The advancements in AI and the tools that have come out of these developments have been rapidly integrated into B2B business workflows and GTM market programs. Some are more successful than others, as seen in that MIT report. Now, I’m not coming on my soapbox here to label AI, LLMs, Agentic, etc., as a mistake that we should go back from (even though there are ethical concerns regarding creativity and intellectual property that have yet to be rectified). What I want to bring up is a growing concern about where the market is.

The Problem

With the execution of AI investment and rollouts, as well as the hidden uses of AI, we have entered a period of diminishing returns from these AI tools that we are using. I’m not referring to a potential market bubble here; I’m specifically referring to the impact that overreliance and uncritical use of AI have on what we are seeing in the market, on the brand voice and content of B2B companies, on GTM initiatives, and on the models themselves. The more we use AI to create content, strategies, emails, etc., uncritically and without creating novel and human-created assets to then put back into the models and systems we’re using, the more generic and milquetoast the outputs we will get from these models, and we’ll likely see more hallucinations as well.

I’m sure everyone has noticed the flush of obvious and generic AI writing bombarding inboxes and LinkedIn posts daily. There are obvious patterns to look out for: EM dashes, random emojis, excessive bolding for emphasis, and the list goes on. For brands, this writing often erases their unique brand voice, and for marketers and salespeople, it renders any outbound initiatives incredibly generic and unlikely to drive engagement.

The Solution

We need to reintroduce some modicum of human input into any workflow that uses AI. It is dangerous in the long term to be entirely reliant on them. This post, for example, was wholly written without AI. That brings me to my second point: we must continue to practice the skills we used before we started to rely on AI models. This blog took me twice as long to write compared to what it used to take when I wasn’t using AI to support. The pattern of people offloading much of what was a daily part of their job that required skill to AI is something we are seeing across industries. The pattern leaves all of us vulnerable not just to something like a major AI outage or market impact, but also to new model rollouts that change the way the AI works, responds, and its output quality.

Potential Opportunity

On the other hand, the pattern and direction in which companies and teams are utilizing AI also leave an opportunity for those who can strike a balance between human creation and AI optimization. People crave authentic human tone and conversation, and the content that doesn’t sound AI-generated stands out. So, while the overreliance on AI models is a concern, I also think there is an opportunity that comes with it.

If you want to chat about CLG or anything in this post, please reach out: acceleration@heinzmarketing.com

The post AI in B2B: The Human Imperative appeared first on Heinz Marketing.

Sometimes it is more important to track what doesn’t work than what does.  Customer marketing is one of these areas, at least in my opinion.  The reason is simple: the relationship is yours to lose.  Every relationship needs boundaries, and customer marketing walks a fine line between offering value and being annoying.  This is especially true when you want them to spend more money.

Don’t get me wrong, it is very important to know what works.  However, if you don’t know where the boundaries are with the various segments of your customers, then you run the risk of overstepping.  Excitement about what works can quickly translate to anguish, and relationships are hard to rebuild.

I like the term “boundary metrics” for the type of thing that shows you were the line is.  So here are some common flags we have encountered and suggestions on what to do about it.

Just getting started?  Here is is a guide on what CLG is, an FAQ on CLG Metrics, and essential tools you need for CLG efforts.

Email Metrics

Email is very trackable and benchmarks are easy to gather.  When you start to see sudden degradation of your performance against those benchmarks, there are a few things you will want to ask your team about.  First off, here are some flags to look for in your email reporting:

• Deliverability below 95%
• Unsubscribe rates at greater than 0.1%
• Open rates at less than 15% (particularly for welcome/education emails)
• Click-to-open ratios of less than 8% (this can really vary by email topic)

These benchmarks I listed are general and for reference only, it is better to compare against your own historical performance.

In these cases, there are a few culprits that would indicate you are bumping up against boundaries in your customer relationships.  Here are some questions to ask:

• What is the frequency of emails going out to customers?
• Do unsubscribe rate increases correlate to frequency of emails in a given period?
• What new accounts were added in the period where your metrics declined?
• Is the open-rate decline over time or does it better connect to topics?
• Has anything else changed in your email program?

As with everything, you need to figure out where the point of diminishing returns is and avoid going too far past it.  This is more than economics, relationships can be damaged and take time to repair.

In-App

This is commonly less visible to marketing.  But hey, it is a good reason to get chummier with your product team.  There isn’t a right-size for this one, but there are some commonalities and themes.  As a rule of thumb these are generally also churn-risk indicators.

• Drops in in-app communication engagement
  • Things like notification muting or unopened messages
• Announcement dwells <1s
• Declining engagement with announcements

Marketing in-app can be a tricky thing.  Users are there to get the value they paid for, and while we want to add value (through selling more services), they might not be open to it.  Cross this boundary and you will associate negative affect with the product.  Yikes.

Ask about:

• What campaigns have happened in-app?
• How frequently are we running in-app campaigns?
  • Has that increased lately?
• What % of announcements have been marketing vs. other?
• Has the messaging in-product changed recently?

As with email, this boils down to relevance and frequency.  Too much frequency or too little relevance will hurt the relationship.  Both at the same time is a big turn-off for users.

Community

Not every tool has, or needs, a user community.  If you don’t, there is still somewhere that professionals using your tool go to complain/rant with others (specific to you or not).  Some examples would be subreddits, professional forums, and private Slack or LinkedIn groups.  These are common.

