How Merge mapped an unconventional qualified market, and built a revenue engine that doubled year over year

The challenge: a qualified account no database could see

Merge is the leading provider of agentic tools and customer-facing integrations for the largest banks, AI companies, HR technology providers, and more.

For Merge, a qualified account isn’t defined by industry, employee count, or geography. It’s defined by a signal that isn’t easy to come by at scale — does this company offer integrations to its users?

Here’s how the Merge team worked out how to identify every qualified account in their market, despite their unconventional qualification criteria, and then used that universe of qualified accounts to fuel a revenue engine that doubled year over year.

Moving beyond proxies

Most revenue teams inherit a market that was built from legacy proxies — the classic three: employee count, industry, geography — because those were the only filters data providers have been able to offer.

The Merge team recognised this. Their ideal customer was more detailed: a software company that already offers integrations. Most tools can tell you “software company,” but that’s still far broader than what Merge needed.

Before formalising their GTM approach, the Merge team had to jump between multiple platforms to understand if an account was qualified:

• Apollo for a broad list of companies, filtered by standard firmographics

• G2 to manually check whether a company fit a relevant software category

• Crunchbase to check funding data, and assess whether a prospect could realistically afford Merge

• Sales Navigator to use LinkedIn industry as a subjective, inaccurate and inconsistent stand-in for fit

The real frustration was that the available signals — is this company in Apollo? Are they in a G2 category that typically needs integrations? Do they have decent Crunchbase funding? — were a poor proxy for what actually mattered.

For a start, Apollo’s database of companies is huge. Not every software company is on G2, and companies can be listed in multiple G2 software categories. And looking at Crunchbase to understand company funding cuts out every bootstrapped company in the market.

Without knowing the true size of Merge’s market, Charlie could not make strategic commercial decisions. How many reps could he hire? Where could he expect revenue to come from?

The solution

Step 1: Mapping the market

The Merge team recognised that they needed to start by defining their real market precisely.

Decoding a qualified account

We took a list of 50 of Merge’s best customers and analysed what data set them apart from other companies — a data-driven, reverse-engineered approach to build a firmographic profile of the ideal Merge buyer.

What we found was very simple. Every Merge customer had an integrations page on their website.

We realised we could bypass the proxy problem entirely. Instead of using traditional data to ask “is this the sort of company that would need to have integrations?”, we could ask the more direct question: “does this company offer integrations that match the Merge offering?”

Building the market

The only way to find “integrations offered” is to look at a company’s actual website. But you can’t scrape every website in the world — you need to start with a list of the right companies. Merge’s problem was that they didn’t have that list, nor a data provider that could reliably identify which companies in their database were software companies in the way Merge defined “software company.”

Goodfit solved Merge’s problem in two steps:

We identified the accounts that matched Merge’s buyer criteria. Merge’s reps went from spending hours manually qualifying accounts across four platforms, to working a pre-qualified, enriched market. Instead of surfacing integration pages by hand for every account, that signal was now a structured field in Salesforce.

And because Merge’s market expands every time they launch a new integration category, the mapping isn’t a one-off. When the team prepared to launch marketing automation integrations, we expanded the market definition to include that category before the product went live, so sales had accounts to work on day one.

Step 2: Prioritising the right accounts

With their list of qualified accounts, the obvious next question is — who do we go after first, and how hard?

Tiered scoring

We worked with Merge’s revenue team to build a tiered scoring model (Tier 1, 2, 3) using enriched Goodfit data as the foundation. The scoring combined signals including:

• Integration breadth
• G2 subcategory
• Funding stage
• Team composition
• Year founded
• And more

Each signal added to or subtracted from an account’s score. The higher the score, the more valuable a company was likely to be, and the higher their Tier.

The Merge team then continued to sharpen their scoring as they won more deals and better understood what made a good customer. The team has now refined their scoring model over two years.

Scoring as an operating system

What makes Merge’s approach distinctive is how deeply scoring is embedded in their commercial model:

An accurate scoring model has also created something many revenue teams struggle to build: a Sales and Marketing team aligned around the same definition of a “good” account.

Step 3: Segmenting accounts by use case

Most teams stop at scoring. They know which accounts to prioritise, but they pitch every account the same way. The Merge team went a step further, recognising that their outbound motion needed to be built around vertical-specific use cases and competitor messaging.

To achieve this, we built approximately 30 tags for Merge’s account base. Tags are dynamically populated based on a combination of integration categories, use case signals, and vertical classifications. Each tag represents a judgment about an account: what problem they’re most likely trying to solve, and which Merge integration categories are relevant to them.

At Merge, vertical-based sequences deliver 3–5x the reply and interest rates of generic outbound. Tags power the entire outbound motion:

The combination of scoring and segmentation gives Merge’s reps something rare. They know which accounts to work and they know what to say. The result is outbound that converts because it speaks directly to the prospect’s actual business context, rather than a generic value prop.

Step 4 — Distributing accounts to reps

With accounts mapped, scored, and tagged, the final step is getting them into reps’ hands. When accounts are recycled or new accounts enter the market — because a company launched integrations, raised funding, or crossed a team-size threshold — they flow into Salesforce pre-scored and pre-tagged, ready to be routed to reps without anyone managing distribution from a spreadsheet.

And because every account already has a use-case tag, reps don’t just know which accounts to work — they know what to say. That’s the system working end-to-end: mapped, scored, segmented, distributed, and refreshing every day.

The results

Mapped, scored, segmented, and distributed, Merge’s qualified accounts now refresh every day and flow straight to reps as the market changes, without anyone managing distribution from a spreadsheet.

That’s taken Merge from a market that was basically every account in Apollo, pulled as one-off lists, to a continuously growing universe of qualified accounts, each pre-scored and pre-tagged before a rep ever sees it.

With mapping, scoring and segmentation working together, Merge’s Sales and Marketing teams now work from a single, shared definition of a good account — and the revenue engine built on that definition has doubled year over year.