, Web 3.0 and NFTs: What marketers need to know

Best inclusive focus: Thought-to-thought

Two teams won awards based around brain computer interface (see the fifth award below) — probably, thought Wu, because there had been a recent conference on the topic. This involves a developing, real world technology aimed at using signals from the brain directly to engage with computers or other external devices.

Wu admits this project was “pretty wild” and one of the most speculative in the contest. “What interests me is that they touch on some ethical elements in this idea, because when you’re sharing your thoughts with other people, you have to build up a trust relationship. You’re enabling people to read your mind.”

Best social impact: Finding heart in Woven cities

Perhaps the cutest element here is the small (and frankly, stealable) robot dreamt up by two architecture students and a computer science student interested in public health. “Basically, the robot is just an agent, just a platform, like a repository for your idea or thought,” said Wu. It can be seen as a mobile billboard.

The robot would roam freely so encounters with it would be spontaneous. The genesis of the idea lay in the isolation of the COVID era and anxiety about speaking with strangers. (Woven City is an imaginary community.)

Most Actionable: Interspace

“Interspace is between intraspace and outer space,” explained Wu. The team were reflecting on the current state of social media that encourages individuals to present an outer face to the world, quite separate from the inner space of their own thoughts and feelings or relationships with close friends and family members.

“Those two spaces can mingle,” said Wu. The final concept was a wristband (inspired by the Tamagotchi device) that easily allows users to toggle between their inner self and outer brand when engaging with others.

Best blue sky: Brain computer interfaces

Being able to detect brain actvity like alpha waves — that’s old technology, Wu said. “I think the newer one will allow you to have a more sophisticated understanding of the signals from the brain and to analyze them.”

The vision here includes the revolutionizing of content creation as well as support for people who are impeded in the use of a normal mouse and keyboard, for example by neurodegenerative disease.

What will digital content look like in the future?

Brandon Watts, a senior manager at Storyblok, explained how this first collaboration with the Jacobs Institute came about. As a headless CMS, Storyblok can send content to all kinds of existing devices and channels. “One of the things we’re most interested in is what type of content platforms are going to exist in the future,” Watts said, “and of course we can speculate all day long, but we thought this was a cool opportunity to partner with Berkeley and get students on the ground to make an educated guess about where things might go.”

The concepts may seem ambitious but there’s always a content management element, Watts explained, because the content needs to be stored and somehow delivered. “With Storyblok, it doesn’t matter what platform it is — it’s all API-driven — so we could be communicating through a robot as easily as through a brain computer interface or anything like that.”

The main takeaway? The future could be much more personal and much more inclusive. “Instead of companies blasting out content on a billion different channels, these visions were all very one-to-one. What does that content journey look like?’

The post Beyond boundaries: The future of digital content appeared first on MarTech.

On May 2, designated as International Martech Day, Scott Brinker unveiled the latest edition of his marketing technology landscape. From 9,932 last year, the total number of solutions in the landscape has grown to 11,038. This represents something like a 7,000% growth in the space over the last 12 years.

One new feature launched alongside the landscape is the Marketing Technology Capability Heatmap. It reveals the most searched-for capabilities, with personalization at number one, and identifies the categories that offer them.

Presenting at Best of Breed Marketing Summit alongside Marketing Tribe founder Frans Riemersma, Brinker, VP platform eco-system at HubSpot, said: “On average, across companies of all sizes, you still have around 291 SaaS subscriptions. Even while there is consolidation of the industry in the economic environment of today, a motivation to rationalize stacks, we still see people using this very wide variety of tools.”

Dig deeper: Why marketers are replacing foundational martech

Other key trends. Among the trends identified within the new landscape, Brinker and Riemersma highlighted the following:

• Two-way data flows between data warehouses and front-line solutions like CDPs.
• Composability emerging in a number of categories including DXPs, commerce and CDPs.
• “AI as your co-pilot is real,” said Brinker; and it’s going to change the relationship between buyers and sellers because buyers will have AI too.
• Generative AI will extend the no-code revolution beyond marketing ops and power users to any marketing or business user.

Why we care. Everyone who knows marketing technology knows the marketing technology landscape. It’s an evolving but iconic image. It’s such a crowded graphic, however, that it simply became hard to read. This is why it’s so valuable that it has become, essentially, interactive. Registered users can explore the actual content of the landscape rather than just gaze on it in wonder.

The landscape is available at Chief Martec and can be sorted, filtered and queried at martechmap.com.

The post Scott Brinker unveils 2023 martech landscape 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.

That’s the key takeaway from a survey of over 500 U.S. and U.K. marketers issued by demand marketing platform Integrate. “These survey results indicate that despite economic challenges, B2B marketers are forging ahead and making do with the resources they have with a focus on their customer,” said John Follett, co-founder of Demand Metric which conducted the survey on behalf of Integrate.

Other findings. B2B marketers are striving for resilience in the face of economic pressures and the threat of burnout. Among other findings reported by the survey:

• 66% reported feeling burnout in the face of current challenges.
• Nevertheless, almost 70% felt optimistic or very optimistic about their team’s performance compared with six months earlier.
• The most favored strategy for optimizing for growth this year was marketing to existing customers (cross-sell/upsell; 57%).
• 35% cited technology as a focus for optimizing for growth.
• In the U.S., marketing ops and technology was the leading area where more spending was expected.
• The area most likely to face cuts? Field and event marketing.

Why we care. When it comes to data-driven B2B marketing, Integrate, as a B2B demand platform, has some skin in the game. Still, this survey supports previous research that finds B2B marketing teams stolidly optimistic in the face of very difficult times.

An infographic representing the results of the survey is here.

Dig deeper: Why B2B buyers now hate traditional B2B selling

The post B2B marketers remain optimistic in the face of major challenges appeared first on MarTech.