Flags to look out for:

• Increasing negative posting about you.
• Complaints about marketing.

That’s it, pretty simple to envision, but harder to gather.  Harder still to analyze, but here are some questions to ask (pro tip – use ChatGPT to do this sort of qual analysis at scale):

• What does the ChatGPT wordcloud of these comments look like?
  • Copy the text of the comments, put it into a doc or directly into ChatGPT and ask it for this.
• What is the overall sentiment of these comments?
  • The official term is “sentiment analysis”
• Does the frequency of these posts correlate to any particular campaigns or trends in customer marketing?
• Are any of the people posting known to us?
• What is specifically mentioned in the posts, and what trends are there among them?

There is a lot to mine in this sort of customer ranting.  Highly recommended work, here.

Sales/CS data

Whoever is responsible for your expansion/renewals is the group to talk to.  There are some quantitative metrics that you can track here, but a lot of it is qualitative and relational.

• Dips in expansion deal win-rates
• Dips in expansion deal qualification rates
• Dips in NPS
• Increasingly negative sentiment in expansion deal notes
• Jumps in cycle time for expansion deals

Not all of these will be directly related to marketing, but there may be correlations there that you wouldn’t know about if you didn’t ask your team.

• Are customers mentioning anything about our marketing?
• Are customers complaining at all about notifications in the system?
• Are leads from our campaigns disqualifying at a higher rate than normal?
• Are deals from our campaigns taking longer to close?
• Is there any timing correlation between NPS dips and customer marketing campaigns?

Get this information early and often, and then plan with your CS/Sales folks to react.

Conclusion

Boundaries matter.  Just like in the buying process, customers are defining more about how the relationship goes than ever before.  AI is only accelerating this as there is increasing parity between product offerings.  Tech is commoditizing, and what will matter more is the relationships you have with your customers.  These take time to build and based on trust.

In customer marketing, it is easy to get focused on the things that drive leads.  To tie it all the way back to the beginning, if you aren’t careful the desire to bring value to your customers can quickly sour them on you.

Remember, what buyers want the most is confidence in the decision they made to go/stick with you.  Don’t endanger it by pushing too hard, too fast.

As always, if you want to chat with me about how you can build these warning systems and the plays to deal with issues, email acceleration@heinzmarketing.com.

The post CLG Metrics: Red Flags and Questions to Ask appeared first on Heinz Marketing.

Choosing the right marketing agency or consulting firm can be overwhelming—there’s no shortage of options, and it’s not always easy to tell who’s truly the right fit for your business. We get it. 

Whether you’re looking for strategic guidance, better execution, or a fresh perspective, the right fit matters. That’s why we put together this guide to help you find the right partner for your marketing needs.

From knowing where to start, what to ask, and how to evaluate fit, here’s a guide to help you confidently select the partner that will help you achieve your marketing goals.

Why should I consider an agency in the first place?

A marketing agency can bring deep expertise, experience, and perspective that many in-house teams don’t have on their own. Agencies stay on top of the latest strategies, tools, and trends, and can quickly apply that knowledge to your business. They also provide additional capacity so your marketing doesn’t stall if your team is stretched thin.

Beyond execution, the right agency acts as a strategic partner, helping you refine goals, uncover opportunities, and build campaigns that drive measurable growth. An agency can help you move faster, smarter, and with greater impact than going it alone.

Where do I begin?

Take a step back and start with clarity about your goals. Before you talk to any agency, take time to define what you want to achieve whether it’s building brand awareness, generating leads, launching a new product, or all of the above. Knowing what success looks like will help you find the right kind of partner.

How do I build and narrow down my list of agencies?

Ask for referrals. Reach out to your professional network or industry peers. Referrals from trusted sources often lead to great agency partnerships.

Conduct online research. Use LinkedIn, agency directories, and even Google or AI assistants to find agencies with strong reputations in your space.

Look for thought leadership. Check out the agency’s blogs, podcasts, and webinars. Agencies that share insights publicly often bring a point of view and expertise that could benefit your business.

Review case studies and client testimonials. Real-world results matter. Look for examples of how they’ve solved problems similar to yours.

Have intro conversations. Talk to a few agencies so you can make some comparisons. The discovery call is as much about how they listen and ask clarifying questions as it is about what they say.

Feel out the vibes. You’re not just hiring a vendor; you’re choosing a partner. Chemistry, communication style, and cultural fit matter just as much as capabilities. 

Consider whether they listen carefully and act as a partner rather than a vendor. Verify that they understand your business goals. The strongest agency relationships are rooted in trust, collaboration, and shared priorities.

What kind of questions should we ask?

What services do you specialize in?

Not every agency does everything. Are you looking for strategy? Campaign execution? SEO? Branding? Make sure their strengths match your needs. A great agency should clearly articulate what they’re best at, and even what they don’t do.

Do you have experience in my industry or solving problems like mine?

You don’t necessarily need an agency that only works in your vertical, but relevant experience helps. Ask for examples of how they’ve tackled similar challenges and what results they achieved.

How do you approach strategy development?

Dig into their process. Do they take time to understand your business, your audience, your funnel, and your competitors? A cookie-cutter approach is a red flag. Strategy work should be highly customized.

Who will I actually be working with?

