What Marketing Teams Should Prioritize in 2025

An Internal Refit Can Amplify Marketing’s Impact on Business Revenue and Growth 

Marketing leaders’ perceptions of their ability to impact their company’s performance produced some of the most eye-opening findings in the 10th Annual Dun & Bradstreet B2B Data Report. Those leaders understand how integral marketing teams are to creating buyer journeys, but a significant percentage admitted to serious concerns about their current processes and data. Left unchecked, those uncertainties and gaps are likely to erode their teams’ ability to build effective marketing campaigns, develop personalized and meaningful messaging and content, and create compelling customer experiences. 

As ongoing economic pressures ratchet up the pressure to engage active, qualified buyers faster than ever, teams are eager for help. Jon Ewing, our own Head of Marketing and a former CTO, CMO, and product development executive, is no stranger to collaborating with B2B and B2C marketers within very diverse companies and industries. In a wide-ranging recent interview, he offered his in-the-trenches perspective on how and where teams can optimize efforts within a wildly unpredictable business landscape. Keep reading for key excerpts and practical insights. 

What tools or processes have the most potential, in your opinion, to help marketers contribute to innovation and business growth in 2025?

There are three main things that I would call out. The first one is probably obvious, and that’s generative artificial intelligence. Generative AI is already being used to produce all kinds of marketing copy, imagery, and video, which I find to be a double-edged sword. While AI output might be faster and cheaper, marketers need to be really careful about how it’s used, particularly in light of copyright, legal, regulatory, and ethics issues. 

In my view, marketing is the art and science of persuasion at scale — and it’s still humans who are best at persuading other humans! However, we are at the point where AI can be a powerful efficiency tool. We should be considering how it can augment and supplement our efforts, not replace them. 

My second callout would be measurement. Yes, it can be a boring topic, but measurement is crucial, and I don’t think the industry as a whole has focused enough on meaningful marketing measurement, especially for B2B organizations.

We should be spending more time measuring and analyzing results, making our decisions based on those results, and showing the wider business the impact marketing really is having. Otherwise, we are at risk of jumping in and launching things and hoping they work. What’s worse is that we may end up just doing the same things over again and believing they work, when in fact they drive no incremental value. Marketing teams have to build out that measurement muscle.

LinkedIn shared in a recent report that most marketers say they have the right measurement tools, but many are still struggling to get the metrics that show the value of their work. Why do you think that’s happening?  

Before marketing teams start thinking about AI and metrics, we have to focus on comprehensive, trustworthy data. High-quality, accurate data is foundational — and it’s also my third call-out. 

In a recent Dun & Bradstreet marketing survey, 85% of marketing and sales executives told us they didn’t have all the data they needed to accomplish their goals, and almost half of them said their companies don’t currently invest in third-party data. Another 42% said integrating any kind of data into their key platform or systems was a big struggle. I find all of that incredibly concerning.   

Marketers need to blend reliable, comprehensive third-party data with first-party data, because third-party data gives us the broad view of who our prospects are. Then within the pool, we can start identifying the people who are actually interested in buying. 

Interest can be signaled in a number of different ways, so if marketing can layer in reliable buyer intent data, we can get actionable insights into why those people are behaving the way they are. Then we can use that to infer back into the wider pool and push those insights into our lead scoring model. But in our survey, about half of marketers and sellers told us buyer intent data wasn’t even available within their companies.  

Marketing teams need to partner with a reputable data provider. That’s how we can strengthen data quality and the data management processes that anchor and inform our lead gen strategies. 

In turn, better lead generation strategies can help us improve two extremely important KPIs: generating more leads, and ensuring a higher percentage of those leads convert into paying customers. We need quantity and quality, and I think those two indicators are particularly effective for making the argument to company leadership for marketing’s value and impact.

You mentioned the potential for AI to boost efficiency. What are some of the ways you see AI, data, and measurement helping marketing teams to think and work differently? 

I think there’s a crucial role that “traditional” AI — machine learning and automated decisioning — can play not only in measuring the effectiveness of marketing campaigns and experiments but also in suggesting and implementing experiments. A great example is allocating spend within, and across, channels. 

Watching how campaigns perform, and deciding to stop or change investment in those campaigns based on performance, is an important part of every marketing team’s job. If we’re doing this manually and periodically, then there are often periods when we’re investing in a campaign that is performing badly pending the next review. AI is very well placed to monitor campaign data minute by minute, and either can suggest in real time how spend could be optimized, or it can be given the ability to make spend decisions autonomously.  

Let’s also recognize that resource constraints prevent many teams from fully leveraging AI or launching data quality initiatives. This has helped lead to explosive growth in Marketing-as-a-Service offerings. I see fractional marketing hires as a cost-effective way for teams to solve for skills gaps or staffing shortages that can limit our work.  

