👋 Hi, it’s Kyle and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.

As AI makes generic outreach cheap and ubiquitous, differentiation comes from precision, relevance, and deep context around a small set of high-value accounts. This sounds wonderful on paper until it’s time to set up a modern account-based GTM engine.

For help I turned to Dan Rosenthal, co-founder of Workflows.io, which builds go-to-market systems for B2B revenue growth. Dan and partner Fivos Aresti bootstrapped Workflows.io to $2.5M ARR in 10 months and have implemented dozens of modern ABM engines. Today Dan shares their extremely tactical ABM playbook. (This post may be cut-off by your email provider; read it in full on the Growth Unhinged website.)

Put simply, account-based marketing is an account-based GTM strategy that works backwards from a list of target accounts. While it has historically been expensive and highly manual, AI agents and data enrichment make it way more accessible.

We now recommend it to any B2B company with a ACV of $50k+ and a constrained addressable market (<20,000 qualified companies). For companies with a TAM of <20,000 companies, the relative low conversion rates of other GTM approaches (e.g. automated outbound, LinkedIn content) make them very hard to rely on in siloes.

I started Workflows.io to help companies build their GTM systems. We’ve personally worked with 250+ companies including dozens of ABM implementations. ABM is one of our most in-demand service lines, and I can confidently say that the technical implementation is still far harder than most expect. But the results are also far more impressive.

So I teamed up with Kyle to teach you how to build a modern, agent-compatible ABM system using HubSpot, Clay, and Claude. Every process will be rooted in real (but anonymized) examples from our clients. I’ll break it down into seven main components, which I recommend following step-by-step.

The primary goal here is to create a Target Account List (TAL) that captures 90%+ of a TAM. When we’re talking a TAM of <20k companies, incremental increases in coverage can have massive implications on revenue so don’t shortchange this.

First, we build a data-supported ICP model. It is surprising how many GTM teams we speak to that don’t have their ICP written down on an agreed-upon document. We’ll assume you have this in-place for the sake of this article, but here are some external resources that can help if you don’t:

Here’s an example of what our own ICP planning doc looks like:

We believe simpler is better when it comes to tiering models. Simple usually means more accuracy and clarity for the sales team. With agreed-upon ICP qualification and scoring parameters, you have everything you need to start pulling the actual lists.

There are four main types of data providers for creating the Target Account List. Generally, we find that smartly combining 3+ data sources massively increases our chances of reaching 90%+ TAM coverage.

The goal is to balance coverage and costs.

But the only way to maximize coverage beyond a certain point is to pull a higher volume of lists. And those lists naturally get broader, including more unqualified companies, but also more qualified companies that targeted searches won't capture. Which increases costs two-fold.

On one side, it’s the credits directly spent with the data providers to pull more data. But also more AI spend required to qualify the TAM list since we deploy AI research agents to qualify each company based on their website content. This is a necessary evil if you want to fully map out your TAM, because the filters / data quality of prospecting databases don't fully suffice (e.g. vertical SaaS on typical databases will be under their industry vertical instead of software development).

After pulling multiple lists, the next step is to merge + dedupe them. We used to do this in Clay, but we recently switched this workflow to Claude Code and find it far easier. We still end up in Clay afterwards, where we spend significant time back-testing and optimizing the centerpiece qualification prompt/research agents.

This powers the initial qualification we do on every company (usually industry and product fit), before enriching and qualifying further based on firmographics, technographics, and account fit signals.

Here is the framework we use to build our qualification prompts:

With a recent Series A SaaS company targeting B2B distributors, we scraped 70+ sources (mostly industry association directories) to maximize coverage. The Clay workbook got pretty hectic, but that's the price it takes.

The last step is to repeat the same process for contacts:

AI made creating a comprehensive account list much easier. It also made account research WAY easier to collect at scale.

Since we already cut down our initial list, our goal is to collect the additional data points that would be useful for (a) campaign personalization or (b) sales reps researching accounts without leaving the CRM.

For most of the companies we work with, that ends up being 30+ custom data points. Which usually means 30+ custom CRM properties that need to be configured.

This is why setting up ABM is resource-intensive on the RevOps side of things, but Claude Code and MCPs have also made it a lot easier.

We typically use a spreadsheet to map out every single data point with their official HubSpot names, as well as additional requirements e.g. overwrite rules. Here’s an example of what that looks like:

This work is all done before we build a single workflow, so we can accurately predict costs.

Doing a full TAM Map with account research can easily cost $2,000+ across data provider, Clay, and AI credits. But it’s a justifiable expense because you can now get extremely precise with the data points you collect.

These are real examples from clients, in order of simple to complex:

We deploy both web research agents (Claygent) and some of the 150+ data providers you can rent in Clay, since some data is not publicly available. In practice, building this requires a lot of prompt engineering and Clay configuration.

We also use the account research properties to set up account scoring directly in HubSpot. This way if a rep manually overwrites a value, the score can be automatically reassessed:

On top of the raw data, we typically apply some formulas or calculations data more actionable for HubSpot.