Sometimes you meet the A-team during the pitch and get handed off to junior staff later. Ask to meet the people who will be doing the work on your project day-to-day and make sure they’re a fit.

What does your typical client relationship look like?

This can reveal how they collaborate. Do they prefer retainer or project-based work? Are they proactive or reactive? Do they act as embedded partners or outside vendors?

How do you handle communication and reporting?

Ask how often you’ll meet, how they share updates, and what reporting looks like. Does it match your needs? Transparency and responsiveness are essential.

In addition, ask what they expect of you as the client. Will it work for your bandwidth?

How do you measure success?

Look for alignment on goals and KPIs. A good agency will tailor metrics to your business outcomes, not just vanity metrics like impressions or clicks.

Can you walk me through a recent campaign or engagement?

Real-world examples say a lot. Ask about strategy, execution, results, and what they’d do differently next time.

What’s your onboarding process like?

A thoughtful, structured onboarding process indicates that they’re organized and experienced. This can include discovery sessions, documentation sharing, stakeholder interviews and early priority planning and goal setting.

What sets you apart from other agencies?

Every agency should have a clear point of view or differentiator. Listen for their unique methods, values, or team strengths.

What’s the best way to compare agencies?

Create a simple scorecard with criteria that matter most to your company. Things like strategic expertise, industry knowledge, cultural fit, and pricing. After conversations with a few agencies, compare them side by side. In the end, the “right” choice is the one that brings both the expertise and chemistry your team needs to succeed.

How much should I expect to invest in a marketing agency?

Budgets vary widely depending on the scope of work, your goals, and whether you need strategy, execution, or both. Some agencies work on monthly retainers, while others bill project by project. What matters most is alignment between your investment and the results you expect…be wary of agencies that promise big outcomes for unrealistically low fees.

How quickly will I see results?

Marketing rarely provides instant results. Paid campaigns may deliver leads quickly, but brand-building, content, and demand generation take time to ramp up. A good agency will help set expectations, show progress along the way, and balance quick wins with long-term growth.

What red flags should I watch for when evaluating agencies?

Look out for agencies that:

• Promise guaranteed results (no one can control every variable).
• Are vague about their process or reporting.
• Push one-size-fits-all solutions.
• Don’t ask thoughtful questions about your business.

Wrapping it up

Choosing a marketing agency is more than checking boxes. It’s about finding a partner who understands your goals and brings both strategic clarity and executional firepower. Ask the right questions, and you’ll find the right fit.

How can I learn more?

We’re happy to answer any questions we’ve missed here. Check out our website, blog, resources, sign up for our newsletter, or follow us on LinkedIn, Facebook, or Instagram.

Want to chat? Email us for a free brainstorm session!

Image by Moondance from Pixabay

The post From Search to Selection: How to Find a Marketing Agency That Fits Your Needs appeared first on Heinz Marketing.

When most people think of AI, they picture massive, general-purpose models like GPT-4, Claude, or Gemini, systems seemingly capable of answering just about anything you throw at them (not to get into issues with AI hallucinations and the use of dubious sources). These large language models (LLMs) dominate headlines for their near-human performance and conversational format.

But Nvidia’s recent research paper makes a bold argument: the future of many AI applications, especially in “agentic” systems, belongs to small language models (SLMs), leaner, faster, more specialized AI tools. Mind you, they’re saying this even as much of their primary valuation is because of their status as critical hardware for data centers used to run complex LLMs, that SLMs don’t require.

This isn’t just a technical shift. If Nvidia is right, it could reshape how businesses deploy and make investments in AI, how marketers build customer experiences, and how organizations approach AI ethics.

Why Nvidia is Betting on Smaller Models

Nvidia’s core thesis is simple:

Most real-world AI use cases don’t require a giant, general-purpose brain; they need a focused, highly efficient specialist.

In “agentic” AI systems (think automated assistants, task bots, and process-driven AI workflows), the job isn’t to hold open-ended conversations but to perform a small set of repetitive, predictable tasks quickly and reliably.

SLMs are ideal for that because they:

• Cost less to run (lower compute, less energy)
• Respond faster (reduced latency)
• Can be deployed on-device or in low-power environments
• Specialize easily through fine-tuning for specific business needs

In Nvidia’s vision, companies will increasingly blend SLMs and LLMs, using SLMs for narrow, high-frequency tasks and reserving the big models for complex reasoning or unpredictable scenarios.

Why B2B Marketers Should Care

For B2B marketers, this shift could have three significant implications:

1. AI-Driven Customer Experiences Become Cheaper and Faster

Always-on chatbots, product recommendation engines, and real-time personalization tools could run on smaller, more efficient models. That means faster responses, reduced infrastructure costs, and fewer budget fights over AI experimentation.

2. Greater Customization Without Enterprise-Level Budgets

SLMs can be fine-tuned to a company’s exact messaging, tone, and product knowledge without the data hunger (and cost) of an LLM. This levels the playing field for mid-market companies who want sophisticated AI without LLM price tags.

3. Smarter Marketing Ops

Behind the scenes, SLMs could power internal marketing workflows, lead scoring, campaign optimization, and competitive monitoring, without draining resources from customer-facing initiatives.

