Foundry Research: Insights that drive engagement

Foundry’s latest research offers powerful insights into the evolving cybersecurity landscape. These insights can help shape your marketing strategies and drive deeper engagement with tech buyers. From shifting budget priorities to emerging threat vectors, Foundry’s studies reveal what’s top of mind for security decision-makers. 

Why security is now a top business priority

Cybersecurity has evolved from an IT concern to a core business imperative. According to Foundry’s��2025 State of the CIO��research,��security management remains the top priority for CIOs, with 91%��anticipating��their involvement in cybersecurity to increase or remain steady over the next year. Cloud migration is also gaining momentum, with 81% of CIOs expecting continued involvement. These findings reflect a broader shift: CIOs are stepping into more strategic roles, balancing innovation with the responsibility to protect their organizations. Their decisions now influence not just infrastructure, but how businesses build trust and deliver value.

For tech marketers, this is a critical insight. If your solutions address cloud or cybersecurity concerns,��lead��with��measurable outcomes such as ROI and risk reduction. These are the metrics that resonate most with IT decision-makers under pressure to secure their organizations while enabling innovation.��

Learn more on how to effectively engage with CIOs throughout the purchase process on our Selling to the CIO page.

Cloud security: What you need to know 

We’ve��just released��our��new�� packed with the latest insights on��top��business��drivers,��cloud��growth areas, and��cloud trends tech marketers��can’t��afford to miss.��With 670 IT respondents, this study dives into the vital role cloud computing plays for IT decision-makers across the globe.��

Cloud security��is��now��the frontline of��cybersecurity strategy, with��IT��buyers prioritizing��protection in their daily operations and long-term strategies.��The surge in demand for cloud computing skills is no��coincidence; it comes as a��direct��response to the gaps in cloud and security��management��that leave organizations exposed and vulnerable.��Understanding��the challenges of cloud��infrastructure, while recognizing its growing role in business innovation, reinforces��the need for security-first solutions. That’s why this Cybersecurity Awareness Month,��you��have a unique opportunity to lead with clarity, educate your customers, and��protect your business. Position yourself as part of the solution.��

Some key findings from the study tech marketers should pay attention to:

• 70% of ITDMs say their organization has accelerated cloud migration in the past year.
• Improving security and governance is the top reason for moving to the cloud.
• 57% plan to invest in cloud-based security in the next year, with 25% ranking it as a top growth area.

Foundry’s 2025 Cloud Computing Study reveals that cloud security is now central to cybersecurity strategy. As cloud adoption expands, so does the attack surface. Understanding the challenges of cloud infrastructure and the demand for security-first solutions is essential for marketers looking to position their products as part of the solution. 

What security leaders are prioritizing in 2025 

As cyberattacks continue to grow and become more advanced, security leaders are faced with more responsibility and complexity. Featuring the insights of 870 security professionals, our 2025 Security Priorities Study couldn’t come at a better time…

Tech marketers, this is your chance to stay ahead of the curve. These insights will help you craft messaging that speaks directly to the evolving needs of security leadership.

Want to learn more? to get an exclusive first look at this new research.  

From awareness to action: A marketer’s perspective 

Stepping into my current role, I’ve quickly realized how central security is to everything we do in tech marketing. Far from being a buzzword, security now stands as a core business priority, influencing how IT leaders make strategic decisions, safeguard data, and build customer trust. Whether your solution is cloud-based, cyber-focused or tied to broader risk management, security is the conversation that matters most right now. What I’ve learned from diving into Foundry’s research is this: educating yourself, your business, and your customers on managing cyber risks is essential to staying ahead of threats and keeping your organization secure.

As tech marketers, you have a unique opportunity right now to address the concerns of CIOs and security professionals and drive meaningful engagement across your organization. So, here’s my advice: Don’t just join the conversation. Lead it. 

Use this time as a chance to show your audience that you understand the risks, recognize their priorities, and support solutions that protect what matters most. 

Checkout CISA’s  with customizable assets like social media posts, email banners, and educational materials that you can use to amplify your message.

A bloomer is someone who believes AI is the next great revolution, bringing innovation, endless possibilities, and will make our lives as we know it, better. A gloomer on the other hand, is someone who believes the opposite, that AI could if not approached carefully, will eventually destroy life as we know it in a doomsday-esque fiery explosion. 

Finding a balance

Naturally, as marketers, we’re expected to jump onto the AI trend, pushing innovation, creativity, and always staying ahead of the curve. Yet, amid the ongoing “Bloomer vs. Gloomer” debate surrounding AI, where should marketing stand? Is it a sprint to rapidly adopt AI and explore every possibility, or should we be cautious, implementing AI only as necessary?

The ultimate question is, how can we as marketers use AI to our advantage? 

As we step into uncharted territory with AI, navigating this landscape requires a strategic approach. To effectively leverage AI’s advantages for marketing success, four critical questions emerge: 

How can marketers use AI in practice today?

Marketers can effectively leverage the power of AI in their daily operations by adopting a strategic and targeted approach. Start by identifying specific areas where AI application aligns with your marketing objectives. AI adoption can begin on a smaller scale, then gradually expand across multiple areas of marketing.  

Here are a few strategies that marketers can start putting into practice today: 

Stay informed

One of the easiest ways marketers can begin to use AI in practice is by optimizing their existing tech stacks by staying informed on AI innovations in the roadmap of existing platforms. One way to do this is by attending conferences and briefings related to marketing tools in use. For example, at Foundry we use for social media management. Recently the platform came out with , an AI tool that helps generate AI captions and winning post ideas in seconds. Once our team knew that tool was available, we were able to integrate and implement it into our existing strategy.  

Enhance buyer experiences 

The rise of AI has made it easier than ever for businesses to create personalized content at scale, allowing you to connect with your audience on a more individualized and meaningful level. addressing unique concerns and aspirations of different stakeholders. Whether it’s the CFO concerned about ROI, the CTO focused on implementation challenges, or the CEO envisioning long-term business impact, the GenAI narrative must speak directly to their priorities. Additionally, experiment with AI image and video tools. These tools, like , allow users to create customized snippets and visuals, creating a more engaging and tailored experience for your audience. 

