There are a couple of ways to interpret this question. One way is process; how do you create a system that lets you operate efficiently, rinse, repeat, and scale? The second is growth; how do you expand from a few accounts to many while still doing “ABM”? I’m going to start with the latter, because it lets me start off with a strong statement: I Do Not Believe ABM Is Real or Effective As Originally Defined. I’ve been doing digital marketing for a long time, in many different ways, and one thing marketers are exceptional at is reframing what we already do, giving it a new name when we’ve only made a small tweak. Email marketing became marketing automation. Now, really great targeting became ABM. Early ABM had a couple of distinct features. At first, it often included literal 1:1 tactics, like sending custom physical packages to a specific person at a specific company. But over time, we got better at scaling those tactics digitally—and suddenly, we called it ABX (Account-Based Experience). So what are we really talking about? It’s not ABM, it’s just really solid marketing with really solid targeting. And that’s something everyone should be doing. Yes, ABM is more B2B-focused, and yes, selling iPhone cases on Instagram doesn’t map to ABM-style targeting in the same way. But the core principle—highly strategic, high-intent targeting—should be the standard for all sophisticated marketing. So, Given That, How Do You Scale It? I think about marketing the same way product managers think about products. Every campaign and audience combination is a separate product. Scaling ABM isn’t about running one play and making it bigger—it’s about building a system where you repeatedly test, optimize, and expand audience and campaign combinations. 1. Start with a strong initial audience-campaign fit—something you’re confident will perform. 2. Build the mechanisms, measurement, and execution for that audience. 3. Test, iterate, and optimize. 4. Once you’ve validated it, add the next audience or campaign. 5. Repeat. Again. And again. Two Critical Things to Watch as You Scale: 1. Maintenance Costs – Every new campaign adds operational overhead (tracking, reporting, optimization). It only works if the ROI outweighs the upkeep. 2. Audience Overlap and Cannibalization – The more you scale, the greater the chance you start targeting the same people in multiple places. You have to manage cross-channel saturation and ensure new campaigns aren’t just stealing conversions from other efforts. When you've achieved the right scale you then get to really have fun, something I've termed "moneyball marketing" which is a fancy way of saying constant tweaking. At some point, you’ll max out one or more of these factors: 1. Total Addressable Market (TAM) – You run out of high-fit accounts. 2. Budget Ceiling – You need to justify more spend by proving incremental ROI. 3. Team Capacity – You don’t have enough hands to execute everything. At this stage, your job is to fine-tune the balance between these constraints: 1. Find and test new audiences. 2. Justify more budget from Finance by proving ROI. 3. Optimize your team’s workflow, automation, and execution velocity. Once you’ve built a scalable ABM engine, you’re no longer just marketing. You’re running a system that continuously finds, targets, and converts the best-fit buyers in a predictable way. Some other things to think about: Don't neglect your audiences, the rinse part also applies to stuff that's been out a while and getting stale, your audiences notice that and you'll lose efficiency. Partner with the sales/revenue org on this. Great marketing of this type requires that side of the playbook to be just as good. One aspect that ABM had is that super personal feel. AI can be a place you rediscover that with the broader scale I'm talking about here. NOTHING is more important than measurement. You can't manage what you can't measure. All these campaigns are not going to work if you can't compare them apples to apples and really prioritize. ABM isn’t some exclusive marketing methodology, it isn't fancy, it’s just high-quality targeting at scale. If you rinse, repeat, and scale effectively, it stops being just marketing and starts becoming a revenue engine. Really you're not making a campaign, you're building a system. Hmm. I wrote all that answering my second interpretation and looking back I think it solves for both, so I'm going to stop here.

When addressing alignment with executive stakeholders it is important to drive clear goals, KPIs, RACIs, and a strategy that outlines the pros and cons. This can include the following: 1. Goal alignment: You need to align with both stakeholders up front on the core problem we are trying to solve. By driving this alignment you ensure that everyone is on the same page around the goals we are trying to achieve. Without this, your strategy won’t align. 2. Organized swimlanes: It is important to build a RACI with an ultimate decision maker, including who can make the final decision and escalation paths as needed if these two stakeholders disagree. 3. Influencer mindset alignment: It is your job to understand their core KPIs and business needs, which you can highlight in the options you share. This includes their personal and professional drivers, which may influence their decision-making later in the process. 4. A company-first strategy: The proposed strategy should include the pros, cons, and risks. Different leaders may assign different values to each of these areas. Ideally, you align these to your company or organization's priorities to make it easier to see from a company-first perspective. Ultimately, when you provide a suggested strategy, it should be the one that provides the overall company with the least amount of risk meeting the core objectives you agreed to solve for. If needed, you can use the escalation paths in your RACI, but ideally, doing the upfront alignment will be needed less often.

