How Quartr built a fair, data-backed territory model — and covered the cost inside six months

The Challenge: 40 reps, no shared definition of fit

Quartr’s early approach to account distribution was relaxed — reps pursued whichever accounts they chose. That works at six AEs. At 40 it created friction: no shared way to tell which accounts were a good fit, no fairness across territories, and falling rep confidence in the books they were handed.

Underneath the fairness problem was a data problem. Quartr’s buyers don’t fit the firmographic filters — industry, headcount, region — that traditional data vendors are built around. Inside “buy-side firm”, the qualifying signal is the presence and size of an equity research team, the number of portfolio managers, or, for the API product, whether a data team exists and is growing.

RevOps carried the weight of it. Rebalancing books meant swapping accounts in and out with no way to know whether the replacements were any better. Kevin had tried AI enrichment in Clay, which classified accounts as buy-side or sell-side and added directional signals like AUM — but reps kept flagging mis-categorised accounts, and every flag cost trust.

The Solution: Qualification criteria per product line, then the whole market mapped against it

Goodfit started by defining what qualified means for each product and persona, rather than starting from a list.

Goodfit then mapped Quartr’s 16,153 CRM accounts against that definition. The CRM held no grading, no buy-side/sell-side classification and no way to filter by investment strategy or AUM. 70.8% of those accounts fell outside the qualified market — accounts the team had been actively working.

The gap ran the other way too: 7,541 accounts that matched Quartr’s criteria weren’t in the CRM at all — a 64% coverage gap, widest on the sell-side.

Put together, the qualified market came to 11,795 accounts — 27% smaller than the CRM, and graded. Kevin used that graded output to build his own scoring model and construct evenly weighted books: Quartr’s first full territory rebalance, live at the start of July 2026.

The results

Between going live in late February and the end of Q2, Goodfit-sourced accounts generated $68K in ARR — $97K by early August — while over 50% of all new business ARR in the period came from Goodfit-enriched accounts. That covered the cost of the investment before the territory model had even reached the team; reps and inbound had found their way to surfaced accounts that hadn’t yet been assigned to anyone.

Multi-license deals, in closed-deal data

Because qualification is built on equity research team size and portfolio manager count rather than AUM alone, Quartr can identify accounts with real multi-license potential — tracked internally as a “likelihood to buy five or more licenses” signal. It shows up on the Pro product’s closed deals.

Pipeline follows the same line: Goodfit-enriched accounts have generated over $8M since go-live — roughly 40% of all pipeline created in the period, with around $600K coming specifically from Goodfit-sourced accounts.

The qualifying work that used to sit with each rep — research from scratch, cross-checked against Salesforce — now arrives with the account.