Elevate employee experiences 

Train employees to use AI effectively, helping to automate repetitive tasks to make work more efficient. AI can help cut down time in various marketing activities like content editing, creating images for blogs, or automating certain tasks. Encourage teamwork for more creativity and efficiency. Numerous programs are available for employees to explore the benefits of AI, one example being Google Cloud’s Introduction to which you can enroll in for free now. 

What AI tools are available to marketers right now? 

Given that close to two-thirds (61%) expect their spending towards AI projects to increase in 2024, marketers but be ready to explore AI tools available and what will integrate best into their existing martech stack. 

AI tools available to marketers right now include: 

• – ChatGPT is a language model developed by OpenAI, designed for natural language understanding and generation, capable of performing various language-related tasks through API interactions. 
• – Jasper AI is an AI writing tool that helps you easily create content. You only need to provide simple inputs, and Jasper will generate original, high-quality content. 
• – Writer makes it easy to create custom apps to support any use case, including digital assistants, content generation, summarization, or data analysis. 
• – Typeface is the generative AI application to supercharge personalized content creation for businesses. 
• – Adobe Firefly is being built into Creative Cloud to give people the tools to both generate results quickly and customize them to fit their unique vision. 
• – Avoma is an AI meeting assistant and revenue Intelligence solution for customer-facing teams for startups and scaleups. 

It’s important to note that the choice of tools may depend on specific marketing needs. Additionally, Generative AI tools are not going to solve all our problems. Additionally, businesses must have the capacity to implement any technology that they are bringing on. 

What are the risks associated with AI?

AI presents significant risks for marketers (ask any gloomer). In fact, only 36% agree that their organization has a policy in place to monitor the use of Gen AI.  
 
This lack of governance can lead to various concerns, including: 

• Security and privacy – As Gen AI evolves and becomes more sophisticated, the risks of cybersecurity threats amplify. Security and privacy concerns are the most pressing ethical implications when implementing Gen AI.  
• Quality – Over-using AI to write content, posts, etc. you risk not matching brand voice, losing your perspective/POV, etc. 
• Trust – Marketers may exploit AI capabilities, leading to manipulative practices, data breaches, and overreliance on automation. This could result in the erosion of trust and long-term brand damage. 
• Channel performance – Certain traditional channels, such as cold outreach and cold outbound, may become less effective as AI-driven personalization increases. 
• Job displacement – As AI automates certain SEO tasks, there is a risk of job displacement for professionals specializing in routine optimization.  

 
To mitigate these risks, organizations may need to develop policies addressing the specific challenges of Gen AI. This could involve establishing ethical guidelines or implementing required training. 

Who should own AI within an organization? 

The ultimate question when it comes to leveraging AI in any organization, “What department or role owns AI?” The answer may look different for every company, determining who owns AI depends on factors like its structure and goals.  

While this is a highly debated topic, here are three possibilities on who could own AI within an organization: 

1. Department leaders – Each department leader is given a mandate to leverage AI technology within their own departments. Assigning AI ownership to department leaders could empower each division to leverage AI tailored to their specific needs and goals. In this case CMO’s would become the driving force behind integrating AI solutions to meet the unique needs and objectives of the marketing department. 

2. Cross-functional teams – Establish cross-functional teams that cut across departments, bringing together individuals with diverse skills, expertise, and perspectives to collectively drive AI innovation across the organization. 

3. Centralized ownership – Centralized ownership of AI involves appointing a dedicated team or individual responsible for overseeing and driving AI innovation across the entire organization. In this model, a centralized AI unit assumes the role of a strategic hub, coordinating efforts, setting standards, and ensuring a cohesive and aligned approach to AI implementation.  

Additionally, organizations should consider implementing some sort of AI ethics, to ensure ethical considerations are prioritized and integrated into AI applications. 

Conclusion

As we move forward into the unknown of AI, it’s not just about the speed of adoption but the mindfulness and strategy we bring to the table. Between Bloomers and Gloomers, the middle ground becomes an opportunity of advantage for marketers. 

Want to hear more about marketers AI advantage? Tune into Inspire to Connect to watch Matt Egan, Editorial Director at Foundry, Katie Berg, VP Marketing at Klue, and Aditya Kothadiya, Founder & CEO of Avoma, share their thoughts on how marketers can better leverage AI.  

Below we’re going to look at one small piece of the research – how employee productivity is a key driver to adoption and how this increased productivity impacts tech buyer engagement.

Investing in AI

Overall, 44% of ITDMs agree that their organization is willing to pay more for AI-infused products from vendors. One of the main reasons they state is due to employee productivity. When asked what business objectives are driving AI investments at their organizations – 48% said improving employee productivity, followed by enabling innovation (43%), and gaining a competitive edge (41%).

With its ability to automate tasks, personalize services, enhance security, and optimize system performance, just to name a few, it is no surprise that a little more than half also say that AI capabilities can and will enable workforce reduction. This increases to 59% for ITDMs at enterprise organizations, likely due to their already larger staff, and is only 50% for SMBs. Thinking about how AI has the power to reduce workforce, we asked respondents to identify their top use cases for AI applications, and leading the top of the list are data analytics, employee productivity, and process automation.

Status of Gen AI

Close to half of respondents to the AI Priorities study say that Gen AI technology is on their radar or being actively researched. The theme of employee productivity continues as 58% agree that they see Gen AI playing a large role in employee productivity and are starting proof of concepts to test. Again, thinking about how IT decision-makers expect Gen AI to support this, respondents stated their primary use cases to be chatbots and virtual assistants, content generation, and industry-specific applications.

We also see technology vendors adding Gen AI capabilities to their already existing software applications and platforms. Thirty-eight percent of ITDMs say that Gen AI capabilities have been added to their productivity/collaboration tools and 86% say that these additions have already resulted in a strong/ somewhat positive impact. More than half (55%) think that these tools would benefit from additional Gen AI capabilities, so it’s clear that they view Gen AI as a productivity tool and expect to see positive benefits from these additions.

These tools don’t only have the ability to reduce workforce, they have the ability to redirect IT to other tasks. Fifty-five percent of ITDMs agree that Gen AI is allowing for employees to refocus on high value-adding tasks. So much of the time, ITDMs are working on functional tasks, such as cost control and improving operating systems. With the use of Gen AI tools, the hope is that they will be able to spend more time developing and refining business strategies, and identifying opportunities for competitive differentiation, to name a few.