Ohhh! Interesting question. Demand generation has come a long way in the last several years thanks to marketing automation, analytics, and more. We can measure, slice and dice data, and optimize in ways we never thought possible. I believe many of these systems will get smarter, likely augmented at least in some part by AI. Data will become even more important going forward. Long gone are the days when a partial picture of campaign performance is acceptable. Campaign and demand generation managers should continue to focus and hone their skills around data manipulation, analysis, optimization, and using data to tell stories. They should understand how tools with AI can help their tech stacks, program performance, and more.

When transitioning from a company with established demand generation to one where you're building it from the ground up, you should expect several significant differences: Resourcing You should expect to find yourself evaluating the true state of available resources. This is precisely why you've been hired - to build and optimize: * Data visibility limitations - Basic information like conversion rates, customer acquisition costs, or even clean prospect lists may need development * Tech stack opportunities - Tools you previously utilized (marketing automation, analytics, etc.) might need implementation or refinement * Content development needs - You'll likely need to create foundational content that speaks directly to buyer pain points Relationships In established demand gen environments, sales and marketing usually have defined workflows. When starting fresh: * Building trust is essential - Sales teams will be looking to see if marketing can deliver tangible value * Alignment opportunities - You can establish shared definitions of what constitutes a "qualified lead" from the beginning * Process creation - Implementing new lead routing, scoring, or follow-up processes is an opportunity to optimize from day one Working with Founders and Leadership * Learn and borrow from the founder's vision - If you're working directly with a founder, absorb their perspective on the category, company values, and how teams work * Identify their strengths - Depending on their functional background, determine specific areas where should leverage their expertise * Setting expectations together - Help leadership understand what will yield results when, potential failure points, and realistic timelines Operational Foundations * Operations & Analytics - These are the foundation to your planning and decision-making. Determine what is "good enough" to start with and prioritize hiring in this area as soon as possible * Balancing gut and data - Respect past work but evaluate programs based on their actual impact. Build conviction around what will work and structure these as experiments * Timeline realities - You'll need to manage the balance between pressure for quick wins and building sustainable programs The Cultural Opportunity Building demand generation often allows you to shape organizational mindset: * Education as leadership - You have the opportunity to establish marketing concepts that will become standard practice * Measurement maturity - You can introduce the data-driven approach that modern demand gen requires * Patience with purpose - Help leadership understand the investment timeline while delivering incremental wins In short, the journey from established to ground-zero demand generation is about strategic vision, stakeholder alignment, and balancing short-term wins with long-term building.

Always be open to feedback. Any feedback either positive or negative is a gift. There's always opportunities to improve and grow no matter how much experience you have. In your case, if you don't agree with the feedback, I would ask for examples on how you could have done something differently, or better. Your boss may not see eye to eye with you and that's okay, but as long as you can show impact through your work, numbers don't lie.

Don't believe the "experts". NO ONE knows the answer to this question with confidence). All that said, here are my hot takes: AI will have an impact on your career as a Demand Generation professional in the medium term. How? -Automation of your routine tasks: AMEN. This is a great thing and will allow us all to spend more time focused on more interesting creative and strategic problem solving. -Enhanced Data Analysis: Again, this is rad. Imagine you have your own, personal data scientist to help you parse the reams of data we collect as marketers, and develop real and impactful business insights. -Personalization at Scale: More great news here. AI will help us personalize sales and marketing interactions beyond the hard limits of 1:1 "account based marketing". That means more net new leads, more qualified leads, more opportunities, higher average deal sizes, more closed won and happier customers. -Improved Lead Generation and Nurturing" See above for personalization; it will have a meaningful impact on our ability to ship the right ad to the right prospect at the right time. It will help us customize our nurture streams and produce the right content. This is great news for all of us. -Content Creation and Optimization: This is probably where you can see the biggest impact RIGHT NOW. If you are not using free, off the shelf tools for content ideation, outline creation, narrative flow, H1 & H2 creation and editing for readability....you are missing out. Will this replace the Content Marketing Manager or Copyeditor role anytime soon? I don't think so. It's just going to make them more productive. -Real-time Customer Insights: I don't think we're here yet, but I think it's coming and it's a GREAT THING. In near-future states, AI will provide real-time insights into customer behavior and engagement, allowing for more us to be responsive and pivot quickly to marketing strategies. The big question for us human working professionals is, "can we increase our agility and be prepared to quickly adjust campaigns based on these insights?" -Skillset Evolution: Ok so this one is INTERESTING. You want to take a risk and get ahead of building an inevitably hot, in demand skill? Invest in your query development skills (I recently heard of a startup shutting down for a month to train their entire staff on this). Get proficient in using AI tools and platforms, understanding AI-driven analytics, and how businesses can integrate AI into your overall strategy. All that said, I don't think AI will replace your role in the next 5-10 years, provided you embrace and leverage the tools that are rapidly becoming made available to you. In the long term - ten plus years - AI will almost certainly transform the the role. Maybe even eliminate it as it stands today. But there's no need to panic. It may be hard to remember, but there were no "Demand Generation Managers" 20 years ago. And Marketers like you and me still have jobs.