The Business Case for Going Small

If your organization is building or buying AI tools, Nvidia’s recommendations are worth noting:

• Prioritize SLMs for repetitive, high-frequency tasks to reduce energy consumption and latency.
• Adopt modular AI architectures that mix SLMs and LLMs; think of it as using the right tool for the right job.
• Fine-tune SLMs quickly to keep pace with changing market demands, seasonal campaigns, or regulatory shifts.

For many B2B companies, the economics here are game-changing: you can scale AI adoption without scaling costs at the same rate.

The Ethical Dimension: Smaller Isn’t Just Cheaper, It’s Cleaner

There’s another reason to pay attention to SLMs: AI ethics and sustainability.

• Lower energy use = lower carbon footprint. LLMs require vast amounts of compute and energy. Training one can emit as much CO₂ as five cars over their lifetime. SLMs drastically cut that load.
• Reduced dependency on centralized AI providers. Smaller models can run locally, giving businesses more control over their data privacy and security.
• Fewer “hallucinations” for repetitive tasks. A model trained for a narrow scope is less likely to produce unpredictable or misleading outputs, which helps with compliance and brand trust.

If you’ve been hesitant to scale AI because of ethical concerns, SLMs offer a path forward that aligns better with responsible AI principles.

The Bottom Line

Nvidia’s research isn’t saying LLMs are obsolete; they’re just not the best fit for every job and are unlikely to dominate the majority of AI use cases in the future.

The real future might be hybrid: SLMs handling most of the load, LLMs stepping in if higher-order reasoning is needed.

For B2B marketers and business leaders, this could mean:

• Faster AI adoption without spiraling costs
• More tailored and consistent customer experiences
• A more straightforward path toward sustainable, ethical AI deployment

The smartest AI strategy in the next few years might not be thinking bigger, it might be thinking smaller.

If you want to chat about any of these, or anything in this post, please reach out: acceleration@heinzmarketing.com

The post Why Nvidia’s SLM Vision Matters for B2B Marketing appeared first on Heinz Marketing.

Every revenue leader has felt the sting of a “ghosted” prospect — a once-promising opportunity that suddenly goes silent. In our recent webinar, “Ghosted to Growth: Revenue Leaders on Buyer Re-Engagement”, Patricia DuChene (Head of Marketing, Sendoso) and Andrew Reed (Director of Global ABM, AVEVA) joined Heinz Marketing President Matt Heinz to tackle one of today’s most persistent B2B challenges: how to cut through the noise, earn buyer attention, and turn early engagement into long-term relationships.

Here’s what stood out — and how you can apply it to your own customer-led growth (CLG) strategy.

Why B2B Buyer Engagement is Harder Than Ever

Buyers today are operating in a state of ruthless prioritization. Patricia noted that even as a buyer herself, she rarely fills out contact forms or takes demos — instead turning to AI summaries, trusted peer recommendations, and self-service research to narrow options long before engaging a vendor.
Andrew echoed the challenge: AI and digital targeting have made it easier to put messages in front of buyers — but also easier for everyone else to do the same. The result? More competition for attention and more pressure to deliver relevance.

Trust-Building Tactics for Better Buyer Engagement

Success means making early B2B buyer engagement value deposits—consistent, non-transactional touchpoints that build trust over time.

• Go beyond procurement: In Andrew’s example, winning a $50M deal required engaging not just the buying office but the end users across regions who would ultimately drive internal demand.
• Act like a person, not just a brand: Patricia shared the importance of showing up in buyers’ existing communities — from subreddits to industry events — offering best practices and insights with no strings attached.

Takeaway: Treat the buying group as a network you need to equip, not just a set of leads you need to convert. Your future advocates are often inside the account already — they just don’t have a budget line yet.

Want to deepen your CLG strategy? Check out “Lessons from Structuring GPTs for Customer-Led Growth”, exploring how to operationalize CLG workflows using AI tools for repetition, consistency, and speed.

Scaling B2B Buyer Engagement Through Systems Thinking

Scaling personalization and relevance is no longer just about better copy or creative — it’s about building systems that can deliver the right message, at the right time, through the right channel.

• Andrew’s ABM team uses AI to scale what used to be 1:1 personalization to dozens of accounts without sacrificing quality.
• Patricia recently moved her Director of Demand Gen into a GTM engineering role to improve data flow, real-time insights, and campaign adaptability.

Takeaway: Systems thinking lets you sustain relevance across the long B2B sales cycle — and ensures your marketing stays responsive to real-time buyer behavior, not just quarterly campaign calendars.

For tactical activation, check out our post on “Operationalizing CLG Signals” — covering signal triage, whitespace mapping, and workflows to prioritize high-value engagement.

Avoiding the Pitfalls

Not every tactic delivers. Both panelists shared lessons learned:

• Over-personalization can backfire. Andrew recounted a hyper-targeted campaign that took nine months to build but didn’t deliver better results than a leaner approach.
• Attribution noise wastes energy. Patricia urged marketers to treat attribution as a decision-making tool, not a political football — and to keep revenue as the ultimate north star.

Curious where your CLG motion stacks up? Our “CLG Maturity Model” helps assess whether you’re reactive, opportunistic, programmatic, or predictable — and what’s needed to level up

Accelerating the Journey Without Forcing It

Velocity doesn’t always come from pushing harder — sometimes it’s about removing internal friction.

Equip your champions with what they need to sell on your behalf to procurement, legal, and finance. As Andrew noted, one “soft no” in a buying group can stall a deal indefinitely — finding and converting those potential blockers is just as critical as nurturing your advocates.