Engaging with AI focused tech buyers

Due to the rising complexity of the technology purchase process, ITDMs are constantly searching for ways to free up some of their time and enhance their productivity during the periods that they do have available for fundamental and strategic tasks. We see that AI tools have the ability to do this, so there is no question as to why ITDMs have such strong research and piloting plans, but there are a few things to keep in mind as a tech marketer. When reaching out to tech buyers to promote these new solutions, or current solutions with AI features added, it’s important for technology marketers to be respectful of ITDMs’ time and provide educational resources that are easily digestible. Just because they may be able to redirect their focus to other tasks due to increased productivity, this does not mean that they now all of of this time to sort through irrelevant content. AI-focused tech decision-makers do not want content filled with empty buzzwords. They seek content that addresses their pain points, integration challenges, and offers “how-to” information.

To properly engage tech buyers as they navigate the new AI landscape and work to incorporate Gen AI tools into their day to day, tech vendors must know where their customers and prospects are in the decision-making process and how they are preparing for this integration. ITDMs state that their greatest challenges to AI implementation are IT integration (governance, maintenance, security, etc.), lack of in-house expertise, and business case/justification. Ensure value by creating content that appeals to these areas and providing training where needed.

Further develop your understanding of ITDMs’ AI and Gen AI plans, their current level of satisfaction, and how you can assist them with their implementation by or contacting Foundry to explore in more detail.

What changes do you hope new technologies (Gen AI, ABM, Intent, etc.) will bring to partner marketing in 2024?

Enhanced partner performance

Matt Davison, Vice President of Marketing, SoftwareOne

I see marketers in 2024 starting to integrate and leverage new technologies like artificial intelligence (AI) to boost marketing performance. For example, AI will allow marketers to provide personalized experiences and tailored messages at scale, improve lead generation efforts with AI-driven lead scoring and predictive analytics, and help reduce operational costs by streamlining processes. Though budget tightening may loosen next year, Marketers will find even more pressure to justify their approach and provide their stakeholders with conversion and insights.

With the need to map marketing spend to outcomes, many marketers will lean into new technologies and data sources in order to create a differentiated, dynamic customer experience. For example, account-based marketing (ABM) is not new, but I see this being heavily used consistently within co-branded marketing campaigns. This direct focus to high-value leads will mitigate the broad efforts of new client acquisition and allow for a stronger alignment across sales and strategic partners.

Additionally, marketers will seek to increase cross-sell and up-sell opportunities by leveraging first-party data and expanding into third-party data. Intent will focus on co-marketing into personas worth investing, overlayed with technographic, consumption, and firmographic details.”

While these approaches can enable marketing teams to uncover opportunities, they are a bit of a red herring. Despite the exciting capabilities of new technologies, they do not guarantee a return (which often is a requirement for the use of funding from partners) and can be expensive if not implemented and executed correctly. They need to be combined with tried-and-true marketing approaches, like content that articulates your value and aligns to challenges. Leverage these tools to test and validate your strategy and lean into partners that support these continuous motions.

Strategic deployment of Gen AI, ABM & Intent data

Anthony DiSibio, Senior Channel Marketing Manager, Coveo

As we look ahead to partner marketing in 2024 our goals remain the same: build stronger relationships with partners, drive higher-quality leads, and ultimately achieve better results. GenAI, ABM, and Intent Data are all powerful tools that can make those goals a reality. However, these technologies must be used in an effective manner to maximize impact. I envision three areas in which GenAI, ABM, and Intent Data can make a difference in 2024.

1. Data-driven decision-making: Tracking partner behavior, campaign performance, and conversion metrics to enable partner marketers to make informed decisions.
2. Scalability: Increased ability to manage a larger number of partners and accounts without sacrificing quality or personalization.
3. Improved ROI: Utilizing advanced technology to target the right partners with the right messaging at the right time can lead to improved partner marketing ROI.

Reshaping collaboration between businesses & partners

Sharal Pinto, Ecosystem Marketing Manager, Red Hat

In the ever-evolving landscape of partner marketing, 2024 promises to be a game-changing year. As we look ahead, emerging technologies like GenAI, Account-Based Marketing (ABM), Intent Analysis, and more are set to reshape how businesses collaborate with their partners.

GenAI, with its unmatched ability to understand and predict customer behavior, will empower marketers to create highly personalized campaigns. This means deeper connections, increased engagement, and stronger partner relationships.

ABM will continue to gain momentum, enabling organizations to target high-value accounts with precision. Expect increased ROI and more meaningful partnerships as businesses tailor their strategies to the unique needs of each partner.

Intent analysis, driven by advanced machine learning algorithms, will unlock unprecedented insights into customer intent. This invaluable information will guide partner marketing efforts, ensuring businesses sync with their audiences’ evolving desires.

In 2024, partner marketing is not just about collaboration but strategic synergy fueled by cutting-edge technology. As we harness the potential of GenAI, ABM, Intent Analysis, and more, we’re poised for a transformative year to redefine how we approach partner marketing.

Elevating partner nurture efforts

Rick Currier, VP of US Sales & Partner Marketing, Foundry

With larger buying teams and extended sales cycles, nurturing is essential for translating leads into valuable pipeline. Yet, many partners lack the resources and expertise needed for effective nurturing, ultimately undermining program results and ROI. In 2024, I hope to see more partner marketers integrating new and emerging technologies into their programs to help supplement their partner nurture gap.

While some partners rely on email follow-up tools for nurturing, this is just a fraction of what nurturing encompasses today. reveals that enterprise technology buying teams have on average 25 individuals from various departments, all evaluating deals. A marketing qualified lead (MQL) might identify initial interest from someone on the buying team, but genuine nurturing means engaging many on the buying team throughout the purchase process.

Successful nurturing involves a mix of Account-Based Marketing (ABM), intent analysis, email marketing, digital display, face-to-face engagements, sales outreach, content engagement and more. Marketers must seamlessly integrate these technologies and strategies into their programs to identify not only hand-raisers but also engage all relevant stakeholders within target accounts through digital and in-person interactions. For example, you might utilize Intent to generate a lead for sales, but how are you using Intent to surround the rest of the buying team while sales follow-up on that one lead?