I read in a Salesforce survey that 83% of sales teams with AI say that they achieved revenue growth in the past year versus 66% of teams without AI.

There’s little doubt that both marketing and sales teams are looking at AI to help them be more precise and successful in terms of who they target and with what. In the Dun & Bradstreet B2B Data Report, 67% of B2B and B2B2C senior leaders indicated their companies were likely to incorporate AI technologies within the next 2-3 years. Of those, almost 30% planned to leverage AI for specifically for sales and marketing, and 25% planned to use AI to improve content marketing. 

Let’s talk about leads for a moment, and how good leads get to sales. There are two ways that tends to happen. The first is to hire sales development representatives (SDRs) who prescreen and then route leads to the relevant sales team. This works really well, but it’s a largely manual process, adds additional steps in the sales process for the customer, and it’s expensive. The second option is lead scoring.

Perhaps this is simply my own experience, but I would argue that lead scoring is the most underused — or badly used —  aspect of marketing today. While AI-supported lead scoring, twinned with high-quality customer data, is not at a stage yet where it can completely replace SDRs, it can certainly supplement and accelerate the overall lead routing process, getting the right leads into the inbox of the right salesperson more quickly and more accurately. More informative and faster lead scoring can help marketing and sales really move the needle. 

It's interesting to think about AI and pipeline also as part of the measurement discussion. When we look at B2B pipeline, we look at the number of prospects who have, for example, completed a form and then we turn that into a sales number. We assume that marketing has helped drive that number. But how many of those people might have come anyway, without any form of marketing? AI can help create the control groups, metrics, and analysis for those kinds of questions, and then we can use that information to prove to others in the company that marketing efforts can, and will, drive incremental benefit.

There’s also an opportunity to improve attribution in B2B marketing and sales, where we increasingly have multiple people — buying groups — involved in purchasing decisions. Let’s say marketing gets a lead from Division X of Company Y, but when the salesperson picks it up, they realize they know someone in Division Z at Company Y.  If the salesperson connects with the Division Z contact and makes the deal, that’s where the attribution analysis can break down. With the right AI and data, marketing can be more successful in tracking and measuring their impacts across that entire buying group.

What do you think marketers can do to help them find better leads in the sales funnel? 

I think marketers need to view that process as more of a sieve. The “funnel” always struck me as a weird metaphor because in the real world, everything you put in the top of a funnel comes out at the bottom!

This isn’t the prettiest image but think about an oil distillery and how the process of fractional distillation works. Crude oil flows in the bottom and then through a series of towers, different products come out. Propane, methane, ethanol, and the petroleum — they distill it out in different ways. And that for me is a better representation of the sifting and concentrating that marketing needs to do to sort for the right people and develop better leads. 

We can think about our total mass of leads as the crude oil flowing in the bottom, and what we're trying to do is distill the leads — the prospects — who are ready to buy right now. Their checkbook is open. They have a pen in their hand. So these are the ones who need to speak with a salesperson right away because these are the ones who are close, on the edge. 

Then there are the leads who are somewhat interested but need more information. They’re not quite ready to purchase yet. We need to separate the ones who are just idly browsing, as well as those who are likely to never buy, and then determine how much energy and what tactics we want to apply. 

The distillation model, I think, is more realistic but also more technical and complex to manage — and why data, artificial intelligence and measurement are essential.   

Based on what you’ve described, some of the best opportunities for marketers to demonstrate their value and contributions are internally focused. What is it about our current business environment that makes this internal focus so important? 

Let’s go back to the definition of marketing as the art and science of persuasion at scale. If we agree on that, then we also need to acknowledge how hard it is to actually influence the way another human being thinks or behaves. And we’re getting fewer chances and less time to do it. Gartner is telling us things like 50% of consumers are significantly limiting use of social media due to quality concerns, and search engine volume is expected to drop this year by 25%. 

Because many marketers are predisposed to act fast and keep moving, it can seem easier to just push out more and more messages and emails and ads and blogs. By and large, that’s not an effective strategy. Customers are feeling inundated and overwhelmed with information. They’re trying to balance the demands of buying groups and organizational requirements for efficiency and cost-effectiveness, and often they’re looking for frictionless, self-service tools. 

So I think successful persuasion is going to require us to really embrace simplicity. By simplicity, I mean succinct, resonant work that stands out for speaking so plainly and irresistibly to customers’ needs and interests. 

There is a necessary discipline to simplicity in marketing. It takes a lot of time and hard work to ensure that in every customer channel, every single piece of content and every single piece of media looks and feels the same and is consistently direct and compelling. 

In the service of simplicity, it’s data, analysis, AI, and measurement that are going to help us ensure the relevance, consistency, and quality of our work. They are critical to gaining more capacity to focus on creativity and exploring better ways to engage with customers.