AI, Search, and the Next Battleground for Visibility

As buyers shift from Google searches to LLM-powered queries, Patricia suggested a simple but powerful tactic: ask the LLMs where they source their answers, then make sure you’re present there. That means:

• Increasing brand mentions across social platforms and industry forums
• Investing in review sites and thought leadership that AI models can easily surface
• Structuring your content for AI-friendly discovery (e.g., clear text on social posts)

For a foundation in CLG strategy, check out “What is CLG and Why It Matters” — it outlines CLG fundamentals, tactics, and why shifting from acquisition-only thinking is vital.

Your CLG Action Plan

Optimize your B2B buyer engagement strategy by equipping internal champions, building scalable systems, and ensuring visibility in AI-driven search. If you’re looking to turn ghosted prospects into growth, start here:

1. Audit your value deposits. Where are you offering insight with no strings attached? Where could you?
2. Equip the full buying group. Create materials for end users, champions, and potential blockers.
3. Invest in system scalability. Build workflows and tech stacks that support personalization at scale.
4. Balance efficiency with experimentation. Test bold ideas, but watch for diminishing returns.
5. Be findable in the AI age. Optimize for the sources LLMs pull from — not just for Google.

Watch the full webinar on demand to hear Patricia, Andrew, and Matt share more real-world examples, cautionary tales, and practical strategies for breaking through buyer silence and driving engagement that leads to revenue. You’ll also get access to some of our gated CLG content. Send us an email if you have any questions, and sign up for our newsletter for info on upcoming webinars and events this fall!

The post B2B Buyer Engagement: Ghosted to Growth with Proven Re-Engagement Strategies appeared first on Heinz Marketing.

Sometimes I struggle with figuring out how to use AI.  There are all of these claims and promises, but where do you even begin?  Obviously I began with simply using ChatGPT Plus to handle basic tasks.  This requires minimal learning. Where it gets interesting is when you start to find use cases, such as in customer-led growth.

A natural next step is to experiment with customGPTs and agents.  That is what this post is about.  In my last post, I wrote about some lessons I learned using customGPTs to assist with campaign development in CLG motions.  Still helpful, but my thinking has evolved.  Now, I am starting to explore agents to pull together diverse data sets.

Let’s talk about that.

LLMs anthropomorphize marketing data

Everyone says they’re a “data person” these days.  I suppose in a literal sense, we all are (DNA and all that).

Marketing data, though, is often extremely difficult to make sense of.  Sure, there are tools and processes that really help.  No doubt about that.  However, marketing data is multiform and distinctly human.  In other words, it is messy.

However, LLMs enable the very exciting opportunity to have a conversational interface with these multiform data sets.  This gives a new meaning to the term “data person”.  Where it used to be that you had to block off hours of time to parse mixed data sets, now you can upload that data and talk to it in plain, simple English.

This is obviously very powerful.  It democratizes the ability to parse and use customer data across your team.  It makes the concept of mixed and multiform data less daunting.  It finds patterns and insights that would take a human a long time, or maybe even would be downright impossible at scale.

Building tools to help you do this is a great move for enabling your team to both do better marketing and further adopt AI into their workflows.

Here are some use cases:

• Designing customer marketing campaigns designed to target specific customer lifecycle moments
• Identifying, understanding, and responding to churn risk
• Planning multi-threading campaigns to expand to other teams at a target account
• Developing and/or updating buyer and user personas
• Onboarding new team members to help them better understand your market

There are numerous ways to do this, but let’s talk about 3 that you probably have access to, in ascending order of complexity: Basic ChatGPT+, customGPTs, and live-API methods (AKA agents).

As always, this assumes that you are being ethical and cautious about data uploads.  Personally identifiable information, company secrets, and anything else proprietary should only be used in accordance with your data policies.  Be smart.

Basic ChatGPT+

If you are just getting started, this is a very easy one to begin with.  I generally recommend doing this only as a proof of concept, since it gets tedious fast.  However, if you want to see what is possible and are evaluating a greater investment, this is a good way to go.

You need the Plus version of the tool, but once you can upload data sets, you are ready to go.  Here are some basic instructions:

1. Pull whatever relevant data you want to put into ChatGPT
2. Clean that data (tedious, but necessary)
3. Upload it all to ChatGPT
4. Start talking to it

You can do this with datasets of any size.  Personally, I started with just basic SFDC data pulls for campaign building, but it quickly evolved.  The more data you feed in, the better the responses will get.

There are limitations to this, like it requiring frequent reuploading, but it does give you the ability to start talking to your data.  Very helpful for getting directional insights.

This is where I started.

Custom GPTs

I wrote about these in my last post, and since then I have really expanded my use.  Formerly they were primarily for things like campaign development.  Still helpful for that, but the real power comes when you include data in the knowledge base.

The big shift from basic CGPT+ to this is that you can create consistency across conversations and then give that tool to your team.  The workflow looks like this:

1. Upload cleaned data sets, personas, positioning, and any other relevant materials to the knowledge base.
2. Write prompts to instruct the GPT on how to interpret and frame the data for users.
3. Write instructions on how it should interface with users.

The copy the link and start testing with your team.  This is the real benefit of doing it via a customGPT rather than just within ChatGPT itself: you can send a link to your team so that everyone can use it.  While the creator needs to have ChatGPT+, anyone can use it if they have a link.