������ý���߹ۿ�, we take a unified approach, leveraging our comprehensive brand-to-demand portfolio and the latest technology to engage buying audiences across their journey through our editorial ecosystem. With a consolidated audience and product platform, we can report on the engagement of buying teams at the account and individual levels, thanks to technologies like ABM orchestration, intent data tools, reverse IP mapping, dynamic content delivery, and more.

Wherever partner marketers engage their audiences in 2024, they need to think about how they’re supplementing their partner’s nurture efforts (or lack thereof) through the use of the latest B2B marketing technologies. The future of partner marketing lies in unified digital tactics (and reporting) to foster genuine opportunities and drive success.

Contact us today to learn more about how Foundry combines media properties, people-based data and technology to fully address needs of partners marketers from brand to demand.

About Foundry Global Partner Marketing Solutions Our dedicated Global Partner Solutions practice can help you navigate the unique and complex challenges of partner marketing, and help you drive positive business outcomes through collaboration. By implementing practical strategies and leading-edge marketing solutions, we help you elevate and communicate the value of your partnerships and drive return on investment. We can be there every step of the way from messaging, content, strategy and production services.

On an almost weekly basis in our organization, in reviewing our campaign progress and numbers for the month, the conversation inevitably turns to the fidelity of our leads. “That’s a prospect, but not a lead.” “That’s a lead, but not an MQL.” It’s a topic for debate that I’ve seen in other organizations I’ve worked for, amongst clients, and across the industry as well—and one that never seems to reach a clear resolution.

As B2B marketers, a top KPI for many of us is the delivery of high-quality leads to the business—leads that have a high propensity for conversion to opportunity and that ultimately contribute to company revenue. Marketing Qualified Leads, or MQLs, are the lifeblood of the data-driven marketing department. So why do we not have a clear definition of what classifies as an MQL?

My team set out to test our theory that the market at large lacks a standard MQL definition. 130+ marketers across LinkedIn proved us right.

Via a , we asked a simple question: How do you define an MQL? We gave four options:

• Activity-based (downloads)
• Intent-based score
• Book a demo / hand-raisers
• Other

While “book a demo / hand-raisers” came out on top with 37% of respondents, there was no one clear winner. “Activity-based (downloads)” earned 30% of responses, “intent-based score” came in third with 22% of votes, and even the ambiguous “other” option garnered 11% of votes. And while we did not conduct this poll with even a fraction of the rigor as we do with our other industry-leading research, we believe the data is strong enough to confirm one thing: there’s a lack of consensus amongst marketers when it comes to the MQL.

So, what should marketers, and revenue teams at large, do to address this? Whereas for other functions of the revenue team there’s less grey area when it comes to funnel stages (prospecting, opportunity, closed won, etc.), for the marketing team, who generally feed the sales pipeline, there’s a wide range of what can classify as a lead.

This is due, in part, to the fact that marketing teams have so much data to measure as a result of their activities. Behavioral intent, digital engagement, content downloads, and contact form fills can all make for valuable leads, but treating each of these leads equally within the organization not only drives tension between marketing and sales, but also causes the funnel to be fragmented.

Insisting that the market agree on a standardized definition of an MQL misses the point. Instead, marketing and sales teams need to work together to develop strategies to make the most of the vast amount of data available to them. A lead should not be seen as disqualified simply because they haven’t raised their hand, because most actual buying isn’t as linear as the funnel model suggests. Doing so only undermines all of the value that marketing creates in the upper funnel— value that strategic sales and SDR teams know how to leverage to drive pipeline.

By broadening the concept of the MQL and designing strategic approaches to nurturing and converting the many variations of qualified leads, revenue teams not only increase the number of viable prospects to engage with, but they also reap the full benefits of marketing’s work.

The solutions being built are geared towards keeping the user on the search screen, and provide the content written by publishers within that interaction and not having the users to click to the publisher’s sites. The landscape of search engines to showcase news or information is changed forever. Instead, we are transitioning to a virtual assistant model where AI-powered platforms directly provide users with answers. This shift, to some degree, is unsettling. But it is here and we can’t stick our heads in the sand and ignore it.

This sudden rise of AI chatbots will upend the audience acquisition model and create a new landscape where only publishers with audiences that seek real human-written content and strong brand loyalty will survive, while those focused solely on content written purely making a run for rankings leveraging keywords alone will be obsolete. As this occurs, consumer dependence on legacy search will wane and search habits will alter. In the long run, this trend spells the end for one specific category of publisher, which I call Clickbait ������ý���߹ۿ�.

Clickbait ������ý���߹ۿ� lacks subject matter authority, brand credibility, helpful content for their audience and their audience’s loyalty. Their visitor and pageview numbers are, for the most part, a result of keyword-based content, rather than quality content. This approach has given rise to no shortage of indistinguishable, low-quality AI-generated content sites. Clickbait ������ý���߹ۿ� typically invests minimally in editorial, chasing low-hanging search fruit for views and clicks to maximize ad revenue. , .

The new AI content recognition browser plugins and search engine algorithm updates will force Clickbait ������ý���߹ۿ� to rethink their reliance on AI-generated content with no human review, facts, insight or quotes. They will have to adapt their strategies to remain relevant. But building a brand authority is certainly more difficult than rank chasing and requires significant investment and a thoughtful, comprehensive audience acquisition strategy. For better or worse, Clickbait ������ý���߹ۿ� isn’t equipped to navigate this shift.

As a result, Clickbait ������ý���߹ۿ� will struggle to survive, while publishers with real content and audiences will gain more market share. This evolution will deliver greater value to publishers who invest in editorial content and adopt a varied approach to audience generation, like offering newsletters, hosting in-person events and expanding their online presence across content discovery platforms.

The winners in this transformed landscape will be publishers with authentic and authoritative  content that inspire loyalty and foster high-quality audiences experiences. These publishers offer their users helpful, people-first content that demonstrates expertise, experience, authoritativeness and trustworthiness. In contrast, Clickbait ������ý���߹ۿ� will find themselves struggling to maintain their relevance with content that is just fluff.

Candidly, I think this change will pave the way for a more robust and engaging content landscape, where quality and authenticity are paramount. It will also lead to a more accurate intent pipeline, as it will be easier to understand what an audience wants and how to deliver on their expectations. In this new era, only publishers who invest in their brands and nurture their audiences will do well. These are publishers who carefully develop high-quality editorial, embrace new channels and platforms for audience engagement and foster a sense of community and loyalty among their readers.