One important sub-component of account research are parent-child associations. When selling to PE-backed companies, you may have to sell through the parent company. We deploy web research agents to find this information and set up HubSpot associations to reflect those relationships.

All of this makes your CRM a more data-rich and pre-filtered prospecting database so your reps don’t ever need to login to Apollo or ZoomInfo themselves (you can just use their data through Clay).

This may seem like a lot of work for a one-time implementation, but this same workflow is converted to something that gives everlasting value: an automated CRM enrichment workflow.

Note: Everything up to this point can also be done in Claude Code with the same data providers, but the evergreen workflows coming after are more robust to build in Clay.

If we go through all the effort of setting up the target account infrastructure mentioned above, we want this same workflow to be applied to every record in the CRM. And every future CRM record.

Surprisingly, we find this workflow fairly straightforward to set up:

We typically set up an “Enriched by Clay” date field on both the company and contact objects, both for easier tracking and to power workflows that re-enroll records when the data freshness window passes (usually after one year).

Before we do any changes in the CRM, e.g. adding records or properties, we typically do a cleanup first. We recommend the following at the bare minimum: (a) hard domain and email duplicate removal, (b) basic property audit to prevent redundancies.

CRM hygiene can get deeper than we have space to explore in this article. In short, these three mediums should cover 99% of your use cases:

Once the CRM cleanup is done, the TAM/stakeholder map is uploaded, and custom enrichment data is automatically flowing, we typically recommend a few quality-of-life improvements to help activate this data:

Up to this point, I’ve just covered static data. For any data that needs to be monitored on an ongoing basis, we’ve found a dedicated signal tracking process to be the best approach.

Activating signals is the key to removing the classic silo between sales and marketing.

If marketing drives 50 ad impressions to an ICP account, and that signal is sent to the sales team, both teams can be aligned to the only stat that really matters when building out an ABM program: ICP pipeline progression. This is an objective read-out we always use as our north star.

These are the 5 main ways we use intent signals:

I can confidently say that in every single of our ABM systems we’ve built out, the reception to intent signals by the sales team has been overwhelmingly positive. We have significant evidence to suggest that at least some intent signals, across every client, are associated to higher conversion rates, and anecdotally reps really enjoy the extra support to help them reach quota.

However, we encounter our fair share of signals that don’t drive results. Either that is because they don’t correlate to actual buying intent, or they don’t occur at a significant enough volume to make a difference. Finding the winning signals comes down to testing, but more importantly the strategic planning around signal tracking.

Since we know what’s out there, we’re usually able to single out the highest-priority signals before we ever start building. Here’s how we map it out:

Many GTM leaders we speak to see the buzz around signals and think they are overhyped, but that’s not what the stats from the wider market are suggesting.

In Growth Unhinged's 2025 State of B2B GTM report, intent-based outbound ranked as the #2 GTM channel that teams are investing more into in 2026. In our best cases, we’ve seen clients where 20-40% of their active pipeline is attributed to signal activation (7-day deal creation windows).

Capturing a signal is actually the easy part. Activating them is 90% of the work. Here is the 13-step process we use for every signal workflow:

Funnily, awareness scoring is a strategy we learned from Growth Unhinged, especially in the case study pieces with Parabola and Emilia Korczynska. We took the frameworks laid out, and operationalized them inside HubSpot and Clay for our clients.

Awareness scoring was quite unclear at first, but we’ve built up a lot of know-how (e.g. using custom events). Here’s how it works across the five stages:

When a company moves stages, we’ve found a few best-practices for activation:

Designing awareness stages can be tricky, but here are some examples you can use to model off of:

This system acknowledges a major shortcoming of lifecycle stages: that they usually start after an opt-in. This means they end up missing out on a significant portion of your warm market.

The core ABM infrastructure is now in place, starting with the target account list and moving to intent signals and awareness scores. This removes manual work that limited ABM in the pre-AI era. Now demand gen can become the core focus.

Some interesting observations we’ve seen after implementing the above-described components:

Anecdotally, a BDR at one of our clients booked four meetings after just one day of cold calling out of the “Aware” lists we had set up for them. Their reps used to book just 1-2 meetings per week (it’s a $100k+ ACV product) so these numbers were massive for them.

Just like every marketing strategy, things may fail before they succeed. There’s are some key lessons I feel are worth sharing across every key channel for ABM:

1:1 demand generation

1:many demand generation

I’ve never met a GTM professional who doesn’t understand the importance of reporting. What is worth sharing: the top six reports we recommend for any company implementing ABM:

AI has lowered the manual work and costs of doing ABM. But you still need the right RevOps infrastructure to be effective. Follow the program we outlined in chronological order.

This is far more than buying an intent dashboard like Bombora and expecting your reps to check it. It’s building out the data pipeline that will power all your GTM activities.

If done right, you’ll start to see a flywheel effect with your GTM efforts:

And I really believe this is the only way to break into a limited market fast enough. We’ve seen it directly with companies like Medrio generating $1.3M in ABM-attributed pipeline in four months.

If you’re looking to build something like this out, I’d start by hiring a GTM engineer or RevOps specialist. Or, of course, an external partner.