There are still a few limitations, primarily around the fact that the data set is still static.  You will need to add more data to the knowledge base as it comes available and refresh it with some frequency.  Not a perfect tool, but the ability to share this tool with your team from just one ChatGPT+ subscription is very nice.

Here is a pro tip: make a short survey to give to your team after they have used the tool.  This will help you gather feedback and improve the tool over time.

Frankly, I think this is the best option for most users.

Agentic

This is the one I am newest at, so my experience here is still evolving.  Take this with a grain of salt.

This is the most advanced approach, and honestly probably not necessary for most folks.  There are surely tools to do this easier and faster, but I like to figure out how stuff works.  You might just want to pay for something.

The key differentiator of this method is that it is an agent that uses tools and can access live data via APIs.  Here are the pros and cons.

Pros:

• Accesses live data via API. Very exciting because it gets rid of reuploads.
• Can be hooked up across your data ecosystem, pulling info from wherever you give it access. This means you can really create a much more complete “data person”.
• Considerably more customizable as you can give the agent more tools to access.

Cons:

• It is technical. I am using ChatGPT to accelerate the programming, but I still have to know the code.
• APIs have security risks. You gotta engage your security folks to stay safe.
• Takes longer to go this route. Making the right tools and giving it the ability to answer open-ended questions is difficult.
• It requires other paid plans like ChatGPT Platform (if you want to use CGPT as the LLM).
• It requires a UI to be usable by your team. I am just working in Jupyter Notebooks, but most marketers are going to need an actual interface.

Overall, this approach gives the most flexibility, real-time answers, and potential impact.  I think building these sorts of systems should be on the radar of every marketer.

While this isn’t a con, you should know: you need to have a good data foundation for this to work.  Garbage-in, garbage-out is the mantra.  Mapping out your data ecosystem is a good first step.  What are all of the sources of customer data you have/can get, where do you store them, and what characteristics exist within that data?  Once you have that, you can start to piece together how to build an agent who can become your “data person”.

This is an ongoing exploration, if you want to talk about it, or hear how we do this sort of thing with clients, please reach out to me at accelerate@heinzmarketing.com.

The post Using AI to “Talk To” Your Customer Data appeared first on Heinz Marketing.

In today’s B2B landscape, the most overlooked revenue engine is hiding in plain sight: your existing customers. While most marketing teams are still focused on top-of-funnel activities, industry data tells a different story. According to Forrester, 73% of B2B revenue now comes from current customers, not net-new business. That makes customer expansion—not just retention—a strategic growth priority.

But what does it actually mean for marketing to own customer expansion? It’s not about simply sending a few upsell emails before renewal. True ownership requires a structured approach—grounded in data, aligned with customer experience, and accountable to revenue.

What It Means for Marketing to Own Customer Expansion

Ownership starts with clarity. Expansion isn’t a vague concept—it’s a set of measurable outcomes: increasing account value through upsells, cross-sells, feature adoption, or renewals with add-ons. And yet, too often, these motions are reactive and fragmented. Marketing might support sales or CS with a few ad-hoc resources, but there’s no cohesive strategy or sustained effort.

To fix this, leading organizations define customer expansion as a joint accountability—one where marketing plays a proactive, not passive, role. This means identifying ideal expansion opportunities, mapping a tailored post-sale journey, running targeted campaigns, enabling front-line teams, and reporting on impact. In other words, treating expansion with the same discipline you’d apply to demand gen.

How to Identify High-Potential Expansion Opportunities

The first sign of true ownership is that marketing understands where expansion is likely to happen. This doesn’t mean guessing or collecting anecdotal stories. It means using first-party data to identify patterns across your customer base. Who is adopting new features quickly? Who’s nearing license limits? Who’s expanded usage into a new department?

For instance, a customer with 85% product adoption may be ready for an upgrade conversation. A buyer who’s clicked through multiple knowledge base articles on a complementary product might be open to a cross-sell. This isn’t just theory—according to OpenView’s SaaS benchmarks (note: SaaS-specific), companies that operationalize product signals into expansion plays see 20–30% higher Net Revenue Retention.

Outside of SaaS, the same principles apply. Usage trends, engagement with marketing content, support interactions, and success metrics all provide expansion cues. When marketing owns expansion, it owns the insights that power this targeting.

Why Mapping the Post-Sale Customer Journey Is Critical for Growth

Many organizations have a well-defined pre-sale funnel, but few have mapped what happens after “closed-won.” That’s a miss. Customer expansion happens along a journey and if marketing doesn’t shape that journey, it risks being invisible when it matters most.

The post-sale lifecycle typically moves through onboarding, adoption, maturity, and advocacy. Each stage offers opportunities for value reinforcement and growth, but only if marketing shows up intentionally. In onboarding, that could mean welcome emails and quick-start content. During adoption, it might be product webinars or use-case guides. At maturity, marketing should surface advanced solutions or cross-functional benefits. By the time advocacy kicks in, customer stories and referral incentives come into play.

McKinsey’s research on growth outperformers found that 80% of value creation comes from expanding existing relationships—not acquiring new ones. Expansion isn’t a side effect of satisfaction. It’s the result of intentional customer journey design.