As I say this, make no mistake that AI isn’t an enemy, but rather an inevitability. To that end, publishers must also explore smart ways to leverage AI to their own benefit. No, that doesn’t mean laying off journalists en masse to cut costs through AI. Rather, use AI tools to optimize content creation, enhance personalization and improve user experience. By embracing AI and incorporating it into their strategies, publishers can stay ahead of the curve and ensure their continued relevance in the industry.

Ultimately, the AI-powered search wars will serve as a catalyst for change, pushing publishers to refocus their efforts on delivering high-quality content, building trust and loyalty, and creating genuine connections with their audiences. Clickbait ������ý���߹ۿ� will face lower crawl budgets due to their low quality content and won’t be around, in the end. This shift will not only benefit the surviving publishers, but will also elevate the overall quality of content available to consumers. The power will truly lay on trustworthy content.

No one can be certain of the full impact Generative AI will have on content production and consumption, but two things are certain: the impact will be huge, and AI won’t replace human creators. Generative AI tools do not replace human beings if you have a sincere concern for your audience. But used with skill ChatGPT and its rivals will have a transformative effect on the way we scale, curate, and access content.   

������ý���߹ۿ� we believe that technology can be a force for good. Leveraged wisely, it can have a positive influence on the world. The rapidly developing story of Generative AI hits close to home, given our 60-year legacy of high-calibre, human generated editorial.  

We are excited about the potential of Generative AI tools such as ChatGPT. Used ethically and with imagination, Generative AI will exponentially increase the value of human-made content for all users. It will bring efficiency and scale to processes, and equality of access to all.     

Generative AI tools are not new: we have been using ChatGPT and others in content ideation, research, and copywriting for some years. GPT tools help our Editorial teams better understand what people need to know. Generative AI supports us as we optimize headlines and FAQs. Such tools help us write social media copy and marketing copy, and to create video and audio scripts from written content.    

We’ve experimented with other use cases such as copy editing, article writing, news summarization and aggregation. In their present state, Generative AI tools cannot deliver the high standards we set ourselves in these scenarios, and that is fine. We always experiment and are never afraid to feel uncomfortable in pursuing new approaches. Technology constantly asks us to adapt and challenges the status quo. Foundry’s willingness to embrace the latest technology without compromising on standards sets us apart.   

Foundry’s ethos is to always go beyond the original connection when building engagement with our audience. We invest in the kinds of content other publishers eschew: in-depth, on the record interviews with IT leaders and practitioners. Multi-vendor case studies that highlight real world projects with good and bad outcomes. Analysis and insight from industry insiders who know because they do. We do this because it is of deep interest to IT buyers, rather than casual interest to a broad audience. Interviews, expert opinion, and case studies provide unique expertise our customers’ customers can find nowhere else.     

Today Generative AI tools can’t provide context or analysis. They can summarize large swathes of unstructured information but can’t source, or fact check that information. Because these tools are generative, they are only as accurate as the sources at which they are pointed: ChatGPT on the open internet not only pulls information from myriad potentially inaccurate and unknown sources, it creates its own information as it goes (so-called ‘hallucinations’).    

You cannot and should not ask a Generative AI tool such as ChatGPT to ‘write’ content based on unknown sources without considering accuracy and even plagiarism.   

However, when applied to Foundry and ‘s original, human-made content on our owned and operated domains, Generative AI will be transformative. It will help users efficiently access the information they seek regardless of where and how it was published. They will be able to find answers to natural language queries from across Foundry and ‘s unsurpassed repository of trusted and tested content.  And we will be completely transparent with our users when a Generative AI mechanism is the writer or the source of a piece of content.    

Think of a chatbot that guides a user to a set of data, a tutorial or a product set based on all our expert content. Imagine being able to consume every piece of content in summary or bullet-point form, or even as a user-initiated audio podcast. Generative AI will allow us to do these types of things and more, enhancing and driving even greater value to our users.   

We are early in the cycle of adoption for Generative AI in content creation, curation and dissemination – but the pace is fast. We will continue to create valuable original content that’s factually correct, ethically sourced and uniquely useful to our audience. Foundry will always endeavor to use technology to enhance the quality and accessibility of our content. Still, while we are quick to embrace the “new thing” we hold true to our values. We are not in the business of cutting corners, and trust is everything to us. 

We are actively and enthusiastically experimenting with Generative AI tools and will continue to do so with this and every other transformative technological development. But as a priority, and as we’ve done for more than 60 years, we will continue to create content authored by, about, and in the service of, real people.  

The Academy Awards is the biggest night in Hollywood. Film industry elites flock together to witness a lucky few receive the prestigious award. As a company obsessed with data, we set out to push the limits of intent data through popular culture. Intent data is ever-evolving in marketing and sales. So, we ran an experiment to test if intent data could predict the winners of the 2023 Oscars.

Intent data is information collected from an individual’s or accounts’ online behavior. This can include the websites and content they visit. By analyzing this data, it’s possible to gain insights into people’s preferences, interests, and even their intentions to purchase. This proves incredibly useful for B2B organizations, like identifying potential buyers. 

The Experiment:

We tracked keywords for the six big award categories: Best Actor, Best Actress, Best Supporting Actor, Best Supporting Actress, Best Director, and Best Picture. 

For each nominee, we tracked four custom keywords. After a few weeks of data collection, we analyzed the results to determine which keywords received the most engagement in each award category. We collected engagements from social media and public web sources.

Predictions and Outcomes: 

Our predictions were determined by which nominees received the largest share of engagements in their categories.

Note: All percentages are rounded to the nearest whole number.

Category: Best Lead Actor

Prediction: Austin Butler, Elvis | Winner: Brendan Fraser, The Whale

Austin Butler and Elvis garnered 11% and 67% of all keyword engagements, respectively, in this category. It’s fair to assume that there is an overlap between those engaging with Elvis the 2022 film and Elvis the artist. The latter is likely earning a fair amount of online engagement at any moment. 

Category: Best Supporting Actor

Prediction: Ke Huy Quan, Everything Everywhere All at Once | Winner: Ke Huy Quan, Everything Everywhere All at Once

Ke Huy Quan made his comeback to the big screen via this bombastic film. Despite a challenging year for box-office profits, the film grossed $108 million USD.