For marketers transitioning into Demand Generation, the key is to demonstrate a strong grasp of marketing fundamentals and a strategic mindset. Start by showcasing your understanding of marketing channels, the funnel, and key Demand Generation metrics like conversion rates, pipeline contribution, and ROI. Employers want to see that you can think both analytically and creatively when executing campaigns. Leverage your existing marketing experience to highlight relevant skills. For example, if you come from product marketing, emphasize your expertise in messaging and positioning—both critical for demand programs. If your background is in content marketing, illustrate how storytelling and content strategy play a role in lead generation. Understanding how different marketing functions collaborate to drive demand is a huge advantage, so demonstrate your ability to work cross-functionally to execute campaigns. Creativity is also essential. Be prepared to discuss how you would activate a campaign in the market, optimize performance, and scale results. Employers value candidates who can not only strategize but also roll up their sleeves to get things done. Finally, show initiative by familiarizing yourself with Demand Generation best practices, tools (such as marketing automation platforms and CRM systems), and emerging trends. Demonstrating a proactive learning mindset will reinforce your ability

Socializing KPIs before and after a campaign is important. It allows all stakeholders to align, prioritize work that impacts these KPIs, and understand performance, including the reasons behind the performance. Before creating KPIs, it's important to understand the following: * Know the campaign messaging and call to action you want users to take: What do we want people to ultimately gain from this campaign? Do you want to drive users to a specific page on your site? Increase free trial sign-ups? Increase demo requests? Is this a top-of-funnel play where you'd want to measure engagement like impressions? It could be a mix of these depending on the campaign, in which case you'd want to account for these various KPIs accordingly. * Have a strong understanding of the different channels promoting the campaign: For example, if email is a large part of the promotion strategy, you'll not only want to include email metrics as KPIs but also include the percentage of sourced traffic from email versus other channels. Aside from determining KPIs, it's also important to establish goals and benchmark comparisons. To do this, you can review performance from similar marketing initiatives or research industry benchmarks and set goals based on these insights. With many moving pieces, it's crucial to communicate your KPIs once you know which ones you want to track and measure. I recommend sharing your KPIs in an easy-to-read template well ahead of your campaign launch to provide time for stakeholders to review and provide feedback. Once a campaign is launched, you can provide an end-of-day KPI performance readout, an end-of-week readout, and an end-of-month readout. Depending on the campaign's longevity, you could also provide additional KPI readouts. For instance, if the campaign is seasonal and only running for a specific amount of time, you'll want to do a final KPI readout once the campaign has ended.

To manage pipeline forecasting differences between sales and demand generation, I'd focus on building a system where sales, demand gen, RevOps, and MarOps work together, combining sales' insights with campaign data for one solid forecast and identifying any root causes. Below are the typical steps that I go through: * Create joint processes, like unified dashboards, to quickly spot and troubleshoot discrepancies. * Ensure a common understanding of key metrics - lead volume, conversion rates, etc. - and the assumptions behind them, such as product launches, P&P changes, or marketing campaign launches, so both teams are aligned. * Troubleshoot data pipeline, which often causes forecasting discrepancies and gaps. Addressing this first saves time before digging deeper. * Review individual metrics alongside historical performance to identify shifts in trends or dynamics, ensuring forecasts reflect current realities. * Set up weekly or bi-weekly check-ins to keep communication going, adjust forecasts as needed, and build trust across teams.

I don't necessary think there are any KPIs that are "unimportant" as everything can be measured and provide value and insights to the business. However, there is a difference between marketing "inputs" and "outcomes." And "outcomes" should ultimately be those KPIs that truly impact that business (e.g. MQLs, SQLs, marketing generated pipeline, product demos or trials downloaded/viewed, etc.). While "inputs" are still important, they are those KPIs that help lead to or drive the business outcomes (e.g. web visits, cold leads, event attendees, etc.). I think marketers fall into the trap of putting too much of an emphasis on the inputs, instead of using them as a directional data point to better understand how you can impact the business outcomes. For instance, I've seen marketers care too much about measuring "leads" generated and spending too much time justifying why they're important, when they should be spending time on understand how MQLs and SQLs are driving results for the business.