How to Build Marketing Campaigns That Drive Customer Expansion

When marketing owns expansion, it doesn’t just react to sales requests or sprinkle in a few generic emails. It builds and runs dedicated campaigns tied to specific expansion goals.

For example, a quarterly upsell push might target accounts with heavy usage and showcase how the next pricing tier unlocks time-saving features. A cross-sell campaign could zero in on a customer segment with shared needs, using relevant case studies to introduce a complementary solution. And a retention campaign might focus on renewals with embedded add-ons to improve stickiness.

These aren’t “one and done” efforts—they’re part of a broader lifecycle marketing calendar that is driven by the voice of the customer. And just like acquisition programs, they have clear objectives, KPIs, and reporting. In organizations where expansion is owned, marketing teams are not only creating content—they’re creating pipeline.

How Marketing Enables Sales and Customer Success to Drive Growth

Customer Success and Account Managers are on the front lines of expansion, but they often lack the messaging, tools, and air cover to act. Marketing can change that.

This means providing sales enablement for post-sale conversations: one-pagers tailored to upgrade scenarios, ROI calculators, email templates, and success story decks. It also means marketing and CS aligning around shared signals and timing. If a customer hits a certain usage threshold, for example, both the CS manager and marketing automation system should act in concert.

Expansion thrives when everyone is working from the same playbook. Marketing is uniquely qualified to write that playbook.

Measuring the ROI of Marketing-Led Customer Expansion

Finally, true ownership is visible in the numbers. Marketing should track and report the key metrics that matter most for the organization:

• Expansion pipeline influenced or sourced
• Campaign-sourced upsell or cross-sell opportunities
• Engagement with post-sale content
• Changes in Net Revenue Retention (NRR)
• Customer Lifetime Value (CLV) growth

Too often, marketing’s impact ends at “leads generated.” But in expansion-oriented orgs, CMOs are showing how their work directly contributes to revenue sustainability. As Bain famously reported back in 2014, a 5% improvement in retention can lead to a 25–95% increase in profits. Talk about speaking the CFO’s language!

Why Customer Expansion Is Marketing’s Growth Imperative

Owning expansion isn’t just about organizational structure. It’s about mindset and accountability. When marketing takes the lead, customers get a better experience, teams collaborate more effectively, and the business grows more predictably.

So here’s the real question: Is your marketing team actively shaping the post-sale journey, running campaigns that create expansion pipeline, enabling customer-facing teams, and reporting on impact? Or are you still handing off customers at the finish line?

Customer expansion is marketing’s job. The teams that embrace it become indispensable, positioning themselves as essential drivers of efficient, sustainable growth well into the future.

Ready to turn your marketing team into a proactive driver of customer-led growth?

We help B2B organizations shift from reactive tactics to strategic, revenue-generating expansion programs. Contact us today to learn how we can help you build a scalable customer expansion engine.

Image from Freepik

The post Owning Customer Expansion: A B2B Marketing Playbook appeared first on Heinz Marketing.

That’s what we explored in “From Signals to Revenue: The Modern Growth Playbook for CMOs and CROs,” an on-demand session hosted by Heinz Marketing’s President Matt Heinz, with guest speakers Julie Persofsky (Growth Advisor, Metadata), and Trey Harnden (Enterprise Account Manager, Folloze), the discussion centered on how B2B teams are turning behavioral and intent signals into pipeline, revenue, and stronger customer relationships.

If you didn’t catch it live, you can watch the full session here.

What’s Getting Missed (and What to Do About It)

Your prospects and customers leave behind useful signals constantly via content engagement, platform activity, buying committee research, and more. But most teams either aren’t tracking those signals well, or don’t have a clear plan for acting on them.

The panel called out a few specific gaps they see even among mature revenue teams:

• Most GTM teams still treat signals as a lead-gen tool. But the best-performing organizations are using them across the entire customer lifecycle from first touch to expansion and renewal.
• There’s too much emphasis on collecting data, and not enough on applying it. Signals aren’t valuable unless they trigger something actionable and measurable.
• Sales and customer success often get left out of the loop. Even when marketing is doing a good job surfacing insights, they often don’t make it into the hands of the people who can act in time.

What Leading Teams Are Doing Differently

Rather than trying to boil the ocean, successful teams narrow in on signal categories that tie directly to revenue moments and align cross-functional plays around them. The panel walked through examples of how this shows up across different teams:

For Marketing

• Tailoring nurture paths based on engagement patterns and content interest
• Weighting lead scoring with intent and behavioral data (not just email opens or pageviews)
• Building modular content journeys that evolve as new signals come in

For Sales

• Triggering outreach based on high-value actions (like repeat product page views or competitor comparisons)
• Equipping reps with “why now” context, not just account names
• Using signals to prioritize outreach timing and messaging

For Customer Success

• Monitoring platform usage and support patterns to flag expansion opportunities or churn risk
• Aligning renewal and upsell plays with in-product behavior
• Using low-lift engagement (like resource nudges or check-ins) tied to specific signals

A Few Real-World Examples of Using Buyer Signals to Drive Growth

The conversation also touched on how teams are putting these ideas into practice:

• A SaaS company identified a rise in usage of premium features among a segment of customers. That signal prompted a proactive outreach from CS that sped up renewals and opened expansion conversations earlier than usual.
• Another team combined first-party engagement (like pricing page activity) with third-party intent (visiting competitor sites) to build 1:1 outbound campaigns. Their conversion rate from previously cold accounts more than doubled.
• One org used signal thresholds to automate internal routing, ensuring accounts showing high buying intent were flagged to sales and removed from nurture, streamlining follow-up and avoiding channel conflict.