Category: Best Lead Actress

Prediction: Michelle Yeoh, Everything Everywhere All at Once | Winner: Michelle Yeoh, Everything Everywhere All at Once 

Her first Oscar nomination, Michelle Yeoh is the second woman of color to win Best Actress. Yeoh has also won awards from the Screen Actors Guild and The Golden Globes for her performance as the multiverse traveling laundromat owner.

Category: Best Supporting Actress

Prediction: Stephanie Hsu, Everything Everywhere All at Once | Winner: Jamie Lee Curtis, Everything Everywhere All at Once

This prediction was a close one. Co-stars on Everything Everywhere All at Once, only a 2% margin separated Jamie Lee Curtis and Stephanie Hsu.

Category: Best Picture

Prediction: Elvis | Winner: Everything Everywhere All at Once

Everything Everywhere All at Once is the second science-fiction film to win Best Picture. Despite receiving eight nominations, Elvis earned no golden statues on Oscar night.

Category: Best Director

Prediction: Daniel Kwan and Daniel Scheinert, Everything Everywhere All at Once | Winner: Daniel Kwan and Daniel Scheinert, Everything Everywhere All at Once

Professionally known as “Daniels” the directing duo has been working together since 2009. Their kung-fu, sci-fi drama left the ceremony with seven awards.

The Takeaway

Tracking keyword engagements on the public web made identifying nominees that generated the most buzz and potential winners possible. In essence, the data showed us which films and nominees garnered the popular share of voice.

Yet, this wasn’t foolproof. Online popularity didn’t choose who won on Oscar night. Winners are the result of votes cast by members of The Academy of Motion Picture Arts and Sciences. The opinions of Academy voters are influenced by a multitude of factors, including personal preferences, industry politics, and cultural trends. While popular opinion can’t choose the Oscar winners, it may have influenced the academy’s voting patterns.

Like the Oscars, within every company is a group of hidden decision-makers choosing who will win their business. However, unlike the Academy Awards, savvy marketers have the power to influence that decision. Popularity does carry weight in the consideration process. But, the results illustrate there is more happening behind the scenes. Intent data is one piece of the puzzle. 

Learn how your data can help refine targeting strategies and develop actionable insights for your next campaign.

Account-based marketing terms you should know

ABM (Account-Based Marketing): a B2B strategy that orchestrates marketing campaigns to drive pipeline and revenue, alongside sales, within a group of target accounts, as opposed to a traditional, leads-based strategy. Get the basics in our .
A/B Test: A/B testing refers to testing 2 different versions of a campaign element to see which is more effective, with A and B referring to the 2 different versions being tested. A proper A/B test uses a randomized sampling of users and tests only one variable at a time, such as web page copy, imagery, an email subject line, etc.

ACV (Average Contract Value): ACV refers to the average amount new contracts are signed for. Knowing your average contract value helps marketing to understand their return on investment from marketing, as well as determine what their goals need to be to turn a profit.

B2B (Business-to-Business): a term that refers to commercial transactions between businesses, rather than between businesses and individual consumers.

B2C (Business-to-Consumer): a term that refers to direct transactions between businesses and consumers who are the end-users of its products and services.

B2G (Business-to-Government): a term that refers to transactions between businesses and the government rather than other businesses or directly to consumers. B2G marketing has its own intricacies, but has many of the same needs/tactics as B2B marketing.

BOFU (Bottom of the Funnel): the third section of the sales and marketing funnel, below TOFU and MOFU, that maps to the “purchase” stage of the buyer journey. The goal is to turn the prospects that are in your pipeline into customers.‍

CAC (Customer Acquisition Cost): how much money it costs to acquire a new customer. This acronym is sometimes used interchangeably with CPA, but , taking into account overall marketing spend, rather than looking specifically at singular campaigns. Customer acquisition cost is defined as the total marketing spend divided by the number of new customers acquired.‍

CLV (Customer Lifetime Value): CLV refers to the average value a customer has to your business. Customer lifetime value can be calculated by multiplying your customer’s average purchase price by their average purchase frequency, by their average lifespan. ‍

CMS (Content Management System): a web application designed to make it easy for non-technical users to create, edit, and manage a website. Common CMSs include WordPress, Webflow, and Drupal.‍

Cold: Referring to email outreach or ad campaigns – cold indicates the lack of having been ‘‘warmed up’ with previous touch points. This means cold email outreach would be outreach to a new contact, and cold advertising would be the first set of ads that a target is seeing, to be followed up by retargeting later on. ‍

CPA (Cost Per Acquisition): how much money it costs to acquire a new customer. This acronym is sometimes used interchangeably with CAC, but is generally used more as a campaign-level metric. The cost per acquisition of a campaign is the total campaign spend divided by the number of conversions attributed to the campaign.‍

CPC (Cost-per-click): the price you pay for every click in a paid advertisement. Often used interchangeably with PPC (pay-per-click), CPC refers to the metric whereas PPC refers to the overall online marketing model.‍

CPM (Cost Per Mille): cost per mille is a common metric used in advertising. Cost per mille measures the cost per thousand impressions, allowing you to measure an ad’s reach in the thousands. An impression is whenever an ad is loaded. ‍

CRM (Customer Relationship Management): A CRM system is software that allows you to manage and keep track of all your company and customer relationships. CRM systems are an important part of the B2B sales process providing data on specific accounts, and customer health. CRM systems are used to track customers throughout their buying journey, so if you’re running account-based marketing, it’s important that your ABM platforms integrates with your CRM platform. For example Foundry integrates with  to allow your revenue teams to see which accounts are buying, what those accounts need, and personalize your messaging to drive conversions.‍

CRO (Conversion Rate Optimization): a systematic process of driving more results from existing website traffic. The goal is to test and alter different design and campaign elements to increase the percentage of traffic that completes a specific, desired action, such as watching a video, contacting sales, or completing a purchase.

‍CTA (Call to Action): refers to the next step that a marketer wants an audience to take, often in the form of a button or text such as “,” “,” “,” etc.‍

CTR (Click-Through Rate): the ratio of clicks on a certain link over the total number of views of that link. Common use cases include measuring digital advertising (clicks / impressions) and email marketing (clicks / email opens).‍

Dark Social: You may have seen this term scrolling on . Dark social is the untrackable actions buyers make on social media (Reddit, LinkedIn, Twitter, Facebook). Dark social is notoriously hard to track, but there are ways to get a better pulse of your social presence by adding “how did you hear about us?” forms or using UTM tracking links.‍

DMP (Data Management Platform): technology used to gather, store, and analyze audience data from multiple sources. For example, digital ad buyers and publishers will gather and store data such as demographics, cookie IDs, etc, to help businesses segment and target certain audiences.‍

DNS (Domain Name System): DNS servers convert domain names to IP addresses, serving as the internet’s phonebook. If “CNAME” followed by a string of numbers seems familiar to you, at some point you probably needed to update your DNS records in order to add subdomains.

DSP (Demand-Side Platform): a system that allows buyers of digital ads to manage multiple ad exchange and data exchange accounts through one interface.‍

ENT (Enterprise): ENT is a commonly used acronym standing for enterprise, generally understood to mean an account that has over 1000 employees, and/or $1B in annual revenue. Marketing to enterprise-level accounts tends to be more personalized and on a larger scale than marketing to small to mid-sized businesses.‍

Gated / Ungated: Gated or ungated refers to whether a piece of content is freely accessible or ‘gated’ by a form where you must supply your contact information. B2B marketers often debate gating/ungating as it’s a trade-off between receiving more leads with gated content vs. having more people consume your content with ungated content.‍

GTM (Go-To-Market): GTM or Go-to-market refers to the plan an organization has for presenting its unique value proposition to its target market. Go-to-market strategies typically involve both the sales and marketing teams.‍

ICP (Ideal Customer Profile): a description of the ideal buyer persona for your product or service. In B2B, common characteristics used in the ICP include geography, firmographics, job seniority, etc. Scroll to the end of  for an ICP worksheet.

KPI (Key Performance Indicator): a quantifiable measure used to track progress towards set goals and evaluate the success of an organization, team, or employee. Common KPIs in B2B marketing include organic website traffic, form conversions, and new opportunities created.‍

LP (Landing Page): A landing page refers to the destination page a user comes to when they click on an ad, with the goal of landing page content being to convert visitors, usually via a form. 

‍L2A (Lead to Account): often refers to technology for lead-to-account matching, mapping, routing, attribution, etc., which automates the process of cleaning prospect data and connecting leads to the right accounts. In B2B marketing, results of L2A technology include more granular segmentation, accurate targeting, and efficient campaigns.

MAP (Marketing Automation Platform): Marketing automation platforms are marketing applications that help marketers automate marketing campaigns on multiple channels, such as email and social, based on preset rules. MAPs also help marketers gather, store, and manage prospect information, much like CRMs do. A few examples of MAPs that you may use yourself include: , , , and .

MarTech (Marketing Technology): Marketing Technology is any service or software product that works to aid your marketing and sales teams in their strategies and goals. An example of Marketing Technology is .The martech space has exploded in recent years, as you can see from the 

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MOFU (Middle of Funnel): the second section of the sales and marketing funnel, in between TOFU and BOFU, that maps to the “consideration” stage of the buyer journey. The goal is to increase purchase consideration in prospects and add them to the sales pipeline.

MQA (Marketing Qualified Account): a target account that marketing deems ready to talk to sales based on the level of engagement from all the stakeholders within that account, not just an individual lead. Learn more about the role of account scoring and MQAs in measuring ABM success .

MQL (Marketing Qualified Lead): a lead that the marketing department determines is in the buying cycle and deems more likely to become a customer than other leads based on the lead’s level of engagement.

NPS (Net Promoter Score): a customer satisfaction metric that measures, on a scale of 0-10, the degree to which people would recommend your company to others.

‍Pipeline Velocity: Pipeline velocity refers to how quickly qualified opportunities move through your pipeline. It’s calculated by multiplying the SQLs in your pipeline by your average deal size and your overall win rate, divided by the average length of your sales cycle.‍

PLG (Product Led Growth): Product led growth is a strategy for SaaS startups to structure their marketing and sales strategy around their product, with the core idea of letting their product ‘do the selling’. Product led growth is most commonly adopted by SaaS startups with a subscription model, with many of them utilizing free trials as a marketing incentive.‍

PPC (Pay-Per-Click): an online advertising model where the advertiser pays a fee each time someone clicks on their ad. Popularly used to drive traffic to websites via search engines and social media platforms‍

RevOps (Revenue Operations): RevOps aims to maximize your organization’s revenue potential through the alignment of marketing, sales, and services. RevOps prevents silos among different departments within your organization. RevOps has become increasingly important with the rise of marketing technology. ‍

ROAS (Return On Ad Spend): ROAS refers to the amount of revenue earned for each dollar spent on marketing. For example, if your ROAS is 4:1 (or 4), that means you earn $4 in revenue for every $1 you spend on marketing.

SEO (Search Engine Optimization): SEO refers to the practice of improving and optimizing website content in order to more easily be found by users using search engines such as Google. SEO is also used as an abbreviation for the people that manage SEO.   

SLA (Service Level Agreement): a contract that establishes a set of deliverables that one party has agreed to provide another. B2B marketers can have SLAs with partner organizations, such as agencies, media partners, and analysts, as well as internal groups such as the sales team with regard to account qualifications.

SMB (Small and Medium-Sized Businesses): describes small businesses (100 or fewer employees) and medium-sized businesses (100 to 999 employees), as opposed to large and enterprise businesses, often used by B2B marketers to segment and target.

SAL (Sales Accepted Lead): a MQL that the sales team approves of. Many companies have a large MQL to SAL drop off. If that happens to be the case for you, check out  about solving your MQL problem.

SMB (Small and Medium-Sized Businesses): describes small businesses (100 or fewer employees) and medium-sized businesses (100 to 999 employees), as opposed to large and enterprise businesses, often used by B2B marketers to segment and target.

‍SRA (Self-Reported Attribution): Self-reported attribution refers to where a customer says they heard of you. The most common way of collecting self-reported attribution data is on a form by asking, “How did you hear about us?”. Self-reported attribution is a popular supplemental way of tracking attribution in addition to software attribution which can be flawed / inaccurate, especially if your company relies on less trackable touch points such as trade shows, podcasts, dark social, etc.

Social selling: Social selling is the use of  to influence, inform, and connect with prospects or customers. Social Selling can help businesses and salespeople reach and engage with their sales targets. The most popular channel for social selling in B2B is by far, , where many B2B prospects go to network and learn about solutions that may help them in their role. 