What CMOs and CROs Should Take Away

Getting value from intent data—and truly using buyer signals to drive growth—isn’t about having the most tools or tracking the most behaviors. It’s about:

• Defining which signals matter and why; what do they indicate, and what outcome should they tie to?
• Aligning teams around those signals; ensure marketing, sales, and CS aren’t duplicating efforts or missing handoffs
• Turning signals into plays; ideally pre-defined and repeatable, so teams can move quickly without needing to reinvent the wheel
• Measuring impact clearly; look beyond surface metrics and ask: which signals actually correlate to revenue movement?

For revenue leaders, that might mean starting with a signal audit, refreshing your scoring model, or rethinking how expansion plays get triggered. The common thread is making signals usable, not just visible.

Final Thoughts

Too many teams are sitting on valuable insight without a clear way to act. This session showed how a few key changes—better alignment, more focused signal strategies, and practical playbooks—can make a real difference in both pipeline and customer growth.

You can watch the full webinar on-demand here for deeper examples and insights and for access to our growth resources. If you have any questions after watching our on-demand webinar and reading through our content, send us an email!

The post Webinar Recap: “From Signals to Revenue: The Modern Playbook from CMOs and CROs” appeared first on Heinz Marketing.

A Practical Guide to B2B Marketing and Sales Alignment By Twelverays Agency
When marketing and sales paddle in sync, growth accelerates—when they don’t, chaos follows. This guide breaks down practical strategies to bridge the gap and turn alignment into revenue growth.

Mind the Lead Nurture Gap: New Strategies for B2B Marketing Success by Matt Hummel
Even in the age of AI and ABM, lead nurturing remains critical. Research shows top-performing teams excel at it, while most struggle to keep up with today’s chaotic buyer journey.

Marketing Must Own The B2B Commercial Operating System: Here’s Why The Future Depends On It by Cameron Partridge
AI won’t fix broken go-to-market systems—it’ll just make the cracks bigger. Forbes argues that marketing must take ownership of the entire commercial system to drive lasting growth.

Strategic Growth: Aligning B2B Marketing & Sales by Proven ROI Marketing Team

Aligned marketing and sales teams can generate 32% more annual revenue. This guide explores why old approaches fail and introduces a new framework for true collaboration.

How to Build a Winning B2B Digital Marketing Strategy for Success by Breakthrough3X
A strong B2B digital marketing strategy combines audience insight, measurable goals, and channels like SEO, content, and email to build trust and generate qualified leads. Success comes from data-driven decisions and consistent, personalized engagement that drives long-term growth.

Have a wonderful weekend and thank you for reading!  If you have B2B news sources you rely on we’d love to hear about them.  Please share them with us.  

The post B2B Reads: Sales & Marketing Alignment, Lead Nurturing, and B2B Digital Marketing Success appeared first on Heinz Marketing.

Soak up one of the last lazy weekends of summer with a few standout reads for CMOs—covering common pitfalls, deal pauses, brand in the age of AI, and a little Ann Handley wisdom to top it off.

6 of the Biggest Marketing Pitfalls for CMOs – and How to Course-Correct  by James Pintsak at DemandGen Report
This piece is worth a read if you’ve ever felt your team is spinning its wheels. It calls out six traps CMOs often fall into—like chasing too many priorities, working in silos, or waiting for “perfect” before launching—and offers simple fixes. The takeaway: focus on a few clear goals, test and learn quickly, and lean on fresh perspectives to keep marketing moving with impact.

Why the Last Year Has Been the Biggest Challenge for CMOs by Greg Jarboe
This read lays out how recent turbulence like shifts in consumer habits, relentless tech change, budget pressure, and a hyper‑competitive digital landscape has made being a CMO suddenly feel like steering through a perfect storm.

Mastering the Silent B2B Deal Pause by Carla A. Fleming
This one dives into a silent reality every CMO faces: nearly 40% of B2B deals stall.  About 60% of lost deals aren’t because you were outmatched, but because the buyer simply decided not to decide. The article gives you a smart game plan to treat those “hibernating” deals not as failures, but as signals worth reengaging.

CMOs Must Protect Their Brand in an AI-First World by Jessica Apotheker
This one’s a reality check: in an AI-driven landscape, your brand’s distinct voice is more fragile than ever, with AI enabling copycats to flood the market effortlessly. The playbook? Lean into the things only humans can do—creativity, values-driven messaging—and back it up with sharpened insights so you don’t get lost in the noise. It’s about leading with brand intent, not letting AI erode who you are.

When to Use AI: Lessons From My Fave Library by Ann Handley
Ann says it best in this charming library tale that shows how AI and human creativity aren’t adversaries but teammates. She reminds us: let AI speed up the tedious stuff, but lean on your uniquely human flair to truly connect. It’s a light-hearted nudge that the secret sauce still lives in our own authentic voices, even in an AI world.

Have a wonderful weekend and thank you for reading!  If you have B2B news sources you rely on we’d love to hear about them.  Please share them with us.  

The post B2B Reads: CMO Challenges, Stalled Deals, Course Corrections and the Humanness of AI appeared first on Heinz Marketing.