TAM (Total Addressable Market): the total addressable market is the revenue opportunity for your product or service- meaning all of the accounts you can reach and engage that exist. An ABM strategy can help you  for outreach.

TOFU (Top of Funnel): the first section of the sales and marketing funnel, above MOFU and BOFU, that maps to the “awareness” stage of the buyer journey. The goal is to get more people within your target market to know about your product or service.

UTM (Urchin Tracking Module):  are short codes appended to the ends of URLs for the purpose of tracking how someone got to a page. UTM codes can track sources, mediums, campaigns, content, and more. 

Organizational role acronyms B2B marketers should know

CCO (Chief Customer Officer): a Chief Customer Officer is responsible for handling customer-centric matters and building a strong relationship between the business and the customers. 

CEO (Chief Executive Officer): a Chief Executive Officer heads the entire company. Their duties consist of implementing strategies, plans, and policies for the entire organization. The future strategy of a company and important decisions will be made by the Chief Executive Officer.

CFO (Chief Financial Officer): a Chief Financial Officer heads the finance department. When new deals are signed, the final sign off on the client side often comes from the CFO, so it’s important for sales teams to understand a CFO’s motivations and goals to avoid losing deals in the final stretches. 

CMO (Chief Marketing Officer): a Chief Marketing Officer oversees all marketing activities within a company. CMOs are important decision makers in all marketing and sales activities.

COO (Chief Operating Officer): a Chief Operating Officer ensures the company is running smoothly, and has what it needs to function properly. The COO will focus on areas such as HR, training, onboarding, payroll, etc. 

CRO (Chief Revenue Officer): a Chief Revenue Officer is responsible for every revenue-generating function in the business to drive growth. Oftentimes when purchasing a new software product (), the CRO will have heavy involvement. This role has been growing immensely in recent years, with the rise of RevOps.

CS (Customer Success): CS refers to customer success, the department that manages the relationship between your company and your customers. A close relationship between CS and marketing allows for better marketing to customers.

CTO (Chief Technology Officer): a Chief Technology Officer is in charge of the organization’s technical needs and requirements. The CTO is also responsible for research and development of the product at hand. 

Well there you have it – 58 acronyms you should know. But as many know and joke about, we in marketing love to make new acronyms… so stay tuned for future updates!

In essence, marketing has always been, and will always be about identifying customer needs, offering solutions out of the desire to solve these needs, and building trust that turns prospects into customers and customers into loyalists. It’s not unlike developing a friendship. Data and technology have made the marketing function measurable, but not altered at its core. Yet as we advance, become more automated in our execution, sharper in our data analysis, more KPI oriented… are we getting increasingly closer, collectively, to a clear pathway to understanding, effectively reaching, and building trust with our buyers? Or have we simply over-tooled ourselves with martech and services that fail to deliver a truly comprehensive view of our prospects—one that would be gained from a strong interpersonal connection with our buyers? In short—can, and will, martech ever lead us to the state of empathy needed in the effective relationship-building we aim for?

Without breaching a dystopian point of singularity, I do believe that there is a future version of martech that can synthesize a variety of subtle and unstructured datapoints (the types that real people produce by the hour), apply something akin to human psychology in its algorithms, and produce a more authentic view of our buyers as thinking, needing, feeling beings. My prediction, and my hope, for 2023 is that we make strides towards this version of martech because marketers will start demanding it.

Martech can have the tendency to create noise while obscuring relevant information on how buyers actually buy. Marketers will be looking to martech to better connect the dots in their data in 2023 and beyond, and to capture and process signals that, while perhaps not highly scored on their own, when combined tell a truer story of the humans immersed in the discovery and purchase process. Some insights we can look to:

• More nuanced views of intent: Marketers are already shifting their focus away from the MQL (the sole lead representing an account, see second-lead syndrome) and towards a more comprehensive view of account propensity and, even more specifically, various opportunities within these accounts. Intent helps marketers understand not just the journey of one buyer but the journeys of buying teams. Metrics of note and intent signals here might be: 
  
  • Contextual behavioral signals indicative of demand and purchases: hiring for certain roles, posting questions to their network, attending an event, announcing new initiatives, following vendors on social media.
  • Buying group signals: assets being shared, email forwards, multiple content engagements from the same account, junior staff participating in research (vetting solutions and downloading assets for decision makers), engagement from decision makers across multiple teams.
    
• Truer measures of engagement: Related to intent is the value and quality of digital engagement. Unfortunately, we’ve been championing the wrong metrics in this area. Clicks and downloads are scored uniformly but provide a false sense of validation because not every click or download will have an equally engaged (if engaged at all) buyer triggering that action. Martech needs to enable us to look deeper into buyer behavior by building context around these actions and allowing us to better understand why users are taking certain actions and how they are experiencing and engaging with brands. Martech should measure behaviors such as session duration, page count, active sessions (scrolling through content), engagement paths, multiple downloads, distributed stakeholder activity—and then help us see these behaviors on a larger stage of intent and engagement.
  
• New metrics and scoring frameworks emerging: In many instances, our existing metrics will remain but it will be up to martech to create a more lifelike picture of intent with what we’re measuring. We should expect our AI technology to better analyze a vast array of signals across channels and across the buying group, and use this data to tell us the detailed story of one group’s specific case, need, and intent. These buying group stories should be the data analysis that our martech aspires to produce. When our technology can help us understand that the same action can have different context among different buying groups, we will make immense strides towards understanding and meeting the unique needs of our buyers.

Technology has certainly allowed us marketers to scale hyper-targeted outreach and be more present on more channels in our buyer’s day-to-day. Yet despite our ability to activate, capture, and measure data better than ever before, I’d argue we’re still circling the drain in terms of gaining human-level insights and understanding about our individual buyers. Yes, customers are more shrewd than ever when it comes to participating in the dance of marketing and voluntarily giving us behavioral datapoints—and this inherent distrust of advertising is a major hurdle that marketers must creatively overcome. But the fact remains that buyers are still out there, in-market, taking action and wanting to be understood and served. It’s up to us as marketers to leverage the data and technological capabilities at our fingertips and start building a more compelling generation of martech that guides us back to the root of marketing—a place of empathy from where we can provide real value.